This article presents a low-cost insurance system developed for small-hold farms in disaster-prone regions, primarily using free Earth Observation (EO) data and Free Open Source Software (FOSS) – collectively termed sustainable geoinformatics - with a proprietary mobile phone app that enables verification. The study examined 30 farms in Risaralda department, Colombia. A Digital Elevation Model (12.5m pixels) from the ALOS PALSAR satellite sensor was used with a Geographical Information System (GIS) to map the terrain, drainage and geohazards of each farming district. Google Earth Engine (GEE) was used to carry out Time Series Analysis of 15 EO and weather datasets for 1998 to 2020. This analysis enabled the levels of risk from hydrometeorological hazards to be determined for each farm of the study, providing key data for the setting of insurance premiums. A parametric insurance product was piloted using GPS-tagged, time-stamped mobile phone photos to verify crop health. Key features of this insurance system are its low operational cost and rapid damage verification, relative to conventional approaches to farm insurance. It is an affordable form of insurance for small-scale farmers, with the rapid verification and payment of claims. This relatively fast, low-cost and affordable approach to insurance for small-scale farming enhances sustainable development by enabling policy-holder farmers to recover more quickly from disasters.

Intense rainfall events frequently occur in Brazil, often leading to rapid flooding. Despite their recurrence, there is a notable lack of sub-daily studies in the country. This research aims to assess patterns related to the structure and microphysics of clouds driving intense rainfall in Brazil, resulting in high accumulations within 1 hour. Employing a 40 mm/h threshold and validation criteria, 83 events were selected for study, observed by both single and dual-polarization radars. Contoured Frequency by Altitude Diagrams (CFADs) of reflectivity, Vertical Integrated Liquid (VIL), and Vertical Integrated Ice (VII) are employed to scrutinize the vertical cloud characteristics in each region. To address limitations arising from the absence of polarimetric coverage in some events, three case studies focusing on polarimetric variables are included. A fourth case study enhances the overall understanding of these events, emphasizing the underexplored nature of short-term intense rainfall studies in Brazil. Results reveal that the generating system significantly influences the rainfall pattern, especially in the Southern, Southeastern, and Central-Western regions. Regional CFADs unveil primary convective columns with 40-50 dBZ reflectivity, extending to approximately 6 km. The microphysical analysis highlights the rapid structural intensification, challenging the event predictability and the issuance of timely specific warnings.

The traditional societies evolved locally with the realms of time in their calendars while modern Gregorian based on the solar movement and some others use lunar or the combination with solar. Unveiling some information from the old manuscripts to gain the realm of traditional Batak people since the ancient times in settlements surrounding the tropical Lake Toba, North Sumatera, Indonesia, revival of cosmos is delivered. Uniquely the ancient Batak count days in a month by observing lunar phase and the period of year is by looking the observable Constellation of Orion & Scorpius within the new phase of the moon. Thus there are years with 12 months and some leap years with 13 months, and we propose to categorize the calendar to be lunar-star, relative to other widely analyzed calendars. In the realization of the use of the cosmogram, we cross the period of the leap year to the examination of contemporary data on agriculture event, i.e.: crop failure, as well as some profiles on weather. The revival of the Batak Calendar opens further interesting conjectures based on the rich cultural and astronomical knowledge embedded in tradition with deep connection between celestial observations and timekeeping.

With the emergence of autonomous functions in road vehicles, there has been an increased use of Advanced Driver Assistance Systems comprising various sensors to perform automated tasks. Light Detection and Ranging (LiDAR) is one of the most important types of optical sensor that detects the positions of obstacles, representing them as clusters of points in 3-dimensional space. LiDAR performance degrades significantly when driving in rain as raindrops adhere to the outer surface of the sensor assembly. The performance degradation behaviors include missing points and reduced reflectivity of the points. It was found that the extent of degradations is highly dependent on the interface material properties, which subsequently affects the shapes of the adherent droplets, causing different perturbations to the optical rays. A fundamental investigation is performed on the protective polycarbonate cover of the LiDAR assembly coated with four classes of materials – hydrophilic, almost-hydrophobic, hydrophobic, and superhydrophobic. Water droplets are controllably dispensed onto the cover to quantify the signal alteration due to each droplet of various sizes and shapes. To further understand the effects of droplet motion on LiDAR signals, sliding droplet conditions are simulated using numerical analysis and validated with physical optical tests using a 905 nm laser source and receiver to mimic the LiDAR detection mechanism. Comprehensive explanations are presented on LiDAR performance degradation in rain from both material and optical perspectives. These can aid component selection and the development of signal enhancing strategies for integrating LiDARs in vehicle designs to minimize the impact of rain.

In this paper major machine learning (ML) tools and the most important applications developed elsewhere for numerical weather and climate modeling systems (NWCMS) are reviewed. NWCMSs are briefly introduced. The most important papers published in this field in recent years are reviewed. The advantages and limitations of the ML approach in applications to NWCMS are briefly discussed. Currently, this field is experiencing explosive growth. Several important papers are published every week. Thus, this paper should be considered a simple introduction to the problem.

The ocean’s mixed layer depth (MLD) plays an important role in understanding climate dynamics, especially during extreme weather occurrences like hurricanes. This study investigates the effects of Hurricane Katrina (2005) on the MLD in the Gulf of Mexico, using the Delft3D modeling system. By integrating hydrodynamics and wave dynamics modules, we simulate the ocean’s response to extreme weather, focusing on temperature, salinity and MLD variations. Our analysis reveals significant cooling and mixing induced by Katrina, resulting in spatial and temporal fluctuations in temperature (~± 4°C) and salinity (~± 2.5 ppt). The MLD is estimated using a simple threshold method, revealing a substantial deepening to ~120 m on August 29th-30th during Hurricane Katrina in the middle of northern Gulf of Mexico, compared to an average MLD of ~20-40 m during pre-storm conditions. It took about 18-days to recover to ~84% of the pre-storm level after Katrina. Compared to the stand-alone FLOW model, the coupled FLOW+WAVE model yields a deeper MLD of ~5%. The MLD recovery and wave effect on the MLD provide insights from various scientific, environmental and operational perspectives, offering a valuable basis for ocean management, planning and applications, particularly during extreme weather events.

Herein, we examined the effects of weather parameters and individual clinicodemographic risk factors on hemorrhagic stroke (HS) incidence, severity on admission, and disability at discharge in a harsh desert climate. In a retrospective design we studied patients admitted to a stroke unit in Arab Emirates in 2016-2019. With a distributed lag nonlinear model we explored immediate and delayed effects of weather on stroke incidence. Supervised machine learning was used to build models predictive of scores in NIHSS and mRS scales. We assessed model performance by calculating ROC AUC, F1 scores, specificity, and sensitivity. HS risk increased after a significant change in any weather parameter. The risk was reduced below baseline after 1-week adjustment to environmental changes. Climate input from the previous 3 days yielded reliable classification models. Humidex was a better predictor of severity than air temperature, whereas body mass index, age, and time of day at stroke onset were the strongest clinical predictors. Weather parameters before admission were stronger predictors of disease severity than individual clinical factors. We addressed limitations of previous studies by analyzing a full set of climate parameters: ambient temperature, relative humidity, humidex, atmospheric pressure, and wind speed. Predictive models may help to optimize patient management and develop preventative strategies.

Solar flares are characterized by sudden bursts of electromagnetic radiation from the Sun’s surface, and caused by the changes in magnetic field states in solar active regions. Earth and its surrounding space environment can suffer from various negative impacts caused by solar flares ranging from electronic communication disruption to radiation exposure-based health risks to the astronauts. In this paper, we address the solar flare prediction problem from magnetic field parameter-based multivariate time series (MVTS) data using multiple state-of-the-art machine learning classifiers that include MINImally RandOm Convolutional KErnel Transform (MINIROCKET), Support Vector Machine (SVM), Canonical Interval Forest (CIF), Multiple Representations SEQuence Learner (Mr-SEQL), and Long Short-Term Memory (LSTM)-based deep learning model. Our experiment is conducted on the on the Space Weather ANalytics for Solar Flares (SWAN-SF) benchmark data set, which is a partitioned collection of MVTS data of active region magnetic field parameters spanning over 9 years of operation of the Solar Dynamics Observatory (SDO). The MVTS instances of the SWAN-SF dataset are labeled by GOES X-ray flux-based flare class labels, and attributed to extreme class imbalance because of the rarity of the major flaring events (e.g., X and M). As a performance validation metric in this class-imbalanced dataset, we used the true skill statistic (TSS) score. Finally, we demonstrate the advantages of the MVTS learning algorithm MINIROCKET, which outperformed the aforementioned classifiers without the need for essential data preprocessing steps such as normalization, statistical summarization, and class imbalance handling heuristics.

Heavy rains have created significant threats to human health and life. Floods and other natural disasters, which have a global impact annually, can be attributed to extended durations of intense precipitation. Accurate rainfall predictions are crucial in nations such as Bangladesh, where agriculture is the predominant occupation. The efficiency of machine learning (ML) methods is enhanced by the non-linearity of rainfall, surpassing the effectiveness of other alternative ways. Machine learning techniques show that individual classifiers exhibit worse accuracy than ensemble learning (EL) methodologies. Ensemble Learning techniques are utilized for rainfall prediction and estimating rainfall quantity and daily average temperature to enhance comprehension of the diverse Machine Learning algorithms. This research implements the machine learning techniques and ensemble-based classifier to predict the rainfall occurrence, along with the machine learning regressor models and ensemble-based regressor for the rainfall amount prediction and daily average temperature prediction, using Bangladesh Weather Dataset. The results of the machine learning and ensemble-based models are compared using the Accuracy and F1 score for rainfall occurrence prediction. In contrast, MAE and RMSE evaluation metrics are used for ensemble regressor and regression algorithms for the rainfall amount and daily average temperature prediction. With an accuracy of 83.41% and a recall of 78.17%, the Ensemble Classifier is the best at predicting when it will rain, but its precision of 51.16% stands in for the lowest. The Ensemble Regression model outperforms Linear Regression, Random Forest, and SVR in rainfall amount prediction, with the lowest MAE of 0.36 and RMSE of 0.90. The Ensemble Regression provides the most precise results for daily average temperature prediction with the lowest MAE 0.42 and RMSE 0.54 highlighting its superiority over the other regression models in forecasting temperature with less error. Ensemble approaches consistently lead task performance metrics.

Agriculture is an industry that heavily relies on the environment. The increasing frequency of ex-treme disasters attributable to climate change has adversely affected agricultural land and dis-rupted the stability of the supply of agricultural products. Therefore, we implemented a research program focused on current strategies for adapting to agricultural meteorological disasters. The primary goal was to actively promote disaster prevention information and techniques through information platforms and applications and to provide refined forecasting and an early warning system to enhance preparedness for and recovery from agricultural disasters. This initiative can transform passive mitigation into dynamic action by disseminating crucial information to farmers. We integrated meteorological data and crop characteristics to develop disaster prevention strate-gies for the predisaster, during-disaster, and postdisaster stages to empower farmers to implement autonomous disaster prevention, ensuring their profitability and minimizing the impact of disas-ters. This paper presents an agricultural disaster prevention system involving cross-disciplinary collaboration to establish an early warning system for crop disasters. However, the effectiveness of the early warning system relies on the availability of complete meteorological and crop infor-mation, necessitating high-density agricultural meteorological stations, refined weather forecast services, historical crop damage data, and ongoing research and development on disaster preven-tion technology. In the system, a diversified social media interface is used to provide local farmers with weather forecast information. When impending disasters are identified, the platform acti-vates disaster prevention measures to reduce agricultural losses. This article paper introduces Taiwan’s agricultural disaster prevention system as a complete disaster prevention strategy.

The cassava whitefly, Bemisia tabaci, causes direct damage to cassava in the tropics by feeding on the phloem of leaves, inducing leaf chlorosis and abscission which can result in considerable yield loss of up to about 50% on susceptible varieties. The pest is also known to impart its indirect damage through sooty mold formation. Most of the improved cassava varieties in Uganda were developed for CMD resistance and CBSD tolerance and little is known about their reaction to whitefly infestation. A study was conducted to evaluate the reaction of 24 improved cassava varieties which were sourced from 5 different countries in the region (Uganda, Kenya, Tanzania, Malawi, and Mozambique) to whitefly infestation. These varieties were evaluated in three agro-ecological zones of Uganda, namely; North-Western Savannah Grassland, Lake Victoria Crescent and the Kyoga Plains in the second rains of 2016. Monthly data were taken on adult and nymph counts, whitefly and sooty mold damage, CMD and CBSD severities from 2 up-to 9 Months after planting (MAP). Results show that the whitefly population was highly significantly (P

This paper aims to study the performance of solar collectors with various sizes under different weather conditions of different Japanese cities, i.e. Kofu city, Nagoya city and Yamagata city. The heat generated from the solar collector was used to conduct a biogas dry reforming reactor in order to produce H2 to feed a solid oxide fuel cell (SOFC). This study has revealed that the output temperature of solar collector, Tfb in April and July was higher than that in January and October irrespective of city. The optimum length of absorber (dx) of the collector was 4 m irrespective of city. It was clarified that Tfb in Yamagata city in January and October, i.e. winter and autumn is lower than that in Kofu city and Nagoya city especially, which is influenced by the tendency of a solar intensity (I) strongly, not velocity of surrounding air (ua). On the other hand, Tfb is almost the same in April and July, i.e. spring and summer, irrespective of city. The amount of produced H2 from the biogas dry reforming reactor and the power generated by SOFC using H2 in spring and summer were higher compared to the other seasons irrespective of city. This study has revealed that the highest available households number per month was 4.7 according to the investigation in this study.

Insurance serves as a mechanism to effectively manage and transfer revenue-related risks. We conducted a study to explore the potential financial advantages of index insurance, which protects agricultural producers, specifically sugarcane, against excessive rainfall. Creation of the index involved utilizing generalized additive regression models, allowing for consideration of non-linear effects and quantile generalized additive regression to evaluate relationships with lower quantiles, such as low yield events. To quantify the financial benefits for farmers, should they opt for excessive rainfall index insurance, we employed efficiency analysis based on metrics such as Conditional Tail Expectation, Certainty Equivalence of Revenue, and Mean Root Square Loss. The results of the regression model demonstrate its accuracy in predicting sugar cane yields, with a split testing R2 of 0.691. Additionally, our study suggests that this type of insurance could also apply to sugarcane farmers and other crop producers in regions where extreme rainfall threatens the financial sustainability of agricultural production.

In recent years, the integration of sensors in agriculture has emerged as a pivotal catalyst for transforming conventional farming practices into precision agriculture. Sensor application is impacting the everyday objects that enhance human life. In this special issue, the main objective was to address recent advantages of sensor application in agriculture covering a wide range of topics in this field. This provides overview of diverse application of sensors in agriculture, highlighting their role in enhancing productivity, sustainability, and resource management. sensors are instrumental in monitoring various agriculture parameter such as soil condition, weather, crop health and livestock wellbeing. By collecting real-time data and providing valuable insight, sensors enable precision farming, optimize resource allocation, and minimize environmental impacts. we have to explore the key sensor types used in agriculture, their applications, benefits, and future prospects, emphasizing their crucial roles in the modernization of agriculture and global quest for food security and crop production.

Land surface temperature (LST) is a critical parameter for land surface and atmospheric interactions. However, the applicability of current land surface temperature estimates for field-level hydrological, agricultural, and ecological operations is still challenging due to their coarse spatiotemporal resolution. In the current article, we have downscaled 100 m LST to 10 m by using high-resolution Sentinel-1,2 optical-microwave data and three different models- 1) Thermal Sharpening (TsHARP); 2) Thin Plate Spline (TPS); and 3) Random Forest (RF). The extensive analysis was performed at agricultural farms in the semi-arid (IARI) region of India during the winter and summer seasons of 2020-21 and 2021-22, arid (CAZRI) and per-humid (UBKV) region (2021-2022). The calibration accuracy of the RF model was shown to better agreement with the coefficient of determination (R2) of 0.982, root means square error (RMSE) 0.181 and normalized root means square error (RMSE) 0.061 with their lower values of standard errors over three diverse agro-climatic zones. The findings demonstrate that the validation accuracy of models' varied according to the agroclimatic zones, although RF and TPS consistently outperformed as compared to the TsHARP models.

The ex situ conservation and sustainable exploitation of neglected or underutilized plant species (NUPs) is urgent and paramount. To this end, we focused on Petromarula pinnata (Campanu-laceae), a vulnerable local endemic of Crete (Greece) garnering interest for its agro-alimentary, medicinal, and ornamental value. A GIS ecological profile was established herein based on its natural distribution in Crete containing detailed information on the climatic conditions (mini-mum, maximum, mean temperature, and precipitation) as well as information on 19 bioclimatic variables shaping its natural adaptation. This profiling contributed to better understanding of the species' ecological requirements and facilitated the germination trials employing stored seeds from four distinct populations (two from lowlands and two from semi-mountainous areas) tested at four temperatures (10, 15, 20 and 25oC) and two light condition treatments. The results herein showed that both temperatures and population as well as their interaction significantly affected seed germination rates. Incubation temperatures of 10 and 15oC were the most appro-priate for the successful seed germination of this species (> 81.25% for both temperatures in three out of four populations), with light conditions not affecting seed germination (86% in light and 80% in darkness). The successful germination protocol of P. pinnata seeds opens avenues for fur-ther sustainable exploitation of this valuable yet vulnerable NUP as a new Greek native crop.

Global food waste has a huge impact on the environment, as it is a source of greenhouse gas (GHG) emissions and wasted natural resources. Across the world, over 30% of food is lost or wasted each year. Aside from this, food industry, as well, is one of the biggest sources of agro-industrial waste and by-products, which can be valorized and used for different purposes. Such waste is a good source of bioactive organic compounds which can be extracted without al-tering their properties, where deep eutectic solvents (DESs) can serve as green solvents and excel-lent replacement for volatile organic solvents. Isolated compounds can be used in innovative food production, chemical production, cosmetics and other industries. DESs have attracted extraordi-nary attention due to their advantages such as environmental friendliness, availability and easy preparation, easy handling and utilization of non-toxic components for their formation. Due to those properties, they are a greener alternative to classic organic solvents for many processes, in-cluding extractions. In this paper, we review the utilization of deep eutectic solvents as potential green media for the extraction of organic compounds such as polyphenols, carbohydrates, pro-teins, alkaloids from by-products of the food industry and agro-industrial-waste.

ABSTRACT Heavy metals have been classified as dangerous environmental pollutants. Interest in feasible ways to reduce heavy metals pollution is a noble goal. Adsorption is an effective method based on its effectiveness and low- cost. Nowadays, low-cost bio-sorbents have been utilized to replace costly expensive adsorbents. Rich cellulose materials are effective heavy metal adsorbents, and their performance can be enhanced via chemical modification. The present work reviews the steady progress in the utilization of agriculture by-product wastes as heavy metal bio-sorbent. The unique advantages of these by-products such as renewability, abundance, simple processing, and low cost, justifier this work. The results confirm that the chemically treated agro-wastes exhibit high- removal efficiency (reaches 95% in most cases). The adsorption isotherm studies indicate that the concentration of the metal ions has a significant effect on the adsorption process. The Freundlich model provides a physical adsorption model of the metal ions on the bio-sorbent surface. Langmuir isotherm offers an estimate of the effect of different factors on the efficiency of the process. The adsorption kinetics studies offer an idea of reaction pathways including the adsorption mechanism and the efficiency of the metal ion removal. Applying the pseudo-first-order and pseudo-second-order models describes the adsorption kinetics. This information can be utilized in developing effective strategies to remove heavy metal ions using chemically treated agro-waste. The data confirmed that chemical modification is a significant parameter in the efficiency of metal ion removal. The feasibility and effectiveness of using these cellulosic by-products as bio-sorbent should be given attention and encouraged.

This study explores the application of the Fully Convolutional Network (FCN) algorithm to the field of meteorology, specifically for the short-term nowcasting of severe convective weather events such as hail, convective wind gust (CG) and thunderstorms, short-term heavy rain(STHR) in Gansu. Training data comes from the European Center for Medium-Range Weather Forecasts (ECMWF) and real-time ground observations, the performance of the proposed FCN model based on 2017 to 2021 training datasets demonstrate a high prediction accuracy, with an overall error rate of 16.6%. Furthermore, the model exhibited an error rate of 18.6% across both severe and non-severe weather conditions when testing against the 2022 dataset. Operational deployment in 2023 has yielded an average Critical Success Index (CSI) of 24.3%, a Probability of Detection (POD) of 62.6%, and a False Alarm Ratio (FAR) of 71.2% for these convective events. It is noteworthy that the predicting performance for STHR was particularly effective with the highest POD and CSI, as well as the lowest FAR. CG and hail predictions had comparable CSI and FAR scores, although the POD for CG surpassed that for hail. The FCN model’s optimal performance for hail prediction occurred at the 4th, 8th, and 10th forecast hours, while for CG, the 6th hour was most accurate, and for STHR, the 2nd and 4th hours were most effective. These findings underscore the FCN model's ideal suitability for short-term forecasting of severe convective weather, presenting extensive prospects for the automation of meteorological operations in the future.

Ecological stoichiometry is an important index to reflect ecological interaction and the balance of various elements in ecological processes. At present, the stoichiometric spatial distribution of soil carbon (C), nitrogen (N), phosphorus (P) and potassium (K) in farmland and grassland of Inner Mongolia and its influencing factors are still unclear. In this study, the spatial distribution of soil nutrient content and stoichiometry and the effects of environmental factors on soil C:N:P:K stoichiometry were analyzed under different land use patterns in four agro-pastoral areas. The results showed that there was a geographical gradient in the ecological stoichiometric relationship between farmland and grassland. RCN, RCP, RCK, RNP and RNK showed an "N" distribution from west to east, while RPK showed a "V" distribution. The difference in spatial distribution was more significant than the difference in land use pattern. The ecological stoichiometric relationship of grassland was mainly affected by the content of C and N, and was related to the similarity of soil properties. The field ecological stoichiometry was mainly affected by the content of nitrogen and phosphorus, and had a great relationship with fertilization. The effects of MAT and MAP on soil ecological stoichiometry were related to the inter-regional environmental heterogeneity. The influence of MAP on farmland and MAT on grassland was more significant. It was of great significance for field management according to local conditions, rational utilization of land resources and sustainable development of agriculture to clarify the ecological stoichiometric characteristics of farmland and grassland in agro-pastoral ecotone.

In a recent paper, we extended previous studies on solar-terrestrial relationships by investigating the origin of the March 2012 heatwave in northeast USA. In the present study we check the possible relationship of solar activity with the early March 2012 bad weather in northeast Thrace, Greece. To this end, we examined data from various remote sensing instrumentation monitoring the Sun (SDO satellite), the Interplanetary Space (ACE satellite), the Earth’s magnetosphere (Earth based measurements, NOAA-19 satellite), the top of the clouds (TERRA and AQUA satellites) and the near ground atmosphere. Our comparative data analysis suggests that: (i) the winter-like weather (rainfall, fast winds, decreased temperature) in Thrace started on 6 March 2012, on the same day as the heatwave starting in USA, (ii) during the March 2012 winter-like event in Thrace (6-15 March), the ACE satellite recorded enhanced fluxes of solar energetic particles (SEPs), while SOHO and PAMELA recorded solar protons at very high energies (> 500 MeV), (iii) Thrace experienced particularly intense cyclonic circulation, during periods of magnetic storms on 8 - 10 and 12-13 March, which occurred after the arrival at ACE of two interplanetary shock waves, on March 8 and March 11, respectively, (iv) at the beginning of the two above mentioned periods large atmospheric electric fields were recorded, with values ranging between ~-2000V/m - ~1800V/m on 8 March, (v) the winter-like 8-10 March 2012 weather occurred after the detection of the main SEP event related with a coronal mass ejection released in the interplanetary space as a result of intense solar flare activity observed by SDO on 7 March 2012, (vi) the 8-10 March weather was related with a deep drop of ~630C in the cloud top temperature measured by MODIS/TERRA, which favors strong precipitation. Finally, we analyzed data from the electric power network in Thrace (~410N) and we found, for the first time sudden voltage changes of ~3.5kV in the electric grid in Greece, during the decay phase of the March 2012 superstorm. We discuss the winter-like March 2012 event in Thrace in terms of solar cosmic ray influence on the low troposphere mediated by positive NAO. Finally, we infer that the novel finding of the geomagnetic effects on the electric power grid in Thrace may open a new window into space weather applications research.

Currently, many researchers have an interest in the investigation of the electric field in the fair-weather electric environment, along with its diurnal and seasonal variations across all regions of the world. However, a similar study in the southern part of Siberia has not yet been carried out. In this regard, the study aims to estimate the mean values of the electric field and their variations in the mountain and steppe landscapes using measurement data from the Khakass-Tyva expedition in 2022. The maximum values of positive ion density were noted at the site in the Iyussko-Shirinsky steppe between Belyo and Tus salt lakes in the Khakass-Minusinsk ba-sin. The maximum values of negative ion density were observed at site in the Shol tract in the center part of the Tyva depression. The potential gradient tends to increase with altitude and reaches a maximum in the highlands. The maximum values of the potential gradient were noted in the highlands plateau near the Mongun-Taiga mountain massif and Khindiktig-Khol lake. The diurnal cycles of potential gradient at different observation sites were divided into two groups: 1) a diurnal cycle in the form of a double wave; 2) a daily cycle with a more complex course due to the strong influence of local factors.

Natural fibers have attracted increasing interest as an alternative to produce environmentally friendly and sustainable materials. Particularly, hemp fibers have revealed to be widely used in various industrial applications due to their extremely unique properties. However, hemp can generate a large amount of agro-waste, and results in an attractive source of biopolymers for the development of low-cost materials in alternative to the raw materials and conventional petroleum-based plastics. In addition, deep eutectic solvents (DESs), a new type of truly green solvents, have been tested to remove gums, lignin, and other non-cellulosic components from hemp fibers. Reusing these components dissolved into the DESs to fabricate new materials directly by electrospinning is a very attractive but still unexplored endeavor. Thus, this innovative research to venture new upcycling pathways is focused on the fabrication of composite nanofibers by electrospinning of a gel-based blend of Poly(vinyl alcohol) (PVA) and hemp agro-waste (HW) dissolved into choline chloride (ChCl) : Glycerol (1:2) and ChCl : Urea (1:2) DES mixtures. The results obtained revealed that the produced nanofibers displayed uniform appearance with diameters ranging from 257.72 ± 65.63 nm to 380.78 ± 133.98 nm. In addition, the mechanical properties of the electro-spun composite nanofibers produced from the gel-based blends of HW dissolved in DESs and PVA (HW-DESs_PVA) were found to be superior, resulting in an enhanced tensile strength and Young’s modulus. Furthermore, the incorporation of HW into the nanofibers was able to provide bioactive antioxidant and antibacterial properties. Overall, this study demonstrated a promising, more sustainable, and eco-friendly way to produce electrospun composite nanofibers using HW in a circular economy perspective.

Climate change impacts threaten sustainable development efforts. The magnitude of the impacts, however, varies with socio-ecological characteristics of locations. This is the reason there is con-sensus on the necessity for climate change adaptive capacity building that is country driven, based on and responsive to local needs. However, information on context specific capacity building needs in developing countries is not readily available. The objective of this study was to establish location specific awareness, training, educational, research and technology capacity building needs for climate change adaptation among small-holder farmers in Uganda. Structured interviews were undertaken with 465 households from five agro-ecological zones selected based on the level of vulnerability of agricultural systems to the main climate variation and change hazards. Results reveal substantial capacity building needs in all the zones. Majority of the farmers needed capacity building for interventions on soil water conservation practices for adapting to drought and un-predictable rainfall. For all zones, education, research, and technology were perceived as key needs. However, the needs varied among zones. These results demonstrate the importance of context specificity in adaptation efforts. The study provides agro-ecological and social system specific in-formation for climate change adaptation planning and policy interventions for effective capacity building

In space application, hybrid pixel detectors of the Timepix family have been considered mainly for measurement of radiation levels and radiation dosimetry in low earth orbits. By the example of the Space Application of Timepix Radiation Monitor (SATRAM), we demonstrate the unique capabilities of Timepix-based miniaturized radiation detectors for particle separation. Using a novel method for proton spectrum reconstruction, we were able to measure the spectrum of protons trapped in the inner Van-Allen radiation belt, for the first time with a single-layer detector. We assess the measurement stability and the resiliency of the detector to the space environment demonstrating that, even though degradation is observed, data quality has not been affected significantly over more than 10 years. Based on the SATRAM heritage and the capabilities of the latest-generation Timepix-series chips, we discuss their applicability for use in a compact magnetic spectrometer for deep-space mission or in the high radiation environment of the Jupiter radiation belts, and their capability for use as single-layer X- and γ-ray polarimeter. The latter was supported by measurement of the polarization of scattered radiation in a laboratory experiment, where a modulation of 80% was found.

With the rapid development of artificial intelligence, machine learning is gradually becoming popular in predictions in all walks of life. In meteorology, It is gradually competing with traditional climate predictions dominated by physical models. This survey aims to consolidate the current understanding of Machine Learning (ML) applications in weather and climate prediction—a field of growing importance across multiple sectors including agriculture and disaster management. Building upon an exhaustive review of more than 20 methods highlighted in existing literature, this survey pinpointed eight techniques that show particular promise for improving the accuracy of both short-term weather and medium-to-long-term climate forecasts. According to the survey, while ML demonstrates significant capabilities in short-term weather prediction, its application in medium-to-long-term climate forecasting remains limited, constrained by factors such as intricate climate variables and data limitations. Current literature tends to focus narrowly on either short-term weather or medium-to-long-term climate forecasting, often neglecting the relationship between the two, as well as general neglect of modelling structure and recent advances. By providing an integrated analysis of models spanning different time scales, this survey aims to bridge these gaps, thereby serving as a meaningful guide for future interdisciplinary research in this rapidly evolving field.

Prescriptions for fuels management are universally applied across forest types in British Columbia, Canada, to reduce fire behaviour potential in the wildland-urban interface. Fuel thinning treatments are assumed to reduce the potential for sustained ignition and crown fire initiation by reducing surface fuel loading. We hypothesized that these prescriptions are not appropriate for the coastal wet forests in the Whistler region of the province. Our study measured the efficacy of fuel thinning treatments in four stands located in the Whistler community forest. We examined several in-stand microclimate variables during snow melt in the spring and at the height of fire danger in late summer, at thinned and unthinned locations paired using GIS- analysis. We found that thinning increased the wildfire risk based on the differences between unthinned and thinned areas in the same forest stand.

Accurate prediction of the degree of airport delays under the influence of convective weather is crucial for collaborative traffic management implementation and improving the efficiency of airport operations. However, existing studies usually only consider numerical-type quantitative features of weather-affected traffic in their models, and lack the introduction of spatial information to comprehensively portray the traffic operation scenarios under the influence of weather. In order to overcome this problem, this paper firstly designs a new image representation of weather-affected air traffic, and constructs a multi-channel traffic and weather scene image (MTWSI) by populating the airspace two-dimensional grid with traffic and meteorological information to represent the overall traffic operation situation in the terminal area under the influence of convective weather; then, a deep convolutional neural network-based airport delay prediction model ( ADLCNN), which takes MTWSI images as input and uses a specific CNN model to extract the deep features that affect traffic operation in it to input into the subsequent classification algorithm to predict the flight delay level; finally, a series of comparative experiments are carried out on the operational data of Guangzhou Baiyun Airport, and the experimental validation shows that, compared with the traditional machine learning methods, the proposed CNN-based airport delay prediction The experimental validation shows that the proposed CNN-based airport delay prediction model has satisfactory prediction performance compared with the traditional machine learning methods, which also proves that the proposed MTWSI method can more comprehensively respond to the real traffic conditions.

Both globally and in Hungary, agriculture is one of the industries that is most vulnerable to weather and climate extremes. Intense temperature rises, spatial and temporal variations in precipitation, and significant changes in extreme climatological and weather parameters have contributed to changes in the conditions of cropland, crop losses, and impacts on crop quality in recent years. This paper depicts the transformation of the domestic agricultural sector due to the extreme drought shock of 2022, as well exploring the adaptation strategies applied. The research is based on official agro-climate database and crop data, and the temperature, precipitation, and radiation during the growing season are all examined. The agro-meteorological properties in Hungary had to be investigated for the entire year and all four of its seasons, with indicator analysis projected onto the ever-increasing and dormant seasons. Long-term climate analysis is necessary to understand the historic drought of 2022 and the success of future adaptation and mitigation techniques. The results can help smallholders effectively reduce the adverse impacts of drought conditions, thereby increasing their adaptation to similar shocks.

As major natural disasters in grassland areas, fires and dust events seriously threaten human safety, property, and animal husbandry. Furthermore, these phenomena may be mutually reinforcing, which can lead to more severe cascading disasters. However, few studies have been on the mechanisms of grassland fire and dust events disaster chain. Therefore, we selected Dornod aimag (province), a typical temperate grassland, as the study area and analyzed the spatiotemporal variation patterns of grassland fires and dust weather, as well as the effect of grassland fires on dust weather based on MCD64A1 Burnt Area data and SYNOP dust data. The mechanism of grassland fires on dust weather was further investigated using the MOD13A3 vegetation index product and ERA5 wind speed, wind direction, and precipitation data. The results revealed that grassland fires and dust weather varied spatially across the study area. Furthermore, grassland fires occurred mainly in spring (April to May), summer (June), and autumn (October), while dust weather mainly occurred in spring (March to May). Moreover, autumn, winter, and spring cumulative grassland fires (both days and area) substantially affected the spring total dust weather days and dust storm days, particularly the spring cumulative dust storm days. Additionally, higher precipitation in the summer of 2014 resulted in higher vegetation coverage and more fuel in the autumn and winter of 2014, and even in the spring of 2015. As a result, the cumulative grassland fire days was higher, and the area was larger from September 2014 to April 2015, leading to a considerable increase in the cumulative dust storm days in May 2015. This study has important implications for disaster prevention and mitigation, ecological and environmental protection, and sustainable development in grasslands.

The spatial and temporal changes in social vulnerability to natural hazards in Mexico are analyzed. To this end, using census data from 2000, 2010, and 2020 and a statistical method, different indices were computed, and with a GIS-based approach, patterns of social vulnerability are examined. In addition, a risk assessment test for severe weather (thunderstorms, hailstorms, and tornadoes) is made out. The results show different common social vulnerability driving factors in the three analyzed years, with root causes that have not been addressed since the beginning of the century. Likewise, a wider gap between Mexico's most and least vulnerable populations is identified. The changes in spatial patterns respond to different historical situations, such as migration, urbanization, and increased population. Also, poverty, ethnicity, and marginalization factors located in very particular regions in Mexico have remained relatively the same in the last few years. These situations have strongly influenced the spatial-temporal distribution of vulnerability in the country. The role of social vulnerability in the disaster risk to extreme events such as thunderstorms, hailstorms, and tornadoes in Mexico is fundamental to understanding changes in disaster distribution at the national level, and it is the first step to generating improvements in integrated risk management.

Grapevine leafroll disease (GLRD) is the most globally prevalent and destructive disease complex responsible for significant reductions in grape yield and quality as well as wine production. GLRD is associated with several positive-strand RNA viruses of the family Closteroviridae, designated grapevine leafroll-associated viruses (GLRaVs). However, the specific etiological role of any of these GLRaVs in GLRD has not been demonstrated. Even though GLRaV-3 is considered the chief GLRD agent, little is known about the molecular, cellular and pathological properties of this virus. Such knowledge gap is due to multiple factors, including unavailability of biologically active virus cDNA clones and the lack of reliable experimental systems for launching grapevine infection using such clones. In this work, we have tested four methods for inoculating tissue cultured grapevine plantlets with cDNA clones of GLRaV-3: i) vacuum agro-infiltration; ii) agro-pricking; iii) agro-drenching and iv) agro-injection. We have shown that vacuum agro-infiltration was the most effective among these methods. Further, we examined the impacts of different experimental conditions on the survival and infectivity rate of grapevines after infiltration. To verify the infectivity rate for different treatments, we used RT-PCR, RT-qPCR, and Western blotting. We have found that humidity plays a critical role in the survival of plantlets after agro-infiltration and that the use of RNA silencing suppressor, and dormancy treatment both had strong effects on the infection rates. To our best knowledge, the experimental protocol reported here is the most effective system for launching infection of grapevine using cDNA clones of grapevine viruses featuring up to 70% infection rate. This system has strong potential to facilitate grapevine virology research including fulfillment of Koch’s postulates for GLRD and other major virus diseases as well as the molecular, cellular, and pathological properties of GLRaVs and potentially other important grapevine viruses.

Measuring the Sun’s magnetic field is a key component of monitoring solar activity and forecasting space weather. The main goal of the research presented in this paper is to investigate the possibility of reducing the dimensions and weight of a solar magnetograph while preserving its optical quality. This article presents a range of different designs, along with their advantages and disadvantages, and an analysis of the optical performance of each. All proposed designs are based on the Magneto Optical Filter (MOF) technique. As a result of the design study, a miniaturised solar magnetograph is proposed with an ultra-compact layout. The dimensions are 345mm × 54mm × 54mm and the optical quality is almost at the diffraction limit. The design has an entrance focal-ratio of F/17.65, with a plate scale of 83.58 arcsec/mm at the telescope image focal plane and produces a magnification of 0.79. The field of view is 1920 arcsec diameter, equivalent to ±0.27degrees, sufficient to cover the entire solar disk.

The brief paper utilises Zimbabwe as a case study to discuss the effects of El Nino in Southern Africa. It offers potential adaptation and mitigation measures for farmers to prepare for the forecasted El Nino-influenced rainy season 2023/24 and the future. To reduce climate and weather hazards connected with El Nino, the brief report suggests anticipatory action methods be applied in southern Africa, using Zimbabwe as a case study. To protect farmers' livelihoods and enhance drought readiness for the forthcoming agricultural seasons, the paper suggests a degree of strategic, tactical, and operational decision-making that the agriculture industry should adhere to. It emphasised the significance of providing farmers with knowledge and advice regarding drought and heat stress, including cultivating crop varieties and livestock and sufficient fire safety precautions. The brief paper calls to advocate for anticipatory action to avert El Nino in Southern Africa.

This article explores the intersection of innovative leadership and climate change, aiming to provide outstanding contributions to the existing body of knowledge in this field. The article identifies the critical role of innovative leadership in driving sustainable solutions and strategies for addressing climate change. It examines the defining characteristics and behaviors of innovative leaders and explores how their transformative leadership approaches can create a positive impact on climate change mitigation and adaptation efforts. The article also highlights the importance of fostering innovation within organizations and societies to tackle the complex challenges posed by climate change. Through a comprehensive review of relevant literature and case studies, this article presents novel insights, theoretical frameworks, and practical implications for policymakers, organizations, and individuals involved in climate change leadership.

We report on ground-based measurements of the atmospheric electric field (Ez= -Potential Gradient (PG)) and current density (Jz) that were conducted at two locations in Israel. One is the Emilio Segre cosmic ray station located on Mt. Hermon (34.45 N, 2020 m AMSL) located in northern Israel near the Syrian-Lebanon border and at the other at the Wise astronomical observatory in the Negev desert highland plateau of southern Israel (31.18 N, 870 m AMSL). We searched for possible effects of strong, short-term solar events on the potential gradient and the vertical current density, as disruption to the Global Electric Circuit are often observed following strong solar events. The first case study (St. Patrick Day, 17 March 2015) was classified as the strongest event of 2015. The second case study (8 Sep 2017) was categorized as the strongest event of 2017 and one of the twenty strongest events on record to date. The results show that the electrical parameters measured at ground level at both stations were not affected during the two massive proton events and the ensuing geomagnetic storms. The magnetospheric shielding in lower latitudes is strong enough to shield against flux of energetic particles from solar events, obscuring any impact that may be noticeable above the local daily variations induced by local meteorological conditions (aerosol concentrations, clouds, high humidity, and wind speed) which were investigated as well.

Rice cultivation serves as a significant anthropogenic source of methane (CH4), a primary greenhouse gas, and nitrous oxide (N2O), a secondary greenhouse gas. Although N2O emissions remain relatively small compared to CH4 emissions, they are remarkably affected by nitrogen-fertilized soil conditions during rice cultivation. To date, investigations are very limited concerning N2O emissions from rice cultivation in relation to environmental factors such as temperature, rainfall, and soil properties. In this case study, we investigated the characteristics of N2O emissions in the central region of South Korea, where a single rice cropping cycle occurs annually over a span of three years, from May 2020 to May 2023. We investigated the impact of variations in temperature and soil moisture on N2O emissions during rice cultivation. In this context, we attempted to discover the complex dynamics of N2O emissions by comparing longer fallow periods with the rice cultivation periods and extended non-dry periods with irrigated periods. We observed that extremely high N₂O flux events encountered during the fallow period appeared to have a substantial impact on the yearly cumulative N₂O emissions, surpassing the impact of cumulative N₂O emissions during the rice cultivation period. We found that high N₂O flux events arose not only from artificial nitrogen inputs but also due to temperature and soil moisture variations influenced by weather conditions. We concluded that assessing N₂O emissions solely based on the rice cultivation period would underestimate annual emissions. To prevent underestimation of N₂O emissions, continual gas collection throughout a year covering both rice cultivation and fallow phases is required in align with monitoring of varying temperature and soil moisture conditions. Based on our findings, we recommend that at least a three whole year evaluation period is needed to ensure estimation accuracy of N₂O emissions under varying nitrogen fertilization rates. Also, the findings from this study would help prepare a further revision or refinement of N2O emission factor from rice cultivation in the national greenhouse gas inventories defined by the inter-governmental panel on climate change (IPCC).

Super Typhoon Saola came very close to Hong Kong on 1 and 2 September 2023, necessitating the issuance of No. 10 hurricane signal, the highest tropical cyclone warning signal, in Hong Kong. While there were widespread damages in Hong Kong, no people were killed in the event with effective early warning. It is rare that a super typhoon came very close to Hong Kong and this paper is the first part in the series of the documentation of Saola to summarize the interesting observations of Saola near Hong Kong for future reference by weather forecasters, including sur-face observations, upper air observations, microburst alert from weather radars, and turbulence intensity based on spectral width measurement of radars.

Deployments of real-world object-detection systems often experience a degradation in performance over time due to concept drift. Systems that leverage thermal cameras are especially susceptible because the respective thermal signatures of objects and their surroundings are highly sensitive to environmental changes. In this study, a conditioning method is investigated. The method aims to guide the training loop of thermal object detection systems by leveraging an auxiliary branch to predict the weather, while directly or indirectly conditioning the baseline detection system. Leveraging such an approach to train detection networks does not necessarily improve the performance of native architectures, however, it can be observed that conditioned networks manage to extract a signal from thermal images that guides the network to detect objects that baseline models miss. As the extracted signal appears to be quite noisy and very challenging to regress accurately, further work is needed to identify an ideal optimization vector.

CO2, CH4 and CO are the most critical atmospheric gases in terms of their impact on the radiative system, air quality and health. This work provides information on the direction of source areas and potential sources of emissions and shows many aspects of these gases by a statistical analysis using bivariate polar diagrams and local weather conditions (e.g., temperature, wind speed and wind direction) recorded at the Lamto station (LTO, 6°31N and 5°02W) in Côte d’Ivoire over the 2014-2018 period. The results show that the main regions contributing to the high concentrations of CH4 (> 1925 ppb) and CO2 (> 420 ppm) in the GSS, GSP, PSS and PSP seasons are the North and Northwest sectors of Lamto. In these directions, CH4 and CO2 concentrations are associated with wind speeds less than 6 m.s-1, due to the influences of local sources as emissions resulting from the degradation of organic matter submerged during the impoundment of the Taabo dam, and/nor human activities linked to the practice of intensive agriculture. In addition, the high concentrations of CO (> 350 ppb) are observed in GSS in the North, North-West, North-East and East sectors for wind speeds less than or equal to 9 m.s-1, due to the influences of both local and distant sources. The correlation coefficients between CH4 and CO, and between CH4 and CO2 are positive and significant in all sectors. However, those calculated between CO2 and CO have showed both low and high values in all seasons.

For autonomous driving, perception is a primary and essential element that fundamentally deals with the insight into the ego vehicle’s environment through sensors. Perception is a challenging task suffering from dynamic objects and continuous environmental changes. The issue gets worse due to interrupting the quality of perception by adverse weather like snow, rain, fog, night light, sand storm, strong daylight, etc. In this work, we have tried to improve camera-based perception accuracy, such as autonomous driving-related object detection in adverse weather. We proposed the improvement of YOLOv8-based object detection in adverse weather through transfer learning using merged data from various harsh weather datasets. Two prosperous open-source datasets (ACDC and DAWN) and their merged dataset were used to detect primary objects on the road in harsh weather. A set of training weights were collected from training on the individual datasets, their merged version, and several subsets of those datasets according to their characteristics. A comparison between the training weights also occurred by evaluating the detection performance on the above-mentioned datasets and their subsets. The evaluation revealed that using custom datasets for training significantly improves the detection performance compared to the YOLOv8 base weights. And using more images through the feature-related data merging technique steadily increases the object detection performance.

Recently, NOAA has included GNSS (Global Navigation Satellite System) Radio Occultation (RO) data as one of the crucial long-term observables for weather and climate applications. To include more GNSS RO data in the numerical weather prediction system, the NOAA Commercial Weather Data Pilot program (CWDP) started to explore the commercial RO data available on the market. After two rounds of pilot studies, the CDWP decided to award the first Indefinite Delivery Indefinite Quantity (IDIQ) contract to GeoOptics and Spire Incs. in 2020. This study examines the quality of Spire data products for weather and climate applications. Spire RO data are collected from commercial CubeSats through careful comparison with the data from Formosa Satellite Mission 7–Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2), ERA-5, and high-quality radiosonde data. The results demonstrated that although with lower Signal-Noise-Ratio (SNR) in general, the pattern of the lowest penetration height for Spire is similar to those for COSMIC-2. The Spire and COSMIC-2 penetrate heights are between 0.6 and 0.8 km altitude at the tropical oceans. Although using different GNSS RO receivers, the precision of Spire STRASP receivers is of the same quality as those of COSMIC-2 Global Positioning System - GPS, GALILEO, and GLObal NAvigation Satellite System – GLONASS (TGRS) receivers. The retrieval accuracy from Spire is very compatible with those from COSMIC-2. We validated Spire temperature and water vapor profiles by comparing them with collocated radiosonde data. Generally, over the height region between 8 km and 16.5 km, the Spire temperature profiles match those from RS41 RAOB very well with temperature biases < 0.02 K. Over the height range from 17.8 to 26.4 km, the temperature differences are ~-0.034 K with RS41 RAOB being warmer. We also estimated the error covariance matrix for Spire, COSMIC-2, and KOMPSAT-5. Results showed that the COSMIC-2 estimated error covariance values are slightly more significant over the oceans at the mid-latitudes (45oN-30oN and 30oS-45oS), which may also be owing to COSMIC-2 SNR being lower at those latitudinal zones.

The biomethane accumulation of several combinations of whey and sugarcane molasses, inoculated with sludge from a treatment facility of one of the dairy enterprises of Imbabura, was assessed in the current experiment at a concentration of 0.5 g/l COD. The whey: molasses (W: M) ratios for each treatment were 0:100, 25:75, 50:50, 75:25, and 100:0, with a constant temperature of 37°C and an initial pH adjustment of 7.5. Half a litre of total mixes was used for each treatment in duplicate. Six kinetic models were evaluated to account biomethane accumulation in anaerobic co-digestion processes in batch of whey and sugarcane molasses. Five of these have been tested by other researchers, and one was developed by modifying a first-order model to consider changes in the biomethane accumulation profile. This proposed model, along with the modified two-phase Gompertz model, resulted in the ones that were best able to adjust the experimental data, obtaining in all cases an R² ≥ 0.949, indicating the accuracy of both models. In addition, the proposed here model has five parameters, one less than the modified two-phase Gompertz model, making it more straightforward and robust.

Thrombosis is characterized by the pathological formation of fibrin clots within a blood vessel, leading to the obstruction of blood flow. Fibrinolytic enzymes from microorganisms have exhibited promising effects to dissolve clots in a more efficient and safe way. Then, the aim of this study was to evaluate the biomass and fibrinolytic enzyme production of Tetradesmus obliquus under autotrophic and mixotrophic conditions using different concentration of corn steep liquor (CSL). Different extraction and precipitating methods were tested, and the enzyme was purified by ion exchange chromatography. More advantageous culture condition was mixotrophic using 0.25% CSL, showing the highest values of biomass productivity (Px = 169.3 ± 44.36 mg∙L-1day-1) and specific growth rate (µmax = 0.17 ± 0.00 day-1), and significant fibrinolytic production (391.34 ± 40.03 U∙mg-1). Moreover, fibrinolytic activity was higher when extracted by homogenization and precipitated using acetone, which exhibited clear zone of fibrin degradation in the fibrinolytic plate assay. Additionally, the purified enzyme showed specific activity of 1176.90 ± 140.37 U∙mg-1 and molecular weight around 97 kDa. Finally, the enzyme has higher enzymatic activity than various fibrinolytic enzymes, and the obtained enzyme has potential to be developed as a therapeutic agent in thrombosis treatment. Additional studies are need to investigate the biochemical properties and biological profile of this enzyme.

From December 2021 to May 2022, MeteoSwiss and Meteomatics conducted a proof of concept to demonstrate the capability of Meteodrones to provide data of sufficient quality and reliability on a routine operational basis. Over 6 months, Meteodrones MM-670 were operated automati-cally 8 times per night at Payerne, Switzerland. 864 meteorological profiles were measured and compared to co-located standard measurements including radiosoundings and remote-sensing instruments. To our knowledge, it is the first time that Meteodrone’s atmospheric profiles are evaluated in such an extensive campaign. The paper highlights two case studies that showcase the performance and challenges of measur-ing temperature, humidity, and wind with a Meteodrone. It also focuses on the overall quality of the drone measurements. Throughout the campaign, the availability of Meteodrone measure-ments was 75.7%, with 82.2% of the flights reaching the nominal altitude of 2000m above sea level. To assess the quality of the Meteodrone measurements, the radiosondes were used as a refer-ence, comparing them to the WMO (World Meteorological Organization) requirements [1]. The temperature measurements by the Meteodrone met the "breakthrough" target, while the humid-ity and wind profiles met the "threshold" target for high-resolution numerical weather predic-tion. The temperature measurement quality was comparable to that of a microwave radiometer, and the humidity quality was similar to that obtained from a Raman Lidar. However, the wind measurements by a Doppler Lidar were more accurate than the estimation provided by the Me-teodrone. During this campaign, the data quality could be improved and at the end of the campaign, Me-teodrone quality reached the “goal” target for temperature and the “breakthrough” target for humidity. This campaign marks a significant step towards the operational use of automatic drones for me-teorological applications.

To provide the population with food, it is very important to re-cultivate "abandoned lands" that have been retired from agricultural use. Conversion of arable land into agricultural abandoned lands in the south of Kazakhstan is, first of all, was primarily associated with salinity. For the purposes of sustainable development, there is a need to develop proposals for re-mastering by studying their current state, reviewing world studies on the reclamation of salt-affected soils. Therefore, this study was devoted to the study of the current environmental and reclamation conditions of the abandoned lands of Otyrar district in the south Kazakhstan, as well as discussion and recommendations on their development. In the course of the research, the historical method is used in the study of the emergence and formation of abandoned lands, the method of geographical analysis during the territorial analysis of the research object, the statistical and comparison methods in showing the complexity and intensity of the problems, the cartographic, geo-informational and field research (reconnaissance) methods in the study of the condition of the abandoned lands in the research object and methods of grouping and analysis were used in the work with scientific data on the topic of research. By deciphering space images with the help of geoinformation technologies, it was revealed that the area of abandoned lands in the research object is 13688.9 ha, including the area of non-saline soils – 83.9 ha, weakly saline soils – 984.4 ha, medium saline soils – 2398.3 ha, highly saline soils – 10222,1 ha. A review of the methods and technologies proposed by scientists for the development and melioration of salt-affected lands was made. Taking into account the ecological and reclamation state of the object of research along with the material and technical capabilities of farms, 2 methods of developing abandoned lands (organic and agro-innovative) in the research object are proposed and the need for their use in case of soil salinization has been scientifically justified.

In the overall context of national eco-conservation civilization development and considering the hills in the Beijing-Tianjin-Hebei-Shanxi region, the local forest coverage rate is gradually on the rise, and so is the risk of forest fire. Every year, satellites can monitor and detect hundreds of fire spots on average. In recent years, serious fire took place for several times, leading to loss of life and personal injury. Then the fire-fighting work consumed huge amount of labor and material resources, exerting negative social impacts in many ways. Thus, the analysis of fire climate background in this region and the forecast based on fire weather indexes constitute an important part in the forest fire risk management. Located in the tropical monsoon climate zone, four provinces and municipalities of Beijing, Tianjin, Hebei and Shanxi are similar in terms of climate characteristics. This study tries to analyze the characteristics about fire spots under satellite monitoring, fire climate and fire weather index. The probability distribution analysis of weather index about fire spots in history and key meteorological observation stations as well as the weather index analysis in the early stage of typical fire indicate that the Fine Fuel Moisture Content (FFMC) and the Initial Spread Index (ISI) in the Fire Weather Index (FWI) system is applicable to the fire risk analysis in the Beijing-Tianjin-Hebei-Shanxi region, which can also effectively indicate the fuel moisture and spread conditions. As regards to the average level in Beijing-Tianjin-Hebei-Shanxi, when the FFMC is smaller than 93.5 and the ISI is also smaller than 5.0, there is a low risk of fire; when the FFMC is above 94 and the ISI is larger than 10.0, there is a high risk of fire; and the fire risk will be extremely high if the FFMC is over 96 and the ISI is above 13.0. In the practical fire-fighting management, the fire danger class can be released in reference to the ISI classes and threshold values of key meteorological observation stations under analysis provided by this study.

Strong convective storms can be a serious threat to modern society, influencing both the economy and human life. Thunderstorms can bring much-needed rain after a dry spell, but the nature of thunderstorm mostly causes damage due to high wind associated with it and heavy intensity rain that mostly causes quick overlandflow and flashfloods. The paper focused on the 2011 Pukkelpop event in Belgium where due to heavy thunderstorm a lot of financial damage occurred, and five casualties. In this paper we look into the basic elements that enforces the creation of a thunderstorm like CAPE and windshear. A representative grid has been taken from the Alaro-0 model of 12.5 km resolution for the analysis of the thunderstorm over Belgium which is detailed out in the methodology. The model runs of Alaro-0 from Royal Meteorological Institute of Belgium (RMI) is used for historical and future years for CAPE and Shear. We look into STEnv which is a composite number that can provide some insight on occurrence of thunderstorm, and STEnv is when CAPE*Shear exceeds certain threshold. In this study the threshold has been chosen as 97%. Only summer months have been taken into account into this study as summer(s) has the most thunderstorm in Belgium. The analysis of CAPE and STEnv over historical period (1977-2005), near future (2041-2069) and far future (2071-2099) shows significant increase in occurrence of thunderstorm in the 2071-2099s, specially in extreme cases such as 97% and 99% quantile. CAPE (J/Kg) increases in all future scenarios compared to the historical period. In 2071-2099s, CAPE increases from 2100 J/Kg to 2300 J/Kg (90% quantile) and from 3500 J/Kg to 3900 J/Kg (99% quantile) compared to the historical period. The number of STEnv increases from 300 to 378 in top 10% storm case (90% quantile), 158 to 218 in top 5% storm (95% quantile), 97 to 153 in top 3% storm (97% quantile) and 33 to 66 in top 1% storm (99% quantile) with a 90% confidence interval overall. Shear mostly remains unchanged. The study also shows spatial variation of CAPE, shear and STEnv over Belgium for present and future scenarios in different extremities (90%, 95%, 97% and 99% quantiles). There is a good indication that increase in CAPE translates to increase in STEnv, which is an accepted measure to predict occurrence of thunderstorm. Future climate change also amplifies the occurrence of thunderstorm, which is coherent with relevant studies. The uncertainty related to the study can be reduced with more detailed dataset and involvement of numerical weather prediction models beyond data statistics.

Despite the important role of offshore renewable energies in the energy transition, the economical viability is still unclear. Therefore, an appropriate site selection is crucial. Besides the energy potential, the impact of operation and maintenance (O&M) aspects on the location can be critical. Traditional accessibility assessment metrics do not allow a comprehensive evaluation. Therefore, the present paper suggests a novel, technology-informed metric, incorporating the overall set of most critical aspects, i.e. metocean conditions, visibility due to sunlight and sea fog, system failures, and O&M logistics. Among the different aspects, limited visibility is shown to be highly relevant with a reduction of up to 60% in accessibility. The study assesses accessibility in 5 different locations across Europe. On the one hand, accessibility is shown to be less sensitive to long-term resource variations with a reduction of 5% in the last 6 decades. On the other hand, accessibility is shown to be inversely proportional to the energy potential overall, meaning that as energy potential increases, accessibility is reduced, increasing downtime, reducing final energy generation and increasing the final cost of energy. As a consequence, site selection should combine energy potential and accessibility assessments, which is enabled by the technology-informed metric presented here.

With advances in the development of autonomous vehicles (AVs), more attention has been paid to the effects caused by adverse weather conditions on them. It is well known that the performance of self-driving vehicles is reduced when they are exposed to stressors that impair visibility or generate water or snow accumulation on sensor surfaces. This paper proposes a model to quantify weather precipitation, such as rain and snow, perceived by moving vehicles based on outdoor data. The modelling covers a wide range of parameters, such as varying wind direction and realistic particle size distributions. The model allows the calculation of precipitation intensity on inclined surfaces of different orientations and on a circular driving path. The modelling results were partially validated against direct measurements carried out by a test vehicle. The model outputs showed strong correlation with the experimental data for both rain and snow. Mitigation strategies for heavy precipitation on vehicles can be developed and correlations between precipitation rate and accumulation level can be traced using the presented analytical model. Dimensional Analysis of the problem has highlighted the critical parameters that can help the design of future experiments. The obtained results highlight the importance of the angle of the sensing surface on the perceived precipitation level. The proposed model is used to analyze optimal orientations for minimization of the precipitation flux, which can help to determine the positioning of sensors on the surface of autonomous vehicles.

The evaluation of suitability for bioclimatic viticulture indices (BVIs) zones, as with any other crop, requires a well-founded knowledge of the spatial variability in climate data, which is also used to assess different grapevine cultivars and to delimit appropriate regions for vineyard production under the current climate and projected climate change. BVIs, on the other hand, are typically calculated using a small number of locations linked by climatic station sites or applied using a coarsely-resolution distributed dataset. Furthermore, often the methods applied to precisely delineate symmetrical regions are not appropriate to generate accurate maps. To provide an analysis using three temperature-based indices and the hydrothermal coefficient (HTC), quantifying their spatial variation, and representing the spatial patterns of each BVI throughout the Jabal Al Arab viticulture—one of the most important Syrian viticulture regions in the Eastern Mediterranean—daily temperature data from 15 meteorological stations and 57 rain gauges, considering the 1984–2014 period, and downscaled future scenarios (RCPs based on CanESM2), considering the 2016–2100 period, were utilized. The statistical method of each BVI was analyzed, and, later, they were mapped by using GIS and a hybrid interpolation (regression-kriging) approach. The regression fitting method revealed that the sum accumulation of heat and the hydro-thermal index during the growing season were highly related to elevation and distance to the seacoast variables; however, the viticulture zones varied spatially depending on which index was used and under suggested future scenarios compared to the current climate. The spatial distribution of climate in the Jabal Al Arab regions exhibits significant variability. The findings suggest that climate change projections indicate a prevalence of warmer conditions in the future. Under the RCP scenarios, the territory can be categorized into up to three bioclimatic classes for both the Heat Index (HI) and the Winter Index-Growing Degree Days (WI-GDD), in contrast to the current climate which has six classes. These results provide valuable insights into the suitability of viticulture within each climatic region and facilitate the identification of homogeneous zones. The utilization of consistent bioclimatic indices and an advanced hybrid interpolation method has enabled the delineation and comparison of bioclimatic variables of Jabal Al Arab with other regions worldwide. Such comparisons should be considered when selecting grapevine varieties and assessing the potential for grape production.

Global warming is increasing the frequency and intensity of compound drought-heat events (CDHEs), potentially leading to larger and more extreme fire seasons in mesic forests. Wildfire activity in subtropical China, under the influence of monsoonal rainfall, was historically limited to dry winters and rare in rainy summers. Here we seek to test that this area is on the brink of a major change in its fire regime characterized by larger fire seasons, extending into the summer, leading to increases in burned area. We analyze fire activity in Chongqing Municipality (46,890 km2), an important area in subtropical China hosting the Three Gorges Reservoir Area. We observed significant increases in summer forest fires under anomalous dry-hot summer conditions, where total burned area was 3-6 higher than the historical annual mean (largely confined to the winter season). Vapor pressure deficit, an indicator of hot and dry conditions, was a strong predictor of fire activity, with major wildfires occurring on days where VPD was higher than 3.5kPa. Results indicate that major wildfire activity may occur in the area as a result of climate change, unless strong fire prevention policies are implemented.

An accurate flux measurement of low-energy charged particles trapped in the magnetosphere is necessary for space weather characterization and to study the coupling between the lithosphere and magnetosphere, which allows for the investigation of the correlations between seismic events and particle precipitation from Van Allen belts. In this work, the project of a CubeSat space spectrometer, the Low-Energy Module (LEM), is shown. The detector will be able to perform an event-based measurement of the energy, arrival direction, and composition of low-energy charged particles down to 0.1 MeV. Moreover, thanks to a CdZnTe mini-calorimeter, the LEM spectrometer also allows for photon detection in the sub-MeV range, joining the quest for the investigation of the nature of Gamma-ray bursts. The particle identification of the LEM relies on the ΔE−E technique performed by thin silicon detectors. This multipurpose spectrometer will fit within a 10 × 10 × 10 cm3 CubeSat frame, and it will be constructed as a joint project between the University of Trento, FBK, and INFN-TIFPA. To fulfil the size and mass requirements, an innovative approach, based on active particle collimation, was designed for the LEM; this avoids the heavy/bulky passive collimators of previous space detectors. In this paper, we will present the LEM geometry, its detection concept, the results from the developed GEANT4 simulation, and some characterisations of a candidate silicon detector for the instrument payload.

Climate data are important in building a hydrological risk assessment model. The ClimInonda web application enables interactive and dynamic visualizations of different data collected from different weather stations in the study area on a single platform, allowing users to explore and analyze data in an easy way. This can assist decision-makers and stakeholders in understanding the current state of the environment and in identifying areas of flooding risk. Visualizations can include different types of data, such as satellite imagery, weather data, and terrain data, and can be displayed using various techniques, such as heat maps, contour maps, and 3D models by providing easy-to-understand visualizations. The different stations of the Gafsa and Kasserine governorates in the study area are included and other stations of the Algerian territory (Tebessa governorate) are incorporated. This web application also provides the capability to include each user's stations.

Agro-ecotourism is a tourism development activity in a location with ecological and agricultural advantages, leading to business utilization to generate economic value. Agro-ecotourism in West Java has a selling point in the form of natural potential, agriculture, arts, handicrafts, to the traditional culinary processing typical of the village. This research aims to analyze the sustainability level and the sensitive factors or attributes that affect the level of sustainability of community-based agro-ecotourism in West Java. The study used a survey method on 237 agritourist actors. Data were analyzed using Multi-Dimensional Scaling in the Rapid Appraisal-Tourism technique. The study results show that community-based agro-ecotourism in West Java is quite sustainable (57.07). This study analyzed sustainability through economic, social, cultural, institutional, ecological, and technological dimensions. The social dimension has the highest value because agriculture-based village tourism is carried out in the community. Meanwhile, the lowest value is the technology dimension. The availability of infrastructure influenced the value obtained and means of transportation as well as environmentally friendly farming techniques.

The European Green Deal has set a concrete strategic plan to increase farm sustainability. At the same time, the current global challenges, due to climate change and fuels and commodity market crisis, combined with the Covid-19 pandemic and the ongoing war in Ukraine, comprise the need for quality food, but also the reduction of negative external effects of agricultural production, with fair remuneration for the farmers. In response, precision agriculture has great potential to contribute to the sustainable development. Precision agriculture is a farming management that provides a holistic system approach to managing the spatial and temporal crop and soil variability within a field to improve the farm’s performance and sustainability. However, farmers are still hesitant to adopt it. On these premises, the study aims to evaluate the impacts of precision agriculture technologies on farm profitability, agronomic and environmental management by farmers adopting (or not) these technologies, using the case study method. In detail, the work focuses on the period 2014-2022 for two farms that cultivate durum wheat in Central Italy. The results suggest that the implementation of precision technologies can guarantee economic and agri-environmental efficiency. Results could serve as a basis for developing a program to start training in farms as well as suggest policy strategies.

The presence of fog in the background can prevent small and distant objects from being detected, let alone tracked. Under safety-critical conditions, multi-object tracking models require faster-tracking speed while maintaining high object-tracking accuracy. The original DeepSORT algorithm used YOLOv4 for the detection phase, and a simple neural network for deep appearance descriptor. Consequently, the feature map generated loses relevant details about the track being matched with a given detection in fog. Targets with a high degree of appearance similarity on the detection frame are more likely to be mismatched, resulting in identity switches or track failures in heavy fog. We propose an improved multi-object tracking model based on the DeepSORT algorithm to im-prove tracking accuracy and speed under foggy weather conditions. First, we employed our camera-radar fusion network (CR-YOLOnet) in the detection phase for faster and more accurate object detection. We proposed an appearance feature network to replace the basic convolutional neural network. We incorporated GhostNet to take the place of the traditional convolutional layers to generate more features and reduce computational complexities and cost. We adopted a segmentation module and fed the semantic labels of the corresponding input frame to add rich semantic information to the low-level appearance feature maps. Our proposed method outperformed YOLOv5 + DeepSORT with a 35.15% increase in multi-object tracking accuracy, a 32.65% increase in multi-object tracking precision, the speed increased by 37.56%, and identity switches decreased by 46.81%.

AVs suffer reduced maneuverability and performance due to the degradation in sensor performances in fog. Such degradation causes significant object detection errors essential for AVs' safety-critical conditions. For instance, YOLOv5 performs significantly well under favorable weather but suffers miss detections and false positives due to atmospheric scattering caused by fog particles. Existing deep object detection techniques often exhibit a high degree of accuracy. The drawback is being sluggish at object detection in fog. Object detection methods with fast detection speed have been obtained using deep learning at the expense of accuracy. The problem of the lack of balance between detection speed and accuracy in fog persist. This paper presents an improved YOLOv5-based multi-sensor fusion network that combines radar's object detection with a camera image bounding box. We transformed radar detection by mapping the radar detections into a two-dimensional image coordinate and projected the resultant radar image on the camera image. Using the attention mechanism, we emphasized and improved important feature representation used for object detection while reducing high-level feature information loss. We trained and tested our multi-sensor fusion network on clear and multi-fog weather datasets obtained from the CARLA simulator. Our result shows that the proposed method significantly enhances the detection of distant and small objects. Our small CR-YOLOnet model best strikes a balance between accuracy and speed with an accuracy of 0.849 at 69 fps.

Three facts characterize the hailstorm of 30 August 2022 in the Catalan village of La Bisbal d'Empordà and its surroundings: first, the most dramatic, the death of a child hit by a hailstone. Second, the damage to most of the roofs and cars in the town. Finally, because of the record of hail size (more than 10 cm) in Catalonia in at least the last 30 years. This research focuses on the radar field comparison and the observations provided by an electronic survey of the study area. The results reveal that weather radar sub-estimated the hail size because of different factors. On the opposite, some reporters provided an inaccurate hour. The difference of three months between the hail event and the electronic survey is the probable cause of this mistake in the time estimation. However, the survey delay helped to avoid answers with larger hail sizes than those provided by the official spotters.

In this study we investigated the association between weather types (WTs) and the Urban Heat Island Intensity (UHII) in the region of Attica (Greece). The application of the methodology results in ten WTs over Attica region. The UHII was calculated for every hour of the day from 2008 to 2017, using a new air temperature dataset produced by Copernicus Climate Change Service. To have more clear results concerning the association between WTs and UHII, we have used also the upper 5% of UHII (Urban Overheating-UO). The UO have been estimated for two-time intervals (daytime and nighttime) and for the warm period (June-September). The UHII frequency distribution as well as the spatial characteristics of the UO were also investigated. It was found that UO was amplified under WT2 during the night while, WT10 was mainly responsible for exacerbated UO magnitude at daytime, in all months. Furthermore, analysis results revealed that the UO effect is more pronounced in Athens during the night, especially at Athens center. The daytime hot-spots identified mainly in sub-urban and rural areas. Therefore, this methodology may help for heat mitigation strategies and climate adaptation measures, in urban environments.

In the framework of the FireStorm project, four portable weather stations were installed in the Lousã/Estrela mountain range. Given that the Portuguese Institute for Sea and Atmosphere’s sur-face network has two weather stations installed in this region, the new data allows an improved monitoring of the vertical variability of near-surface variables in this mountainous region. As most of the wildfires in mainland Portugal affect areas with complex terrain with elevations below 1200 m and major fires continue to burn overnight, it is also relevant to monitor the vertical changes of meteorological variables in the nighttime period, as these may exhibit large variability. This study provides the first assessment of the available data, with focus on the summer seasons of 2021 and 2022. The results highlight the large variability that was observed in the region and suggest that the risk of extreme fire behaviour in the nighttime period may be underestimated.

Accurately predicting the power of solar power generation can greatly reduce the impact of the randomness and volatility of power generation on the stability of the power grid system, which is beneficial for the balanced operation and optimized dispatch of the power grid system, and reduces operating costs. Solar PV power generation depends on weather conditions, which are prone to large fluctuations under different weather conditions. Its power generation is characterized by randomness, volatility and intermittency. Recently, the demand for further investigation and effective use on the uncertainty of short-term solar PV power generation prediction has been getting increasing attention in many application of renewable energy sources. In order to improve the predictive accuracy of output power of solar PV power generation and develop a precise predictive model, the authors worked predictive algorithms for the output power of a solar PV power generation system. Moreover, since short-term solar PV power forecasting is one of the important aspects for optimizing the operation and control of renewable energy systems and electricity markets, this review focuses on the predictive models of solar PV power generation, which can be verified in the daily planning and operation of a smart grid system. In addition, the predictive methods in the reviewed literature are classified according to the input data source used for accurate predictive models, and the case studies and examples proposed are analyzed in detail. The contributions, advantages and disadvantages of the predictive probabilistic methods are compared. Finally, the future studies of short-term solar PV power forecasting is proposed.

In foggy weather scenarios, the scattering and absorption of light by water droplets and particulate matter cause object features in images to become blurred or lost, presenting a significant challenge for target detection in autonomous driving vehicles. To tackle this problem, this study proposes a foggy weather detection method, YOLOv5s-Fog, based on the YOLOv5s framework. The model enhances the feature extraction and expression capabilities of YOLOv5s by introducing a novel target detection layer, SwinFocus. Additionally, this research incorporates decoupled head into the model and replaces the conventional non-maximum suppression method with Soft-NMS. Experimental results demonstrate that these improvements effectively enhance the detection performance for blurry objects and small targets in foggy weather conditions. Compared to the baseline model YOLOv5s, YOLOv5s-Fog achieves a 5.4% increase in mAP on the RTTS dataset, reaching 73.4%. This method provides technical support for rapid and accurate target detection in adverse weather conditions, such as foggy weather, for autonomous driving vehicles.

The effects of global warming combined with the progressive expansion of urbanization have considerably increased exposure to urban flooding and runoff widespread erosion risk, also causing shallow landslides and mud flows, respectively in urbanized areas of lowland and hill/foothill environments. Increasing urban flooding resilience has become a priority at virtually all levels of governance. The analysis of a different hazard scenarios, in which various hydro-meteorological conditions and management alternatives are examined, should act as the basis for the effective design and evaluation of interventions to improve urban flooding resilience. Turin Metropolitan Area (TMA), located in north-western Italy, represents a unique case due to its complex orography, with a mountainous sector in the west side and a flat or hilly part in the east side. During the warm season, these environmental conditions make the urban area prone to severe atmospheric convection causing frequent hailstorms and rainstorms of high intensity that may impact on urban infrastructures (i.e., drainage system and road network), thus requiring an adequate management as a key factor to reducing risk and losses. The urban areas of TMA are monitored by polarimetric Doppler weather radars and by a dense rain gauges network. Analyzing several case studies of urban flooding, this research work assesses the feasibility of a meteorological radar early warning system based on the identification of rainfall thresholds that characterize urban flooding, occurring in the lowlands, and the runoff erosion phenomena affecting the urbanized hills and foothills.

The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

Vehicle safety promises to be one of the Advanced Driver Assistance System (ADAS) biggest benefits. Higher levels of automation remove the human driver from the chain of events that can lead to a crash. Sensors play an influential role in vehicle driving as well as in ADAS by helping the driver to watch the vehicle’s surroundings for safe driving. Thus, the driving load is drastically reduced from steering as well as accelerating and braking for long-term driving. The baseline for the development of future intelligent vehicles relies even more on the fusion of data from surrounding sensors such as Camera, Lidar and Radar. These sensors not only need to perceive in clear weather but also need to detect accurately adverse weather and illumination conditions. Otherwise, a small error could have an incalculable impact on ADAS. As most of the current study is based on indoor or static testing. In order to solve this problem, this paper designs a series of dynamic test cases with the help of outdoor rain and intelligent lightning simulation facilities to make the sensor application scenarios more realistic. As a result, the effect of rainfall and illumination on sensor perception performance is investigated. As speculated, the performance of all automotive sensors is degraded by adverse environmental factors, but their behaviour is not identical. Future work on sensor model development and sensor information fusion should therefore take this into account.

Location-specific weather and climate information together with related advisory services are one of the crucial drivers of development in the 21^st century particularly in Sub-Saharan Africa. However, there still exist significant gaps in provision of weather and climate information at scales that effectively address the needs of local people for instance; local farmers and pastoralists. This therefore force local people to rely on indigenous knowledge (IK), to observe and forecast weather conditions. This situation does not exclude Uganda, and thus the current study assessed the efficacy of integrating indigenous knowledge systems into modern weather observational instruments in order to boost and act as a backup mechanism for modern weather observational instruments to increase accuracy and wide coverage of weather observations within Uganda. Results indicated that, respondents across the two pilot sites use a combination of plants, animals, insects, and human behaviours, meteorological and astrological indicators to observe and predict local prevailing weather conditions. Majority of the respondents particularly in Masaka district, believed that the use of indigenous knowledge to observe weather and climatic events is very reliable compared to their counterparts in Entebbe Municipality. Therefore, the integration of indigenous knowledge system in scientific weather observations is very vital.

The winged bean is an underutilized legume that has the potential to contribute to nutrition and food security globally, especially in sub-Saharan Africa (SSA). However, very little research attention is paid to exploiting its full potential due to a lack of adequate knowledge of the existing genetic diversity in the available winged bean germplasm. To bridge this gap, seed morphometric and selected agro-morphological traits which are crucial for yield and seed quality determination in crop plants were used to assess the genetic diversity of thirty accessions of winged bean sourced from the Gene bank of the International Institute of Tropical Agriculture (IITA), Ibadan. The thirty winged accessions were evaluated under field trials at three agro-ecological zones in Nigeria. The agro-morphological data as well as the seed morphometric dataset obtained from a Multi-Spectral Imaging (MSI) system were subjected to Analysis of Variance (ANOVA), Principal Component (PC) analysis, cluster analysis, and correlation analysis. Significant differences (p < 0.05) were observed among the accessions for all measured traits. The first three PCs accounted for 93% of the variation observed among the accessions for all measured traits. Accessions were grouped into three clusters based on the agro- morphological traits and three clusters under the MSI system. Seed yield per plant had the highest estimates of significant positive correlation with hundred seed weight, pod weight per plant, etc. at both phenotypic and genotypic levels. TPt-31 had the highest seed yield per plant, pod weight per plant, and early maturity while TPt-7 had an extended flowering and maturity period, the highest number of pods per peduncle and pods per plant as well as the lowest seed yield. These accessions could serve as a good resource for future winged bean improvement programs. The result also confirmed that the MSI system is an invaluable tool for discriminating among accessions of the same crop species.

This paper presents an analysis of future precipitation patterns over the Lake Victoria Basin using bias-corrected CMIP6 model projections. A mean increase of about 5% in mean annual (ANN) and seasonal [March-May (MAM), June-August (JJA), and October-December (OND)] precipitation climatology is expected over the domain by mid-century (2040-2069). The changes intensify towards the end of the century (2070-2099) with an increase in mean precipitation of about 16% (ANN), 10% (MAM), and 18% (OND) expected, relative to the 1985-2014 baseline period. Additionally, the mean daily precipitation intensity (SDII), the maximum 5-day precipitation values (RX5Day), and the heavy precipitation events, represented by the width of the right tail distribution of precipitation (99p-90p) show an increase of 16%, 29%, and 47%, respectively, by the end of the century. The projected changes have a substantial implication for the region - which is already experiencing conflicts over water and water-related resources.

Observations of ocean surface winds from Indian scatterometer SCATSAT-1 have been combined with background wind field from a numerical weather prediction (NWP) model available at National Centre for Medium Range Weather Prediction (NCMRWF) to generate a 6-hourly gridded hybrid wind product. A distinctive feature of the study is to produce a global gridded wind field from SCATSAT-1 scatterometer passes with spatio-temporal data gaps at regular synoptic hours relevant for forcing models and other NWP studies. This is done by making use of concepts from the modern particle filter technique, which does not represent the model probability density function (PDF) following the Gaussian technique. The 6 hourly hybrid wind is generated for the entire year of 2018 and is validated using the wind speed from daily gridded level-4 SCATSAT-1 winds (L4AW), Cross Calibrated Multi-Platform dataset (CCMP) and global buoy data from National Data Buoy Centre (NDBC). The results indicate potential of the technique to produce scatterometer winds at the desired temporal frequency with significantly less noise and along swath biases. The study shows the generated hybrid winds have very high quality with respect to the already existing daily product available from ISRO.

This paper studies pandemic viruses that spread during the period (1759–2020) according to solar activity cycles. Our findings and results include the following: (1) The severity of a pandemic correlates negatively with the strength of solar activity; (2) Pandemic viruses are classified into three types based on their compatibility with solar activity associations. Most of them spread through the quiet Sun, where viruses survive better in cold and rainy weather, and in stable geomagnetic fields without strong disturbances; (3) The emergence of new strains of influenza viruses was manifested in two ways. First, the annual epidemics due to antigenic drift. Second, pandemics recur every 1–12 solar cycles (about 11–120 years) due to viral reassortment of new subtypes, which results in antigenic shifts; (4) Pandemic viruses have two groups according to their recurring period: first, recurring in nine solar cycles; second, recurring in twelve solar cycles. Furthermore, we reassort pandemic viruses from their previous spread in the same periodic classification. Moreover, we derive a periodicity formula for each subtype of the pandemic virus as a spread date.

Over the last 20 years, begomoviruses have emerged as devastating pathogens, limiting the production of different crops worldwide. Weather conditions increase vector populations, with negative effects on crop production. In this work we evaluated the relationship between the in-cidence of begomovirus and climatic conditions before and during the crop cycle. Soybean and bean fields from the northwest (NW) of Argentina were monitored for 14 years and classified as moderate (≤50%) and severe (> 50%) according to the relative incidence. Two hundred bio-meteorological variables were constructed, summarizing meteorological data in 10-day peri-ods from June to March of each crop year. The studied variables included temperature, precipi-tation, relative humidity, wind (speed and direction), pressure, cloudiness and visibility. For bean, high maximum winter temperatures, low spring humidity and precipitation 10 days before planting correlated with severe incidence. In soybeans, high late winter and pre-planting tem-peratures, and low spring precipitations were found to be good predictors of high incidence of begomovirus presence. The results suggest that temperature and pre-sowing precipitations can be used to predict incidence status [predictive accuracy: 82% (bean) and 75% (soybean)]. Thus, these variables can be incorporated in early warning systems for crop management deci-sion-making to reduce the virus impact on bean and soybean crops.

There is great interest in automatically detecting road weather and understanding its impacts on the overall safety of the transport network. This can, for example, support road condition-based maintenance or even serve as detection systems that assist safe driving during adverse climate conditions. In computer vision, previous work has demonstrated the effectiveness of deep learning in predicting weather conditions from outdoor images. However, training deep learning models to accurately predict weather conditions using real-world road-facing images is difficult due to: (1) the simultaneous occurrence of multiple weather conditions; (2) imbalanced occurrence of weather conditions throughout the year; and (3) road idiosyncrasies, such as road layouts, illumination, road objects etc. In this paper, we explore the use of focal loss function to force the learning process to focus on weather instances that are hard to learn with the objective to help address data imbalance. In addition, we explore the attention mechanism for pixel based dynamic weight adjustment to handle road idiosyncrasies using state-of-the-art vision transformer models. Experiments with a novel multi-label road weather dataset show that focal loss significantly increases the accuracy of computer vision approaches for imbalanced weather conditions. Furthermore, vision transformers outperforms current state-of-the-art convolutional neural networks in predicting weather conditions with a validation accuracy of 92% and F1-score of 81.22%, which is impressive considering the imbalanced nature of the dataset.

Accurate knowledge of the rain amount is an crucial driver in several hydro-meteorological applications. This is especially true in complex orography territories, which are typically impervious, thus leaving ungauged most of the mountain areas. Thanks to their spatial and temporal coverage, weather radars can potentially overcome such an issue. However, weather radar, if not accurately processed, can suffer from several limitations (e.g., beam blocking, altitude of the observation, path attenuation, indirectness of the measurement) that can hamper the reliability of the rain estimates performed. In this study, a comparison between rain gauge and weather radar retrievals is performed in the target area of the Abruzzo region in Italy, which is characterized by a heterogeneous orography ranging from the sea side to Apennine ridge. Consequently, the Abruzzo region has an inhomogeneous distribution of the rain gauges, with station density decreasing with the altitude reaching up to approximately 1500 m a.s.l. Notwithstanding, pluviometric inflow spatial distribution shows a sub-regional dependency as a function of four climatic and altimetric factors: coastal, hilly, mountain, and inner plain areas (i.e., Marsica). Such areas are used in this analysis to characterize the radar retrieval vs. rain gauge amounts in each of those zones. Compared to previous studies on the topic, the analysis presented an attention to the importance of an accurate selection of the climatic and altimetric sub-regional areas where undertake the radar vs. rain gauge comparison. This aspect is not only of great importance to correct biases in radar retrievals in a more selective way, but it also paves the way for more accurate hydro-meteorological applications (e.g., hydrological model initialization, quantify the aquifers recharge etc.) which, in general, require the accurate knowledge of rain amounts upstream of a basin. To fill the gap caused by the uneven rain gauge distribution, Ordinary Kriging has been applied on a regional scale to obtain 2D maps of rainfall data, which are cumulated on a monthly and yearly base. Weather radar data from the Italian mosaic are considered as well, in terms of rain rate retrievals and cumulations performed on the same time frame used for rain gauges. The period considered for the analysis is two continuous years: 2017 and 2018. The output of the elaborations are raster maps for both radar and interpolated rain gauges, where every pixel contains a rainfall quantity. Although the results show a general underestimation in the weather radar data especially in mountain and Marsica areas, even though within the 95% confidence interval of the OK estimation. Our analysis highlights that the average bias between radar and rain gauges, in terms of precipitation amounts, is a function of altitude and is almost constant in each of the selected areas. This achievement suggests that after a proper selection of homogeneous target areas, the radar retrievals can be corrected using the denser network of rain gauges typically distributed at lower altitudes and extend such correction at higher altitudes without loss of generality.

We identified six natural "control knobs" for global weather and climate, which clearly regulate, increase and decrease global temperatures, thus performing a true temperature "control" on Earth. Identified control knobs act as Earth's orbital forcing. We present a detailed Earth orbital model, from where the analysis proceeds. Based on our orbital model, we compare meteorological data from all over the globe, in order to detect orbital forcing fingerprints. Ninety temperature graphs and bar charts demonstrate the results. Empirical meteorological data covers the Pacific ocean, the Atlantic ocean, Arctic and Antarctic sea ice extent, and presents land based meteorological measurements from all continents for the year 2020, as well as multi-decadal data from global daily temperature datasets. A supplementary file provides another 100 additional graphs of weather data and temperature charts. The global orbital "temperature control" is clearly observable in all graphs and charts. Until today, orbital forcing has not been integrated in weather models and CMIP6 climate models.It is concluded that the proposed new orbital forcing should be part of weather and climate forecasting models.

The modeling and prediction of crop seed yield can be a vital improvement in the precision agriculture industry as it provides reliable assessments of the effectiveness of agro-traits. Here, multiple machine learning (ML) techniques are established for predicting sesame (Sesamum indicum L.) seed yield (SSY) and incorporating agro-morphological features. Models utilized for coupled PCA-ML (Principal component analysis-Machine Learning) methods were compared with original ML models to evaluate predicted efficiency. The Gaussian process regression (GPR) and Radial basis function neural network (RBF-NN) models exhibited the most accurate SSY predictions with determination coefficients or R2 values of 0.99 and 0.91, respectively. The root-mean-square error (RMSE) for the ML models fluctuated between 0 to 0.30 t/ha (metric tons/hectare) for the varied modeling process phases. Estimation of sesame seed yield with coupled PCA-ML models improved performance accuracy. The K-fold process suggested the utilization of datasets with the lowest error rates to ensure the continued accuracy of GPR and RBF models. Sensitivity analysis revealed the capsule number per plant (CPP), seed number per capsule (SPC), and 1000-seed weight (TSW) were the most significant seed yield determinants.

A time integration scheme based on semi-Lagrangian advection and Laplace transform adjustment has been implemented in a baroclinic primitive equation model. The semi-Lagrangian scheme makes it possible to use large time steps. However, errors arising from the semi-implicit scheme increase with the time step size. In contrast, the errors using the Laplace transform adjustment remain relatively small for typical time steps used with semi-Lagrangian advection. Numerical experiments confirm the superior performance of the Laplace transform scheme relative to the semi-implicit reference model. The algorithmic complexity of the scheme is comparable to the reference model, making it computationally competitive, and indicating its potential for integrating weather and climate prediction models.

Psychiatric patients are particularly vulnerable to strong weather stimuli, such as foehn, a hot wind that occurs in the alps. However, there is a dearth of research regarding its impact on mental health. This study investigated the impact of foehn wind among patients of a psychiatric hospital located in a foehn area in the Swiss Alps. Analysis was based on anonymized datasets obtained from routine records on admission and discharge, including the Brief Symptom Checklist (BSCL) questionnaire, as well as sociodemographic parameters (age, sex, and diagnosis). Between 2013 and 2020 a total of 10,456 admission days and 10,575 discharge days were recorded. All meteorological data were extracted from the database of the Federal Office of Meteorology and Climatology of Switzerland. We estimated the effect of foehn on the BSCL items using a distributed lag model. Significant differences were found between foehn and non-foehn admissions in obsession-compulsion, Interpersonal Sensitivity, depression, Anxiety, Phobic Anxiety, Paranoid Ideation, and General Severity Index (GSI) (p <.05). Our findings suggest that foehn wind events may negatively affect specific mental health parameters in patients. More research is needed to fully understand the impact of foehn’s events on mental health.

Weather forecast has a big impact on the global economy, accurate and timely weather forecast is required by all, it affects many aspects of human livelihood and lifestyle, it also plays a critical role in decision making for severe weather management and for primary and secondary sectors like agriculture, transportation, tourism, and industry as they rely on good weather conditions for production and operations. The erratic and uncertain complex nature of the weather makes traditional weather forecasting tedious and a challenging task, traditional weather forecast involves applying technology and scientific knowledge on numerical weather prediction (NWP), and weather radar to solve complex mathematical equations to obtain forecasts based on current weather conditions. These traditional processes utilize expensive, complex physical and computational power to produce forecasts, which can be inaccurate and have various catastrophic impacts on society. In this research, a machine learning-based weather forecasting model was proposed, the model was implemented using 4 classifier algorithms which include Random Forest classifier, Decision Tree Algorithm, Gaussian Naïve Bayes model, and Gradient Boosting Classifier, these algorithms were trained using a publicly available dataset from Kaggle for the city of Seattle for the period 2012 to 2015. The model’s performance was evaluated; the Gaussian Naive Bayes algorithm proved to be the best performing algorithm with a predictive accuracy of 84.153 %.

The study of long-term average weather patterns is known as climatology. It is a distinct field of study from meteorology and can be broken down into several subdivision. In order to predict the future, the knowledge of climatology is essential. In other words, with the help of climatology, we can figure out how likely it is that snow and hail will fall to the ground, and how much solar thermal radiation can reach a certain location etc. Climatology often focuses on how the climate has changed over time and how it has affected people and events. Both meteorology and climatology fall under the general term "meteorology", in particular, they are subdivision of research in the same field. In case of predicting the weather, meteorologists use variables such as humidity, air pressure, and temperature. This article's primary objective is to familiarize engineers with the fundamentals of climate and its processes so that they can effectively apply this knowledge to comprehend the climatic impact on water resources systems.

Forest fires though part of a natural forest renewal process, when frequent and assuming large-scale proportions have detrimental impacts on biodiversity, agroforestry systems, soil erosion, air and water quality, infrastructures, and economy. Portugal (PT) endures extreme forest fires, with a record of burned area in 2017. These extreme wildfire events (EWE) concentrated in few days but with high burned areas, are among other factors, linked to severe fire weather conditions. In this study a comparison between several fire danger indices is performed for a reference period 2001‒2021 and 2017 (May‒October) for the Fire Weather Index (FWI), Continuous Haines Index (CHI), Keetch-Byram drought index (KBDI), Burning Index (BI) and Fire Danger index (FDI). A daily analysis for the so-called Pedrogão Grande wildfire (June 17th) and the October major fires (October 15th) included the Spread Component (SP) and Ignition Component (IC). Results revealed high above average values for all indices for 2017 in comparison with 2001‒2021 particularly, for October. High statistically significant monthly correlations between FWI, FDI and BI were found, along with lower between FWI and CHI. These correlations are depicted in the spatial patterns between FWI and FDI for the two EWE. The spatial distribution of FDI, SC and IC had the best performance in capturing the locations of the occurrence of the two EWEs’. The outcomes allowed to conclude, that since fire danger depends on several factors a multi-index’s diagnosis is highly relevant, though calibration and scale adjustment are needed for PT. The implementation of a Multi-index’s Prediction System should be able to further enhance the ability of tracking and forecast unique EWE, since the shortcomings of some indices are compensated by the information retrieved by others as shown in this study. Overall, a new forecast system can help ensuring the development of appropriate spatial preparedness plans, proactive responses by the civil protection regarding firefighter’s management, suppression efforts to minimize the detrimental impacts of wildfires in Portugal.

Biochar is a carbon allotrope obtained by pyrolysis of biomass, usually agro-waste. Owing to the demand for sustainable development, biochar is continuously raising much hope in the scientific community. However, in order to impart it with new properties, its modification is required, either in situ during pyrolysis, or after the carbonization process. Herein, we propose a new direct approach to obtain bimetallic copper/nickel nanoparticle-loaded on olive stone biochar. The bimetallic-coated biochar and the reference materials bare biochar, copper-loaded and nickel-loaded biochar were prepared at 400 °C under a stream of dinitrogen from olive pit powder particles impregnated first with citric acid (CA), and then with copper and nickel nitrates. We have employed citric acid in the process in order to check its effect on the structural and textural properties of biochar supporting the metallic nanoparticles. Surprisingly, citric acid induced the formation of agglomerated or even raspberry-shaped, bimetallic copper/nickel nanoparticles. Large 450-500 nm-sized agglomerates of ~80 nm bimetallic CuNi NPs were noted for B-CA@CuNi. Interestingly, for biochar material prepared with initial Cu/Ni=10 molar ratio (B-CA@CuNi10/1), the bimetallic NPs formed unusual nano-raspberries (1748 nm in size) which are agglomerates of individual 10-20 nm-sized CuNi10/1 nanoparticles. The B-CA@CuNi and reference materials were characterized by Raman spectroscopy, scanning electron microscopy (SEM)/energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD) and magnetometry. The B-CA@CuNi and B-CA@Ni materials could be attracted efficiently with a magnet, but not B-CA@CuNi10/1 due to a low nickel loading. B-CA@CuNi was tested as a catalyst for the degradation of methyl orange (MO). Discoloration was noted within 10 min, much faster than a similar material prepared in the absence of CA. B-CA@CuNi could be recycled at least 3 times with exhibit as fast discoloration catalysis performance. This paper stresses the important role of citric acid in shaping the bimetallic nanoparticles loaded in situ on biochar during the slow pyrolysis process and to enable faster catalysed discoloration of organic dye solution.

In the past years, our knowledge of coastal environments has been enriched by remotely sensed data. However, to successfully extract information from a combination of different sensors systems, it should be understood how these interact with the common coastal environment. In this research we co-analyze two sensor systems: Terrestrial Laser Scanning (TLS) and satellite based Synthetic Aperture Radar (SAR). TLS shows large potential for examining coastal processes thanks to the possibility to retrieve repeated, accurate and dense topographic information in a rapid and non-invasive manner. However, TLS presents some limits due to its high economic costs and limited field of view. SAR systems are among the most used active remote sensor system for Earth Observation. Despite their relatively low resolution, SAR systems provide the ability to monitor and map coastal areas with complete, repeated and frequent coverage, penetrating through clouds and providing all weather monitoring. Moreover, Sentinel-1 SAR images are freely available. The availability of a permanently installed TLS system (PLS, Permanent Laser Scanner) allows us, to extensively compare Sentinel-1 SAR data and topographic laser scans during different conditions on a sandy beach. PLS data are compared with simultaneous Sentinel-1 SAR images in order to investigate the combined use of PLS and SAR in coastal environments. The purpose of this comparison is the investigation of a possible relation between PLS and SAR data: knowing their relation, SAR dataset could be correlated to beaches characteristics. Meteorological and surface roughness have also been taken into consideration in the evaluation of the correlation between PLS and SAR data. The permanently installed laser scanner for the present study is located in Noordwijk (the Netherlands). A generally positive but low correlation exists between the two variables. When considering weather phenomena, their correlation increases and shows a dependence on wind directions and speed. The correlation with the surface roughness, evaluated in terms of root-mean squared height, also depends on specific wind speed and directions.

The Compact Midwave Imaging System (CMIS) is a wide field of view, multi-angle, multi-spectral pushframe imager that relies on the forward motion of the satellite to create a two-dimensional (2D) image swath. An airborne demonstration of CMIS was successfully completed in January-February 2021 on the NASA Langley Research Center Gulfstream III. The primary objective of the four-flight campaign was to demonstrate the capability of this unique instrument to perform stereo observations of clouds and other particulates (e.g. smoke) in the atmosphere. It is shown that the midwave infrared (MWIR) spectral bands of CMIS provide a unique 24/7 capability with high resolution for accurate stereo sensing. The instrument relies on new focal plane array (FPA) technology, which provides excellent sensitivity at much warmer detector temperatures than traditional technologies. This capability enabled a compact, low-cost design that can provide atmospheric motion vectors and cloud heights to support requirements for atmospheric winds in the 2017-2027 Earth Science Decadal Survey. Applications include day/night observations of the planetary boundary layer, severe weather, and wildfires. A comparison with current space-based earth science instruments demonstrates that the SWIR/MWIR multi-spectral capability of CMIS is competitive with larger, more expensive instrumentation. Imagery obtained over a controlled burn and operating nuclear power plant demonstrated the sensitivity of the instrument to temperature variations. The system relies on a mature stereoscopic imaging technique applied to the same scene from two independent platforms to unambiguously retrieve atmospheric motion vectors (AMVs) with accurate height assignment. This capability has been successfully applied to geostationary and low-earth orbit satellites to achieve excellent accuracy. When applied to a ground-point validation case, the accuracy for the CMIS aircraft observations was 20 m and 0.3 m/s for cloud heights and motion vectors, respectively. This result was confirmed by a detailed error analysis with analytical and covariance models. The results for CMIS cases with underflights of Aeolus, CALIPSO and Aqua provided a good validation of expected accuracies. The paper also showed the feasibility of accommodating CMIS on CubeSats to enable multiple instruments to be flown in a leader-follower mode.

The capabilities of a quadcopter in the hover mode for low-altitude sensing of atmospheric turbulence with high spatial resolution in urban areas characterized by complex orography are investigated. The studies were carried out in different seasons (winter, spring, summer, and fall), and the quadcopter hovered in the immediate vicinity of ultrasonic weather stations. The DJI Phantom 4 Pro quadcopter and AMK-03 ultrasonic weather stations installed in different places of the studied territory were used in the experiment. The smoothing procedure was used to main regularities in the behavior of the longitudinal and lateral spectra of turbulence in the inertial and energy production ranges. The longitudinal and lateral turbulence scales were estimated by the least-square fit method with the von Karman model as a regression curve. It is shown that the turbulence spectra obtained with DJI Phantom 4 Pro and AMK-03 generally coincide with minor differences observed in the high-frequency region of the spectrum. In the inertial range, the behavior of the turbulence spectra shows that they obey the Kolmogorov-Obukhov “5/3” law. In the energy production range, the longitudinal and lateral turbulence scales and their ratio measured by DJI Phantom 4 Pro and AMK-03 agree to a good accuracy. Discrepancies in the data obtained with the quadcopter and the ultrasonic weather stations at the territory with complex orography are explained by the partial correlation of the wind velocity series at different measurement points and the influence of the inhomogeneous surface.

Tephra plumes can cause a significant hazard for surrounding towns, infrastructure, and air traffic. The current work presents the use of a small and compact X-band Multi-Parameter (X-MP) radar for the remote tephra detection and tracking of two eruptive events at Merapi Volcano, Indonesia, in May and June 2018. Tephra detection was done by analysing the multiple parameters of radar: copolar correlation and reflectivity intensity. These parameters were used to cancel unwanted clutter and retrieve tephra properties, which are grain size and concentration. Real-time spatial and temporal forecasting of tephra dispersal was performed by applying an advection scheme (nowcasting) in the manner of Ensemble Prediction System (EPS). Cross-validation was done using field-survey data, radar observations, and Himawari-8 imagery. The nowcasting model computed both the displacement and growth and decaying rate of the plume based on the temporal changes in two-dimensional movement and tephra concentration, respectively. Our results with ground-based data, where the radar-based estimated grain size distribution fell within the range of in-situ data. The uncertainty of real-time forecasted tephra plume depends on the initial condition, which affects the growth-and decaying rate estimation. The EPS improves the predictability rate by reducing the number of missed and false forecasted events. Our findings and the method presented here are suitable for early warning of tephra fall hazard at the local scale.

Solar radiation prediction is an important process in ensuring optimal exploitation of solar energy power. Numerous models have been applied to this problem, such as numerical weather prediction models and artificial intelligence models. However, well-designed hybridization approaches that combine numerical models with artificial intelligence models to yield a more powerful model can provide a significant improvement in prediction accuracy. In this paper, we propose novel hybrid machine learning approaches that exploit auxiliary numerical data. The proposed hybrid methods invoke different machine learning paradigms, including feature selection, classification, and regression. Additionally, numerical weather prediction (NWP) models are used in the proposed hybrid models. Feature selection is used for feature space dimension reduction to reduce the large number of recorded parameters that affect estimation and prediction processes. The rough set theory is applied for attribute reduction and the dependency degree is used as a fitness function. We investigate the effect of the attribute reduction process with thirty different classification and prediction models in addition to the proposed hybrid model. Then, different machine learning models are constructed based on classification and regression techniques to predict solar radiation. Moreover, other hybrid prediction models are formulated to use the output of the numerical model of Weather Research and Forecasting (WRF) as learning elements in order to improve the prediction accuracy. The proposed methodologies are evaluated using a data set that is collected from different regions in Saudi Arabia.

Background: Heat waves are correlated with increased mortality in the aged population. Social isolation is known as a vulnerability factor. This study aims at evaluating the correlation between an intervention to reduce social isolation and the increase in mortality in the population over 80 during heat waves. Methods: The study adopts a retrospective ecologic design. We compared the excess mortality rate (EMR) in the over 80 population during heat waves in urban areas of Rome (Italy), where a program to reduce social isolation was implemented compared to others where it was not implemented. We measured mortality of the summer periods from 2015 to 2019 compared with 2014 (a year without heat waves). Winter mortality, cadastral income and proportion of over 90 were included in the multivariate Poisson regression. Results: The EMR in the intervention and controls was 2.70% and 3.81%, respectively. Rate ratio 0.70 (c.i. 0.54 - 0.92, p-value 0.01). The Incidence Rate Ratio (IRR) of the interventions with respect to the controls is 0.76 (c.i. 0.59 - 0.98). After adjusting for other variables, the IRR was 0.44 (c.i. 0.32 - 0.60). Conclusions: Reducing social isolation could limit the impact of heat waves on the mortality of the elderly population.

Environmental effects and climate change are lately representing an increasing strain of the coastal areas which topography strongly depends on these conditions. However, the processes by which weather and environmental phenomena influence the highly variable beach morphology are still unknown. A continuous monitoring of the beach environment is necessary to implement protection strategies. This paper presents the results of an innovative study performed on a coastal area using satellite remote sensing data with the aim of understanding how environmental phenomena affect beaches. Two-years of synthetic aperture radar (SAR) Sentinel-1 images are used over a test area in Noordwijk, the Netherlands. At the same time as the SAR acquisitions, information on tidal and weather conditions are collected and integrated from nearby meteorological stations. Dedicated codes are implemented in order to understand the relationship between the SAR amplitude and the considered phenomena: wind, precipitation, tidal conditions. Surface roughness is taken into account. The results indicate a strong correlation between the amplitude and the wind. No particular correlation or trend could be noticed in the relation with the precipitation. The analysis of the amplitude also shows a decreasing trend moving from the dry area of the beach towards the sea and the correlation coefficient between the amplitude and the tide level gets negative with the increase of the water content.

Biofortification refers to the increase in the amount of essential vitamins or provitamins or minerals in crops to improve the nutritional status of the people, which is largely intended to alleviate the problem of micronutrient malnutrition. I argue that biofortification may not be an effective weapon to fight against the hidden hunger since it demonstrates limited capacity on nutritional enhancement and can negatively impact the socio-economic fabric of the society in many different ways. Finally, I suggest a couple of alternatives that might meet the challenge more efficiently than biofortified crops.

Boulder dynamics may provide essential data for the coastal evolution and hazards assessment and can be focused as a proxy for the onshore effect of intense storm waves. In this work, detailed observations of currently available satellite imagery of the Earth surface allowed to identify several coastal boulders displacements in the Southern Apulia coast (Italy), in a period between July 2018 and June 2020. Field surveys confirmed the displacements of several dozens of boulders up to several meters in size, also allowing the determination of the initial position for many of them. Archive weather analyses identified two possible causative storms during the same period, and calculations based on analytical equations are found in agreement with the displacement by storm waves for most of the observed boulders. The results help to give insights about the onshore effect of high storm waves on the coastal hydrodynamics and the possible future flooding hazard in the studied coast.

The understanding of weather and climate extremes provides academics, decision makers, international development agencies, nongovernmental organizations and civil society the necessary information for monitoring and giving early warning to prevent or minimize the risks associated with weather related hazards. Different researches were carried out to provide vital information that will further enhance the assessment of vulnerability and its impacts. Lack of proper understanding of weather and climate extremes was realized to be responsible for the huge and devastating losses that could have being averted or minimized over the past decades. Different countries and institutions have put in place a number of ways to increase sensitization and awareness of weather extremes. This became necessary in order to reduce the losses associated with these extremes both on local and regional scales.

Mountain systems within the Mediterranean region, e.g. the Pyrenees, are very sensitive to climate change. In the present study, we quantified the magnitude of extreme precipitation events and the number of days with torrential precipitation (daily precipitation ≥ 100 mm) in all the rain gauges available in the Pyrenees for the 1981-2015 period, analyzing the contribution of the synoptic scale in this type of events. The easternmost (under the Mediterranean influence) and north-westernmost (under Atlantic influence) areas of the Pyrenees registered the highest number of torrential events. The heaviest events are expected in the eastern part, i.e. 400 mm day-1 for a return period of 200 years. Northerly advections over the Iberian Peninsula, which present a low zonal index, i.e. im-plying a stronger meridional component, give rise to torrential events over the western Pyrenees; and easterly advections favour extreme precipitation over the eastern Pyrenees. The air mass travels a long way, from the east coast of North America, bringing heavy rainfall to the western Pyrenees. In the case of the torrential events over the eastern Pyrenees, the air mass causing the events in these areas is very short and originates in the Mediterranean Basin. The NAO index has no influence upon the occurrence of torrential events in the Pyrenees, but these events are closely related to certain Mediterranean teleconnections such as the WeMO

Significant problems have arisen in the last years, such as climate change, global warming, and hunger. These complications are correlated with the depletion and exploitation of natural resources and environmental contamination. Due to overcrowding, the list of challenges for the next few years is growing. A comprehensive approach was made to the agro-industrial production of Avocado (Persea americana) and the management of all its biomass waste. So, bioprocesses and biorefinery can be used to produce high added-value products. A large number of residues are composed of lignin and cellulose. They have many potentials to be exploited sustainably for chemical and biological conversion; physical, chemical, and natural treatments improve the following operations. There are some applications to many fields such as pharmaceutical, medical, material engineering, and environmental remediation. Possible pathways are mentioned to take advantage of Avocado as biofuels, drugs, bioplastics, and even in the environmental part and emerging technologies such as nanotechnology using bioprocesses and biotech. In conclusion, Avocado and its waste could be transformed into high value-added products in industries above to mitigate global warming and save non-renewable energy.

This review seeks to explain four features of viral respiratory illnesses that have perplexed generations of virologists: (1) the seasonal timing of respiratory illness; (2) the common viruses causing respiratory illness worldwide, including year-round disease in the Tropics; (3) the rapid response of outbreaks to weather, specifically temperature; (4) the rapid arrival and termination of epidemics caused by influenza and other viruses. The inadequacy of the popular explanations of seasonality is discussed, and a simple hypothesis is proposed, called Temperature Dependent Viral Tropism (TDVT), that is compatible with the above features of respiratory illness. TDVT notes that viruses can transmit themselves more effectively if they moderate their pathogenicity (thereby maintaining host mobility) and suggests that endemic respiratory viruses accomplish this by developing thermal sensitivity within a range that supports organ-specific viral tropism within the human body, whereby they replicate most rapidly at temperatures below body temperature. This allows them to confine themselves to the upper respiratory tract and to avoid infecting the lungs, heart, gut etc. Biochemical and tissue-culture studies show that “wild” respiratory viruses show such natural thermal sensitivity. The typical early autumn surge of colds and the existence of respiratory illness in the Tropics year-round at intermediate levels are explained by the tendency for strains to adapt their thermal sensitivity to their local climate and season. The TDVT hypothesis has important practical implications for preventing and treating respiratory illness including Covid-19. TVDT is testable with many options for experiments to increase our understanding of viral seasonality and pathogenicity.

There is consensus that climate variability and change is impacting food security in Eastern Africa, and that conventional extension approaches, based on top-down model of information dissemination and technology transfer, are too inadequate to help smallholder farmers tackle increasingly complex agro-climatic adversities. Innovative service delivery options exist but are mostly operated in silos with little effort to explore and blend them. There are efforts to develop a blended Climate-Resilient Farmers Field School methodology to address the gaps, with objective to improve participants’ knowledge, skills and attitude to apply the blended approach and to sensitize actors on what needs to be advocated at the policy level. Some 661 local trainers/facilitators (ToT/ToFs), 32% of them women and 54% youth, were trained across Kenya, Tanzania, and Uganda, with additional 76 Master Trainers (MToTs) trained to backstop the ToT/ToFs. Through the implementation, the process reached 36 agribusinesses covering some 237,250 smallholder farmers trained across Kenya, Tanzania, and Uganda on CSA technologies, practices, and innovations by the end of 2020. The blended approach offers lessons to transform extension to help farmers improve food security and resilience. Preliminary findings indicate that the process is rapidly shaping individual adaptive behavior and group adaptive thinking. Lessons also show a strong need for agronomists to work more closely with agro-meteorologists to ensure that farmers are properly guided to participate appropriately in the co-generation and application of climate information and agro-weather advisories, which they can interpret easily and utilize for their agricultural production purposes. Experience from this initiative can be leveraged to develop scalable participatory extension and training models

This review seeks to explain four features of viral respiratory illnesses that have perplexed many generations of microbiologists: (1) the seasonal occurrence of viral respiratory illness; (2) the occurrence of respiratory illness year-round in the Tropics; (3) the rapid response of illness to temperature drops in temperate regions; (4) the explosive arrival and rapid termination of epidemics caused by influenza and other respiratory viruses. I discuss the inadequacy of the popular explanations of seasonality, and propose a simple hypothesis, called Temperature Dependent Viral Tropism (TD-VT), that is compatible with the above and other features of respiratory illness. TD-VT notes that viruses can often transmit themselves more effectively if they moderate their pathogenicity (thereby maintaining the mobility of their hosts) and suggests that most endemic respiratory viruses accomplish this by developing thermal sensitivity, in the sense that they normally replicate rapidly only at temperatures below normal body temperature. This allows them to confine themselves to the upper respiratory tract and to avoid infecting the lungs, heart, gut etc. I review biochemical and tissue-culture studies that found that “wild” respiratory viruses often show natural thermal sensitivity within a range that supports organ-specific tropism within the human body, and I discuss the evident tendency for viral strains to adapt their thermal sensitivity to their local climate and season. I also explore the possible misinterpretation of early experiments where volunteers were inoculated nasally with viral samples and then chilled. Next, I discuss the practical implications of the TD-VT hypothesis for preventing and treating respiratory illness. Finally, I note that the hypothesis is very testable and make suggestions for the most important experiments to increase our understanding of the seasonality and pathogenicity of viral respiratory illness.

This study investigated the efficacy of advanced oxidation process (AOP) for the reduction of pollution loads in mixed agro-food industrial wastewaters (dairy and slaughterhouse) in Nablus city, Palestine. Bench-scale Jar tests using an advanced oxidation process (AOP) were performed as a pretreatment stage. Initial results on direct applications of Fenton’s process on mixed agro-food wastewater (COD: 15400-18200 mg/l) were unsatisfactory. Hence, the performance of the Fenton process was applied on three mixed wastewater samples with different pre-treatment trials: (A) coagulant (FeCl3.6H2O) addition, (B) settling (2h), and use of flocculent (lime Ca(OH)2) in sample (C). Preceded with lime, Fenton`s process (Sample C) was most effective in the removal of organic carbon and nitrogen (89% COD; 80% TKN). The removal efficiency in inorganic loads (91% TSS; 62% TS) were achieved under H2O2/COD (w/w ratio 2:1), H2O2/Fe+2 (w/w ratio 10:1) and acidic conditions (pH = 3). The adoption of AOP technology by agro-food industries could ensure compliance with municipal by-laws and acquire connection permits to sewerage networks.

Silicone RTV-based engineering rubber composite products have been widely used for several applications in various fields as a major component such as structure, automotive, and medical. In its application, the rubber composite product is placed in an open area that is directly exposed to sunlight and rain. It has a significant negative impact on changes in chemical and rheological properties, making the product life of rubber composite products shorter. Therefore, in this study, changes in the chemical and rheological properties of both isotropic and anisotropic magnetorheological elastomer (MRE) treated with accelerated weathering were studied compared to untreated specimens with specimens that had been treated. MRE specimens with 40% by weight CIP were prepared with no current excitation and another sample were made under 1.5 T of magnetic flux density. Each specimen was treated in an accelerated weathering machine Q-Sun Xe-1 Xenon Test Chamber with a UV light exposure cycle for 102 minutes and 18 minutes of UV light combined with water spray for 24 hours followed by a condensation cycle of 4 hours in a dark period. Material characterization was carried out using FTIR and Rheometer to determine the changes in chemical and rheological properties. The morphological analysis results showed that the surface was rough and more cavities occurred after being given weather treatment. Rheometer test results showed a decrease in storage modulus in each MRE specimen that had been treated compared to untreated MRE specimens. Meanwhile, FTIR testing showed a change in wave peak between untreated and treated MRE specimens.

This study investigated the reduction of organic loads from mixed agro-food industrial wastewaters (dairy and slaughterhouse) of Nablus city using advanced oxidation process (AOP), a high- rate chemical oxidation reaction. Bench-scale Jar tests using an advanced oxidation process (AOP) were performed as a pretreatment stage. Direct applications of classical Fenton’s process on mixed raw agro-food wastewater samples (COD: 15400-18200 mg/l) revealed unsatisfactory results. The performance of the Fenton process was evaluated using three mixed samples with different pre-treatment trials: (A) coagulant (FeCl3.6H2O) addition, (B) settling (2h) allowed, and use of flocculent (lime Ca(OH)2) in sample (C). Compared with other partial treatments, sample (C), Fenton`s process lime preceded, was the most effective in the removal of organic (89% COD; 80% TKN) and inorganic loads (91% TSS; 62% TS) under H2O2/COD (w/w ratio 2:1), H2O2/Fe+2 (w/w ratio 10:1) and acidic conditions (pH =3). Obtained results comply with Nablus municipal by-law (COD below 2000 mg/l), which help decision-makers within the agro-food industries install pollution reduction systems. Investment in the Fenton-based peroxidation process, allow agro-food industries to obtain connection permits to sewage networks.