Environmental and Earth Sciences

Abstract: We processed the life expectancy data of age group less than 1 year old from the Institute for Health Metrics and Evaluation (IHME) in contiguous USA and a set of 33 environmental variables (or features ) from the European Centre for Medium-Range Forecasts (ECMWF) from the years 2003 through 2019. Visualizing the IHME data we identified the massive disparity in life expectancy in contiguous USA where counties in southern states have relatively less life expectancy compared to counties in northern states. We made use of machine learning to estimate the life expectancy and obtained moderate accuracy as coefficient of determination (R2) and Root Mean Square Error (RMSE) between the true and estimated values were found to be 0.77 and 1.18 year respectively in an independent test set using only a set of 5 environmental variables. Our key finding shows that apart from well-known pollutants such as particulate matter (PM), ozone, carbonmonoxide, it is essential to reduce pollutants such as formaldehyde, sulphate aerosols, dust aerosols; increase vegetation areas, and good working condition such as lower wet-bulb temperature can potentially increase life expectancy in the US. Future work can include socio-economic variables such as household income, poverty rate and other relevant features to create a comprehensive set of variables to improve the results and livelihood of people.

Abstract: Precipitation variability in the VMD is a critical determinant of agricultural productivity, freshwater availability, and flood and drought dynamics in one of Southeast Asia's most climate-vulnerable regions. Teleconnections between PPTA and three dominant climate modes (Niño 3.4, DMI and PDO) were quantified at ten meteorological stations from 1981 to 2025 using Pearson lag-correlation and WTC. ENSO is identified as the primary interannual driver, exhibiting a peak negative correlation at a lag of two months (r = −0.304, p < 0.001; 9.2% variance explained). The IOD exerts a secondary, delayed influence, peaking at lags of 11 to 12 months (r = 0.186, p < 0.001; 3.5% variance). The PDO functions as a persistent decadal modulator: positive phases suppress annual precipitation by 4.6%, while negative phases enhance it by 14.5% relative to the long-term mean (6.4% variance). WTC analysis reveals non-stationary coherence at 2–5 year (ENSO) and 8–16 year (PDO) periodicities. Compound El Niño and positive PDO events result in the most severe precipitation deficits, with non-linear responses during strong ENSO phases. These results establish a multi-index teleconnection framework that supports seasonal drought early warning and climate-adaptive water resource management in the VMD.

Abstract: Climate change and variability pose serious threats to natural and human systems. The Mediterranean region and North Africa (MNA) are among the world’s climate change hotspots. An in-depth understanding of decadal climate variability over this region is critical for climate prediction to support planning and management, as well as adaptation in important sectors such as water resources. Therefore, the observation-based ERA5 precipitation and the outputs of the CORDEX-CORE regional models are used to characterise the decadal precipitation variability over these regions. The influence of a set of large-scale climatic indices (Atlantic Meridional Mode: AMM, Atlantic Multidecadal Oscillation: AMO, Arctic Oscillation: AO, Interdecadal Pacific Oscillation: IPO, North Atlantic Oscillation: NAO, Pacific Decadal Oscillation: PDO, Scandinavia: SCAND, and Western Mediterranean Oscillation: WeMO) on the decadal precipitation is also examined. The results reveal certain discrepancies between ERA5 and the CORDEX-CORE models’ values. Overestimations and underestimations are found between the models and observations, depending on the region and season. However, the capability of multi-model mean (MME) is better in capturing observation patterns over MNA, SMED, and NMED at all time scales; while REMO-Nor and MME perform better than the remaining models over two regions (SAH and WNA) at the annual time scale. As revealed by ERA5, MME confirmed that AMO, NAO, SCAND, and WeMO have more influence on precipitation over the entire region than others. These findings are useful for climate modelling enhancement and predictions in the region that still needs development of multi-annual to decadal time scales, especially in North Africa.

Abstract: Real-time quantitative precipitation estimation (QPE) from weather radar is essential for hydrological forecasting, flash flood warning systems, and water resource management. Despite significant advances in radar technology and signal processing, operational QPE systems face persistent challenges including non-meteorological clutter contamination, signal attenuation, vertical profile biases, and systematic errors that require integration with ground-based rain gauge networks. This review synthesizes recent developments in open-source frameworks for radar QPE, spanning the complete processing chain from raw signal correction to operative hydrological validation. We examine state-of-the-art methods for clutter removal (polarimetric fuzzy logic, CLEAN-AP, neural network quality control), C-band attenuation correction (self-consistent and KDP-based approaches), and vertical profile of reflectivity (VPR) correction for warm-rain events. We compare gauge-radar merging techniques including mean field bias adjustment, spatially variable corrections, Kriging with External Drift (KED), and Conditional Merging, with emphasis on real-time applicability and look-back window strategies. The review identifies key open-source Python libraries (wradlib, Py-ART, pySTEPS, radproc, weatherDataHarmonizer) and documents operational latency constraints for flash flood warning systems. A critical research gap is identified: current open-source solutions lack documented workflows for integrating delayed 24-hour manual gauge readings into real-time QPE streams while maintaining low latency. This review provides researchers and practitioners with a comprehensive roadmap for developing robust, open-source, real-time radar QPE systems suitable for operational hydrological applications.

Abstract: This study presents a comprehensive assessment of longwave radiation variability in the Arctic based on unique measurements collected at the North Pole drifting station SP‑28 in 1987. The primary objective is to compare these historical observations with modern datasets from the Surface Heat Budget of the Arctic Ocean (SHEBA, 1997–1998) and the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC, 2019–2020) to evaluate long‑term changes in the Arctic radiation regime. Continuous longwave radiation measurements were obtained using high‑precision spectral pyrgeometers to identify Arctic haze. The results show that in 1987, Arctic haze layers enhanced the downward longwave flux by 15–20 W·m-2 and increased atmospheric emissivity. In contrast, MOSAiC observations reveal emissivity values that closely match aerosol‑free model calculations, indicating a substantial decline in Arctic haze and the disappearance of radiatively significant aerosol layers. This shift is in alignment with the long-term reduction of global anthropogenic sulfur dioxide emissions across the Northern Hemisphere.

Abstract: Due to randomness factors in the machine learning model construction process, reproducibility is compromised. This study investigates the impact of randomness on model stability and evaluates techniques for reducing this impact, using the widely adopted shallow neural network (NN) model as a testbed. Randomness in this NN model arises from three events: randomly initializing model parameters, randomly selecting a validation subset, and randomly sampling batches for parameter updates. Among these, batch randomness exerts a significantly weaker impact than the other two factors. In this study, the model training is stopped when the validation performance fails to improve or when a preset threshold for loss or epoch-number is met. The final model stability is significantly better using threshold criteria than using validation criterion, as the former avoids the randomness associated with validation subset. Sensitivity experiments show that scaling the model's initial parameters to 0.1 times their original values can mitigate the impact of initialization randomness, thereby significantly improving model stability while also markedly enhancing predictive skill. Furthermore, weight decay and multi-model ensembles, which are two commonly used techniques, can also significantly enhance model stability. Moreover, the inherent instability of individual sub-models may actually benefit the overall predictive skill of a multi-model ensemble.

Abstract: Continuous monitoring and nowcasting of tornadic near-storm environments remain challenging, particularly in regions with limited ground-based weather radar coverage. High-spatiotemporal-resolution geostationary satellite remote sensing offers a valuable approach to track the evolution of severe convective storms. Combining 10-minute cloud-top brightness temperature (TBB) data from the Himawari-8 satellite and ERA5 reanalysis, this study investigates the atmospheric environments of 177 documented tornadoes in China from 2016 to 2023. Tracking storm convective centers using TBB minima reveals clear regional differences in tornadogenesis paradigms. Southern China tornadoes exhibit a "dynamically driven" pattern within quasi-steady, warm, and moist environments. These environments feature low Lifted Condensation Levels (LCL; ~790 m) and weak Convective Inhibition (CIN). Intense low-level wind shear and storm-relative helicity (SRH) dominate the convective triggering. Northern China tornadoes follow a "coupled thermodynamic-kinematic" paradigm under relatively drier and cooler backgrounds. Their initiation relies on the rapid, synchronized accumulation of Mixed-Layer convective available potential energy (MLCAPE) and deep-layer SRH. Furthermore, intensity-based comparative analysis indicates that significant tornadoes (Enhanced Fujita [EF] scale, EF ≥ 2) are favored by higher MLCAPE, deep-layer shear, and lower LCLs compared to weak ones (EF ≤ 1). Himawari-8 TBB data capture a more rapid pre-storm convective cloud-top cooling for strong tornadoes, with medians reaching -73 °C. This study demonstrates that combining high-frequency satellite observations with reanalysis data provides quantitative precursor signals for regional severe tornado nowcasting.

Abstract: This study investigates the mechanical and thermodynamic effects of the evaporating ocean spray on the structure and dynamics of a hurricane marine atmospheric boundary layer using Eulerian multifluid and mixture model approaches coupled with the E−ϵ turbulence closure. The multifluid framework treats air and spray as interpenetrating phases, enabling a physically consistent representation of air–droplet interactions governing momentum transfer, enthalpy exchange, and turbulence modulation. The mixture approach is based on a simplified description that captures only part of the underlying physics, yet offers an advantage in its ability to yield analytical insight. Mechanically, spray produces competing effects: on one hand, droplet inertia causes wind deceleration, on the other, the spray-induced turbulence attenuation, primarily resulting from the air–droplet friction, leads to strengthening the wind. Analytical and numerical results show that the latter effect prevails for typical spray droplet sizes leading to wind acceleration and drag reduction at hurricane wind speeds. Thermodynamically, evaporating droplets redistribute total heat flux in favor of its latent component, with effects strongly dependent on the droplet size. Small droplets suppress turbulence and reduce the total enthalpy flux, whereas large ones enhance it. Furthermore, spray significantly increases the total enthalpy-to-drag coefficient ratio with wind speed, which agrees with field observations.

Abstract: Climate-related disasters are escalating in frequency and severity, yet global adaptation finance remains reactive and insufficient. This review synthesizes disaster loss data, climate finance flows, and financial instrument evidence to address a core research question: How can innovative financial instruments and a risk-layered architecture shift climate resilience finance from reactive to anticipatory? We integrate data from the Emergency Events Database (EM-DAT), the Climate Policy Initiative (CPI), the OECD Development Assistance Committee, the Green Climate Fund, and the Artemis Deal Directory across 2010–2025. Pearson correlation analysis confirms a reactive financing pattern (r = 0.71). Fixed-effects panel regressions show that high climate policy uncertainty suppresses private adaptation investment by approximately 30%. Guarantee and catastrophe bond instruments mobilize up to 6.5 times more private capital per public dollar than concessional loans. Parametric insurance grew at a 24% compound annual growth rate (2010–2025), outpacing catastrophe bonds (7%). A 12–14 times scale-up of current annual flows (USD 25 billion) is required to meet 2030 needs (USD 325 billion). We propose a risk-layered climate finance architecture aligning instruments with distinct hazard tiers. Credible policy signals, strategic public investment, and systematic integration of insurance mechanisms are essential preconditions for unlocking scalable, anticipatory resilience finance.

Abstract: Atmospheric nitrogen dioxide (NO₂) is an important component of reactive nitrogen and plays a key role in the atmospheric nitrogen cycle outside major emission regions. However, its variability under remote background conditions remains poorly characterized, as most observational studies focus on urban or continental environments. This study investigates the background variability of in situ NO₂ measurements at a remote North Atlantic island (Azores) over the period 2015–2024 and examines its association with large-scale atmospheric transport regimes. Monthly NO₂ concentrations were classified into background Atlantic conditions and months influenced by continental air masses using an objective PM₁₀ percentile-based criterion. Differences between regimes were assessed using non-parametric statistics. Although NO₂ concentrations were systematically higher during months associated with continental transport, the differences did not reach statistical significance. Wind speed analysis for the overlapping period 2018–2024 showed consistently higher values during continental transport months, supporting enhanced large-scale advection during these periods. Overall, the results indicate that background NO₂ levels in this remote insular environment exhibit modest but coherent modulation associated with atmospheric transport regimes. These findings contribute to improving the interpretation of reactive nitrogen variability in remote marine settings and highlight the value of island observatories for studying the atmospheric nitrogen cycle.

Abstract: The present study investigates the statistical distribution of slopes in radar reflectivity [S-Ze] in the lower troposphere at the west coast of India using the C-band radar during pre- monsoon months to monsoon months, which spans the different meteorological conditions, including from a drier atmosphere to moist atmosphere. To investigate the S-Ze, we calculated the difference in Ze between 4 to 2 km altitudes in the lower troposphere. For positive [negative] S-Ze, the Ze decreases [increases] towards the surface. The differences in S-Ze in the lower troposphere during pre-monsoon, monsoon onset and monsoon months reveals the precipitation variability. Among all the months, a higher fraction of +ve S-Ze are observed during March and April months compared to other months, and showed that in drier atmosphere the for most of the time Ze tends to decrease towards the surface. However, the average S-Ze shows the highest -ve average -ve S-Ze, during March and April months near the coastal boundaries and associates with the lesser number of profiles. May and June months have a higher fraction of -ve S-Ze [>60%] is observed over the northern latitudes of the study periods, whereas southern AS has a higher fraction of +ve S-Ze. August has the highest fraction of -ve S-Ze, over land and topographic features. September has the highest fraction of +ve S-Ze at the southern latitudes, and at the same time, the study regions are characterized by the drier atmosphere with less updraft. During the pre-monsoon months thermodynamic conditions are more important, where in the drier atmosphere Ze tends to decrease towards the surface. During the monsoon months the dynamics of convective and stratiform precipitation, and either evaporation during the stratiform precipitation along with the convective outburst may increase the lower level RH. Monsoonal months have the less increase or decrease in the hydrometeors size compared to pre-monsoon months, whereas precipitation is more of a convective nature. The results presented here would be an extension of the study from the satellite based observations, and reveals the extension climatology of inclusion of stratiform precipitation.

Abstract: The 27 day and the 11 year solar cycles in extreme ultraviolet radiation (EUV) of the Sun are influencing the Earth’s middle atmosphere. For the first time, the solar cycle influences on geopotential height (or pressure) are analysed by using the Aura Microwave Limb Sounder (Aura/MLS) observations from 2004 to 2021. Composite analysis shows that the mesospheric 27 day variation of the global mean geopotential height is correlated with the 27 day variation of solar radio flux (F10.7cm index) which is a proxy of solar EUV. The maximum of the geopotential height has a phase lag of 4 days with respect to the maximum of EUV. The 11 year solar cycle has a sensitivity of 492m/100sfu in global mean geopotential height at about 94km height. Similarly, the solar cycle influences of the global means of middle atmospheric temperature, ozone, and water vapour are derived and discussed.

Abstract: Accurate liquid water path estimates derived from backscattered solar radiation require knowledge of the vertical structure of cloud droplet effective radius, yet standard bispectral retrievals assume a vertically homogeneous cloud and overestimate liquid water path by up to 45% compared with in situ measurements. We developed a Gauss-Newton optimal estimation retrieval that simultaneously estimates vertical profiles of cloud droplet effective radius and above-cloud integrated water vapor from hyper-spectral solar backscatter measurements in the visible and shortwave infrared. The retrieval solves for effective radius at cloud top and base, cloud optical thickness, and above-cloud integrated water vapor in logarithmic space, using an a priori covariance matrix with off-diagonal elements derived from VOCALS-REx in-situ measurements. Tested on 69 simulated HySICS reflectance spectra constructed from in situ cloud microphysics, the hyperspectral retrieval reduces the average liquid water path error to 17.7%, compared to 45.2% for the standard bispectral method. Applied to 603 EMIT hyperspectral measurements over the southeast Pacific, MODIS-retrieved liquid water path exceeds the hyperspectral estimate by 25.6% on average. These results demonstrate that simultaneous retrieval of above-cloud water vapor is necessary for accurate droplet profile retrievals, and that the upcoming CLARREO Pathfinder instrument, with its 0.3% radiometric uncertainty, should enable routine vertical profiling of cloud droplet size.

Abstract: Reference evapotranspiration (ET0) is most commonly estimated using the FAO-56 Penman-Monteith (PM) equation. However, its application is often limited by the lack of required meteorological parameters. Due to their flexibility, ability to operate with limited input, and high accuracy in estimating ET0, machine learning models have become increasingly relevant in scientific research, offering a practical alternative under limited data conditions. In this study, artificial neural networks (ANNs) were applied to estimate daily ET0 using meteorological data from the Novi Sad station in Vojvodina (Serbia). The dataset consisted of eight meteorological variables relevant to evapotranspiration processes. Analysis showed that some variables had a stronger influence on ET0 prediction than others. To evaluate their combined effect, a series of ANN models with different input combinations was developed and tested. The FAO-56 PM method was used as a benchmark, and model performance was evaluated using R2, NSE, RMSE, and MAE. The highest accuracy was achieved when all variables were included, providing the model with maximum information. The best performance was obtained using a two-hidden-layer architecture with 32 and 16 neurons, resulting in R2 = 0.98, NSE = 97.86%, RMSE = 0.25 mm day-1, and MAE = 0.17 mm day-1.

Abstract: Lightning activity reflects the occurrence of severe convective events and represents the most prominent physical feature of thunderstorm weather. This study utilizes FY-4B/AGRI multi-channel infrared brightness temperature data (with a temporal resolution of 15 minutes and spatial resolution of 4 km) combined with ground-based lightning observation data to construct the AGToLightM model for predicting the probability of thunderstorm occurrence within the next 60 minutes. Based on lightning event characteristics, the model incorporates a Convolutional Block Attention Mechanism (CBAM) to enhance its ability to extract key spatial and spectral features at cloud tops. An adaptive weighted loss function is employed to address the class imbalance issue caused by sparse positive lightning samples. Three study regions—North China, East China, and South China—were selected, utilizing summer 2025 data for model training and validation. Results demonstrate that AGToLightM effectively captures the spatial distribution and evolution trends of thunderstorms, achieving a best Critical Success Index (CSI) of 0.327. In case studies, areas with forecast probabilities exceeding 60% showed spatial consistency with regions exhibiting radar echoes above 50 dBZ. This study validates the effectiveness of the AGToLightM model in integrating multi-source meteorological data for severe convective forecasting, providing technical guidance for enhancing the reliability of short-term thunderstorm forecasts.

Abstract: Using the new theory of "cosmic expansion - atomic expansion" as a tool, this paper summarizes the evolution law of planetary atmospheres and draws the following main conclusions: All the planets in the solar system were born at the same time and had a primordial atmosphere of hydrogen and helium when they were born. The subsequent atmospheres and oceans of all planets are formed by the mixing of the primordial atmosphere with the gases continuously released by the material inside the planets due to the expansion of the universe. The planet's atmosphere and oceans have been in dynamic equilibrium, on the one hand, the atmospheric molecules continue to escape into space, on the other hand, the internal materials of the planet continue to release various gases into the atmosphere. The composition of the planet's atmosphere has been developing in the direction of increasing molecular weight, first dominated by small molecular weight molecules such as hydrogen and helium, and then dominated by medium molecular weight molecules such as nitrogen and water, and then dominated by large molecular weight molecules such as carbon dioxide. Once the planet is completely solidified, the planet's atmosphere will quickly disappear.

Abstract: Climate-related extremes such as floods and droughts have been the main causes of natural disasters in southern Africa in recent years, with noticeable trends in climate extremes being observed. The Limpopo Province in South Africa has been especially prone to these extremes. The extreme weather in Limpopo is mainly caused by a mix of intense tropical weather systems, La Niña conditions, and exacerbated by climate change. Climate change exacerbates current water challenges across the province by affecting rainfall precipitation patterns, distribution, timing and intensity, leading to extreme climate events such as floods and drought. Historical and future trends of precipitation and relevant extreme indices using observed data from the South African Weather Service and CORDEX ensemble model simulations under the RCP4.5 and RCP8.5 scenarios were examined. An analysis of all precipitation data suitable for the study of long-term variability and trend, indicates that most areas underwent drying to various degrees over the last century, especially the central and western parts. Drier conditions over the eastern parts have be-come more prevalent over the last 50 years. Also, more extremes on a sub-seasonal basis were experienced. Regarding future scenarios, three projected time periods were examined: Current climatology (2006 – 2035), near-future (2036 – 2065), and far-future (2066 – 2095), compared to the baseline period (1976-2005). Most areas will experience a further decrease in precipitation under both emission scenarios, especially in the south-east, central and extreme northern parts. In addition, these areas are expected to experience a decrease in the frequency of heavy precipitation days for all periods under both RCP scenarios, mainly due to drying. Consecutive dry days are expected to increase significantly. Transitioning to renewable energy and enhancing natural carbon sinks can reduce emissions, while prioritizing resilience through renewable energy, water management, and climate-smart agriculture will help address climate change challenges in the province.

Abstract: Based on the Multi-Sensor Reanalysis Version 2 dataset, this study analyzes variations in monthly mean total ozone column (TOC) over Northeast China (40–53°N, 115–135°E) during 2015–2024. The study area in winter lies in the transition zone between high polar and low subtropical TOC in East Asian mid-latitudes. Key results indicate that the TOC over Northeast China is consistently higher than the zonal mean TOC of the same latitude band and seasonal cycle demonstrates TOC maximum (minimum) in February (August), one month (two months) earlier than for the Northern Hemisphere midlatitudes. The important role of Brewer–Dobson circulation and quasi-stationary wave (QSW) structure in the TOC distribution over Northeast China is confirmed by the 10-year climatology for January–March. The QSW pattern is characterized by the TOC decrease from the northeastern (~415 DU) to southwestern (~330 DU) parts of the region. The strongest positive (negative) correlations approaching r = 0.9 (r = –0.8) exist between TOC and ozone concentration (temperature) at 50 hPa and 100 hPa, as well as at the surface. These findings can be applied to analyze the ozone observations and stratosphere–surface couplings in the Northeast China region.

Abstract:

Nova Scotia, a province on Canada’s Atlantic coast, has proposed Wind West, a plan to initiate the province’s offshore wind industry. A regional offshore wind report identified eight potential development areas (PDAs), of which four were chosen. The areas were selected to avoid ecologically significant and conflict-of-use areas; however, no consideration was given to tropical cyclones (TCs) and hurricanes (intense tropical cyclones). This paper evaluates the effects of climate change and TCs on offshore wind turbines sighted on Nova Scotia’s continental shelf by analysing historical TC track data to assess the intensity and frequency of extreme wind and wave events on the continental shelf. Correlations between SSTs and extreme weather events were also examined. The findings show no clear long-term trends in TC intensity or frequency in the selected areas, although there is a clear upward trend in sea-surface temperatures (SSTs) since 1950. No strong correlation between rising SSTs and increased storm intensity or frequency within the available datasets were found, though similar studies suggest that these variables have some correlation on aggregate. While climate change is causing conditions for hurricanes to become favorable along the Scotian Shelf, current TC data shows no clear correlation with increasing intensity and frequency over time. The results are affected by the quality of the data. High uncertainty, spatial resolution, and temporal resolution leave large portions of TC tracks unmeasured. Uncertainty associated with pre- and post-1950 data makes conclusions from the results difficult. We propose a measuring buoy in each of the four selected potential development areas cost C$200,000 to develop and C$35,000 to maintain. Each buoy would have a representative radius of 50km, slightly larger than that of each of the four wind energy zones. The additional data collected would allow developers to pick appropriate design standards based on available environmental data and could additionally be used for climate change research. Currently, Nova Scotia faces many limitations developing its offshore; supplying accurate data to assess the risk from extreme weather events to offshore wind turbines is one of the first steps to ensuring success.

Abstract: Warm rain events occur when moist air masses containing elevated precipitable water produce high rainfall rates capable of generating local flash floods. Catalonia, located on the northeastern Mediterranean coast of the Iberian Peninsula, is regularly affected by such episodes: approximately 70% of daily precipitation events exceeding 10 mm with fewer than ten cloud-to-ground lightning flashes can be classified as warm rain. The current research aimed to identify the meteorological conditions most conducive to heavy warm rain episodes in Catalonia. These cases are commonly associated with flash flood episodes in the study region. We have utilized rain gauges, lightning data, radar, and model fields, combined with radio sounding profiles. First, we have identified and characterized warm rain cases, and secondly, we have selected some relevant cases to characterize the phenomenon. These events occur predominantly along the Catalan coast during the warm season, typically following the passage of a cold front, and are associated with shallow convective clouds producing little or no lightning. However, the key determining factor is a characteristic vertical thermodynamic profile: a moist and saturated lower troposphere with high precipitable water beneath a low- to mid-level thermal inversion, weak instability concentrated near the surface. Furthermore, local wind convergence plays a principal role in the rainfall pattern.