Abstract: Air quality makes a huge difference in human health, ecological environment, economic 2 development, and global climate governance. This study introduced Time-Weighted 3 Ensemble model into the air quality prediction model and achieved good results. The 4 prediction results were consistent with reality, and the R-square of prediction is 0.54, pro- 5 viding a new reference for people to avoid air pollution. And because of the the original Air 6 Quality Index (AQI) has limited using scope and results are inaccurate, this thesis establish 7 a brandnew evaluation system, called Adaptive Air Quality Index (AAQI), which takes 8 concentration, correlation, time, and cooperation into consideration. It is more comprehen- 9 sive and advanced than the existing system. Data on six pollutants were collected from 10 six cities, namely Brasilia, Cairo, Dubai, London, New York, and Sydney, and then prepro- 11 cessed the above data using KNN interpolation, Unit transformation and normalization, 12 and calculated the correlations among them by using Mutual Information, Spearman’s 13 Rank Correlation and Kendall’s Tau Correlation. Afterwards, we incorporated it into the 14 AAQI and obtained their air quality. Among them, Sydney had the best air quality, while 15 Dubai and Cairo had relatively poor air quality. This research should be promoted and 16 applied in air quality monitoring in real life.

Abstract:

Extreme precipitation poses a major natural hazard in the western Mediterranean, particularly along the Valencia coast, where torrential events recur with significant societal impacts. This study evaluates the feasibility and added value of an explicitly spatial approach for estimating return periods of extreme precipitation in the Júcar and Turia basins, moving beyond traditional point-based or micro-catchment analyses. Our methodology consists of progressive spatial aggregation of time series within a basin to better estimate return periods of exceeding specific catastrophic rainfall thresholds. This technique allows us to compare 10-min rainfall data of a reference station (e.g. Turis, València, 29 October 2024 catastrophe) with long-term annual maxima from 98 stations. Temporal structure is characterized using the fractal−intermittency n index, while tail behavior is modeled using several extreme-value distributions (Gumbel, GEV, Weibull, Gamma, and Pareto) and guided by empirical errors. Results show that n ≈ 0.3−0.4 is consistent for extreme rainfall, while return periods systematically decrease as stations are added, stabilizing with about 15-20 stations, once the relevant spatial heterogeneity is sampled. Specifically, the probability of exceeding extreme thresholds is between 3 and 10 times higher for the areal than the point approach. Overall, the results demonstrate that spatially-integrated return-period estimation is operational, physically consistent, and better suited for basin-scale risk assessment than purely point-based approaches.

Abstract: A composite, eighteen-year long record on in-situ surface meteorology and computed bulk air-sea fluxes of heat, freshwater, and momentum from an ocean site windward of Oahu are presented. Data were logged every minute over eighteen years. Methods and data quality are discussed. Statistics of one-minute, one-hour, and one-day times series are presented, and daily averaged time series provide an overview of this trade wind site. Mean wind was 6.8 m s−1 toward the west southwest, mean ocean heat gain was 23.2 W m−2 with freshwater loss of 1.2 m yr−1. Energetic sub-diurnal variability was found, with spectral peaks in solar insolation and sea level pressure, and transient, short-lived signals including insolation above the clear sky value, short periods of warm air, and downdrafts of dry air. Mean daily cycles are presented. Longer lasting events, including periods of ocean cooling, ocean heating, and hurricanes, are explored. Mean annual cycles are presented. The ocean loses heat from January through early May; then gains heat until late October and returns to loosing heat. Normalized by duration, the events examined have potential for significant contributions to the heat, freshwater, and mechanical energy exchanges.

Abstract: A retrospective ozone simulation was conducted with the Microscale Forward and Adjoint Chemical Transport (MicroFACT) model for an industrialized area of Detroit, Michigan, USA using a 24 km × 24 km horizontal × 1.5 km vertical grid. The domain encompassed a regulatory monitoring station at East 7 Mile Rd at the northern edge of the grid. The episode day was 30 June 2022, when the station-measured 8-hour ozone reached 76 ppb during predominantly southwesterly wind. The ozone impacts of mobile, point, nonpoint, and biogenic emissions were simulated at 400 m horizontal resolution. Simulation results were compared against station measurements of ozone, nitrogen oxides, and total reactive nitrogen. Local nitrogen oxide sources were found to titrate ozone, while ozone turbulently entrained to the surface from ~500 m aloft enhanced surface Ozone Production Efficiency and led to extended periods of high ozone concentrations very similar to observations. Volatile Organic Compound emission reductions produced only weak decreases in maximum 8-hour ozone, suggesting that radicals were enhanced mostly by photolysis of subsiding ozone. Entrainment of ozone layers aloft may thus be critical in explaining historical ozone exceedances of the United States National Ambient Air Quality Standard at the East 7 Mile Rd station.

Abstract: Aridity represents a fundamental climatic constraint governing water resources, eco-system functioning, and agricultural systems in transitional climate zones. This study examines the spatial organization and temporal variability of aridity and thermal con-tinentality in North Macedonia using observational records from 13 meteorological stations distributed across contrasting altitudinal and physiographic settings. The analysis is based on homogenized monthly and annual air temperature and precipitation series covering the period 1991–2020. Aridity and continentality were quantified using the De Martonne Aridity Index (IDM), the Pinna Combinative Index (IP), and the Johansson Continentality Index (JCI). Temporal consistency and trend behavior were evaluated using Pettitt’s nonparametric change-point test, linear regression, the Mann–Kendall test, and Sen’s slope estimator. Links between aridity variability and large-scale atmospheric circulation were examined using correlations with the North Atlantic Oscillation (NAO) and the Southern Oscillation Index (SOI). The results show a spatially consistent and statistically significant increase in mean annual air temper-ature, with a common change point around 2006, while precipitation displays strong spatial variability and limited temporal coherence. Aridity patterns display a strong altitudinal control, with extremely humid to very humid conditions prevailing in mountainous western regions and semi-humid to semi-dry conditions dominating lowland and southeastern areas, particularly during summer. Trend analyses do not reveal statistically significant long-term changes in aridity or continentality over the study period, although low-elevation stations exhibit weak drying tendencies. A mod-erate positive association between IDM and IP (r = 0.66) confirms internal consistency among aridity indices, while summer aridity shows a statistically significant relationship with the NAO. These results provide a robust climatic reference for North Mace-donia, establishing a first climatological baseline of aridity conditions based on multi-ple indices applied to homogenized observations, and contributing to regional assess-ments of hydroclimatic variability relevant to climate adaptation planning.

Abstract: Atmospheric rivers (ARs) play a critical role in producing high-impact weather events including extreme precipitation, flooding, gusty winds, and rapid temperature changes. Building upon the recently published EDARA (ERA5-based Dataset for Atmospheric River Analysis), we present S-EDARA, a supplementary dataset that enhances AR impact assessment capabilities through a newer AR detection algorithm and additional impact-related metrics. S-EDARA includes AR shapes identified by the tARget version 4 (ARS4) algorithm, strong integrated vapour transport (SIVT) indicators, and pseudo total precipitation rate (PTPR) fields. The dataset features both numerical data and interactive graphical catalogues displaying ARS4, SIVT, PTPR, gusty winds, and 24-hour temperature changes at 6-hourly intervals. These enhancements enable more comprehensive analysis of AR impacts and characteristics, particularly for regions experiencing rapidly changing meteorological conditions during AR events. The dataset covers the period from 1940 to present and is publicly available through the Federated Research Data Repository.

Abstract: Airborne microplastics (AMPs) undergo ultraviolet (UV)–driven physicochemical aging during atmospheric transport, influencing cloud processes, greenhouse-gas release, and potential respiratory health impacts. Quantifying this transformation is particularly challenging for particles smaller than 10 µm and for polymers such as polyethylene terephthalate (PET), whose intrinsic ester carbonyl band obscures newly formed acid carbonyls in conventional infrared analyses. Here, we develop a µFTIR attenuated total reflection (µFTIR-ATR) imaging method combined with a fourth-derivative oxidation index (carbonyl ratio at 1701/1716 cm⁻¹) that resolves these overlapping bands and enables sensitive, quantitative evaluation of PET surface oxidation. The approach automates detection, identification, and oxidation analysis of particles down to ~2 µm. Laboratory UV irradiation experiments show a systematic increase in this derivative-based oxidation index with exposure dose. Application to ambient PET collected from Mt. Fuji, Tokyo, Osaka (Japan), and Siem Reap (Cambodia) reveals clear regional differences corresponding to local UV-A environments: PET from Siem Reap exhibited the highest oxidation, whereas particles from the Japanese sites showed moderate but variable aging. These results demonstrate that derivative-based µFTIR-ATR imaging provides a practical and highly sensitive tool for quantifying photo-oxidative degradation in fine airborne microplastics and highlight the value of chemical-aging metrics for interpreting atmospheric processing and transport pathways.

Abstract: We present an improved algorithm based on the POlarization LIdar PHOtometer Networking (POLIPHON) method to retrieve cloud condensation nuclei (CCN) concentration profiles from spaceborne lidar observations. Our previous paper, which was the first study to demonstrate the feasibility of using measurements from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) to retrieve CCN, is revisited. Our results focus on the Evaluation of CALIPSO’s Aerosol Classification scheme over Eastern Mediterranean (ACEMED) research campaign that took place over Thessaloniki, Greece, in September 2011. We compare our results with our earlier retrievals, discussing the critical changes that have been made and the importance of using the proper conversions factors. We also demonstrate the use of conversion factors acquired based on CALIPSO aerosol typing for CCN retrievals. The analysis highlights the strong influence of smoke on CCN concentrations and shows that the assumed aging state of the smoke can significantly alter the retrieval outcome.

Abstract: The Philippines experiences intense rainfall but has limited ground-based monitoring infrastructure for flood prediction. Satellite rainfall products provide broad coverage but contain systematic biases that reduce operational usefulness. This study evaluated three correction methods—Quantile Mapping (QM), Random Forest (RF), and Hybrid Ensemble—for improving Satellite Rainfall Monitor (SRM) estimates in the Cagayan de Oro River Basin, Northern Mindanao. When trained on comprehensive 2019-2020 data, Random Forest and Hybrid Ensemble substantially outperformed Quantile Map-ping, achieving excellent calibration accuracy (R² = 0.71 and 0.76 versus R² = 0.25 for QM). However, when tested on an independent year with substantially different rain-fall patterns (2021: 120% higher mean rainfall, 33% increase in rainy-day frequency), performance rankings reversed completely. Quantile Mapping maintained satisfactory operational performance (R² = 0.53, RMSE = 5.23 mm), showing improvement over training conditions, while Random Forest and Hybrid Ensemble both failed dramati-cally, with R² dropping to 0.46 and 0.41 respectively despite their excellent training performance. This highlights that training accuracy alone poorly predicts operational reliability under changing rainfall regimes. Quantile Mapping's percentile-based cor-rection naturally adapts when rainfall patterns shift without requiring recalibration, while machine learning methods learned magnitude-specific patterns that failed when conditions changed. For flood early warning in basins with limited data, equipment failures, and variable rainfall, only Quantile Mapping proved operationally reliable. This has practical implications for disaster risk reduction across the Philippines and similar tropical regions where standard validation approaches may systematically mislead model selection by measuring calibration performance rather than operational transferability.

Abstract: Tropical cyclones pose major risks to life and property, especially as coastal populations grow and climate change increases the likelihood of intense storms, making seasonal prediction of tropical cyclones an important scientific and societal goal. This study uses HURDAT best-track records from 1950–2024 to quantify annual tropical cyclone, hurricane, and major hurricane counts across the Atlantic basin, Caribbean Sea, and Gulf of Mexico. These nine targets are paired with 34 monthly climate predictors from NOAA and NASA GISS—including SST and ENSO indices, Main Development Region (MDR) wind and pressure fields, and latent heat flux empirical orthogonal functions—evaluated under nine predictor-set configurations. Four forecasting approaches are developed and tested under operationally realistic conditions: Lasso regression, K-nearest neighbors (KNN), an artificial neural network (ANN), and XGBoost, using a 30-year sliding-window cross-validation design and a Poisson log-likelihood skill score relative to climatology. Lasso performs reliably with concise, physically interpretable predictors, while XGBoost provides the most consistent overall skill, particularly for basin-wide total cyclone and hurricane counts. The skill of ANN is limited by small sample sizes, and KNN offers only marginal improvements. Forecast skill is the highest for basin-wide storm totals and decreases for regional and major-hurricane targets due to lower event frequencies and stronger predictability limits.

Abstract: Temperature and related thermal comfort metrics at a representative 9-member ensemble of airports in Europe are presented using a combination of historical (1985-2014) and future projection (2035-2064) timescales under a variety of forcing scenarios. Data are shown for summer (June-July-August) and the 9 sites are further grouped into 'oceanic', 'continentally influenced', and 'Mediterranean coastal' climate types which ameliorates visualisation and provides more generalised policy-relevant results. Using the Humidex metric, it is shown that some airports in southern Europe may enter a 'dangerous' (>45C) regime of human discomfort. This would be accompanied by economic impacts related to longer mandated rest periods for ground workers, as well as increased water intake and changes to health and safety training. The coincidence of the 38C flash point of kerosene jet fuel with perturbed daily maximum temperature occurrence thresholds at some sites will likely also have knock-on effects on safety practises since some sites may experience 70% of future summer days with temperatures exceeding this value. Using an 18C threshold for defining cooling and heating 'degree days', increases in cooling requirements are projected to be larger than reductions in heating for continental and Mediterranean sites and heatwave occurrence (3 or more days at or above the 95th historical percentile) may increase by a factor of 10. From a building and infrastructure services perspective, increased temperature variability around larger average values has the potential to reduce safe runway lifetimes and increase structural fatigue in large-span steel terminal buildings.

Abstract: Mixed-phase clouds, in which liquid droplets and ice crystals coexist at temperatures between −38∘C and 0∘C, play a critical role in Earth’s radiation budget. In this work, we compare observations from the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS), with one global climate model, the Community Atmosphere Model version 6 – Oslo configuration (CAM6-Oslo), and three storm-resolving models: the ICOsahedral Non-hydrostatic model (ICON), the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM), and the Goddard Earth Observing System model (GEOS). Specifically, we compare mixed-phase cloud occurrence, thermodynamic partitioning, and hemispheric contrast, and introduce joint histograms of supercooled liquid fraction (SLF) and liquid effective radius (r_liq) to analyse microphysical behavior. Our results show that all models reproduce the geographic distribution of mixed-phase clouds, but differ significantly in detail. CAM6-Oslo yields the closest agreement in hemispheric SLF contrasts and SLF–r_liq relationship. Our results highlight the role of aerosol-cloud interactions and microphysics schemes in determining model performance, and demonstrate that storm-resolving models do not solve the ongoing challenge of representing mixed-phase clouds at global scales.

Abstract: On-road vehicles are a primary source of urban air pollution. It is known that high-emitting vehicles represent a fraction of the fleet but contribute significantly to the total emissions. Usually, road transportation emission inventories do not capture the impact of these types of vehicles, underestimating emissions. This study introduces a simple method to refine vehicle emission inventories by incorporating data from Ecuador's Inspection and Maintenance (I/M) program. We analyzed I/M data from Quito to develop a correction factor for the Vehicular Emissions INventory (VEIN) model, accounting for the higher emissions from vehicles that fail inspection. Our analysis shows that while less than 10% of gasoline and 20% of diesel vehicles failed inspection, their emissions were substantially higher; for instance, accounting for reproved vehicles produced 60% more CO, 18% more NMVOC, 40% more PM2.5, and 34% more PM10 . These findings demonstrate that incorporating I/M data is crucial for accurately quantifying vehicular pollution. The proposed methodology offers a way to create more accurate emission estimates, providing a tool for policymakers to manage air quality.

Abstract: Antarctic ice core data reveal a consistent pattern across glacial cycles: atmospheric CO₂ does not immediately track temperature decline as interglacial conditions give way to glaciation. The most dramatic example occurs during the Last Interglacial (Eemian, MIS 5e), where CO₂ remained essentially constant at 275–280 ppm for approximately 13,000 years while temperature fell 7°C. This paper examines whether similar behavior can be detected during cooling from earlier interglacials. Using harmonic fits to temperature and CO₂ data spanning 350,000 years, phase plots are constructed of CO₂ versus temperature that isolate the warming and cooling branches of each glacial cycle. The analysis reveals that the Eemian is the clearest but not unique example: MIS 9 shows comparable behavior, while the MIS 7 complex presents an instructive exception that may reflect extreme orbital forcing conditions. The asymmetry between rapid CO₂ release during warming and slow CO₂ absorption during cooling suggests rate-limited processes govern the return of atmospheric carbon to oceanic and terrestrial reservoirs. These observations are inconsistent with CO₂ acting as the primary driver of temperature change on glacial-interglacial timescales.

Abstract: Knowledge of the maximum gust expected over a period of years is essential for off-shore structures design. Because long records of gust speed are not normally availa-ble, maximum gusts have traditionally been estimated by multiplying the maximum expected hourly or 10-minute wind speed by a gust factor. That calculation ignores the possibility that the highest gust might not occur in the hour with the highest mean wind speed. A similar problem arises in the estimation of the maximum expected in-dividual wave height. By analogy with the accepted method of calculating maximum wave heights, we demonstrate how maximum gusts can be calculated from time series of average wind speed and wind gust distributions. We used measurements from the IJmuiden meteorological mast offshore The Netherlands to find wind gust distribu-tions. The IJmuiden data is particularly useful for studying gusts because four years of measurements were made at a sampling frequency of 4 Hz. Those distributions were used to predict extreme values of gusts in a storm using methods similar to those used in wave height calculations. The resulting extreme values closely matched ex-treme values calculated directly from the measured maximum gusts in each storm. The methods described here can calculate extreme gust speeds more accurately than the methods currently in use.

Abstract: In South Asia, smog has become a serious environmental problem that endangers public health, ecosystems, and the regional climate. To determine the primary causes of smog formation in Lahore in October and November, this study develops a dual analytical framework that combines cutting-edge machine learning with sector- and pollutant-specific emission analysis. To assess their relationship with AQI and create a high-accuracy predictive model, meteorological factors and emission data from key sectors were used to build Random Forest and XGBoost models. The study evaluates the joint effects of weather and emission loads on AQI variability by integrating atmospheric dynamics with comprehensive emission profiles. The XGBoost model forecasts important pollutants from the transportation, industrial, and agricultural sectors, including CO2, NOx, VOCs, and particulate matter, in the second analytical tier. The models consistently identified particulate matter, NOx, and transport-related pollutants as the major determinants of AQI, with high prediction performance (R² > 0.97). The transportation sector accounts for around 90% of Lahore's yearly emissions. These results offer policymakers a useful tool to anticipate air quality, identify important emission sources, and execute targeted initiatives to minimize smog and promote a healthier urban environment. They also clearly demonstrate the causes of atmospheric and sectoral pollution.

Abstract: A harmonic analysis of Antarctic ice core proxy temperature, CO₂ and CH₄ data is presented spanning 350,000 years. To ensure consistent phase comparison, CO₂ data were converted from AICC2012 to EDC3 chronology using depth as the invariant coordinate. Using a greedy algorithm to select periodic components, the analysis initially obtained 59 periods for temperature but subsequently refined this to 55 periods after removing four components (22,150, 9,000, 8,000, and 4,540 years) that exhibited high correlations in the normalized covariance matrix. This refinement ensures stable, well-conditioned parameter estimates while maintaining excellent fits: R² = 0.952 for temperature and R² = 0.964 for CO₂ (truncated at 1515 CE), R² = 0.873 for CH₄. The algorithm independently recovers the canonical Milankovitch orbital periods (approximately 100,000, 41,000, and 23,000 years) without prior specification, validating both the methodology and the orbital pacing of ice ages (Milankovitch, 1941). Phase analysis reveals a systematic pattern of CO₂ lagging temperature at orbital timescales, with mean lag approximately 1,700 years, consistent with the hypothesis that temperature drives CO₂ through ocean degassing rather than the reverse. Examination of the Last Interglacial (Eemian) reveals a striking asymmetry: CO₂ remained elevated at 275–280 ppm for approximately 13,000 years while temperature declined by 7°C. An R² analysis clearly reveals the Mid-Pleistocene Transition and justifies limiting the input to the fit. The modern CO₂ spike, which departs dramatically from the 350,000-year orbital envelope, is clearly anomalous relative to the harmonic structure of the paleoclimate record.

Abstract: : Due to the lack of in-situ observations in mountainous locations, the use of remote sensing data is an alternative to analyze rainfall distribution patterns during the passage of major hurricanes. In this work, gridded precipitation data from the CHIRPS database are evaluated by comparing with observations from weather stations during the passage of category 3–5 hurricanes for the period 1980–2024. The comparison between estimated and observed values is performed by regression analysis and the use of the K and K0 coefficients. An advantage of using K-ratio and K0-ratio is the identification of overestimated or underestimated precipitation in the pixel records. The distribution of daily precipitation helped in a more concise way to better understand how well CHIRPS reproduced the observed rainfall patterns. Results show that correlations between observations and database estimates are in the range of 0.42–0.67, for eastern Pacific hurricanes, and 0.29–0.74 for Atlantic hurricanes all of which are statistically significant; however, these results do not imply congruence between observations and estimates since CHIRPS fails to adequately reproduce the position of the highest precipitation core. In the initial stages of a tropical cyclone, near- zero correlations between observations and estimates indicate that CHIRPS is not able to reproduce the observed rainfall. It is recommended to use CHIRPS with caution when the focus is on analyzing rainfall patterns during the development of intense tropical cyclones.

Abstract: The North American Prairies are a region of critical importance to continental hydroclimate and agriculture, exhibiting high sensitivity to variability in atmospheric moisture transport. This study investigates the seasonal and interannual variability of integrated moisture flux over the Prairie region (246°–264°E, 49°N–53°N) using the National Centers for Environmental Prediction (NCEP) Reanalysis dataset from 1979 to 2022. We employ a combination of composite analysis and Empirical Orthogonal Function (EOF) analysis to identify the dominant modes of variability and their associated large-scale synoptic drivers. Our results confirm a strong seasonal reversal: winter moisture flux is predominantly zonal (westerly), contributing an average of 26.2% to total inbound flux, while summer flux is primarily meridional (southerly), contributing a dominant 72.6%. Composite analysis of extreme moisture years reveals that anomalously high moisture winters are associated with an intensified Aleutian Low and a strengthened pressure gradient off the North American west coast, facilitating enhanced westerly flow. Conversely, a strengthened continental high-pressure system characterizes anomalously low moisture winters by. During summer, high moisture years are driven by an enhanced southerly component of flow, likely linked to a strengthened Great Plains Low-Level Jet (GPLLJ). The first EOF mode for winter explains 36.4% of the variance in eastward flux and is characterized by a pattern consistent with the El Niño Southern Oscillation (ENSO) teleconnection pattern. These findings underscore the control of Pacific-centric circulation patterns on Prairie hydroclimate and have significant implications for predicting seasonal water availability.

Abstract: The advance and delay of the rainy season is among the most frequently cited effects of climate change by Ecuadorian farmers. However, its assessment is not feasible using the conventional indicators recommended by the standardized indices of the Expert Team on Climate Change Detection and Indices (ETCCDI). This study aims to analyze such advances and delays through harmonic analysis in Tungurahua, a predominantly agricultural province in the Tropical Central Andes, where in-situ data are scarce. Daily in-situ data from five meteorological stations were used, including precipitation, maximum, and minimum temperature records spanning 39 to 68 years. The study involved an analysis of the region’s climatology, climate change indices, and harmonic analysis using Cross Wavelet Transform (XWT) and Wavelet Coherence Transform (WCT) to identify seasonal patterns and their variability (advance or delay) by comparing historical and recent time series, and Kriggin for regionalization. The year 2000 was used as a breakpoint for comparing past and present trends. Results show a generalized increase in both minimum and maximum temperatures. In the case of extreme rainfall events, no significant changes were detected. Harmonic analysis was found to be sensitive to missing data. Furthermore, the observed advances and delays in seasonality were not statistically significant and appeared to be more closely related to the geographic location of the stations than to temporal shifts.