Search Results (482)

Accurate correction of daily satellite-derived precipitation estimates in data-scarce tropical regions remains a critical challenge for climate monitoring, agriculture, and public health. Satellite products such as CHIRPS offer broad spatial coverage but exhibit systematic biases relative to ground-based observations particularly in complex terrain under bimodal tropical regimes influenced by ENSO. We propose a Functional Generalised Additive Mixed Model (FGAMM) that corrects CHIRPS-derived precipitation estimates by treating the annual accumulated precipitation curve as a functional response and the satellite accumulation curve as a functional covariate, while incorporating station-level random effects and the Southern Oscillation Index. This functional formulation targets the systematic, slowly varying bias between satellite and ground-station accumulation, the quantity most relevant for water-balance applications such as reservoir management and agricultural planning rather than day-to-day storm nowcasting. Applied to 62 IDEAM stations in the Valle del Cauca department of Colombia (2012–2020), the FGAMM achieves a mean cross-validation RMSE of 0.68 mm/day (95% bootstrap CI: 0.61–0.75), a substantially lower error than linear regression, SVM, and Random Forest within this dataset, where the gap is statistically significant across all competing methods. This magnitude of advantage is not reproduced when applying the same fitting-and-differencing pipeline, via a simplified concurrent approximation, to an independent national-network dataset; we discuss the methodological factors that likely contribute to this discrepancy—including an inherent smoothness asymmetry between the penalised-spline FGAMM fit and the unconstrained benchmark models, and differences in validation design between the two checks—in the Discussion, and treat the true size of the FGAMM’s advantage as an open question pending a fully controlled comparison. Corrected estimates are currently restricted to the calibrated station locations; because CHIRPS provides near-global daily coverage from 1981 to the present, we discuss how the same modelling approach could in principle be applied to other tropical or subtropical regions with a sparse reference station network, including areas of Latin America, sub-Saharan Africa, and South Asia where station density is similarly limited. Full article

This study presents a comprehensive statistical characterization of cloud vertical structure and phase occurrence over Limassol, Cyprus, using 18 months of continuous ground-based remote sensing observations from the Cyprus Cloud Aerosol and Radiation Experiment (CyCARE) campaign (October 2016–March 2018). The Eastern Mediterranean represents a climatologically complex, understudied subtropical region characterized by strong seasonal variability and frequent exposure to diverse aerosol mixtures, including mineral dust, biomass-burning smoke, marine particles, and anthropogenic pollution. Utilizing the standardized Cloudnet target classification framework, synergistically combining lidar and cloud radar observations, 1,393,659 vertical profiles are analysed, finding that 35% contained hydrometeors. Within these profiles, ice-phase targets occurred in 83.8% of cloud-containing profiles, followed by mixed-phase (33.5%) and liquid-phase (29.2%) targets, with precipitation detected in 32% of cases (these percentages are not mutually exclusive). Cloud occurrence exhibited pronounced seasonality: 57.7% in winter, 23.9% in spring, 17.8% in autumn, and virtually absent in summer (0.6%). Among five mutually exclusive cloud-layer classifications, pure ice clouds were the most frequent (38.4%), narrowly ahead of mixed-phase clouds (36.2%); combined, mixed-phase and mixed-phase precipitating layers together (41.0%) constituted the most frequent phase family overall. These results establish an 18-month observational baseline for evaluating climate model parameterizations, validating satellite-derived cloud products, and guiding future aerosol–cloud interaction studies in this climate-sensitive region. Full article

The use of remote sensing data in hydrological applications has increased, especially in regions with limited ground-based observations. Satellite precipitation products (SPPs) provide extensive temporal and spatial coverage but may contain biases that can affect their performance in hydrological simulations. This study evaluates the performance of four near-real-time SPPs for daily streamflow modeling in the Tulijá River Basin (TRB), Mexico: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS), PERSIANN-Dynamic Infrared Rain Rate near real-time (PDIR-Now), and the Early Run and Late Run products of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG). The SPPs were first compared with meteorological station precipitation data and subsequently bias-corrected using the Linear Scaling (LS) method. The CEQUEAU hydrological model simulated streamflow using three precipitation datasets: meteorological stations, original SPPs, and bias-corrected SPPs. For simulations using observed precipitation, the model was calibrated for 1991–2014 and validated for 1968–1990; for SPP-based simulations, calibration and validation were performed for 2003–2011 and 2012–2014, respectively. Model performance was assessed using the Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), and coefficient of determination (R²). The results show that CEQUEAU performance varies by precipitation dataset. Simulations using observed precipitation yielded NSE values close to 0.70 during both calibration and validation, whereas the original SPPs yielded NSE values below 0.18, including negative values. After bias correction, IMERG-Early and IMERG-Late yielded NSE values of approximately 0.55 during both periods. These findings highlight the importance of analyzing the performance of near-real-time SPPs in hydrological applications, especially in tropical regions with complex topography. Full article

Drought monitoring in arid regions commonly relies on climatic indices, yet the accumulation timescale that best represents surface soil moisture drought may vary spatially. This study evaluated the Standardized Precipitation Evapotranspiration Index (SPEI) at 1-, 3-, and 6-month accumulation periods against satellite-derived surface soil moisture across Saudi Arabia during 2003–2024. Temporal correspondence, grid-cell Spearman correlation, and receiver operating characteristic area under the curve (ROC-AUC) were evaluated. The analysis included 1462 grid cells meeting the minimum paired-observation criterion, representing 53.4% of the 2736 cells in the national domain. SPEI-1 consistently showed the strongest overall performance, with the highest temporal correspondence with surface soil moisture drought extent ($\rho =0.50$, versus 0.37 for SPEI-3 and 0.30 for SPEI-6), median grid-cell correlation ($\tilde{\rho }=0.290$, versus 0.255 and 0.175), and median ROC-AUC (0.629, versus 0.618 and 0.580). SPEI-1 was also the nominal highest-AUC timescale in 52.5% of analyzed cells, compared with 29.5% for SPEI-3 and 17.9% for SPEI-6. However, the median AUC difference between the first- and second-ranked timescales was only 0.033, and paired bootstrap comparisons showed that 94.8% of cells had no uniquely supported AUC winner. Regionally, nine of the 13 administrative regions showed a nominal majority preference for SPEI-1, whereas four had no single-timescale majority. The overall SPEI-1 > SPEI-3 > SPEI-6 ordering was also preserved under alternative soil moisture drought thresholds and temporal out-of-sample validation. These findings indicate that shorter SPEI accumulation periods generally provide the closest representation of near-surface soil moisture drought within the observed Saudi domain. The results provide practical guidance for selecting SPEI accumulation periods according to the land-surface drought process being monitored. Full article

Accurate daily gridded precipitation estimates are needed to characterize spatial rainfall variability and support retrospective hydrological analysis in Southeast Asia, where complex rainfall regimes and sparse rain gauge coverage remain major challenges. This study develops an Extended Triple Collocation Analysis (ETCA)-based merged daily precipitation dataset for Southeast Asia using 15 multi-source gridded precipitation products for 2000–2014. The products include gauge-based, satellite-based, reanalysis-based, and merged datasets, all regridded to a common 10 km × 10 km grid. ETCA was applied to all 455 possible three-product combinations to estimate product-level reliability, expressed as the squared correlation coefficient and error variance at each grid cell. The results reveal substantial spatial variability in product reliability. The final ETCA-merged product was generated through pixel-wise reliability-based product selection, quantile-based distributional adjustment using the locally highest-ranked product as an internal reference, and equal-weight averaging of the selected adjusted products. Validation against GSOD daily observations shows that the ETCA-merged product achieves lower RMSD of 2.95 mm day⁻¹, compared with 2.96 mm day⁻¹ for the simple all-product ensemble and 3.01 mm day⁻¹ for the ensemble of the five most regionally reliable products. The corresponding temporal correlations are 0.79, 0.81, and 0.78, respectively. The merged product also improves the representation of high-percentile rainfall, although very intense rainfall remains underestimated. Spatial climatology and annual cycle analyses indicate that the ETCA-merged product preserves the main rainfall patterns and seasonal evolution of Southeast Asia while introducing local adjustments based on product reliability. These findings demonstrate that ETCA provides a useful framework for developing uncertainty-informed precipitation datasets in regions with sparse gauge observations and spatially heterogeneous product performance. Full article

Air quality assessment for atmospheric particulate matter (PM) faces significant metrological challenges due to the lack of Certified Reference Materials and sampler design discrepancies. This study evaluates the comparability of the EN 12341 gravimetric reference method using field data from a national interlaboratory comparison organized by the Italian Institute for Environmental Protection and Research (ISPRA) at Castel Romano with 18 Italian regional environmental agencies. The results revealed systematic deviations driven primarily by weather sensitivity—where relative humidity and precipitation compromised PM thermodynamic and mass stability—and, to a lesser extent, a minor filter matrix effect. Because gravimetric measurements provide the sole traceability source for automated measurement systems (AMS), these biases pose potential risks under Directive (EU) 2024/2881. The post-2030 regulatory framework drastically tightens AMS residual uncertainty budgets for daily and annual PM₁₀ and PM_2.5 metrics. Near these lower limits, unmitigated humidity effects and declining PM concentrations geometrically amplify residual errors in EN 16450 orthogonal regression calculations, potentially increasing equivalence test failures for certain instruments. To safeguard network data validity and legal defensibility by 2030, agencies should adopt Best Available Techniques (BAT) for sampling and filter conditioning, upgrade QA/QC protocols, and train technical personnel. Minimizing ground-level uncertainty is also crucial for atmospheric modeling and satellite retrieval validation. Full article

Drought is a complex and periodic issue that has a significant impact on agriculture and water resources, particularly in semi-arid areas. This study evaluated meteorological and agricultural drought conditions in the Thanjavur district by combining rainfall data with remote-sensing methods. Meteorological drought was analyzed using 33 years of rainfall data and the Standardized Precipitation Index (SPI) (1992–2024) using 20 rainfall stations for the data available between 1992 and 2024. The spatial variation in rainfall was analyzed using Kriging interpolation in GIS. Agricultural droughts were analyzed using the Normalized Difference Vegetation Index (NDVI) and Vegetation Con0dition Index (VCI) using multi-temporal Landsat satellite images (Landsat 5 and Landsat 8). Land Use and Land Cover (LULC) classification was included to determine drought vulnerability across different land types. The NDVI and VCI indices showed an intensification of agricultural drought in 2010. The results demonstrated temporal and spatial differences in drought conditions for the years 1992, 1997, 2004, 2009, 2014, 2019, and 2024 and indicated that the region experienced periodic severe drought conditions of 3%, 3%, 3%, 8%, 19%, 9%, and 11% in the study area, respectively. During the drought period, the vegetation indices showed a strong sensitivity of agricultural areas to changes in rainfall, and low NDVI and VCI values indicated increased vegetation stress. Meteorological and agricultural droughts were integrated using the Analytical Hierarchy Process (AHP) method by combining various indicators to analyze the drought condition across the Thanjavur district. The multiple criteria decision-making (MCDM) method uses pairwise comparisons of various factors, such as giving high importance to SPI and rainfall, followed by vegetation indices and LULC. The consistency ratio validated the reliability of the weighting term. This method shows that combining meteorological and remote sensing indicators advances a robust framework for monitoring and assessing droughts. Conceptual droughts illustrate how meteorological droughts are associated with the development of agricultural droughts. The results of this study can be adopted for effective drought management, irrigation planning, and sustainable agricultural practices in this region. Full article

Accurate precipitation data are critical for hydrological and climatic studies in Guangdong Province, where complex terrain and frequent extreme rainfall pose substantial challenges. However, the performance of gridded precipitation products is still not well understood. This study evaluates nine products, including gauge-based (CHM_PRE, CN05.1, GMCP, NOAA CPC), satellite-based (IMERG-E, IMERG-F, TMPA RT, TMPA 3B42), and ERA5 reanalysis against NCDC observations from 2001 to 2019 using metrics including trend significance, correlation (R), root mean square error (RMSE), categorical statistics (POD, FAR, ETS), and relative bias across rainfall intensities. The results indicate that, based on validation against NCDC observations, CHM_PRE performs the best across all temporal scales, capturing significant increasing trends (p < 0.05) and achieving the highest consistency with observations at the annual (R = 0.99), monthly (R = 0.99), and daily (R = 0.89) scales. Using CHM_PRE as the reference, CN05.1 shows the highest spatial consistency with it, especially for extreme events. NOAA CPC exhibits the best performance in monthly event detection (ETS = 0.42; BIAS ≈ 1). Satellite products show acceptable performance at the monthly scale but exhibit intensity-dependent biases and high daily variability, with pronounced “light rain overestimation and heavy rain underestimation.” ERA5 shows limitations, particularly in its severe underestimation of extreme precipitation. CHM_PRE is thus identified as the most suitable dataset for Guangdong based on its agreement with NCDC observations. With CHM_PRE as the reference, CN05.1 provides a reliable alternative for spatial analyses; NOAA CPC performs the best in monthly event detection. Satellite products suit monthly use but require daily-scale caution; ERA5 shows a relatively poor performance. Full article

Atmospheric water vapor is a major driver of Earth’s climate, yet despite its vital role in driving extreme weather and informing Numerical Weather Prediction (NWP) models, precise quantification of Precipitable Water Vapor (PWV) remains a challenge due to its high spatial and temporal variability. To provide an upgraded evaluation reflecting the most recent data and next-generation instrumentation, this study evaluates the accuracy, relative to radiosonde measurements, in calculating PWV values across six different instruments: Microwave Radiometer (MWR), NOAA-21, TROPOMI, Pandora spectrometer, AERONET sun-photometer, and GNSS/GPS against the bias-corrected Vaisala RS-92 and RS-41 radiosondes over Beltsville, Maryland, utilizing an updated 2024–2025 database. As a certified GRUAN site, HUBC adheres to strict international observation standards designed specifically to provide reference-quality data and comprehensive corrections for systematic errors. This rigorous framework justifies their application as the definitive ‘referent truth’ benchmark for remote sensing validation. RMSE and bias were the primary metrics used to assess relative accuracy. GPS measurements provided the highest level of relative accuracy, yielding the lowest RMSE (1.50 mm) and a near-unity linear fit (y = 0.98x). NOAA-21 and TROPOMI exhibit higher random noise when compared to ground-based instrumentation, yet both obtain high relative accuracy retrievals with negligible biases. AERONET and Pandora also showed strong performance with low RMSEs and R² values of 0.986 and 0.988, respectively, while slightly underestimating PWV. In contrast, the Radiometer performed with the lowest relative accuracy, characterized by the highest RMSE (6.19 mm) and a significant negative bias (−5.55 mm). Although all instruments maintained high correlation coefficients (R² ≥ 0.904), these results indicate that satellite and ground-based remote sensing provide reliable PWV retrievals, while GPS presents the most robust benchmark for high-accuracy PWV retrievals relative to radiosonde measurements. The findings also underscore the need for instrument-specific calibration constants to better align remote sensing retrievals with in situ observations. Full article

The decline in groundwater storage (GWS) poses a critical threat to water security in semi-arid regions where increasing agricultural water demand and climate variability are increasing pressure on aquifers. This study presents a novel hybrid modeling framework integrating multi-source satellite and climate data (GRACE, GLDAS, TerraClimate, and MODIS) with machine learning and explanatory artificial intelligence techniques for the long-term assessment and interpretation of GWS anomalies in the data-poor Iğdır Basin. Three different modeling approaches were developed: XGBoost, Long Short-Term Memory (LSTM) networks, and their combined model, and interpreted using the Shapley Additive Explanations (SHAP) method. The results showed a significant long-term decreasing trend in groundwater storage anomalies at a rate of −0.87 mm per month during the 2002–2016 period, indicating continuous depletion. The LSTM model demonstrated the best performance with R² of 0.59, RMSE of 19.5 mm, and MAE of 15.1 mm, revealing the dominant role of temporal dependencies in groundwater systems. SHAP analysis identified lagged groundwater anomalies (especially GWS_lag3) as the most effective predictors; this may reflect the memory effect and lagged response specific to semi-arid aquifer systems, but this interpretation needs to be validated in different study areas. Snow water equivalent and total water storage anomalies also emerged as significant determinants, while the direct effect of instantaneous precipitation was found to be limited. This study addresses significant gaps in the literature by combining sequence-based modeling with model interpretability in a semi-arid closed basin. The findings highlight the necessity of using system memory and explainable artificial intelligence together for reliable groundwater prediction. While the proposed hybrid approach has the potential for application in other semi-arid regions, its broader usability needs to be supported by independent validation studies under different hydrogeological and climatic conditions. Full article

Remote sensing of clouds and precipitation is undergoing a significant transition from descriptive observations toward process-oriented diagnoses. This transition has not progressed linearly, but has gradually taken place alongside the rapidly developing multi-source observation capabilities over recent decades. In terms of clouds and precipitation observational platforms, satellites provide continuous global-scale monitoring, airborne platforms complement high-resolution sampling of key processes, and ground-based observations offer long-term vertical structure evolution, which form an integrated space–air–ground observation system. In terms of clouds and precipitation retrieval algorithms, active–passive combination remote sensing significantly improves the ability to retrieve macro- and microphysical characteristics and structures, and machine learning methods further expand parameter estimation capabilities in complex scenarios. Nevertheless, key bottlenecks still persist in retrieval non-uniqueness, sensor trade-offs, cross-platform calibration, and validation over oceans, mountains, and polar regions. Based on the above background, this paper provides a systematic review of recent progress in clouds and precipitation physics remote sensing, focusing on the development of multi-platform collaborative observations, the evolution of microphysical parameter retrieval methods, and the improvements in remote sensing characterization of cloud and precipitation formation mechanisms. It further points out that future development will increasingly rely on improved uncertainty quantification, incorporation of physical constraints into retrieval frameworks, and the establishment of standardized multi-source datasets. Full article

This study systematically evaluates the performance of GPM_IMERG and GSMaP_Gauge hourly precipitation products in typhoon rainfall over Mainland China. Using hourly gauge observations from the China Meteorological Administration (CMA)’s national basic meteorological stations as reference, 32 landfalling typhoons during 2021–2025 are analyzed. A multi-layered evaluation framework is established based on 50 km annular stratification from 0 to 500 km relative to typhoon centers, multiple statistical metrics, and dual thresholds for light rain and extreme precipitation. Results indicate systematic underestimation of typhoon rainfall by both products, with GSMaP_Gauge exhibiting more severe negative bias that intensifies nonlinearly with increasing rainfall intensity. Spatially, widespread overestimation occurs in North China, while underestimation dominates elsewhere, with large negative biases concentrated in high-observation regions. Monthly variations show predominantly negative deviations across most months, with GSMaP_Gauge demonstrating persistent negative anomalies except for sporadic positive outliers. Regarding precipitation detection capability, both products perform adequately for light rain, but their capability to capture extreme precipitation remains rather limited, as evidenced by sharply declining Critical Success Index (CSI) across all distance ranges and omission of over 60% extreme precipitation events. GPM_IMERG shows only sporadic high CSI values in the inner-core region during June and October. Error distributions exhibit significant spatiotemporal non-stationarity: errors attenuate markedly within 0–100 km of typhoon centers; seasonally, June and September show higher correlation coefficients but larger RMSE, whereas August presents lower correlation yet smaller errors; diurnally, the 0–50 km zone displays a “three-peak–two-valley” pattern with error maxima in the afternoon, early morning, and evening. In conclusion, both products estimate light typhoon precipitation with reasonable accuracy but still have considerable room for improvement in estimating heavy and extreme rainfall. Dynamic error models based on three-dimensional stratification of distance–season–diurnal phase, coupled with bias correction, are imperative before their application to hydrometeorological modeling, disaster investigation, and climate research. Full article

Effective conservation of the Near Threatened (NT) black-necked crane (Grus nigricollis) requires standardized frameworks for organizing multi-scale biodiversity data. This study proposes the Essential Biodiversity Variables (EBV) system as an optimal structure for archiving and sharing biodiversity data such as Habitat Suitability Index (HSI). Developed by the Group on Earth Observations Biodiversity Observation Network (GEO BON), the EBV framework is an emerging system offering a robust solution for standardizing data exchange. Based on 483,592 valid location records of 106 black-necked cranes using satellite telemetry, we apply species distribution models and demonstrate how the multi-dimensional EBV architecture accommodates distinct life-stage preferences: breeding sites favor mid-elevations modulated by temperature; migration staging relies on precipitation regimes; and wintering grounds are driven by moisture availability and the avoidance of human-modified landscapes. The EBV NetCDF (Network Common Data Form) format functions as a self-describing hypercube that captures spatial, temporal, and life-stage dimensions while ensuring metadata transparency. This integration facilitates critical applications, including the identification of priority conservation areas and climate vulnerability assessments, thereby bridging the gap between species-specific modeling and global biodiversity monitoring standards. Full article

HY–4A is China’s first ocean salinity remote-sensing satellite, launched into orbit in November 2024 and currently in operational service. The LASMR (L-Band Aperture Synthesis Microwave Radiometer) is the L-band synthetic aperture radiometer onboard the HY–4A satellite. This study validates the LASMR Level-2 SSS (sea surface salinity) product using in situ salinity observations from Argo floats, covering the period from November 2024 to December 2025. Global analysis indicates that the LASMR SSS retrieval uncertainties show a distinct zonal distribution, which primarily reflects the impact of sea surface temperature (SST) and sea surface wind speed on SSS retrieval accuracy. A lower SST reduces the sensitivity of brightness temperature (TB) to SSS variations, and a high wind speed degrades the sea surface roughness correction. Both factors lead to increasing uncertainties in SSS retrieval. Furthermore, atmospheric parameters including water vapor content and precipitation also affect the SSS retrieval uncertainty. The influence of water vapor may originate from its coupling with SST/wind speed and inherent uncertainties in the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data. The effect of precipitation is more complex: it increases ocean TB through rain-induced surface freshening and additional rain-induced roughening, which aliases into the satellite signal. Moreover, precipitation-enhanced vertical salinity gradients amplify the vertical representativeness error arising from the depth difference between satellite sensing and Argo measurements. Meanwhile, impacted by land brightness temperature contamination and radio-frequency interference (RFI), the SSS retrieval accuracy of HY–4A decreases significantly in coastal waters compared with the open ocean. Since the traditional buoy–satellite dual-matching method tends to overestimate uncertainties in satellite data, an Argo/HY–4A/SMAP (Soil Moisture Active Passive) triple-collocation dataset is used to estimate the LASMR SSS retrieval uncertainties. The triple-collocation method yields robust uncertainty estimates for both satellites (HY–4A and SMAP) over the global ocean and high-salinity-variability regions. In conclusion, the global uncertainty of the HY–4A LASMR SSS product is 0.35 psu. These results provide a reference for future product refinement and improvements in HY–4A SSS retrieval algorithms. Full article

Accurate rainfall information is essential for water-related hazard assessment in mountainous regions, where complex terrain and sparse gauge networks limit monitoring reliability. This study evaluated long-term rainfall characteristics and the performance of three satellite precipitation products—CHIRPS, GPM IMERG, and GSMaP—along the National Highway 6 corridor in northern Vietnam. Daily gauge observations from 11 meteorological stations with station-dependent records between 1961 and 2024 were used to characterize rainfall variability and heavy-rainfall frequency. Matched gauge–satellite records from 2001 to 2024 were evaluated using continuous statistical indicators, contingency-table metrics, empirical cumulative distribution functions, and percentile-based analysis. Data from 2025 were additionally examined as an extreme-rainfall case study, supplemented by visual gauge–satellite comparisons; however, these data were not used as an independent satellite-validation period. The long-term gauge records showed marked spatial variability in rainfall magnitude and threshold-exceedance frequency. In 2025, all 11 stations recorded daily rainfall exceeding 50 mm, while eight stations recorded events exceeding 100 mm. Satellite-product performance varied substantially among stations, rainfall thresholds, and evaluation metrics, and no product was uniformly superior. At the 100 mm/day threshold, the probability of detection ranged from 0.024 to 0.276, whereas the false alarm ratio ranged from 0.781 to 0.916. Upper-tail analysis showed contrasting product-specific behavior: at the 99th percentile, CHIRPS and GPM IMERG underestimated gauge rainfall by 20.95 and 8.95 mm, respectively, whereas GSMaP overestimated it by 35.57 mm. These findings demonstrate that local validation, uncertainty assessment, and application-specific adjustment are necessary before satellite precipitation products are used for flash-flood, rainfall-induced landslide, drainage-risk, and transportation-infrastructure assessments in mountainous regions. Full article