Search Results (604)

Dry climate regions face heightened risks of flooding and infrastructure damage even with minimal rainfall. Climate change is intensifying this vulnerability by increasing the duration, frequency, and intensity of precipitation events in areas that have historically experienced arid conditions. As a result, accurate precipitation estimation in these regions is critical for effective planning, risk mitigation, and infrastructure resilience. This study evaluates the performance of five satellite- and model-based precipitation products by comparing them against in situ rain gauge observations in a dry-climate region: The fifth generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5) (analyzing maximum and minimum precipitation rates separately), the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), the Western Land Data Assimilation System (WLDAS), and the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG). The analysis focuses on both average daily rainfall and extreme precipitation events, with particular attention to precipitation magnitude and the accuracy of event detection, using a combination of statistical metrics—including bias ratio, mean error, and correlation coefficient—as well as contingency statistics such as probability of detection, false alarm rate, missed precipitation fraction, and false precipitation fraction. The study area is Palm Desert, a mountainous, arid, and urban region in Southern California, which exemplifies the challenges faced by dry regions under changing climate conditions. Among the products assessed, WLDAS ranked highest in measuring total precipitation and extreme rainfall amounts but performed the worst in detecting the occurrence of both average and extreme rainfall events. In contrast, IMERG and ERA5-MIN demonstrated the strongest ability to detect the timing of precipitation, though they were less accurate in estimating the magnitude of rainfall per event. Overall, this study provides valuable insights into the reliability and limitations of different precipitation estimation products in dry regions, where even small amounts of rainfall can have disproportionately large impacts on infrastructure and public safety. Full article

This study presents a practical early-warning approach for heavy rainfall detection using the temporal dynamics of satellite-derived Cloud-Top Temperature (CTT). A rapid rise followed by a sharp fall in CTT is identified as a precursor signal of convective intensification. By quantifying the $rise-peak-fall-trough$ pattern and the $peak$-to-$trough$ amplitude ($swing$), a $\mathrm{W}\mathrm{A}\mathrm{T}\mathrm{C}\mathrm{H}$ window—representing a potential heavy-rainfall candidate period—is defined. The observed lead time between the onset of CTT decline and the subsequent radar-observed rainfall surge is calculated, while an estimated lead time is inferred from the steepness of CTT fall in the absence of a surge. Application to eight heavy rainfall events in Korea (July 2025) yielded a probability of detection (POD) of 87.5%, indicating that potential heavy rainfall could be detected approximately 1.3–8.6 h in advance. Compared with radar-based nowcasting, the CTT $\mathrm{W}\mathrm{A}\mathrm{T}\mathrm{C}\mathrm{H}$ method retained predictive skill up to 3 h before numerical model guidance became effective, suggesting that satellite-based signals can bridge the forecast gap in short-term prediction. This work demonstrates a clear methodological novelty by introducing a physical interpretable, pattern-based metric. Quantitatively, the $\mathrm{W}\mathrm{A}\mathrm{T}\mathrm{C}\mathrm{H}$ method improves early-warning capability by providing 1–3 h of additional lead time relative to radar nowcasting in rapidly evolving convective environments. Overall, this framework provides an interpretable, low-cost module suitable for operational early-warning systems and flood preparedness applications. Full article

Satellite and reanalysis rainfall estimates (SREs) are valuable alternatives to gauge data in data-scarce regions; however, their reliability in areas with complex terrain and variable precipitation remains uncertain. This study evaluated six SREs (CHIRPS v2, ERA5, ERA5-Land, IMERG v07, MSWEP v2.8, and TRMM 3B42) against gauge observations over the period 2005 to 2019. The evaluation was conducted using multiple statistical, categorical, and distributional metrics at daily to seasonal timescales. Terrain-based classification and rainfall intensity categories were used to explore the influence of topography and event magnitude on product performance. The accuracy of SREs improves with temporal aggregation, the monthly scale offering the highest reliability for water resource management. However, their tendency to overestimate light and underestimate heavy daily rainfall requires careful bias adjustment in flood and extreme event analysis. MSWEP, CHIRPS, and IMERG provided balanced and consistent performance across all metrics, rainfall intensities, and terrain zones. Notably, ERA5 and ERA5-Land consistently overestimated average rainfall. All SREs identified dry days well, and their performance declined with increasing intensity. No significant performance variation was observed across different altitudes. This study provides valuable insights into the selection of rainfall products, supporting climate and hydrological studies in data-scarce areas of the Ethiopian highlands. Full article

Accurate identification and monitoring of rice cultivation areas are essential for food security and sustainable agricultural development. However, regions with frequent cloud cover, high rainfall, and fragmented fields often face challenges due to the absence of temporal features caused by cloud and rain interference, as well as spectral confusion from scattered plots, which hampers rice recognition accuracy. To address these issues, this study employs a Satellite Image Time Series Transformer (SITS-Former) model, enhanced with the integration of diverse phenological features to improve rice phenology representation and enable precise rice identification. The methodology constructs a rice phenological feature set that combines temporal, spatial, and spectral information. Through its self-attention mechanism, the model effectively captures growth dynamics, while multi-scale convolutional modules help suppress interference from non-rice land covers. The study utilized Sentinel-2 satellite data to analyze rice distribution in Wuxi City. The results demonstrated an overall classification accuracy of 0.967, with the estimated planting area matching 91.74% of official statistics. Compared to traditional rice distribution analysis methods, such as Random Forest, this approach outperforms in both accuracy and detailed presentation. It effectively addresses the challenge of identifying fragmented rice fields in regions with persistent cloud cover and heavy rainfall, providing accurate mapping of cultivated areas in difficult climatic conditions while offering valuable baseline data for yield assessments. Full article

Satellite remote sensing is an important approach for monitoring lake water environments. However, in regions with frequent cloud and rainfall, optical remote sensing imagery often suffers from extensive data gaps caused by cloud cover, rainfall, and sun glint, which severely limit its continuity and reliability for long-term monitoring. To address this issue, this study uses Lake Taihu—a typical eutrophic lake located in a cloudy and rainy region—as a case study and systematically compares four representative gap-filling methods: Kriging Interpolation, Savitzky–Golay (SG) Filtering, Data Interpolating Empirical Orthogonal Functions (DINEOF), and the Data Interpolating Convolutional Auto Encoder (DINCAE). The results show that traditional methods retain some accuracy under low missing-data conditions (for Kriging: R = 0.84, RMSE = 7.85 μg/L; for SG Filtering: R = 0.88, RMSE = 6.67 μg/L), but tend to produce over-smoothing or distorted estimations in cases of extensive gaps or highly dynamic environments. In contrast, both DINEOF and DINCAE capture the spatiotemporal variability of chlorophyll-a more effectively, maintaining relatively high accuracy and robustness even when the missing rate exceeds 60% (for DINEOF: R = 0.84, RMSE = 6.91 μg/L; for DINCAE: R = 0.79, RMSE = 8 μg/L). Based on the optimal algorithm, a seamless long-term dataset of chlorophyll-a concentration covering Lake Taihu can be constructed, providing a solid data foundation for eutrophication trend analysis and algal bloom early warning. This study demonstrates the effectiveness of integrating statistical and deep learning approaches for lake water color remote sensing data reconstruction, offering important implications for enhancing continuous monitoring of lake water environments and supporting ecological management decisions. Full article

This study examines long-term trends in maximum (Tmax) and minimum (Tmin) near-surface air temperatures and precipitation across South America, focusing on Brazilian biomes and national capitals, using ERA5 reanalysis data for 1979–2024. To isolate the underlying climate signal, seasonal cycles were removed using Seasonal-Trend decomposition based on Loess (STL), which effectively separates short-term variability from long-term trends. Temperature trends were quantified using ordinary least squares (OLS) regression, allowing consistent estimation of linear changes over time, while precipitation trends were assessed using the non-parametric Mann–Kendall test combined with Theil–Sen slope estimation, a robust approach that minimizes the influence of outliers and serial correlation in hydroclimatic data. Results indicate widespread but spatially heterogeneous warming, with Tmax increasing faster than Tmin, consistent with reduced cloudiness and evaporative cooling. A meridional precipitation dipole is evident, with drying across the Cerrado, Pantanal, Caatinga, and Pampa, contrasted by rainfall increases in northern South America linked to ITCZ shifts. The Pantanal emerges as the most vulnerable biome, showing strong warming (+0.51 °C decade⁻¹) and the steepest rainfall decline (−10.45 mm decade⁻¹). Satellite-based fire detections (2013–2024) reveal rising wildfire activity in the Amazon, Pantanal, and Cerrado, aligning with the “hotter and drier” climate regime. In the capitals, persistent Tmax increases suggest enhanced urban heat island effects, with implications for public health and energy demand. Although ERA5 provides coherent spatial coverage, regional biases and sparse in situ observations introduce uncertainties, particularly in the Amazon and Andes, these do not alter the principal finding that the magnitude and persistence of the 1979–2024 warming lie well above the range of interdecadal variability typically associated with the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO). This provides strong evidence that the recent warming is not cyclical but reflects the externally forced secular warming signal. These findings underscore growing fire risk, ecosystem stress, and urban vulnerability, highlighting the urgency of targeted adaptation and resilience strategies under accelerating climate change. Full article

Accurate rainfall estimation remains a critical challenge in hydrology, particularly in Italy, where complex topography and uneven rain-gauge distribution introduce major uncertainties. To address this gap, this study assessed five widely used satellite precipitation products, CHIRPS, GPM, HSAF, PDIRNOW, and SM2RAIN, against the high-resolution SCIA-ISPRA ground dataset. These products were selected because they represent distinct retrieval approaches (infrared–station hybrid, microwave integration, geostationary blending, neural-network infrared, and soil–moisture inversion) and offer diverse temporal and spatial resolutions suitable for both research and operational monitoring. The evaluation, conducted at daily, seasonal, and annual scales using categorical, continuous, and extreme-event indices, revealed that no single product performs optimally across all metrics. GPM achieved the most balanced and reliable performance overall, whereas PDIRNOW and SM2RAIN provided strong detection but frequent overestimation. CHIRPS yielded conservative estimates with few false alarms, while HSAF was less consistent, especially during winter. The results underscore that product suitability depends on the intended application: detection-oriented systems like PDIRNOW are preferable for flood forecasting, whereas conservative datasets like CHIRPS better support drought monitoring. Overall, integrating multiple products or adopting hybrid approaches is recommended to enhance precipitation assessment accuracy over complex Mediterranean terrains. Full article

The Dvorak technique has represented a fundamental tool for understanding the power of tropical cyclones based on their shape and geometric evolution. However, it should be noted that the Dvorak technique is purely morphological in nature and was developed for wind, not precipitation. The role of shape methods in precipitation prediction remains uncertain, particularly in the context of modern multi-sensor capabilities. This uncertainty forms the motivation for the present study. In an attempt to enrich Dvorak’s technique, this study proposes a novel hypothesis. This study tests the hypothesis that higher precipitation intensity is associated with more organized cloud-system morphology, as captured by simple geometric descriptors and indicative of dynamically coherent convection. A total of 3419 cloud-system objects (after size filter) were utilized to establish geometric relationships in each of them. For the case study of Hurricane Patricia over the Mexican coast in 2015, 3858 geometric shapes were processed. The cloud-system morphology was derived from geostationary imagery (GOES-13) and collocated with satellite precipitation estimates in order to isolate intense-rainfall objects (>50 mm/h). For each object, simple geometric descriptors were computed, and shape variability was summarised via Principal Component Analysis (PCA). The present study sought to evaluate the associations with rain-rate metrics (mean, mode, maximum) using rank correlations and k-means clustering. Furthermore, sensitivity analyses were conducted on the rain threshold and minimum object size. A Shape Descriptor: ratio between perimeter and diameter was identified as a promising tool to enhance early prediction models of extreme rainfall, contributing to enhanced meteorological risk management. The study indicates that cloud shape can serve as a valuable indicator in the classification and forecasting of intense cloud systems. Full article

Accurate satellite-based precipitation estimates are crucial for climate studies and water resource management, particularly in regions with sparse meteorological station coverage. This study evaluates the improvements of the Integrated Multi-satellite Retrievals for GPM (IMERG) Final Run version 07 (V07) relative to the previous version (V06). The evaluation employed gridded data from the Brazilian Daily Weather Gridded Data (BR-DWGD) product and ground observations from 58 rain gauges distributed across the Parnaíba River Basin in Northeast Brazil. The analysis comprised three main stages: (i) an intercomparison between BR-DWGD gridded data and rain gauge records using correlation, bias, and Root Mean Square Error (RMSE) metrics; (ii) a comparative assessment of the IMERG Final V06 and V07 products, evaluated with statistical metrics (correlation, bias, and RMSE) and complemented by performance indicators including the Kling-Gupta Efficiency (KGE), Probability of Detection (POD), and False Alarm Ratio (FAR); and (iii) the application of cluster analysis to identify homogeneous regions and characterize seasonal rainfall variations across the basin. The results show that the IMERG Final V07 product provides notable improvements, with lower bias, reduced RMSE, and greater accuracy in representing the spatial distribution of precipitation, particularly in the central and southern regions of the basin, which feature complex topography. IMERG V07 also demonstrated higher consistency, with reduced random errors and improved seasonal performance, reflected in higher POD and lower FAR values during the rainy season. The cluster analysis identified four homogeneous regions, within which V07 more effectively captured seasonal rainfall patterns influenced by systems such as the Intertropical Convergence Zone (ITCZ) and Amazonian moisture advection. These findings highlight the potential of the IMERG Final V07 product to enhance precipitation estimation across diverse climatic and topographic settings, supporting applications in hydrological modeling and extreme-event monitoring. Full article

Ground-based weather radars are essential to better understand precipitation systems, to improve the Quantitative Precipitation Estimation (QPE), and to subsequently provide input to hydrological models. However, reflectivity measured by radars is typically affected by various sources of uncertainty, including attenuation and calibration errors. Due to these limitations, the two ground-based X-band weather radars of Cyprus, namely, at Rizoelia (LCA) and Nata (PFO), have not yet been employed for QPE. This study presents a dual neural network framework with the ultimate goal of converting the ground-based radar raw reflectivity to rainfall rate, using satellite and in situ observations. The two ground-based radars are aligned with GPM DPR using the volume-matching method. Preliminary results demonstrate the feasibility of converting raw ground-based radar reflectivity to rainfall estimates using neural networks trained with spaceborne and in situ observations. Full article

Satellite Precipitation Products (SPPs) play a crucial role in hydrological modeling, particularly in data-scarce and climate-sensitive basins such as the Magat River Basin (MRB), Philippines—one of Southeast Asia’s most typhoon-prone and infrastructure-critical watersheds. This study presents the first full-cycle evaluation of nine widely used multi-source precipitation products (2000–2024), integrating raw validation against rain gauge observations, bias correction using quantile mapping, and post-correction re-ranking through an Entropy Weight Method–TOPSIS multi-criteria decision analysis (MCDA). Before correction, SM2RAIN-ASCAT demonstrated the strongest statistical performance, while CHIRPS and ClimGridPh-RR exhibited robust detection skills and spatial consistency. Following bias correction, substantial improvements were observed across all products, with CHIRPS markedly reducing systematic errors and ClimGridPh-RR showing enhanced correlation and volume reliability. Biases were decreased significantly, highlighting the effectiveness of quantile mapping in improving both seasonal and annual precipitation estimates. Beyond conventional validation, this framework explicitly aligns SPP evaluation with four critical hydrological applications: flood detection, drought monitoring, sediment yield modeling, and water balance estimation. The analysis revealed that SM2RAIN-ASCAT is most suitable for monitoring seasonal drought and dry periods, CHIRPS excels in detecting high-intensity and erosive rainfall events, and ClimGridPh-RR offers the most consistent long-term volume-based estimates. By integrating validation, correction, and application-specific ranking, this study provides a replicable blueprint for operational SPP assessment in monsoon-dominated, data-limited basins. The findings underscore the importance of tailoring product selection to hydrological purposes, supporting improved flood early warning, drought preparedness, sediment management, and water resources governance under intensifying climatic extremes. Full article

Global Navigation Satellite System (GNSS) meteorology has emerged as a valuable tool for atmospheric monitoring, providing high-resolution, near-real-time data that can significantly improve precipitation nowcasting. This study aims to enhance short-term precipitation forecasting by integrating GNSS-derived Precipitable Water Vapor (PWV)—a key indicator of atmospheric moisture—with traditional meteorological observations. A novel two-step machine learning framework is proposed that combines a Random Forest (RF) model and a Long Short-Term Memory (LSTM) neural network. The RF model first estimates current precipitation based on PWV, surface weather parameters, and auxiliary atmospheric variables. Then, the LSTM network leverages temporal dependencies within the data to predict precipitation for the subsequent hour. This hybrid method capitalizes on the RF’s ability to model complex nonlinear relationships and the LSTM’s strength in handling time series data. The results demonstrate that the proposed approach improves forecasting accuracy, particularly during extreme weather events such as intense rainfall and thunderstorms, outperforming conventional models. By integrating GNSS meteorology with advanced machine learning techniques, this study offers a promising tool for meteorological services, early warning systems, and disaster risk management. The findings highlight the potential of GNSS-based nowcasting for real-time decision-making in weather-sensitive applications. Full article

This study presents a water vapor gradient (WVG) retrieval method based on Global Navigation Satellite System (GNSS) tropospheric parameter estimation. A case study examined the method’s applicability to the extreme rainstorm event in North China in July 2023. Precipitable water vapor (PWV) and WVG data from 332 GNSS sites in this area were retrieved. Radar and precipitation data were combined to perform a spatiotemporal comparison study. The results show that GNSS PWV and WVG of this weather process were highly consistent with radar reflectivity and precipitation. When a high PWV (>60 mm) was accompanied by WVG convergence, radar reflectivity was significantly strong and precipitation occurred at the leading edge of large gradients and the convergence region. Based on the edge of big WVGs, observed by multiple GNSS stations, the location and movement of rainfall could be identified. In case of large amounts of PWV accompanied by plummeting WVG (down to 0.1–0.4 mm/km), high or persistent precipitation occurs. During the event, compared to the northern plateau, the plain region demonstrated higher PWV, lesser WVG variation, and more intense precipitation, likely caused by the topographic dynamic effect. GNSS PWV and WVG can be key indicators for short-range weather forecasting of extreme rainstorm events. Full article

The satellite-derived climatic variables offer extensive spatial and temporal coverage for research; however, their inherent biases can subsequently reduce their accuracy for water balance estimate. This study evaluates the effectiveness of bias correction in improving the Tropical Rainfall Measuring Mission (TRMM) rainfall and the Global Land Data Assimilation System (GLDAS) land surface temperature (LST) data and illustrates their long-term (2000–2019) hydrological assessment. The novelty lies in coupling the bias-corrected climate variables with the Thornthwaite–Mather water balance model as well as land use land cover (LULC) for improved predictive hydrological modeling. Bias correction significantly improved the agreement with ground observations, enhancing the R² value from 0.89 to 0.96 for temperature and from 0.73 to 0.80 for rainfall, making targeted inputs ready to predict hydrological dynamics. LULC mapping showed a predominance of agricultural land (64.5%) in the area followed by settlements (20.0%), forest (7.3%), barren land (6.5%), and water bodies (1.7%), with soils being silt loam, clay loam, and clay. With these improved datasets, the model found seasonal rise in potential evapotranspiration (PET), peaking at 120.7 mm in June, with actual evapotranspiration (AET) following a similar trend. The annual water balance showed a surplus of 523.8 mm and deficit of 121.2 mm, which proves that bias correction not only enhances the reliability of satellite data but also reinforces the credibility of hydrological indicators, with a direct, positive impact on evidence-based irrigation planning and flood mitigation and drought management, especially in data-scarce regions. Full article

Probable Maximum Storms (PMS) are synthetic design storms represented by idealized hyetographs. They play a critical role in assessing extreme rainfall events over extended durations and are widely applied in the hydraulic design of infrastructure such as dams, culverts, and bridges. PMS provide essential input for estimating Probable Maximum Floods (PMF), vital for analyzing worst-case flood scenarios with the potential to cause catastrophic loss of life and property. Despite their importance, the estimation of design storms at ungauged locations, particularly across synoptic scales, remains a major scientific and engineering challenge. This study addresses this gap by utilizing the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) dataset, which provides near-global estimated precipitation coverage. IMERG’s 24 h design storm hyetographs (expressed as cumulative percentage of precipitation throughout a 24 h period) were modeled and compared with similar reference data from NOAA Atlas 14 across twenty-eight regions and seven larger zones covering most of the conterminous United States (CONUS). Across the regions, the average root mean square error (RMSE) was 3.7%, with a mean relative bias (RB) of 1.4%. The mean normalized storm loading index (NSLI) from NOAA Atlas 14 was −7.7%, indicating that 57.7% of the total precipitation was received during the first 12 h of the storm, whereas IMERG storms exhibited a mean NSLI of −4.1%, suggesting they are also frontloaded but to a lesser extent. Across the broader zones, the mean RMSE was 4.8% and the mean RB was 1.1%. The mean NSLI values were −9.7% for NOAA Atlas 14 and −5.7% for IMERG, again indicating that IMERG storms are less frontloaded. When design storm families were estimated corresponding with different degrees of frontloading (corresponding to the 10, 20, …, 90% deciles of NSLI), the 40th to 60th percentile range exhibited the strongest agreement between IMERG and NOAA Atlas 14 hyetographs. Full article