Search Results (459)

The Dual-frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) mission provides valuable three-dimensional precipitation structure data on a global scale and has been widely used in hydrometeorological research. However, due to its spatial resolution limitations and inherent algorithmic assumptions, the accuracy of GPM precipitation estimates can exhibit systematic biases, especially under complex terrain conditions or in the presence of variable precipitation structures, such as light stratiform rain or intense convective storms. In this study, we evaluated the near-surface precipitation rate estimates from the GPM-DPR Level 2A product using over 1440 min of disdrometer observations collected across China from 2021 to 2023. Based on three years of stable stratiform precipitation data from the Jinghe station, we developed a least squares linear correction model for radar reflectivity. Independent validation using national disdrometer data from 2023 demonstrated that the corrected reflectivity significantly improved rainfall estimates under light precipitation conditions, although improvements were limited for convective events or in complex terrain. To further enhance retrieval accuracy, we introduced a regionally adaptive R–Z relationship scheme stratified by precipitation type and terrain category. Applying these localized relationships to the corrected reflectivity yielded more consistent rainfall estimates across diverse conditions, highlighting the importance of incorporating regional microphysical characteristics into satellite retrieval algorithms. The results indicate that the accuracy of GPM precipitation retrievals is more significantly influenced by precipitation type than by terrain complexity. Under stratiform precipitation conditions, the GPM-estimated precipitation data demonstrate the highest reliability. The correction framework proposed in this study is grounded on ground-based observations and integrates regional precipitation types with terrain characteristics. It effectively enhances the applicability of GPM-DPR products across diverse environmental conditions in China and offers a methodological reference for correcting satellite precipitation biases in other regions. Full article

The Amazon Basin plays a crucial role in the global hydrological cycle, where seasonal and interannual variations in terrestrial water storage (TWS) are essential for understanding climate–hydrology coupling mechanisms. This study utilizes data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its follow-on mission (GRACE-FO, collectively referred to as GRACE) to investigate the spatiotemporal dynamics of hydrological mass changes in the Amazon Basin from 2002 to 2021. Results reveal pronounced spatial heterogeneity in the annual amplitude of TWS, exceeding 65 cm near the Amazon River and decreasing to less than 25 cm in peripheral mountainous regions. This distribution likely reflects the interplay between precipitation and topography. Vertical displacement measurements from the Global Navigation Satellite System (GNSS) show strong correlations with GRACE-derived hydrological load deformation (mean Pearson correlation coefficient = 0.72) and reduce its root mean square (RMS) by 35%. Furthermore, the study demonstrates that existing hydrological models, which neglect groundwater dynamics, underestimate hydrological load deformation. Principal component analysis (PCA) of the Amazon GNSS network demonstrates that the first principal component (PC) of GNSS vertical displacement aligns with abrupt interannual TWS fluctuations identified by GRACE during 2010–2011, 2011–2012, 2013–2014, 2015–2016, and 2020–2021. These fluctuations coincide with extreme precipitation events associated with the El Niño–Southern Oscillation (ENSO), confirming that ENSO modulates basin-scale interannual hydrological variability primarily through precipitation anomalies. This study provides new insights for predicting extreme hydrological events under climate warming and offers a methodological framework applicable to other critical global hydrological regions. Full article

Precipitation concentration reflects the uneven temporal distribution of rainfall. It plays a critical role in water resource management and flood–drought risk under climate change. However, its long-term trends, associations with atmospheric teleconnections as potential drivers, and links to extreme heavy precipitation events remain poorly understood in complex basins like the Yangtze River Basin. This study analyzes these aspects using ground station data from 1960 to 2019 and conducts a comparison using the Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM (GPM IMERG) satellite product. We calculated three indices—Daily Precipitation Concentration Index (PCID), Monthly Precipitation Concentration Index (PCIM), and Seasonal Precipitation Concentration Index (SPCI)—to quantify rainfall unevenness, selected for their ability to capture multi-scale variability and associations with extremes. Key methods include Mann–Kendall trend tests for detecting changes, Hurst exponents for persistence, Pettitt detection for abrupt shifts, random forest modeling to assess atmospheric teleconnections, and hot spot analysis for spatial clustering. Results show a significant basin-wide decrease in PCID, driven by increased frequency of small-to-moderate rainfall events, with strong spatial synchrony to extreme heavy precipitation indices. PCIM is most strongly associated with El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). GPM IMERG captures PCIM patterns well but underestimates PCID trends and magnitudes, highlighting limitations in daily-scale resolution. These findings provide a benchmark for satellite product improvement and support adaptive strategies for extreme precipitation risks in changing climates. Full article

This study presents a comprehensive analysis of the microphysical structures of Meiyu heavy rainfall (near-surface rainfall intensity > 8 mm/h) across different life stages in the Yangtze-Huaihe River Valley (YHRV). We classified the heavy rainfall events into three life stages of developing, mature, and dissipating using ERA5 reanalysis and IMERG precipitation estimates, and examined vertical microphysical structures using Dual-frequency Precipitation Radar (DPR) data from the Global Precipitation Measurement (GPM) satellite during the Meiyu period from 2014 to 2023. The results showed that convective heavy rainfall during the mature stage exhibits peak radar reflectivity and surface rainfall rates, with the largest near-surface mass weighted diameter (D_m ≈ 1.8 mm) and the smallest droplet concentration (dBN_w ≈ 38). Downdrafts in the dissipating stage preferentially remove large ice particles, whereas sustained moisture influx stabilizes droplet concentrations. Stratiform heavy rainfall, characterized by weak updrafts, displays narrower particle size distributions. During dissipation, particle breakups dominate, reducing D_m while increasing dBN_w. The analysis of the relationship between microphysical parameters and rainfall rate revealed that convective heavy rainfall shows synchronized growth of D_m and dBN_w during the developing stage, with D_m peaking at about 2.1 mm near 70 mm/h before stabilizing in the mature stage, followed by small-particle dominance in the dissipating stage. In contrast, stratiform rainfall exhibits a “small size, high concentration” regime, where the rainfall rate correlates primarily with increasing dBN_w. Additionally, convective heavy rainfall demonstrates about 22% higher precipitation efficiency than stratiform systems, while stratiform rainfall shows a 25% efficiency surge during the dissipation stage compared to other stages. Full article

The Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), and European Space Agency-Climate Change Initiative (ESA–CCI) soil moisture (SM) products are widely used in agricultural drought monitoring, water resource management, and climate analysis applications. However, the performance of these SM products varies significantly across regions and environmental conditions, due to in sensor characteristics, retrieval algorithms, and the lack of localized calibration. This study proposes a multi-source long short-term memory (MLSTM) for improving ASCAT, ESA–CCI, and SMAP SM products by combining in-situ SM measurements and four key auxiliary variables: precipitation (PRE), land surface temperature (LST), fractional vegetation cover (FVC), and evapotranspiration (ET). First, the in-situ measured data from four in-situ observation networks were corrected using the LSTM method to match the grid sizes of ASCAT (0.1°), ESA–CCI (0.25°), and SMAP (0.1°) SM products. The RPE, LST, FVC, and ET were used as inputs to the LSTM to obtain loss data against in-situ SM measurements. Second, the ASCAT, ESA–CCI, and SMAP SM datasets were used as inputs to the LSTM to generate loss data, which were subsequently corrected using LSTM-derived loss data based on in-situ SM measurements. When the mean squared error (MSE) loss values were minimized, the improvement for ASCAT, ESA–CCI, and SMAP products was considered the best. Finally, the improved ASCAT, ESA–CCI, and SMAP were produced and evaluated by the correlation coefficient (R), root mean square error (RMSE), and standard deviation (SD). The results showed that the RMSE values of the improved ASCAT, ESA–CCI, and SMAP products against the corrected in-situ SM data in the OZNET network were lower, i.e., 0.014 cm³/cm³, 0.019 cm³/cm³, and 0.034 cm³/cm³, respectively. Compared with the ESA–CCI and SMAP products, the ASCAT product was greatly improved, e.g., in the SNOTEL network, the Root Mean-Square Deviation (RMSD) values of 0.1049 cm³/cm³ (ASCAT) and 0.0662 cm³/cm³ (improved ASCAT). Overall, the MLSTM-based algorithm has the potential to improve the global satellite SM product. Full article

The Global Precipitation Measurement (GPM) mission’s Dual-Frequency Precipitation Radar (DPR) serves as a critical benchmark for calibrating satellite-based precipitation products, with its retrieval quality directly governing the accuracy of global precipitation estimates. While the updated version 07 (DPR-V07) algorithm introduces substantial refinements over its predecessor (DPR-V06), systematic evaluations of its operational advancements in precipitation monitoring remain limited. This study utilizes ground-based rain gauge data from Mainland China (2015–2018) to assess improvements of DPR-V07 over its predecessor’s (DPR-V06) effects. The results indicate that DPR-V07 reduces the high-altitude precipitation underestimation by 5% (vs. V06) in the west (W) and corrects the elevation-linked overestimation via an improved terrain sensitivity. The seasonal analysis demonstrates winter-specific advancements of DPR-V07, with a 3–8% reduction in the miss bias contributing to a lower total bias. However, the algorithm updates yield unintended trade-offs: the High-Sensitivity Scan (HS) mode exhibits significant detection performance degradation, particularly in east (E) and midwest (M) regions, with Critical Success Index (CSI) values decreasing by approximately 0.12 compared to DPR-V06. Furthermore, summer error components show a minimal improvement, suggesting unresolved challenges in warm-season retrieval physics. This study establishes a systematic framework for evaluating precipitation retrieval advancements, providing critical insights for future satellite algorithm development and operational applications in hydrometeorological monitoring. Full article

The measurement of atmospheric moisture content is essential for the monitoring of severe weather events and hydrological studies. This paper proposes a multivariate linear regression correction model that integrates elevation data with Global Navigation Satellite System (GNSS)-derived precipitable water vapor (PWV) to refine the water vapor content based on FY-4A satellite remote sensing data, thereby improving its accuracy. Taking Hong Kong as an experimental area, we investigated the correlation between GNSS PWV and FY-4A PWV, confirming the feasibility of utilizing GNSS PWV to calibrate FY-4A PWV. Subsequently, by examining the differences between the two PWV values, we found that the elevation of the stations affects the consistency of PWV measurement. Based on this finding, the elevation data are introduced to construct a multivariate linear regression correction model with a first-order polynomial. To evaluate the performance of the proposed model, a comparison with other correction models is made, including second-order polynomials and power functions. The results indicate that the elevation-integrated water vapor correction model improves the root mean square error (RMSE) by 27.4% and the MAE by 26.7%, and reduces the bias from 0.592 to nearly 0. Its accuracy surpasses that of second-order polynomial and power function models, demonstrating a considerable improvement in the precision of FY-4A. Full article

Advanced Synthetic Aperture Radar (SAR) has become an essential modality in remote sensing, offering all-weather capabilities and sensitivity to vegetation biophysical parameters and surface conditions, while effectively complementing optical sensor data. This study evaluates the impact of precipitation on the Normalized Polarimetric Radar Vegetation Index (NPRVI) using dual-polarization Sentinel-1 C-band SAR data from agricultural fields at the Beltsville Agricultural Research Center (BARC). Field-measured precipitation and Global Precipitation Measurement (GPM) precipitation datasets were temporally aligned with Sentinel-1 acquisition times to assess the sensitivity of radar signals to precipitation events. NPRVI exhibited a strong sensitivity to precipitation, particularly within the 1 to 7 h prior to the satellite overpass, even for small amounts of precipitation. A quality assessment (QA) framework was developed to flag and correct precipitation-affected radar observations through interpolation. The adjusted NPRVI values, based on the QA framework using precipitation within a 6 h window, showed strong agreement between field- and GPM-derived data, with an RMSE of 0.09 and a relative RMSE of 19.8%, demonstrating that GPM data can serve as a viable alternative for quality adjustment despite its coarse spatial resolution. The adjusted NPRVI for both soybean and corn fields significantly improved the temporal consistency of the time series and closely followed NDVI trends, while also capturing crop-specific seasonal variations, especially during periods of NDVI saturation or limited variability. These findings underscore the value of the proposed radar-based QA framework in enhancing the interpretability of vegetation dynamics. NPRVI, when adjusted for precipitation effects, can serve as a reliable and complementary tool to optical vegetation indices in agricultural and environmental monitoring. Full article

In recent years, satellite products have played an increasingly significant role in monitoring and estimating global extreme weather events, owing to their advantages of an excellent spatiotemporal continuity and broad coverage. This study systematically evaluates the Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for the GPM Late Run (IMERG-L) product for regional precipitation events based on the observations in Zhejiang Province from 2001 to 2020. In this study, seven typical precipitation indices with seven accuracy evaluation indexes are applied to analyze the performance of IMERG-L from multiple perspectives in terms of the precipitation intensity, frequency and spatial distribution dimensions. The results show that IMERG-L is capable of capturing the spatial distribution trends, especially in the frequency-based precipitation indices (CWD, R10mm and R20mm), which can depict the regional wetness and precipitation pattern. However, the product suffers from a systematic overestimation in capturing heavy precipitation and an extreme precipitation intensity, with a high false alarm rate and unstable accuracy, especially in heavy rainfall and above class events, where the Probability of Detection (POD) drops significantly, showing an obvious reduction in the recognition capability and risk of misclassification. Specifically, IMERG-L failed to reproduce the observed eastward-increasing trends in the annual maximum precipitation for both one-day (RX1day) and five-day (RX5day) durations, demonstrating its limitations in accurately capturing extreme precipitation patterns across Zhejiang Province. Overall, furthering the optimization and improvement of IMERG-L in reducing the intensity-dependent biases in heavy rainfall detection, increasing spatial inhomogeneity in trend representations and improving the false alarm suppression for extreme events are needed for the accurate monitoring and quantitative estimation of high-intensity extreme precipitation events. Full article

In previous studies, the possibilities of Ku-band radar measurements at low incidence angles were investigated for the task of sea ice detection. In this paper, the sensitivity of normalized radar cross-section to sea ice concentration is investigated at various wind conditions. The data of Ku-band radar onboard GPM satellite are used, and the sea ice concentration product from Bremen University website is implemented as reference data and the information on wind speed from reanalysis was applied. Simple analytical parameterization was obtained for the normalized radar cross-section depending on sea ice concentration and wind speed for various incidence angles using the regression method. The threshold behavior of the normalized radar cross-section with increase in wind speed was revealed and preferable wind conditions for sea ice concentration detection were identified. Full article

Accurate prediction of Global Navigation Satellite System-derived precipitable water vapor (GNSS-PWV), which is a crucial indicator for climate change monitoring, holds significant scientific value for climate disaster prevention and mitigation. In the study of GNSS-PWV prediction, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm within a decomposition–integration framework effectively addresses the non-stationarity and complexity of PWV sequences, enhancing prediction accuracy. However, residual noise and pseudo-modes from decomposition can distort signals, reducing the predictor system’s reliability. Additionally, independent modeling of all decomposed components decreases computational efficiency. To address these challenges, this paper proposes a hybrid model combining the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), autoregressive integrated moving average (ARIMA), and long short-term memory (LSTM) networks. Enhanced by local mean optimization and adaptive noise regulation, the ICEEMDAN algorithm effectively suppresses pseudo-modes and minimizes residual noise, enabling its decomposed intrinsic mode functions (IMFs) to more accurately capture the multi-scale features of GNSS-PWV. Sample entropy (SE) is used to quantify the complexity of IMFs, and components with similar entropy values are reconstructed into the following three sub-sequences: high-frequency, low-frequency, and trend. This process significantly reduces modeling complexity and improves computational efficiency. We propose different modeling strategies tailored to the dynamics of various subsequences. For the nonlinear and non-stationary high-frequency components, the LSTM network is used to effectively capture their complex patterns. The LSTM’s gating mechanism and memory cell design proficiently address the long-term dependency issue. For the stationary and weakly nonlinear low-frequency and trend components, linear patterns are extracted using ARIMA. Differencing eliminates trends and moving average operations capture random fluctuations, effectively addressing periodicity and trends in the time series. Finally, the prediction results of the three components are linearly combined to obtain the final prediction value. To validate the model performance, experiments were conducted using measured GNSS-PWV data from several stations in Hong Kong. The results demonstrate that the proposed model reduces the root mean square error by 56.81%, 37.91%, and 13.58% at the 1 h scale compared to the LSTM, EMD-LSTM, and ICEEMDAN-SE-LSTM benchmark models, respectively. Furthermore, it exhibits strong robustness in cross-month forecasts (accounting for seasonal influences) and multi-step predictions over the 1–6 h period. By improving the accuracy and efficiency of PWV predictions, this model provides reliable technical support for the real-time monitoring and early warning of extreme weather events in Hong Kong while offering a universal methodological reference for multi-scale modeling of geophysical parameters. Full article

Passive Microwave Imagers (PMWIs) aboard meteorological satellites have been instrumental in advancing the understanding of Earth’s atmospheric and surface processes, providing invaluable data for weather forecasting, climate monitoring, and environmental research. This review examines the relevance, applications, and benefits of PMWI data, focusing on their practical use and benefits to society rather than the specific techniques or algorithms involved in data processing. Specifically, it assesses the impact of PMWI data on Tropical Cyclone (TC) intensity and structure, global precipitation and extreme events, flood prediction, the effectiveness of tropical storm and hurricane watches, fire severity and carbon emissions, weather forecasting, and drought mitigation. Additionally, it highlights the importance of PMWIs in hydrometeorological and real-time applications, emphasizing their current usage and potential for improvement. Key recommendations from users include expanding satellite networks for more frequent global coverage, reducing data latency, and enhancing resolution to improve forecasting accuracy. Despite the notable benefits, challenges remain, such as a lack of direct research linking PMWI data to broader societal outcomes, the time-intensive process of correlating PMWI use with measurable societal impacts, and the indirect links between PMWI and improved weather forecasting and disaster management. This study provides insights into the effectiveness and limitations of PMWI data, stressing the importance of continued research and development to maximize their contribution to disaster preparedness, climate resilience, and global weather forecasting. Full article

This study aimed to compare and evaluate three satellite-based precipitation estimation products: Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Early Run (IMERG-Early Run), Climate Prediction Center MORPHing technique Real Time (CMORPH-RT), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Dynamic Infrared Rain rate Now (PDIR-Now) to identify the optimal integration strategies to improve the extreme rainfall estimation during Super Typhoon Yagi (September, 2024) in Hanoi, Vietnam, using validation data from 25 ground stations. In-depth analysis of three extreme rainfall series during Typhoon Yagi (6–9 September 2024), examining 93 extreme rainfall events at the 95th percentile precipitation threshold (R95p = 21.78 mm/h), combined with statistics at lower percentile thresholds (R1p, R5p, R10p, and R90p) and upper percentile threshold (R99p), revealed IMERG-Early best captured the peak rainfall, CMORPH-RT achieved highest total rainfall accuracy, while PDIR-Now offered the best spatial analysis. However, limitations included time lags, inability to detect rainfall events above R99p (41.69 mm/hour), and low detection rates (8–12%) in areas first impacted by the typhoon. This study identified that integration strategies combining different satellite products based on their strengths at specific time scales showed potential for improved rainfall estimation: PDIR-Now with IMERG-Early (1–3 h) and IMERG-Early with CMORPH-RT (6–12 h). These integration approaches accounted for each product’s unique capabilities in capturing different aspects of extreme rainfall during super typhoon events. Full article

This paper describes the benefits of using a microwave radiative transfer model (RTM) to improve the inter-satellite radiometric calibration (XCAL) between two independent satellite microwave radiometers. Because this work was sponsored by the NASA Global Precipitation Mission, the emphasis of this paper is on radiometer channels that are used for atmospheric precipitation retrievals; however, this technique is applicable for microwave remote sensing in general, over a wide range of satellite remote-sensing applications. An XCAL example is presented for the NASA Global Precipitation Mission, whereby the GPM Microwave Imager is used to calibrate another microwave radiometer (TROPICS) within the GPM constellation of satellites. This approach involves intercomparing near-simultaneous measured brightness temperatures from these radiometers viewing a common homogeneous ocean scene. The double difference between observed and theoretical brightness temperature, derived using a radiative transfer model, is used to establish a radiometric calibration offset or bias. On-orbit comparisons are presented for two different approaches, namely, with and without the aid of the RTM. The results demonstrate significant improvements in the XCAL biases derived when using the RTM, and this is especially beneficial when one radiometer produces anomalous brightness temperatures. Full article

In subtropical regions, typhoons and tropical storms can generate massive rainstorms resulting in thousands of landslides, often termed as Multiple-Occurrence of Regional Landslide Events (MORLE). Understanding the hazards, their location, and their triggering mechanism can help to mitigate exposure and potential impacts. Extreme rainfall events and earthquakes frequently trigger destructive landslides that cause extensive economic loss, numerous fatalities, and significant damage to natural resources. However, inventories of rainfall-induced landslides suggest that they occur frequently under climate change. This study proposed a semi-automated time series algorithm that integrates Sentinel-2 and Integrated Multi-satellite Retrievals for Global Precipitation Measurements (GPM-IMERG) data to detect rainfall-induced landslides. Pixel-wise NDVI time series data are analyzed to detect change points, which are typically associated with vegetation loss due to landslides. These NDVI abrupt changes are further correlated with the extreme rainfall events in the GPM-IMERG dataset, within a defined time window, to detect RIL. The algorithm is tested and evaluated eight previously published landslide inventories, including both those manually mapped and those derived from high-resolution satellite data. The landslide detection yielded an overall F1-score of 0.82 and a mean producer accuracy of 87%, demonstrating a substantial improvement when utilizing moderate-resolution satellite data. This study highlights the combination of using optical images and rainfall time series data to detect landslides in remote areas that are often inaccessible to field monitoring. Full article