Precipitation Measurement Research Articles

Evaluation of IMERG climate trends over land in the TRMM and GPM eras

Abstract The Integrated Multi-satellitE Retrievals for global precipitation measurement (GPM) mission (IMERG) is a global precipitation product suite consisting of both near-real-time and research-grade products with high spatiotemporal resolution. However, the IMERG developers note that it is designed as a high resolution precipitation product (HRPP), not a Climate Data Record, and its ability to capture climate trends remains uncertain. Therefore, it is imperative to explore and quantify IMERG’s capability in capturing climate trends for our community. This study examines the climatological performance of IMERG Final (Version 06) by analyzing annual precipitation trends over land from 2001 to 2019 using the Mann-Kendall analysis, taking gauge records as ground truth. Three different matching strategies are applied: at gauge locations, at 0.1° pixels, and at 1.0° pixels. Additionally, this study compares the performances during the TRMM (2001–2014) and GPM eras (2015–2019). Our results find: (1) IMERG daily data exhibits high spatial consistency with gauge records at both gauge locations and 0.1° resolution, consistent with its Global Precipitation Climatology Centre calibration, with a conversion rate of approximately 89.3%; (2) IMERG performs much better overall in the GPM era than in the tropical rainfall measuring mission (TRMM) era, evidenced by a lower proportion of unreliable samples (∼10.2% vs. ∼21.2%); (3) The proportion of samples showing consistent trends with gauge data is 86.7% in the GPM era, much higher than the 70.5% and 75.3% shown by the entire record and the TRMM era, respectively. This improvement in the GPM era suggests that the within-mission consistency of IMERG is higher than the between-mission consistency, likely due to residual differences in the calibration methods used during the TRMM and GPM missions. This study broadens the perspective on IMERG, showcasing its additional potential for analyzing climate trends despite its design only as a HRPP. Crucially, it highlights and reconfirms how the GPM era has enhanced IMERG’s capacity for accurately tracking global precipitation trends.

Crowdsourced Data Reveal Shortcomings in Precipitation Phase Products for Rain and Snow Partitioning

AbstractReanalysis products support our understanding of how the precipitation phase influences hydrology across scales. However, a lack of validation data hinders the evaluation of a reanalysis‐estimated precipitation phase. In this study, we used a novel dataset from the Mountain Rain or Snow (MRoS) citizen science project to compare 39,680 MRoS observations from January 2020 to July 2023 across the conterminous United States (CONUS) to assess three precipitation phase products. These products included the Global Precipitation Measurement (GPM) mission Integrated Multi‐satellitE Retrievals for GPM (IMERG), the Modern‐Era Retrospective Analysis for Research and Applications (MERRA‐2), and the North American Land Data Assimilation System (NLDAS‐2). The overall critical success indices for detecting rainfall (snowfall) for IMERG, MERRA‐2, and NLDAS‐2 were 0.51 (0.79), 0.49 (0.77), and 0.54 (0.53), respectively. These indices show that IMERG and MERRA‐2 reasonably classify snowfall, whereas NLDAS‐2 overestimates rainfall. All products performed poorly in detecting subfreezing rainfall and snowfall above 2°C. Therefore, crowdsourced data provides a unique validation source to improve the capabilities of reanalysis products.

Sea Surface and Snowflakes as Natural Targets Connecting FY‐3G and GPM‐CO Dual‐Frequency Radars

AbstractThe Precipitation Measurement Radar (PMR) onboard FengYun‐3G consists of a Ku‐/Ka‐band radar, which is characterized by similar configurations with the Dual‐frequency Precipitation Radar (DPR) carried by Global Precipitation Measurement mission Core Observatory. However, directly comparing observations from two radars is challenging due to a scarcity of their coincidences. In this study, sea surface echoes in their track intersections were employed to cross‐calibrate PMR. Then, we show that the dual‐frequency ratio (DFR) in snow stably increases with Ku‐band reflectivity in statistics, allowing for an assessment of the consistency between PMR and DPR observations. Surprisingly, our results reveal a underestimation of DFR in DPR inner swath, while observations from PMR are in good agreement with those from DPR outer swath. This study demonstrates the novel use of natural targets for spaceborne dual‐frequency radar calibration, and presents a unique view into the connection between the two spaceborne precipitation radar missions in operation.

Vertical microphysical structures of summer heavy rainfall in the Yangtze-Huaihe River Valley from GPM DPR data

Increasing heavy rainfall poses significant challenges in the Yangtze-Huaihe River Valley (YHRV). There is a need for more specific insights into the vertical microphysical structures and their influence on heavy rainfall to enhance the accuracy of numerical simulations and forecasts. Using data from the Dual-frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) satellite from 2014 to 2023, this study investigated the vertical microphysical structures of different types of heavy summer rainfall (> 8 mm/h) and elucidated their impacts on the rain rate in the YHRV. Based on the radar reflectivity thresholds at different altitudes, heavy summer rainfall was classified into four types: deep convective, shallow convective, stratiform rainfall, and warm rainfall. In the YHRV region, shallow convective rainfall contributed the most to total heavy rainfall (39.1 %) and had the highest occurrence (44.7 %) of extreme rainfall (>50 mm/h). Stratiform rainfall occurred most frequently but decreased rapidly with increasing rain rates, while warm rainfall contributed little to heavy rainfall. For the vertical microphysical structure of heavy rainfall, deep convective rainfall exhibited rapid growth of large particles above the melting layer, resulting in the largest average mass-weighted diameter (Dm) near the surface (2.2 mm), but the smallest average droplet concentration (recorded as dBNw in the decibel scale), approximately 37. Below the melting layer, the Dm of small particles in the shallow convective rainfall increased rapidly, and the impact of coalescence was much greater than that of break-up. Except for warm rainfall, the average Dm for other types of heavy rainfall remained relatively high, exceeding 1.5 mm both within and below the melting layer. The average dBNw increased consistently as altitude decreased. As rainfall intensified to extreme rainfall, the average rain rate of shallow convective rainfall slightly surpassed that of deep convective rainfall. This was due to a decrease in average dBNw for deep convective rainfall, while the average dBNw of shallow convective rainfall continued to increase.

Weather conditions in southern Poland at the turn of the 20th century — Insights from archived observational records

This study examines weather conditions in Małopolska (Lesser Poland, southern Poland) from 1861 to 1919, utilizing historical meteorological materials stored in the Archives of the Institute of Meteorology and Water Management — National Research Institute, Poland. The region developed an extensive meteorological network in the latter half of the nineteenth century, preserving daily and sub-daily measurements of air temperature and precipitation, along with notes on socio-economic events and environmental issues. Despite the loss of many early records, the study focuses on 14 measurement points, including the Kraków–Observatory, which has been in operation since 1792 and served as the reference station. Data were digitized through the HISTKLIM project. Analysis revealed that air temperatures in the late 1800s and early 1900s were lower than present-day values, with varied rainfall patterns. The study highlights the substantial value of these historical records, despite gaps and the absence of metadata. Comparing historical weather conditions to current climate normals (1991–2020), the findings contribute to understanding regional climate variability and underscore the importance of preserving archival meteorological data for future research.

A comparative analysis of rainfall data from IMERG early run and ground-based rain gauges on Bali Island

Abstract Ground-based precipitation measurements encounter challenges in various parts of Bali due to the limited number of gauge stations. Therefore, it is essential to identify dependable alternatives like satellite-derived precipitation data, which offer continuous precipitation time series with high spatial resolution. This study assessed the effectiveness of near real-time global satellite precipitation products, specifically the Integrated Multi-satellite Retrievals for Global Precipitation Measurement-Early Run (IMERG-E) compared to gauge data from 43 stations across Bali Province. To evaluate IMERG-E datasets, traditional point-to-pixel comparison techniques were employed, alongside statistical metrics including correlation coefficient (CC), mean absolute error (MAE), relative bias (RB), and Nash-Sutcliffe efficiency (NSE). The comparative analysis showed that the daily IMERG-E dataset performs moderately well, as evidenced by weak to moderate correlation and low MAE. IMERG-E showed evidence of underestimating rainfall, as indicated by the RB value. Conversely, IMERG-E demonstrates poor accuracy according to the NSE value. It is necessary to explore effective correction methods for IMERGE-E to establish it as a viable alternative data source.

Comparisons of Energetic Electron Observations Between FIREBIRD‐II CubeSats and POES/MetOp Satellites From 2018 to 2020

AbstractPrecipitation into the atmosphere is one of the main processes by which high energy electrons trapped in Earth's inner magnetosphere are lost from the system. Precipitating electrons can affect the chemical composition of the atmosphere and provide insight into the complex dynamics of the Van Allen radiation belts. This study compares energetic electron precipitation measurements at low‐Earth‐orbit by the Focused Investigations of Relativistic Electron Burst Intensity, Range, and Dynamics (FIREBIRD‐II) CubeSats with NOAA Polar‐orbiting Operational Environmental Satellite (POES) and ESA Meteorological Operational satellite (MetOp) satellites, which are equipped with the Medium‐Energy Proton Electron Detector (MEPED). The analysis considers 51 high quality conjunction events at >300 keV during times of low to moderate geomagnetic activity. The spacecraft capture similar electron flux variability, and FIREBIRD‐II observations fall between POES/MetOp and telescopes, likely a result of FIREBIRD‐II sampling both precipitating and mirrored electrons due to uncertainties in pointing direction. Results demonstrate the value of high‐resolution differential energy observations of electron precipitation by low‐cost CubeSats such as FIREBIRD‐II, especially during periods of low flux.

Nationwide Landslide Prediction in India Using Neural Networks and Multi-Source Satellite Data.

Landslides are a prevalent and devastating natural hazard in mountainous regions, particularly in high-risk areas such as Uttarakhand and Meghalaya, India, where factors like intense rainfall, steep slopes, and changing land cover significantly contribute to landslide occurrences. Traditional ground-based monitoring methods provide limited coverage and scalability, underscoring the need for a more extensive and real-time approach to landslide prediction. This study addresses this gap by developing an advanced predictive model using neural network architectures and multi-source satellite data, including Synthetic Aperture Radar (SAR) from Sentinel-1 for ground deformation, optical imagery from Sentinel-2 for vegetation and land cover analysis, and Digital Elevation Models (DEM) for slope assessment. Additionally, rainfall data from the Global Precipitation Measurement (GPM) mission was integrated to evaluate precipitation as a potential landslide trigger. Through a comparative analysis of Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM model, we found that the CNN-LSTM hybrid model achieved superior performance, with an F1 score of 0.91. This model effectively captures both spatial and temporal patterns, enabling early detection of landslide-prone conditions and surpassing the predictive accuracy of traditional methods. The proposed approach provides a scalable solution for real-time, large-scale monitoring, with the potential to enhance disaster preparedness and resilience in vulnerable communities. By advancing landslide prediction capabilities, this model contributes a valuable tool for proactive risk management and offers significant potential for integration into regional early warning systems.

Potential effects of climate change on cacti distribution and conservation in North American drylands

Climate change is expected to adversely impact dryland biodiversity by reducing habitat suitability, especially for cacti species. One way to mitigate such effects on cacti is to reinforce the integrity of areas of high importance for their conservation, also known as high-value conservation areas (HVCA). To better understand the distribution of HVCA and their potential vulnerability to climate change, we produced habitat suitability maps for 393 cacti species across North American drylands. We then used spatial analysis to assess hot spots of cacti' habitat suitability and their overlap and mismatch. A total of 217 (55.2%) species had their habitat suitability primarily influenced by measures of precipitation (170 species), precipitation seasonality in particular, and temperature (77 species). Our study reveals climate change will impact cacti habitat suitability across North America's drylands. We also report a strong overlay among HVCA in current and future climate scenarios. Results show that most HVCA is not represented by protected areas. This research emphasizes the need for managers and conservationists to consider the influence of climate when selecting areas for conservation and to anticipate the potential effects of climate change on the spatial configuration of priority areas for the conservation of cacti species.

The Performance of GPM IMERG Product Validated on Hourly Observations over Land Areas of Northern Hemisphere from Global Meteorological Stations

The integrated multi-satellite retrievals for the global precipitation measurement (IMERG) data, which is the latest generation of multi-satellite fusion inversion precipitation product provided by the Global Precipitation Measurement (GPM) mission, has been widely applied in hydrological research and applications. However, the quality of IMERG data needs to be validated, as this technology is essentially an indirect way to obtain precipitation information. This study evaluated the performance of IMERG final run (version 6.0) products from 2001 to 2020, using three sets of gauge-derived precipitation data obtained from the Integrated Surface Database, China Meteorological Administration, and U.S. Climate Reference Network. The results showed a basic consistency in the spatial pattern of annual precipitation total between IMERG data and gauge observations. The highest and lowest correlations between IMERG data and gauge observations were obtained in North Asia (0.373, p < 0.05) and Europe (0.308, p < 0.05), respectively. IMERG data could capture the bimodal structure of diurnal precipitation in South Asia but overestimates a small variation in North Asia. The disparity was attributed to the frequency overestimation but intensity underestimation in satellite inversion, since small raindrops may evaporate before arriving at the ground but can be identified by remote sensors. IMERG data also showed similar patterns of interannual precipitation variability to gauge observation, while overestimating the proportion of annual precipitation hours by 2.5% in North America, and 2.0% in North Asia. These findings deepen our understanding of the capabilities of the IMERG product to estimate precipitation at the hourly scale, and can be further applied to improve satellite precipitation retrieval.

A multi-method and multi-duration trend analysis of temperature and precipitation in Istanbul, Turkey, by using meteorological records, MERRA-2 reanalysis, and IMERG estimations

In this study, temperature and precipitation trends in Istanbul were analyzed using data from a ground-based station for the 1960–2019 interval, Integrated Multi-SatellitE Retrievals for Global Precipitation Measurement (GPM IMERG) for 2000–2019, and Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) temperature reanalysis for 1980–2019. Single-duration analysis revealed significant increasing temperature trends with minor downward and major upward tendencies in precipitation. Multi-duration analysis resulted with dominant decreasing and increasing temperature trends, respectively, in 1980–1999 and 2000–2019, alongside decreasing precipitation trends in 1980–1999 winter and 2000–2019 spring, which were undetected in the single-duration analysis. As a novel attempt, trends based on MERRA-2 and IMERG data were compared with ground-based records in Istanbul and revealed better compatibility in MERRA-2, especially in 2000–2019 compared to IMERG data. By comparing various statistical methods and data sources, this study offers valuable insights into hydrometeorological trend analysis and aids transition from conventional to novel data sources.

Correction: Space‑borne RADARs for Precipitation Measurement

Correction: Space‑borne RADARs for Precipitation Measurement

Detecting the Vertical Structure of Extreme Precipitation in the Headwater Area of Yellow River Using the Dual‐Frequency Precipitation Radar Onboard the Global Precipitation Measurement Mission

ABSTRACTIn the context of global warming, the rise in extreme precipitation events in high‐altitude headwater areas has introduced greater hydrological uncertainty. However, the limited understanding of the physical mechanisms driving extreme precipitation in these areas hinders efforts to mitigate the potential rise in future precipitation risks. This study analysed the extreme precipitation events in the headwater area of the Yellow River (HAYR) from May to September each year from 2015 to 2020 using satellite‐based data from Dual‐frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) Core Observatory and Integrated Multi‐satellite Retrievals for GPM (IMERG). The results show that stratiform precipitation (SP) determines the spatial extent of extreme precipitation events, while convective precipitation (CP) largely affects the rainfall intensity. Statistical analysis from different extreme precipitation events indicates that the rain rate of CP is 2 to 3 times higher than that of SP, thus zones of intense precipitation in the study area are normally dominated by CP. Vertically, the topographic lifting in complex mountainous regions exerts opposite effects on the precipitation rates of SP and CP, weakening the precipitation intensity of SP while enhancing that of CP. The peak precipitation rate in the midstream and downstream regions is observed at approximately 5 km, whereas the upstream region displays a distinctive double‐peaked distribution, with one peak at 8.5 km and another near the surface. This study provides a better understanding of the interior structure evolution process of plateau precipitation, as well as the associated microphysical properties, and highlights some insights to improve microphysical parameterization in the future model developments.

Estimating Rainfall Anomalies with IMERG Satellite Data: Access via the IPE Web Application

This study assesses the possibilities of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-GPM) to estimate extreme rainfall anomalies. A web application, the IMERG Precipitation Extractor (IPE), was developed which allows for the querying, visualization, and downloading of time-series satellite precipitation data for points, watersheds, country extents, and digitized areas. The tool supports different temporal resolutions ranging from 30 min to 1 week and facilitates advanced analyses such as anomaly detection and storm tracking, an important component for climate change study. To validate the IMERG precipitation data for anomaly estimation over a 22-year period (2001 to 2022), the Rainfall Anomaly Index (RAI) was calculated and compared with RAI data from 2360 NOAA stations across the conterminous United States (CONUS), considering both dry and wet climate regions. In the dry region, the results showed an average correlation coefficient (CC) of 0.94, a percentage relative bias (PRB) of −22.32%, a root mean square error (RMSE) of 0.96, a mean bias ratio (MBR) of 0.74, a Nash–Sutcliffe Efficiency (NSE) of 0.80, and a Kling–Gupta Efficiency (KGE) of 0.52. In the wet region, the average CC of 0.93, PRB of 24.82%, RMSE of 0.96, MBR of 0.79, NSE of 0.80, and KGE of 0.18 were computed. Median RAI indices from both the IMERG and NOAA indicated an increase in rainfall intensity and frequency since 2010, highlighting growing concerns about climate change. The study suggests that IMERG data can serve as a valuable alternative for modeling extreme rainfall anomalies in data-scarce areas, noting its possibilities, limitations, and uncertainties. The IPE web application also offers a platform for extending research beyond CONUS and advocating for further global climate change studies.

Airborne Observations of Highly Variable and Complex Freezing Drizzle and Mixed-Phase Environments

Abstract Airframe icing caused by interactions with supercooled cloud droplets and precipitation can pose a risk to aviation operations and life safety. The In-Cloud Icing and Large-drop Experiment (ICICLE) was conducted in January–March 2019 to capture measurements in freezing conditions in support of the Federal Aviation Administration (FAA) Terminal Area Icing Weather Information for NextGen (TAIWIN) program. The National Research Council of Canada’s Convair-580 research aircraft fulfilled the airborne data collection requirements for the ICICLE campaign and sampled icing clouds and atmospheric conditions over the midwestern United States. ICICLE flight 18, conducted on 17 February 2019, collected cloud and precipitation measurements during a widespread storm that generated supercooled small drops and freezing drizzle (FZDZ) within both liquid and mixed-phase regions. Supercooled liquid water content (LWC) typically ranged 0.30–0.45 g m−3 and exceeded 0.70 g m−3 in one instance. Maximum FZDZ diameters of 300–400 μm were commonly sampled near the base of clouds. Missed approaches performed at four Illinois airfields provided measurements of conditions from near ground level to above cloud top and supplied information regarding FZDZ formation and evolution. FZDZ was found to form at altitudes featuring relatively high LWC and sufficiently low droplet number concentrations. FZDZ formation zones were sometimes collocated with regions of atmospheric instability and/or wind shear. Flight through highly variable supercooled cloud droplet and FZDZ conditions resulted in significant Convair-580 airframe icing, highlighting the risk that icing conditions can pose to aircraft safety.

Performance Assessment of Satellite-Based Precipitation Products in the 2023 Summer Extreme Precipitation Events over North China

In the summer of 2023, North China experienced a rare extreme precipitation storm due to Typhoons Doksuri and Khanun, leading to significant secondary disasters and highlighting the urgent need for accurate rainfall forecasting. Satellite-based quantitative precipitation estimation (QPE) products like Integrated Multi-Satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) from the Global Precipitation Measurement (GPM) Mission have great potential for enhancing forecasts, necessitating a quantitative evaluation before deployment. This study uses a dense rain gauge as a benchmark to assess the accuracy and capability of the latest version 7B IMERG and version 8 GSMaP satellite-based QPE products for the 2023 summer extreme precipitation in North China. These satellite-based QPE products include four satellite-only products, namely IMERG early run (IMERG_ER) and IMERG late run (IMERG_LR), GSMaP near-real-time (GSMaP_NRT), and GSMaP microwave-infrared reanalyzed (GSMaP_MVK), along with two gauge-corrected products, namely IMERG final run (IMERG_FR) and GSMaP gauge adjusted (GSMaP_Gauge). The results show that (1) GSMaP_MVK, IMERG_LR, and IMERG_FR effectively capture the space distribution of the extreme rainfall, with relatively high correlation coefficients (CCs) of approximately 0.77, 0.75, and 0.79. The IMERG_ER, GSMaP_NRT, and GSMaP_Gauge products exhibit a less accurate spatial pattern capture (CCs about 0.66, 0.73, and 0.67, respectively). Each of the six QPE products tends to underestimate rainfall (RBs < 0%). (2) The IMERG products surpass the corresponding GSMaP products in serial rainfall measurement. IMERG_LR demonstrates superior performance with the lowest root-mean-square error (RMSE) (about 0.38 mm), the highest CC (0.97), and less underestimation (RB about −6.37%). (3) The IMERG products at rainfall rates ≥ 30 mm/h, GSMaP_NRT and GSMaP_MVK products at rainfall rates ≥ 55 mm/h, and GSMaP_Gauge products at ≥ 40 mm/h showed marked limitations in event detection, with a near-zero probability of detection (POD) and a nearly 100% false alarm ratio (FAR). In this extreme precipitation event, caution is needed when using the IMERG and GSMaP products.

Deep Learning for Opportunistic Rain Estimation via Satellite Microwave Links.

Accurate precipitation measurement is critical for managing flood and drought risks. Traditional meteorological tools, such as rain gauges and remote sensors, have limitations in resolution, coverage, and cost-effectiveness. Recently, the opportunistic use of microwave communication signals has been explored to improve precipitation estimation. While there is growing interest in using satellite-to-earth microwave links (SMLs) for machine learning-based precipitation estimation, direct rainfall estimation from raw signal-to-noise ratio (SNR) data via deep learning remains underexplored. This study investigates a range of machine learning (ML) approaches, including deep learning (DL) models and traditional methods like gradient boosting machine (GBM), for estimating rainfall rates from SNR data collected by interactive satellite receivers. We develop real-time models for rainfall detection and estimation using downlink SNR signals from satellites to user terminals. By leveraging a year-long dataset from multiple locations-including SNR measurements paired with disdrometer and rain-gauge data-we explore and evaluate various ML models. Our final models include ensemble approaches for both rainfall detection and cumulative rainfall estimation. The proposed models provide a reliable solution for estimating precipitation using Earth-satellite microwave links, potentially improving precipitation monitoring. Compared to the state-of-the-art power-law-based models applied to similar datasets reported in the literature, our ML models achieve a 46% reduction in the root mean squared error (RMSE) for event-based cumulative precipitation predictions.

Daily Rainfall Patterns During Storm “Daniel” Based on Different Satellite Data

Extreme rainfall from a long-lived weather system called storm “Daniel” occurred from 4th to 11th September 2023 over the central and eastern Mediterranean, leading to many devastating flood events mainly in central Greece and the western coastal parts of Libya. This study analyzes the daily rainfall amounts over all the affected geographical areas during storm “Daniel” by comparing three different satellite-based rainfall data products. Two of them are strictly related to Meteosat multispectral imagery, while the other one is based on the Global Precipitation Measurement (GPM) satellite mission. The satellite datasets depict extreme daily rainfall (up to 450 mm) for consecutive days in the same areas, with the spatial distribution of such rainfall amounts covering thousands of square kilometers almost during the whole period that the storm lasted. Moreover, the spatial extent of the heavy rainfall patterns was calculated on a daily basis. The convective nature of the rainfall, which was also recorded, characterizes the extremity of this weather system. Finally, the intercomparison of the datasets used highlights the satisfactory efficiency of the examined satellite datasets in capturing similar rainfall amounts in the same areas (daily mean error of 15 mm, mean absolute error of up to 35 mm and correlation coefficient ranging from 0.6 to 0.9 in most of the examined cases). This finding confirms the realistic detection and monitoring of the different satellite-based rainfall products, which should be used for early warning and decision-making regarding potential flood events.

Precipitation Retrieval from FY-3G/MWRI-RM Based on SMOTE-LGBM

Using the FY-3G/MWRI-RM observations, this paper proposes a precipitation retrieval method that combines the Synthetic Minority Over-sampling Technique with Light Gradient Boosting Machine (SMOTE-LGBM) and analyzes the impact of MWRI-RM channel settings on precipitation retrieval. The SMOTE-LGBM-based model consists of two LGBM models for precipitation identification and estimation, respectively. The SMOTE method is used to address the imbalance between precipitation and non-precipitation samples. Using the Integrated Multi-Satellite Retrievals for the Global Precipitation Measurement (IMERG) product as a reference, we validate the retrieved precipitation by the SMOTE-LGBM-based model with an independent testing dataset. The critical success indexes are 0.483 and 0.526, and the Pearson correlation coefficients are 0.611 and 0.645 for the ocean and land regions, respectively. The spatial distributions of the retrieved and IMERG accumulated precipitation in the testing dataset are similar. In addition, we visualize and analyze the cases of Meiyu and two typhoons. The results indicate that the SMOTE-LGBM-based model effectively represents the spatial distribution characteristics of precipitation and achieves high agreement with IMERG precipitation products. Overall, the SMOTE-LGBM-based model successfully retrieves precipitation from MWRI-RM and provides accurate precipitation products for FY-3G/MWRI-RM for the first time.

Intercomparison of reanalysis and satellite precipitation products in endorheic and exorheic basins on the Tibetan Plateau

Study Region: Endorheic and exorheic basins of the Tibetan Plateau (TP). Study Focus: Reanalysis and satellite precipitation products provide alternatives for regions of sparse ground precipitation observation, but pose a tough task to select a suitable one for the TP. This study conducts a multi-scale evaluation of six reanalysis and satellite precipitation products in endorheic and exorheic basins using water balance and extended triple collocation (ETC) methods. The reanalysis precipitation products include ECMWF Re-Analysis version 5 (ERA5-Land), China Meteorological Forcing Dataset (CMFD), Global Land Data Assimilation Systems (GLDAS), and High-resolution Precipitation dataset for the Third Pole region (TPHiPr). The satellite precipitation data include Global Precipitation Measurements (GPM) and Tropical Rainfall Measuring Mission (TRMM) products. New Hydrological Insights for the Region: The precipitation products vary in accuracy from basin to basin, with better performance in exorheic than endorheic basins. Reanalysis-based ERA5-land, TPHiPr, and CMFD perform well in most basins at annual scale, among which TPHiPr performs best at daily scale. At regional scale, GPM performs well in endorheic region, and ERA5-land in exorheic region. While all the products increase significantly in accuracy from basin to regional scale in endorheic region, ERA5-land shows best performance at annual and multi-year scales in the entire region. Our findings provide valuable supports for precipitation product selection in the Tibetan endorheic and exorheic basins.