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Journal articles on the topic 'Satellite-based precipitation'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Saavedra, Oliver, and Jhonatan Ureña. "Generation of Combined Daily Satellite-Based Precipitation Products over Bolivia." Remote Sensing 14, no. 17 (2022): 4195. http://dx.doi.org/10.3390/rs14174195.

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This study proposes using Satellite-Based Precipitation (SBP) products and local rain gauge data to generate information on the daily precipitation product over Bolivia. The selected SBP products used were the Global Satellite Mapping of Precipitation Gauge, v6 (GSMaP_Gauge v6) and the Climate Hazards Group Infrared Precipitations with Stations (CHIRPS). The Gridded Meteorological Ensemble Tool (GMET) is a generated precipitation product that was used as a control for the newly generated products. The correlation coefficients for raw data from SBP products were found to be between 0.58 and 0.6

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Hieu, Bui Thi. "Study on quantification of areal mean precipitation using satellite-gauge merging precipitation." Journal of Science and Technology in Civil Engineering (STCE) - NUCE 12, no. 5 (2018): 117–26. http://dx.doi.org/10.31814/stce.nuce2018-12(5)-12.

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Satellite based precipitation product (GSMaP-MVK) can be reliably used to estimate the Areal Mean Precipitation error based on “Sample Design method” (Esdd) with the effort to mitigate the problem of sparse data, especially severe in poorly gauged river basins. In addition, the satellite-gauge merging precipitation would reduce significantly the magnitude gaps between the satellite rainfall estimations and the rain gauge data. In this study, the capability of satellite-gauge merging precipitation using GSMaP-MVK and local dense rain gauge data with bias reduction approach to evaluate the AMP i

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Smith, Thomas M., Phillip A. Arkin, John J. Bates, and George J. Huffman. "Estimating Bias of Satellite-Based Precipitation Estimates." Journal of Hydrometeorology 7, no. 5 (2006): 841–56. http://dx.doi.org/10.1175/jhm524.1.

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Abstract Systematic biases in satellite-based precipitation estimates can be the dominant component of their uncertainty. These biases may not be reduced by averaging, which makes their evaluation particularly important. Described here are several methods of evaluating the biases and their characteristics. Methods are developed and tested using monthly average precipitation estimates from several satellites. Direct estimates of bias are obtained from analysis of satellite–gauge estimates, and they indicate the general bias patterns and magnitudes over land. Direct estimates cannot be computed

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Gao, Yanbo, Jiping Guan, Fuhan Zhang, Xiaodong Wang, and Zhiyong Long. "Attention-Unet-Based Near-Real-Time Precipitation Estimation from Fengyun-4A Satellite Imageries." Remote Sensing 14, no. 12 (2022): 2925. http://dx.doi.org/10.3390/rs14122925.

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Reliable near-real-time precipitation estimation is crucial for scientific research and resistance to natural disasters such as floods. Compared with ground-based precipitation measurements, satellite-based precipitation measurements have great advantages, but precipitation estimation based on satellite is still a challenging issue. In this paper, we propose a deep learning model named Attention-Unet for precipitation estimation. The model utilizes the high temporal, spatial and spectral resolution data of the FY4A satellite to improve the accuracy of precipitation estimation. To evaluate the

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Errico, Ronald M., George Ohring, Fuzhong Weng, et al. "Assimilation of Satellite Cloud and Precipitation Observations in Numerical Weather Prediction Models: Introduction to the JAS Special Collection." Journal of the Atmospheric Sciences 64, no. 11 (2007): 3737–41. http://dx.doi.org/10.1175/2007jas2622.1.

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Abstract To date, the assimilation of satellite measurements in numerical weather prediction (NWP) models has focused on the clear atmosphere. But satellite observations in the visible, infrared, and microwave provide a great deal of information on clouds and precipitation. This special collection describes how to use this information to initialize clouds and precipitation in models. Since clouds and precipitation often occur in sensitive regions for forecast impacts, such improvements are likely necessary for continuing to acquire significant gains in weather forecasting. This special collect

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Zhang, Xinxuan, and Emmanouil N. Anagnostou. "Evaluation of Numerical Weather Model–Based Satellite Precipitation Adjustment in Tropical Mountainous Regions." Journal of Hydrometeorology 20, no. 3 (2019): 431–45. http://dx.doi.org/10.1175/jhm-d-18-0008.1.

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Abstract The study evaluated a numerical weather model (WRF)-based satellite precipitation adjustment technique with 81 heavy precipitation events that occurred in three tropical mountainous regions (Colombia, Peru, and Taiwan). The technique was applied on two widely used near-real-time global satellite precipitation products—the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center morphing technique (CMORPH) and the Global Satellite Mapping of Precipitation project (GSMaP)—for each precipitation event. The WRF-adjusted satellite products along with the near-real-t

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Tamrakar, Bijaya, and Knut Alfredsen. "Satellite-Based Precipitation Estimation for Hydropower Development." Hydro Nepal: Journal of Water, Energy and Environment 12 (October 29, 2013): 52–58. http://dx.doi.org/10.3126/hn.v12i0.9033.

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Runoff is one of the major factors that govern the capacity of a hydropower project. Precipitation data are needed for estimation of runoff through runoff simulation using a hydrological model. Dense setup of rain gauge network in a mountainous topography is difficult and expensive. An alternative for this problem is the use of Satellite precipitation data with high spatial and temporal resolution. They have an additional advantage that they represent areal precipitation. But, these data should be duly evaluated before using them. In this study, Tropical Rainfall Measuring Mission (TRMM 3B42)

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Heymsfield, Andrew J., Carl Schmitt, Chih-Chieh-Jack Chen, et al. "Contributions of the Liquid and Ice Phases to Global Surface Precipitation: Observations and Global Climate Modeling." Journal of the Atmospheric Sciences 77, no. 8 (2020): 2629–48. http://dx.doi.org/10.1175/jas-d-19-0352.1.

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Abstract This study is the first to reach a global view of the precipitation process partitioning, using a combination of satellite and global climate modeling data. The pathways investigated are 1) precipitating ice (ice/snow/graupel) that forms above the freezing level and melts to produce rain (S) followed by additional condensation and collection as the melted precipitating ice falls to the surface (R); 2) growth completely through condensation and collection (coalescence), warm rain (W); and 3) precipitating ice (primarily snow) that falls to the surface (SS). To quantify the amounts, dat

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Lu, C., H. Yuan, E. I. Tollerud, and N. Wang. "Scale-Dependent Uncertainties in Global QPFs and QPEs from NWP Model and Satellite Fields." Journal of Hydrometeorology 11, no. 1 (2010): 139–55. http://dx.doi.org/10.1175/2009jhm1164.1.

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Abstract Global precipitation forecasts from numerical weather prediction (NWP) models can be verified using the near-global coverage of satellite precipitation retrievals. However, inaccuracies in satellite precipitation analyses complicate the interpretation of forecast errors that result from verification of an NWP model against satellite observations. In this study, assessments of both a global quantitative precipitation estimate (QPE) from a satellite precipitation product and corresponding global quantitative precipitation forecast (QPF) from a global NWP model are conducted using availa

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Li, Jingjing, Kuo-Lin Hsu, Amir AghaKouchak, and Soroosh Sorooshian. "Object-Based Assessment of Satellite Precipitation Products." Remote Sensing 8, no. 7 (2016): 547. http://dx.doi.org/10.3390/rs8070547.

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Yucel, I., and A. Onen. "Evaluating the extreme precipitation events using a mesoscale atmosphere model and satellite based precipitation product." Natural Hazards and Earth System Sciences Discussions 1, no. 6 (2013): 6979–7014. http://dx.doi.org/10.5194/nhessd-1-6979-2013.

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Abstract. Quantitative precipitation estimates are obtained with more uncertainty under the influence of changing climate variability and complex topography from numerical weather prediction (NWP) models. On the other hand, hydrologic model simulations depend heavily on the availability of reliable precipitation estimates. Difficulties in estimating precipitation impose an important limitation on the possibility and reliability of hydrologic forecasting and early warning systems. This study examines the performance of the Weather Research and Forecasting (WRF) model and the Multi Precipitation

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Tian, Bingru, Hua Chen, Xin Yan, Sheng Sheng, and Kangling Lin. "A Downscaling–Merging Scheme for Monthly Precipitation Estimation with High Resolution Based on CBAM-ConvLSTM." Remote Sensing 15, no. 18 (2023): 4601. http://dx.doi.org/10.3390/rs15184601.

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Satellite products have mediocre performance in precipitation estimation, while rain gauges are incapable of describing continuous spatial precipitation distributions. To obtain spatially continuous and accurate precipitation data, this paper proposes a two-step scheme incorporating environmental variables, satellite precipitation estimations, and rain gauge observations for the calibration of satellite precipitation data. First, the GPM data are downscaled from 0.1° to 0.01° based on the seasonal RF models to minimize the spatial differences between the satellite estimations and the rain gaug

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Pfeifroth, Uwe, Jörg Trentmann, Andreas H. Fink, and Bodo Ahrens. "Evaluating Satellite-Based Diurnal Cycles of Precipitation in the African Tropics." Journal of Applied Meteorology and Climatology 55, no. 1 (2016): 23–39. http://dx.doi.org/10.1175/jamc-d-15-0065.1.

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AbstractPrecipitation plays a major role in the energy and water cycles of the earth. Because of its variable nature, consistent observations of global precipitation are challenging. Satellite-based precipitation datasets present an alternative to in situ–based datasets in areas sparsely covered by ground stations. These datasets are a unique tool for model evaluations, but the value of satellite-based precipitation datasets depends on their application and scale. Numerous validation studies considered monthly or daily time scales, while less attention is given to subdaily scales. In this stud

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Chen, Jie, Ziyi Li, Lu Li, et al. "Evaluation of Multi-Satellite Precipitation Datasets and Their Error Propagation in Hydrological Modeling in a Monsoon-Prone Region." Remote Sensing 12, no. 21 (2020): 3550. http://dx.doi.org/10.3390/rs12213550.

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This study comprehensively evaluates eight satellite-based precipitation datasets in streamflow simulations on a monsoon-climate watershed in China. Two mutually independent datasets—one dense-gauge and one gauge-interpolated dataset—are used as references because commonly used gauge-interpolated datasets may be biased and unable to reflect the real performance of satellite-based precipitation due to sparse networks. The dense-gauge dataset includes a substantial number of gauges, which can better represent the spatial variability of precipitation. Eight satellite-based precipitation datasets

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Kolbe, Christine, Boris Thies, Sebastian Egli, Lukas Lehnert, Hans Schulz, and Jörg Bendix. "Precipitation Retrieval over the Tibetan Plateau from the Geostationary Orbit—Part 1: Precipitation Area Delineation with Elektro-L2 and Insat-3D." Remote Sensing 11, no. 19 (2019): 2302. http://dx.doi.org/10.3390/rs11192302.

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The lack of long term and well distributed precipitation observations on the Tibetan Plateau (TiP) with its complex terrain raises the need for other sources of precipitation data for this area. Satellite-based precipitation retrievals can fill those data gaps. Before precipitation rates can be retrieved from satellite imagery, the precipitating area needs to be classified properly. Here, we present a feasibility study of a precipitation area delineation scheme for the TiP based on multispectral data with data fusion from the geostationary orbit (GEO, Insat-3D and Elektro-L2) and a machine lea

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Usowicz, Bogusław, Jerzy Lipiec, Mateusz Łukowski, and Jan Słomiński. "Improvement of Spatial Interpolation of Precipitation Distribution Using Cokriging Incorporating Rain-Gauge and Satellite (SMOS) Soil Moisture Data." Remote Sensing 13, no. 5 (2021): 1039. http://dx.doi.org/10.3390/rs13051039.

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Precipitation data provide a crucial input for examining hydrological issues, including watershed management and mitigation of the effects of floods, drought, and landslides. However, they are collected frequently from the scarce and often insufficient network of ground-based rain-gauge stations to generate continuous precipitation maps. Recently, precipitation maps derived from satellite data have not been sufficiently linked to ground-based rain gauges and satellite-derived soil moisture to improve the assessment of precipitation distribution using spatial statistics. Kriging methods are use

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Lu, Sha, Marie-claire ten Veldhuis, Nick van de Giesen, Arnold Heemink, and Martin Verlaan. "Precipitation Regime Classification Based on Cloud-Top Temperature Time Series for Spatially-Varied Parameterization of Precipitation Models." Remote Sensing 12, no. 2 (2020): 289. http://dx.doi.org/10.3390/rs12020289.

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Satellite and reanalysis precipitation products perform poorly over regions with low-density ground observation networks. In order to improve space-dependent parameterization of precipitation estimation models in data-scarce environments, the delineation boundaries of precipitation regimes should be accurately identified. Existing approaches to characterize precipitation regimes by seasonal or other climatological properties do not account for small scale spatial-temporal variability. Precipitation time series can be used to account for this small-scale variability in regime classification. Un

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Yan, Yan, Guihua Wang, Nergui Nanding, and Weitian Chen. "Hydrological Evaluation of Satellite-Based Precipitation Products in Hunan Province." Remote Sensing 14, no. 13 (2022): 3127. http://dx.doi.org/10.3390/rs14133127.

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The quality of satellite-based precipitation products including TMPA 3B42, IMERG-early, IMERG-final, and CMORPH-CRT, is evaluated by comparing with gauge observations in Hunan province of China between 2017 and 2019. By using the outputs of the Dominant River Routing Integrated with VIC Environment (DRIVE) model, the hydrological applications of gauge- and satellite-based precipitation products are analyzed by comparing them with streamflow observations. Furthermore, we conduct a case study considering Typhoon Bailu. It is found that IMERG-final can produce better results compared to the other

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Joshi, Bharat Badayar, Munawar Ali, Dibit Aryal, Laxman Paneru, and Bhaskar Shrestha. "Spatial Pattern of Precipitation in GPM-Era Satellite Products against Rain Gauge Measurements over Nepal." Jalawaayu 1, no. 2 (2021): 39–56. http://dx.doi.org/10.3126/jalawaayu.v1i2.41009.

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Precipitation in a mountainous region is highly variable due to the complex terrain. Satellite-based precipitation estimates are potential alternatives to gauge measurements in these regions, as these typical measurements are not available or are scarce in high elevation areas. However, the accuracy of these gridded precipitation datasets need to be addressed before further usage. In this study, an evaluation of the spatial precipitation pattern in satellite-based precipitation products is provided, including satellite-only (Integrated Multi satellite Retrievals for GPM IMERG-UCORR and Global

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Fu, Tongxing, Zhixu Wu, Peng Hu, and Xin Zhang. "Fluctuation of Lower Ionosphere Associated with Energetic Electron Precipitations during a Substorm." Atmosphere 12, no. 5 (2021): 573. http://dx.doi.org/10.3390/atmos12050573.

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In this paper, using the combined observations of the NOAA 16, LANL-01A, IMAGE satellites, VLF radio wave, and ground-based riometers, we study the fluctuation of lower ionosphere-associated precipitating energetic electrons during a geomagnetic storm on 8 November 2004. Associated with the substorm dispersion injection observed by the LANL-01A satellite, the riometers observed obvious enhancements of ionospheric absorption within the electron isotropic zone, which they attributed to the tail current sheet scattering (TCS) mechanism. Through observations of the NOAA 16 satellite, we found a sh

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Wang, Jianxin, Walter A. Petersen, and David B. Wolff. "Validation of Satellite-Based Precipitation Products from TRMM to GPM." Remote Sensing 13, no. 9 (2021): 1745. http://dx.doi.org/10.3390/rs13091745.

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The global precipitation measurement mission (GPM) has been in operation for seven years and continues to provide a vast quantity of global precipitation data at finer temporospatial resolutions with improved accuracy and coverage. GPM’s signature algorithm, the integrated multisatellite retrievals for GPM (IMERG) is a next-generation of precipitation product expected for wide variety of research and operational applications. This study evaluates the latest version (V06B) of IMERG and its predecessor, the tropical rainfall measuring mission (TRMM) multisatellite precipitation (TMPA) 3B42 (V7)

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Zhang, Dan, Xiaomang Liu, Peng Bai, and Xiang-Hu Li. "Suitability of Satellite-Based Precipitation Products for Water Balance Simulations Using Multiple Observations in a Humid Catchment." Remote Sensing 11, no. 2 (2019): 151. http://dx.doi.org/10.3390/rs11020151.

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This study assesses the suitability of five popular satellite-based precipitation products in modeling water balance in a humid region of China during the period 1998–2012. The satellite-based precipitation products show similar spatial patterns with varying degrees of overestimation or underestimation, compared with the gauged precipitation. A distributed hydrological model is used to evaluate the suitability of satellite-based precipitation products in simulating streamflow, evapotranspiration and soil moisture. The simulations of streamflow and evapotranspiration forced by the MSWEP precipi

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Wang, Helong, Wenlong Chen, Zukang Hu, Yueping Xu, and Dingtao Shen. "Optimal Rain Gauge Network Design Aided by Multi-Source Satellite Precipitation Observation." Remote Sensing 14, no. 23 (2022): 6142. http://dx.doi.org/10.3390/rs14236142.

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Optimized rain gauge networks minimize their input and maintenance costs. Satellite precipitation observations are particularly susceptible to the effects of terrain elevation, vegetation, and other topographical factors, resulting in large deviations between satellite and ground-based precipitation data. Satellite precipitation observations are more inaccurate where the deviations change more drastically, indicating that rain gauge stations should be utilized at these locations. This study utilized satellite precipitation observation data to facilitate rain gauge network optimization. The dev

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Derin, Yagmur, and Koray K. Yilmaz. "Evaluation of Multiple Satellite-Based Precipitation Products over Complex Topography." Journal of Hydrometeorology 15, no. 4 (2014): 1498–516. http://dx.doi.org/10.1175/jhm-d-13-0191.1.

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Abstract This study evaluates the performance of four satellite-based precipitation (SBP) products over the western Black Sea region of Turkey, a region characterized by complex topography that exerts strong controls on the precipitation regime. The four SBP products include the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis version 7 experimental near-real-time product (TMPA-7RT) and post-real-time research-quality product (TMPA-7A), the Climate Prediction Center morphing technique (CMORPH), and the Multisensor Precipitation Estimate (MPE) of the European Org

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Petković, Veljko, Marko Orescanin, Pierre Kirstetter, Christian Kummerow, and Ralph Ferraro. "Enhancing PMW Satellite Precipitation Estimation: Detecting Convective Class." Journal of Atmospheric and Oceanic Technology 36, no. 12 (2019): 2349–63. http://dx.doi.org/10.1175/jtech-d-19-0008.1.

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AbstractA decades-long effort in observing precipitation from space has led to continuous improvements of satellite-derived passive microwave (PMW) large-scale precipitation products. However, due to a limited ability to relate observed radiometric signatures to precipitation type (convective and stratiform) and associated precipitation rate variability, PMW retrievals are prone to large systematic errors at instantaneous scales. The present study explores the use of deep learning approach in extracting the information content from PMW observation vectors to help identify precipitation types.

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Fu, Hanjia, Li Zhu, Vincent Nzabarinda, Xiaoyu Lv, and Hao Guo. "Error Characteristic Analysis of Satellite-Based Precipitation Products over Mainland China." Atmosphere 13, no. 8 (2022): 1211. http://dx.doi.org/10.3390/atmos13081211.

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Satellite-based precipitation products (SPPs) provide valuable precipitation information for various applications. Their performance, however, varies significantly from region to region due to various data sources and production processes. This paper aims to evaluate four selected SPPs (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Gauge-adjusted Global Satellite Mapping of Precipitation (GSMAP-gauge), and Global Precipitation Measurement (GPM)) over mainland C

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Ray, Ram L., Rajendra P. Sishodia, and Gebrekidan W. Tefera. "Evaluation of Gridded Precipitation Data for Hydrologic Modeling in North-Central Texas." Remote Sensing 14, no. 16 (2022): 3860. http://dx.doi.org/10.3390/rs14163860.

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Over the past few decades, several high-resolution gridded precipitation products have been developed using multiple data sources and techniques, including measured precipitation, numerical modeling, and remote sensing. Each has its own sets of uncertainties and limitations. Therefore, evaluating these datasets is critical in assessing their applicability in various climatic regions. We used ten precipitation datasets, including measured (in situ), gauge-based, and satellite-based products, to assess their relevance for hydrologic modeling at the Bosque River Basin in North-Central Texas. Eval

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Jia, Zhaoying, Shengpeng Yang, Jinglin Zhang, et al. "PRSOT: Precipitation Retrieval from Satellite Observations Based on Transformer." Atmosphere 13, no. 12 (2022): 2048. http://dx.doi.org/10.3390/atmos13122048.

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Precipitation with high spatial and temporal resolution can improve the defense capability of meteorological disasters and provide indispensable instruction and early warning for social public services, such as agriculture, forestry, and transportation. Therefore, a deep learning-based algorithm entitled precipitation retrieval from satellite observations based on Transformer (PRSOT) is proposed to fill the observation gap of ground rain gauges and weather radars in deserts, oceans, and other regions. In this algorithm, the multispectral infrared brightness temperatures from Himawari-8, the ne

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Katiraie-Boroujerdy, Pari-Sima, Nasrin Nasrollahi, Kuo-lin Hsu, and Soroosh Sorooshian. "Evaluation of satellite-based precipitation estimation over Iran." Journal of Arid Environments 97 (October 2013): 205–19. http://dx.doi.org/10.1016/j.jaridenv.2013.05.013.

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Oreggioni Weiberlen, Fiorella, and Julián Báez Benítez. "Assessment of satellite-based precipitation estimates over Paraguay." Acta Geophysica 66, no. 3 (2018): 369–79. http://dx.doi.org/10.1007/s11600-018-0146-x.

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Nwachukwu, Pius Nnamdi, Frederic Satge, Samira El Yacoubi, Sebastien Pinel, and Marie-Paule Bonnet. "From TRMM to GPM: How Reliable Are Satellite-Based Precipitation Data across Nigeria?" Remote Sensing 12, no. 23 (2020): 3964. http://dx.doi.org/10.3390/rs12233964.

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In this study, 16 satellite-based precipitation products (SPPs) comprising satellite, gauge and reanalysis datasets were assessed on a monthly time step using precipitation data from 11 gauge stations across Nigeria within the 2000–2012 period as reference. Despite the ability of some of the SPPs to reproduce the salient north–south pattern of the annual rainfall field, the Kling–Gupta efficiency (KGE) results revealed substantial discrepancies among the SPP estimates. Generally, the SPP reliability varies spatially and temporally, with all SPPs performing better over part of central Nigeria d

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Behrangi, Ali, Bin Guan, Paul J. Neiman, Mathias Schreier, and Bjorn Lambrigtsen. "On the Quantification of Atmospheric Rivers Precipitation from Space: Composite Assessments and Case Studies over the Eastern North Pacific Ocean and the Western United States." Journal of Hydrometeorology 17, no. 1 (2015): 369–82. http://dx.doi.org/10.1175/jhm-d-15-0061.1.

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Abstract Atmospheric rivers (ARs) are often associated with extreme precipitation, which can lead to flooding or alleviate droughts. A decade (2003–12) of landfalling ARs impacting the North American west coast (between 32.5° and 52.5°N) is collected to assess the skill of five commonly used satellite-based precipitation products [T3B42, T3B42 real-time (T3B42RT), CPC morphing technique (CMORPH), PERSIANN, and PERSIANN–Cloud Classification System (CCS)] in capturing ARs’ precipitation rate and pattern. AR detection was carried out using a database containing twice-daily satellite-based integra

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Dandridge, Chelsea, Venkat Lakshmi, John Bolten, and Raghavan Srinivasan. "Evaluation of Satellite-Based Rainfall Estimates in the Lower Mekong River Basin (Southeast Asia)." Remote Sensing 11, no. 22 (2019): 2709. http://dx.doi.org/10.3390/rs11222709.

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Satellite-based precipitation is an essential tool for regional water resource applications that requires frequent observations of meteorological forcing, particularly in areas that have sparse rain gauge networks. To fully realize the utility of remotely sensed precipitation products in watershed modeling and decision-making, a thorough evaluation of the accuracy of satellite-based rainfall and regional gauge network estimates is needed. In this study, Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42 v.7 and Climate Hazards Group InfraRed Precipita

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Lee, Y. R., D. B. Shin, J. H. Kim, and H. S. Park. "Precipitation estimation over radar gap areas based on satellite and adjacent radar observations." Atmospheric Measurement Techniques 8, no. 2 (2015): 719–28. http://dx.doi.org/10.5194/amt-8-719-2015.

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Abstract. Continuous rainfall measurements from ground-based radars are crucial for monitoring and forecasting heavy rainfall-related events such as floods and landslides. However, complete coverage by ground-based radars is often hampered by terrain blockage and beam-related errors. In this study, we presented a method to fill the radar gap using surrounding radar-estimated precipitation and observations from a geostationary satellite. The method first estimated the precipitation over radar gap areas using data from the Communication, Ocean, and Meteorological Satellite (COMS); the first geos

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Lee, Y. R., D. B. Shin, J. H. Kim, and H. S. Park. "Precipitation estimation over radar gap areas based on satellite and adjacent radar observations." Atmospheric Measurement Techniques Discussions 7, no. 6 (2014): 6299–325. http://dx.doi.org/10.5194/amtd-7-6299-2014.

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Abstract. Continuous rainfall measurements from ground-based radars are crucial for monitoring and forecasting heavy rainfall-related events such as floods and landslides. However, complete coverage by ground-based radars is often hampered by terrain blockage and beam-related errors. In this study, we presented a method to fill the radar gap using surrounding radar-estimated precipitation and observations from a geostationary satellite. The method first estimated the precipitation over radar gap areas using data from the Communication, Ocean, and Meteorological Satellite (COMS); the first geos

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Wang, Yibing, Xianhong Xie, Shanshan Meng, et al. "Magnitude Agreement, Occurrence Consistency, and Elevation Dependency of Satellite-Based Precipitation Products over the Tibetan Plateau." Remote Sensing 12, no. 11 (2020): 1750. http://dx.doi.org/10.3390/rs12111750.

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Satellite remote sensing is a practical technique to estimate global precipitation with adequate spatiotemporal resolution in ungauged regions. However, the performance of satellite-based precipitation products is variable and uncertain for the Tibetan Plateau (TP) because of its complex terrain and climate conditions. In this study, we evaluated the abilities of nine widely used satellite-based precipitation products over the Eastern Tibetan Plateau (ETP) and quantified precipitation dynamics over the entire TP. The evaluation was carried out from three aspects, i.e., magnitude agreement, occ

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Vallejos, Andrés G., Samuel A. Ancalle, Ana C. Escalera, and Oliver C. Saavedra. "ANALYZING AREAL PRECIPITATION AT KEY BASINS IN COCHABAMBA USING SATELLITE-BASED PRECIPITATION." INVESTIGACION & DESARROLLO 16, no. 1 (2016): 25–38. http://dx.doi.org/10.23881/idupbo.016.1-2i.

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Tian, Yudong, Christa D. Peters-Lidard, and John B. Eylander. "Real-Time Bias Reduction for Satellite-Based Precipitation Estimates." Journal of Hydrometeorology 11, no. 6 (2010): 1275–85. http://dx.doi.org/10.1175/2010jhm1246.1.

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Abstract A new approach to reduce biases in satellite-based estimates in real time is proposed and tested in this study. Currently satellite-based precipitation estimates exhibit considerable biases, and there have been many efforts to reduce these biases by merging surface gauge measurements with satellite-based estimates. Most of these efforts require timely availability of surface gauge measurements. The new proposed approach does not require gauge measurements in real time. Instead, the Bayesian logic is used to establish a statistical relationship between satellite estimates and gauge mea

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Lockhoff, M., O. Zolina, C. Simmer, and J. Schulz. "Representation of Precipitation Characteristics and Extremes in Regional Reanalyses and Satellite- and Gauge-Based Estimates over Western and Central Europe." Journal of Hydrometeorology 20, no. 6 (2019): 1123–45. http://dx.doi.org/10.1175/jhm-d-18-0200.1.

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Abstract This paper evaluates several daily precipitation products over western and central Europe, identifies and documents their respective strengths and shortcomings, and relates these to uncertainties associated with each of the products. We analyze one gauge-based, three satellite-based, and two reanalysis-based products using high-density rain gauge observations as reference. First, we assess spatial patterns and frequency distributions using aggregated statistics. Then, we determine the skill of precipitation event detection from these products with a focus on extremes, using temporally

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Zhu, Huiqin, Sheng Chen, Zhi Li, Liang Gao, and Xiaoyu Li. "Comparison of Satellite Precipitation Products: IMERG and GSMaP with Rain Gauge Observations in Northern China." Remote Sensing 14, no. 19 (2022): 4748. http://dx.doi.org/10.3390/rs14194748.

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Extreme precipitation events have increasingly happened at global and regional scales as the global climate has changed in recent decades. Accurate quantitative precipitation estimation (QPE) plays an important role in the warning of extreme precipitation events. With hourly rain gauge observations as a reference, this study compares the performance of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) quantitative precipitation estimation (QPE) products over Northern China in 2021. The Probabil

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Tobin, Kenneth J., and Marvin E. Bennett. "Adjusting Satellite Precipitation Data to Facilitate Hydrologic Modeling." Journal of Hydrometeorology 11, no. 4 (2010): 966–78. http://dx.doi.org/10.1175/2010jhm1206.1.

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Abstract Significant concern has been expressed regarding the ability of satellite-based precipitation products such as the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 products (version 6) and the U.S. National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center’s (CPC) morphing technique (CMORPH) to accurately capture rainfall values over land. Problems exist in terms of bias, false-alarm rate (FAR), and probability of detection (POD), which vary greatly worldwide and over

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Behifar, M., A. A. Kakroodi, M. Kiavarz, and F. Amiraslani. "COMBINATION OF METEOROLOGICAL INDICES AND SATELLITE DATA FOR DROUGHT MONITORING IN TWO DIFFERENT ENVIRONMENTS IN IRAN." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 197–200. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-197-2019.

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Abstract. The main problem using meteorological drought indices include inappropriate distribution of meteorological stations. Satellite data have reliable spatial and temporal resolution and provide valuable information used in many different applications. The Standardized precipitation index has several advantages. The SPI is based on rainfall data alone and has a variable time scale and is thus conducive to describing drought conditions for different application.This study aims to calculate SPI using satellite precipitation data and compare the results with traditional methods. To do this,

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Salles, Leandro, Frédéric Satgé, Henrique Roig, Tati Almeida, Diogo Olivetti, and Welber Ferreira. "Seasonal Effect on Spatial and Temporal Consistency of the New GPM-Based IMERG-v5 and GSMaP-v7 Satellite Precipitation Estimates in Brazil’s Central Plateau Region." Water 11, no. 4 (2019): 668. http://dx.doi.org/10.3390/w11040668.

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This study assesses the performance of the new Global Precipitation Measurement (GPM)-based satellite precipitation estimates (SPEs) datasets in the Brazilian Central Plateau and compares it with the previous Tropical Rainfall Measurement Mission (TRMM)-era datasets. To do so, the Integrated Multi-satellitE Retrievals for GPM (IMERG)-v5 and the Global Satellite Mapping of Precipitation (GSMaP)-v7 were evaluated at their original 0.1° spatial resolution and for a 0.25° grid for comparison with TRMM Multi-satellite Precipitation Analysis (TMPA). The assessment was made on an annual, monthly, and

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Zhang, Xinxuan, Emmanouil N. Anagnostou, and Humberto Vergara. "Hydrologic Evaluation of NWP-Adjusted CMORPH Estimates of Hurricane-Induced Precipitation in the Southern Appalachians." Journal of Hydrometeorology 17, no. 4 (2016): 1087–99. http://dx.doi.org/10.1175/jhm-d-15-0088.1.

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Abstract Satellite-retrieved precipitation has the potential to support flood modeling in mountainous areas. However, to reach this potential satellite estimates need to be corrected for the severe underestimation exhibited in orography-induced heavy precipitation events (HPEs). This paper assesses an existing satellite precipitation error correction technique driven by high-resolution numerical weather prediction (NWP) simulations of HPEs in complex terrain. The study is based on NOAA Climate Prediction Center morphing technique (CMORPH) high-resolution precipitation estimates of six such eve

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Tao, Yumeng, Kuolin Hsu, Alexander Ihler, Xiaogang Gao, and Soroosh Sorooshian. "A Two-Stage Deep Neural Network Framework for Precipitation Estimation from Bispectral Satellite Information." Journal of Hydrometeorology 19, no. 2 (2018): 393–408. http://dx.doi.org/10.1175/jhm-d-17-0077.1.

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Abstract Compared to ground precipitation measurements, satellite-based precipitation estimation products have the advantage of global coverage and high spatiotemporal resolutions. However, the accuracy of satellite-based precipitation products is still insufficient to serve many weather, climate, and hydrologic applications at high resolutions. In this paper, the authors develop a state-of-the-art deep learning framework for precipitation estimation using bispectral satellite information, infrared (IR), and water vapor (WV) channels. Specifically, a two-stage framework for precipitation estim

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Wei, Guanghua, Haishen Lü, Wade T. Crow, Yonghua Zhu, Jianbin Su, and Li Ren. "Comprehensive Evaluation and Error-Component Analysis of Four Satellite-Based Precipitation Estimates against Gauged Rainfall over Mainland China." Advances in Meteorology 2022 (May 10, 2022): 1–29. http://dx.doi.org/10.1155/2022/9070970.

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The Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) V06 product has been widely studied, but the errors and the source of the errors within IMERG over diverse climate regions still need to be quantified. To this end, the final run gauge-calibrated IMERG V06 (V06C) and uncalibrated IMERG V06 (V06UC) products are comprehensively evaluated here against 2088 precipitation gauges acquired between March 2014 and June 2018 over China. Moreover, V06C and V06UC rainfall estimates are compared against the Precipitation Estimation from Remotely Sensed Imagery using Arti

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Huang, Yang, Yansong Bao, George P. Petropoulos, Qifeng Lu, Yanfeng Huo, and Fu Wang. "Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest." Remote Sensing 16, no. 7 (2024): 1267. http://dx.doi.org/10.3390/rs16071267.

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Precipitation is the basic component of the Earth’s water cycle. Obtaining high-resolution and high-precision precipitation data is of great significance. This paper establishes a precipitation retrieval model based on a random forest classification and regression model during the day and at night with FY-4B/AGRI Level1 data on China from July to August 2022. To evaluate the retrieval effect of the model, the GPM IMERG product is used as a reference, and the retrieval results are compared against those of the FY-4B/AGRI operational precipitation product. In addition, the retrieval results are

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Tian, Yudong, Christa D. Peters-Lidard, Robert F. Adler, Takuji Kubota, and Tomoo Ushio. "Evaluation of GSMaP Precipitation Estimates over the Contiguous United States." Journal of Hydrometeorology 11, no. 2 (2010): 566–74. http://dx.doi.org/10.1175/2009jhm1190.1.

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Abstract Precipitation estimates from the Global Satellite Mapping of Precipitation (GSMaP) project are evaluated over the contiguous United States (CONUS) for the period of 2005–06. GSMaP combines precipitation retrievals from the Tropical Rainfall Measuring Mission satellite and other polar-orbiting satellites, and interpolates them with cloud motion vectors derived from infrared images from geostationary satellites, to produce a high-resolution dataset. Four other satellite-based datasets are also evaluated concurrently with GSMaP, to provide a better perspective. The new Climate Prediction

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Ryo, Masahiro, Oliver C. Saavedra Valeriano, Shinjiro Kanae, and Tinh Dang Ngoc. "Temporal Downscaling of Daily Gauged Precipitation by Application of a Satellite Product for Flood Simulation in a Poorly Gauged Basin and Its Evaluation with Multiple Regression Analysis." Journal of Hydrometeorology 15, no. 2 (2014): 563–80. http://dx.doi.org/10.1175/jhm-d-13-052.1.

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Abstract The study demonstrates that the temporal downscaling of rain gauge–measured precipitation with satellite-based precipitation estimates enhances the accuracy of hydrological simulations, especially for flood duration. Multiple regression analysis was examined to predict which hydrometeorological parameters have a significant influence on accuracy. The approach was examined at the Hương River basin in Vietnam (1520 km2), which is a mountainous region subject to heavy rainfall. The multisensor algorithm Global Satellite Mapping of Precipitation, version Moving Vector with Kalman (MVK; GS

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Michaelides, Silas. "Editorial for Special Issue “Remote Sensing of Precipitation: Part III”." Remote Sensing 15, no. 12 (2023): 2964. http://dx.doi.org/10.3390/rs15122964.

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This Special Issue of Remote Sensing, which is the third in a series entitled “Remote Sensing of Precipitation”, comprises a collection of ten papers devoted to remote sensing applications for measuring precipitation; these include new satellite technologies for the remote sensing of precipitation, the validation of satellite-based precipitation estimates using rain gauge measurements and surface radar estimates, and comparisons between gridded precipitation data [...]

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