GSMaP Hourly dataset is obtained from the Dual-frequency Precipitation Radar (DPR) sensor onboard Global Precipitation Measurement (GPM), other GPM constellation satellites, and Geostationary satellites produced by the Japan Aerospace Exploration Agency (JAXA). The GSMaP is generated based on a multi-satellite algorithm under the GPM mission, and the accuracy has been improved by DPR data and information. It offers a map of global precipitation by combining: estimated precipitation based on multiple microwave radiometers (imager/sounder) and cloud moving information obtained from geostationary infrared (IR) data. The GSMaP algorithm can be roughly divided into the following three algorithms: microwave imager (MWI) algorithm, microwave sounder (MWS) algorithm, and microwave-Infrared (IR) combined (MVK) algorithm. A global satellite mapping of precipitation can be subject to standard processing or near real-time processing. In standard processing, hourly observation data is processed then the data is averaged monthly. Near real-time processing provides a higher data frequency than standard processing (every hour). The provided formats are HDF5, text, GeoTIFF and NetCDF. The Sampling resolution is 0.1 degree grid. The projection method is EQR. The statistical period is 1 hourly. The current version of the product is Version 5. The Version 4 is also available. The generation unit is global.

Global Satellite Mapping of Precipitation (GSMaP) provides a global hourly rain rate with a 0.1 x 0.1 degree resolution. GSMaP is a product of the Global Precipitation Measurement (GPM) mission, which provides global precipitation observations at three hour intervals. Values are estimated using multi-band passive microwave and infrared radiometers from the GPM Core Observatory satellite and with the assistance of a constellation of other satellites. GPM's precipitation rate retrieval algorithm is based on a radiative transfer model. The gauge-adjusted rate is calculated based on the optimization of the 24h accumulation of GSMaP hourly rain rate to daily precipitation by NOAA/CPC gauge measurement. This dataset is processed by GSMaP algorithm version 6 (product version 3). See GSMaP Technical Documentation for more details. This dataset contains provisional products GSMaP_NRT that are regularly replaced with updated versions when the GSMaP_MVK data become available. The products are marked with a metadata property called ''status''. When a product is initially made available, the property value is ''provisional''. Once a provisional product has been updated with the final version, this value is updated to ''permanent''. For more information please refer General Documentation

GSMaP Monthly dataset is obtained from the Dual-frequency Precipitation Radar (DPR) sensor onboard Global Precipitation Measurement (GPM), other GPM constellation satellites, and Geostationary satellites produced by the Japan Aerospace Exploration Agency (JAXA). The GSMaP is generated based on a multi-satellite algorithm under the GPM mission, and the accuracy has been improved by DPR data and information. It offers a map of global precipitation by combining: estimated precipitation based on multiple microwave radiometers (imager/sounder) and cloud moving information obtained from geostationary infrared (IR) data.The GSMaP algorithm can be roughly divided into the following three algorithms: microwave imager (MWI) algorithm, microwave sounder (MWS) algorithm, and microwave-Infrared (IR) combined (MVK) algorithm. A global satellite mapping of precipitation can be subject to standard processing or near real-time processing. In standard processing, hourly observation data is processed and data is averaged monthly. Near real-time processing provides a higher data frequency than standard processing (every hour).The provided format is HDF5, GeoTIFF and NetCDF. The Sampling resolution is 0.1degree grid. The projection method is EQR. The statistical period is 1 monthly. The current version of the product is Version 5. The Version 4 is also available. The generation unit is global.

The GPM Ground Validation Global Satellite Mapping of Precipitation (GSMaP) IFloodS dataset consists of rainfall rate estimates from the GSMaP project. The GSMaP global rain rate maps are derived by a collection of algorithms that utilize microwave (MW) radiometer data and geostationary Infrared (IR) data. The GSMaP Precipitation data product is provided on a 0.1 degree spatial resolution every hour and was made available for use during the Global Precipitation Measurement (GPM) Ground Validation Iowa Flood Studies (IFloodS) field campaign. These data are available in netCDF-4 and binary formats from April 22, 2013 through June 30, 2013. The near real-time GSMaP data can be obtained from the JAXA GSMaP web page.

This NetCDF4 dataset contains gridded rainfall estimates created from a blend of Global Satellite Mapping of Precipitation (GSMaP) satellite rainfall and Australian Gridded Climate Dataset (AGCD) rain gauge analysis data. The blending process consisted of a two-step method. The first step involved correcting the data through the use of multiplicative ratio grids. For each month, the ratio of the satellite data to the rain gauge data was found at each station. These ratios were then converted into a grid using Ordinary Kriging. The ratio grid was then applied onto the original GSMaP data to form the corrected GSMaP data. The second step involved blending the corrected GSMaP data and AGCD data. The blend is formed from the weighted average of the two datasets using weights derived from their error variances. The weights were inversely proportional to the error variances of the respective datasets. The error variances were calculated on a seasonal basis using the Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset as truth. The weighted average is the final blended product. The temporal coverage of the data spans a total of 20 years from January 2001 to December 2020, on a monthly basis. The spatial domain of the data is a rectangular domain centred over Australia. The latitude ranges from 108 to 156 degrees east while the longitude ranges from -45 to -9 degrees north. The resolution is 0.1 degrees. The data was created in an attempt to provide better representation of rainfall away from rain gauges whilst retaining strong correlations to rain gauges where they exist. The algorithm described earlier was performed using Python 3. This is version 1 of the data. Refinements are planned in the future.

This dataset is a processed data in TIFF (.tiff) files, that used in our study. We used the SPI to determine meteorological drought conditions in the study area, that calculated by using the open-source module Climate and Drought Indices in Python.

China_Flood_Data_2012~2024.

Today's Earth (TE) is JAXA's terrestrial hydrological simulation system developed in collaboration with University of Tokyo. The system derives land surface states/fluxes and river conditions from numerical simulation based on satellite observation and atmospheric reanalysis and/or forecast. The system consists of the land surface model MATSIRO (Minimal Advanced Treatments of Surface Interaction and Runoff, Takata et al., 2003) and river routing model CaMa-Flood (Catchment-based Macro-scale Floodplain, Yamazaki et al., 2011).

Today's Earth - Global (TE-Global) is a deterministic simulation product with three different versions for global coverage. JRA-55 ver. is a product that utilizes the Japan Meteorological Agency (JMA) JRA-55- the Japanese 55-year Reanalysis dataset as input for the TE-Global simulation. Among the JRA-55 variables, the version that uses GSMaP (Global Satellite Mapping of Precipitation) for rainfall is called the GSMaP ver. Similarly, the version that uses MODIS (Terra/Aqua MODIS Global Solar Radiation Product) for downward shortwave radiation is called the MODIS ver. For the validation result of the product, please refer to Ma et al., 2024.

Version, Release date (Temporal coverage)

JRA-55 ver., 2018-03-31 (1958 ~ 2023) GSMaP ver., 2018-03-31 (2001 ~ 2023) MODIR ver., 2018-03-31 (2003 ~ 2023)

People have used rainwater harvesting (RWH) technology for generations to a considerable extent in semi-arid and arid regions. In addition to meeting domestic needs, this technology can be utilized for agricultural purposes as well as soil and water conservation measures. Modeling the identification of the appropriate pond’s location therefore becomes crucial. This study employs a Geo Information System (GIS) based multi-criteria analysis (MCA) approach and satellite rainfall data, Global Satellite Mapping of Precipitation (GSMaP) to determine the suitable locations for the ponds in a semi-arid area of Indonesia, Liliba watershed, Timor. The criteria for determining the location of the reservoir refer to the FAO and Indonesia’s small ponds guideline. The watershed’s biophysical characteristics and the socioeconomic situation were taken into consideration when selecting the site. According our statistical analysis, the correlation coefficient results of satellite daily precipitation were weak and moderate, but the results were strong and extremely strong for longer time scales (monthly). Our analysis shows that about 13% of the entire stream system is not suitable for ponds, whereas areas that are both good suitability and excellent suitability for ponds make up 24% and 3% of the total stream system. 61% of the locations are partially suited. The results are then verified against simple field observations. Our analysis suggests that there are 13 locations suitable for pond construction. The combination of geospatial data, GIS, a multi-criteria analysis, and a field survey proved effective for the RWH site selection in a semi-arid region with limited data, especially on the first and second order streams.

This dataset is a raw data in NetCDF (.nc) files, that used in our study.

This dataset is a raw data in TIFF (.tiff) files, that used in our study.

GSMap provides hourly global rainfall maps (Rainfall rate (mm/hr)) in near real time (about four hours after observation) using the combined MW-IR algorithm with TRMM TMI, Aqua AMSR-E, DMSP SSM/I and GEO IR data.

IntroductionIn complex mountain basins, hydrological forecasting poses a formidable challenge due to the intricacies of runoff generation processes and the limitations of available data. This study explores the enhancement of short-term runoff forecasting models through the utilization of long short-term memory (LSTM) networks.MethodsTo achieve this, we employed feature engineering (FE) strategies, focusing on geographic data and the Soil Conservation Service Curve Number (SCS-CN) method. Our investigation was conducted in a 3,390 km2 basin, employing the GSMaP-NRT satellite precipitation product (SPP) to develop forecasting models with lead times of 1, 6, and 11 h. These lead times were selected to address the needs of near-real-time forecasting, flash flood prediction, and basin concentration time assessment, respectively.Results and discussionOur findings demonstrate an improvement in the efficiency of LSTM forecasting models across all lead times, as indicated by Nash-Sutcliffe efficiency values of 0.93 (1 h), 0.77 (6 h), and 0.67 (11 h). Notably, these results are on par with studies relying on ground-based precipitation data. This methodology not only showcases the potential for advanced data-driven runoff models but also underscores the importance of incorporating available geographic information into precipitation-ungauged hydrological systems. The insights derived from this study offer valuable tools for hydrologists and researchers seeking to enhance the accuracy of hydrological forecasting in complex mountain basins.

The rainfall data set of the southern Qinghai Tibet Plateau is fused by the satellite and the ground station. The data is in ASCII format, with a temporal resolution of 1 day and a horizontal spatial resolution of 0.1 °， The time coverage is from June 10 to October 31 in 2014-2019, which can provide driving data for rainfall verification and hydrological simulation in the southern Tibetan Plateau. The data set is based on the rainfall data of China Meteorological Administration and Hydrological Bureau of the Ministry of water resources after strict quality control , which is the highest density ground station network in the region so far. Dynamic Bayesian Model Average method is used to merge satellite precipitation products, i.e., GPM-IMERG, GSMaP, and CMORPH, based on the likelihood measurements of a high-density rainfall gauge network. The statistical accuracy evaluation and hydrological simulation verification of the merged data preforms better than the source satellite data, and also better than the popular reanalysis data CHIRPS and MSWEP.

The geologic map was created in GSMAP at Socorro, New Mexico by Orin Anderson and Glen Jones and published as the Geologic Map of New Mexico 1:500,000 in GSMAP format in 1994. This graphic file was converted to ARC/INFO format by Greb Green and GlenJones and released as the Geologic Map of New Mexico in ARC/INFO format in 1997. This shapefile only shows the volcanic vents for New Mexico, that are used on the Digital Geologic Map for New Mexico.

Dataset for hydrologic modelling using SWMM

• Historical SWMM Model and Results
  This simulation using 10-years historical data from GSMaP. This dataset includes rainfall data (.DAT), SWMM model (.inp and .ini), simulation result (.xlsx), and map background (.jpg)
• Future SWMM Model and Results
  This simulation using CIMP6's IPSL-CM6A-LR rainfall projections. This dataset includes rainfall data (.DAT), SWMM model (.inp and .ini), simulation result (.txt), and map background (.jpg)

Dataset for Multi-Task Learning for "Simultaneous Retrievals of Passive Microwave Precipitation Estimates and Rain/No-Rain Classification".

Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) and Goddard Profiling Algorithm (GPROF) were provided from NASA Global Precipitation Measurement Precipitation Data Directory (https://gpm.nasa.gov/data/directory). The original data used for this study have been supplied by JAXA’s GSMaP.

The source code for preprocessing and model training is available at https://doi.org/10.5281/zenodo.7627112

Annual and monthly precipitation data from the 92 stations and the 6 products considered (CHIRPS, IMERG, TERRACLIMATE, NASAPOWER, GSMaP, and ERA5) in the study titled "Evaluating rainfall estimations of six gridded products across space and time."

Important Notice: These old dataset versions (V2.0.x) have been superseded. A new version, CHM_PRE V2.1, is now available and is recommended for all users (https://doi.org/10.5281/zenodo.14632156). The updated version (V2.1) extends the data coverage to 2024 and incorporates adjusted precipitation values for the southern foothills of the Himalayas.

The CHM_PRE V2 dataset is a new high-precision, long-term, daily gridded precipitation dataset for mainland China.

The long-term daily observation from 3,476 gauges and incorporated 11 related precipitation variables were utilized to characterize the correlations of precipitation. Then, the dataset was developed by employing an improved inverse distance weighting method combined with the machine learning-based light gradient boosting machine (LGBM) algorithm.

CHM_PRE V2 demonstrates strong spatiotemporal consistency with existing gridded precipitation datasets, including CHM_PRE V1, GSMaP, IMERG, PERSIANN-CDR, and GLDAS. Validation against 63,397 high-density gauges confirms its high accuracy in both precipitation values and events. The dataset achieves a mean absolute error of 1.48 mm/day and a Kling-Gupta efficiency coefficient of 0.88. In terms of event detection capability, CHM_PRE V2 achieves a Heidke skill score of 0.68 and a false alarm ratio of 0.24. Overall, CHM_PRE V2 significantly enhances precipitation measurement accuracy and reduces the overestimation of precipitation events, providing a reliable foundation for hydrological modeling and climate assessments.

The CHM_PRE V2 dataset provides daily precipitation data with a resolution of 0.1°, covering the entire mainland China (18°N–54°N, 72°E–136°E). This dataset covers the period of 1960–2023, and will be continuously updated annually. The daily precipitation data is provided in NetCDF format, and for the convenience of users, we also offer annual and monthly total precipitation data in both NetCDF and GeoTIFF formats.

References:
1. Hu, J., Miao, C., Su, J., Zhang, Q., Gou, J., and Sun, Q.: A new upgraded high-precision gridded precipitation dataset considering spatiotemporal and physical correlations for mainland China, Earth System Science Data Discussions. [preprint], https://doi.org/10.5194/essd-2025-20, in review, 2025

2. Zhang, Q., Miao, C., Su, J., Gou, J., Hu, J., Zhao, X., & Xu, Y. (2025). A new high-resolution multi-drought-index dataset for mainland China. Earth System Science Data, 17(3), 837–853. https://doi.org/10.5194/essd-17-837-2025

3. Han, J., Miao, C., Gou, J., Zheng, H., Zhang, Q., & Guo, X. (2023). A new daily gridded precipitation dataset for the Chinese mainland based on gauge observations. Earth System Science Data, 15(7), 3147–3161. https://doi.org/10.5194/essd-15-3147-2023

The geologic map was created in GSMAP at Socorro, New Mexico by Orin Anderson and Glen Jones and published as the Geologic Map of New Mexico 1:500,000 in GSMAP format in 1994. This graphic file was converted to ARC/INFO format by Greb Green and GlenJones and released as the Geologic Map of New Mexico in ARC/INFO format in 1997.