N: 90 S: -90 E: 180 W: -180
Description
Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.
The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.
The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1°x0.1° (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean to correct known biases.
The half-hourly intercalibrated merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian time interpolation scheme based on work by the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate (PDIR) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the KF morphing (quasi-Lagrangian time interpolation) scheme.
The KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. Motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours within each of the precipitation (PRECTOT), total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data fields provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, else from TQL, if available, else from TQV. The KF uses the morphed data as the “forecast” and the IR estimates as the “observations”, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about ±90 minutes away from the overpass time. Variable averaging in the KF is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of KF morphed precipitation to the local histogram of forward- and backward-morphed microwave data and the IR.
The IMERG system is run twice in near-real time:
"Early" multi-satellite product ~4 hr after observation time using only forward morphing and
"Late" multi-satellite product ~14 hr after observation time, using both forward and backward morphing
and once after the monthly gauge analysis is received:
"Final", satellite-gauge product ~4 months after the observation month, using both forward and backward morphing and including monthly gauge analyses.
In V07, the near-real-time Early and Late half-hourly estimates have a monthly climatological concluding calibration based on averaging the concluding calibrations computed in the Final, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitation, is the data field of choice for most users.
Precipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure.
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Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Ojos del Salado: how active is this sleeping giant? | Murray-Bergquist, L, Thorwart, M, Garcia, A, Ulloa, C, Van Ginkel, J, Van Huisstede, L I, Beniest, A | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Snow or rain? hybrid AI deciphers surface precipitation phase from satellite observations | Yang, Chunlei, Li, Haoran, Zhu, Runzhe, Wang, Yan, Zhang, Feng, Gu, Mingjian, Jiang, Geng-Ming, Zhang, Renhe, Tang, Xu | Atmospheric Water Vapor, Precipitation, RADAR, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessing Debris-Flow Susceptibility at Local and Global Scales: A | Nienkotter, Andreas, Bian, Ang, Di, Baofeng, Li, Jierui, Deng, Tian | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Vegetation Water Content, Soil Moisture/Water Content, Skin Temperature, Terrain Elevation, Digital Elevation/Terrain Model (DEM), Topographical Relief Maps, Plant Phenology, Vegetation Index, Plant Phenological Changes, Enhanced Vegetation Index (EVI) | |
| Assessing future heat wave patterns in India: insights from a high-resolution regional climate model | Jayasankar, C. B., Misra, Vasubandhu, Hopp, Jacob | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Accuracy Assessment of CMORPH and GPCP Satellite Precipitation Products Across Iran | Yousefnezhad, Mohammad Ramyar, Farajzadeh, Manuchehr, Rahimi, Yousef Ghavidel | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Integrating machine learning and physically based hydrodynamic modeling for flood hazard mapping: a case study of the Takkalasi watershed, Indonesia | Soma, Andang Suryana, Nursaputra, Munajat, Rahmat, Syaeful, Rasyid, Abdul Rachman, A, Chairil, Sakamoto, Jun | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Mapping Paddy Rice Cropping Intensity and Planting Dates in Monsoon Asia at 20 m Resolution during 20182021 from Multi-source Satellite Data | Chen, Yongzhe, Liang, Shunlin, Liu, Jia, Ma, Han, Li, Wenyuan, Sucharitakul, Phuping, Luo, Ning, Chen, Zhongxin, Fang, Husheng, Zhang, Fengjiao, Xu, Jianglei | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Marine heatwave in the Bohai-Yellow Seas enhanced the July 2025 rainstorm in Beijing and its surrounding areas | Yang, Mengzhou, Zhao, Ning, Wang, Shuya, Li, Shixue, Zhou, Xiaohui, Qiao, Yu-Xiang, Lu, Er | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Sea Ice Concentration, Sea Surface Temperature, Sea Ice Concentration, Sea Surface Temperature | |
| Model-Based Spatial Data Fusion | Gelfand, Alan E., Schliep, Erin M. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Improvements in tropical cyclone forecasting using CCAM compared with the operational Unified Model: A case study of tropical cyclone Idai | Makgati, Lebogang N., Bopape, Mary-Jane M., Phaduli, Elelwani, Rambuwani, Gift, Maisha, Robert, Reason, Christopher J. C. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impacts of Climatic Phenomena and Terrain on December 2021 Extreme Rainfall over Peninsular Malaysia | Chen, Yixiao, Chan, Andy, Li, Li, Ooi, Maggie Chel Gee, Diong, Jeong Yik, Wong, Soon Yee, Teo, Fang Yenn | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Developing a Database of Hydrological, Meteorological, and Physiographic Characteristics for River Catchments of the Russian Federation | Abramov, D. V., Kurochkina, L. S., Moreido, V. M. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessing Uncertainty in Multi-Source Precipitation for a Semi-Arid Mediterranean Catchment Using SWAT | Gharnouki, Ines, Benabdallah, Sihem, Aouissi, Jalel, Ghosh, Sudoy Kumer, Das, Anjon | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Biases in Southern Ocean Precipitation From Shallow Convection: The Role of Cloud Morphology | Alinejadtabrizi, T., Huang, Y., Lang, F., Sreenath, A. V., Poulsen, C., Siems, S., May, P. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Enhanced atmospheric water cycle and intensified future change over the Tibetan Plateau in convection-permitting regional climate simulations | Zou, Liwei, Jin, Guoqi, Zhou, Tianjun, Zhao, Yin | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation of a Novel Climate-Driven SIR Model for Cholera Prediction | Magers, Bailey, Brumfield, Kyle D., Colwell, Rita R., Jutla, Antarpreet S. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Future Changes in Power Grid Exposure to Urban Flooding Over Eastern Coastal China | Luo, Na, Lu, Zhenghui, Ren, Xinran, Wu, Xinying, Wu, Wentao, Duan, Ruixin | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Future Changes to Rainfall Extremes Over Puerto Rico in a Convection-Permitting Model | Dougherty, E. M., Prein, A. F., OGorman, P. A. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evolution mechanism of the flash flood-debris flow disaster chain triggered by high-elevation shallow landslides: a case study of the Huangya Gully event in Yuzhong ... | Wang, Yukun, Liu, Xingrong, Xiao, Ziyang, Wang, Yu, Ma, Yanjie, Huang, Jinyan, An, Yapeng, Li, Boyu | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Diagnosing cloudbursts over Indian megacities near land-sea boundary | Rakshit, Gargi, Paul, Debjit, Jana, Soumyajyoti, Dubey, Sarvesh, Pattanaik, Dushmanta Ranjan, Mukhopadhyay, Parthasarathi | Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| SM2RAINdual: a global rainfall fusion product derived from multi-source satellite soil moisture observations | Miao, Linguang, Wei, Zushuai, Meng, Lingkui, Li, Linyi, Zhang, Wen, Wang, Xi, Wang, Hui, Wang, Zhe, Zhang, Zhen, Brocca, Luca | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Roles of Surface Latent Heat Flux and Gravity Waves in Offshore MCS Development in the Coastal Eastern Tropical Pacific | Hu, Jingyi, Chen, Xingchao, Peng, ChinHsuan, Leung, L. Ruby | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Validation of Satellite-based and Gridded Precipitation Products for Gap-filling in Precipitation Series in the Eastern Amazon | de Souza, Giselle Nerino Brito, de Franca e Silva, Julie Andrews, Ribeiro, Kaleb Lima, de Oliveira, Leonardo Ramos, da Silva, Paulo Ricardo Teixeira, Castellani, Debora Cristina, de Sousa Bueno Filho, Julio Silvio, Santiago, Alailson Venceslau, Vasconcelos, Steel Silva, Teixeira, Wenceslau Geraldes, de Araujo, Alessandro Carioca | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Extending dam-system efficiency in arid regions via sediment-connectivity modeling and multi-objective optimization | Pal, Debasish, Dash, Sonam S., Ivanovic, Nikola, Galelli, Stefano, Marttila, Hannu, Beck, Hylke E. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Global climate modeling with improved precipitation characteristics by | Wang, Yiming, Zhang, Yi, Han, Yilun, Xue, Wei, Chen, Tianru, Zhou, Yihui, Li, Xiaohan, Chen, Haishan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain |
Variables
The table below lists the variables contained within a single granule for this dataset. Variables often contain observed or derived geophysical measurements collected from a variety of sources, including remote sensing instruments on satellite and airborne platforms, field campaigns, in situ measurements, and model outputs. The terms variable, parameter, scientific data set, layer, and band have been used across NASA’s Earth science disciplines; however, variable is the designated nomenclature in NASA’s Common Metadata Repository (CMR). Variable metadata attributes such as Name, Description, Units, Data Type, Fill Value, Valid Range, and Scale Factor allow users to efficiently process and analyze the data. The full range of attributes may not be applicable to all variables. Additional information on variable attributes is typically available in the data, user guide, and/or other product documentation.
For questions on a specific variable, please use the Earthdata Forum.
| Name Sort descending | Description | Units | Data Type | Fill Value | Valid Range | Scale Factor | Offset |
|---|---|---|---|---|---|---|---|
| Grid/Intermediate/IRinfluence | Grid/Intermediate/IRinfluence | N/A | int16 | -9999 | N/A | N/A | N/A |
| Grid/Intermediate/IRprecipitation | Grid/Intermediate/IRprecipitation | mm/hr | float32 | -9999.900390625 | N/A | N/A | N/A |
| Grid/Intermediate/MWobservationTime | Grid/Intermediate/MWobservationTime | minutes | int16 | -9999 | N/A | N/A | N/A |
| Grid/Intermediate/MWprecipitation | Grid/Intermediate/MWprecipitation | mm/hr | float32 | -9999.900390625 | N/A | N/A | N/A |
| Grid/Intermediate/MWprecipSource | Grid/Intermediate/MWprecipSource | N/A | int16 | -9999 | N/A | N/A | N/A |
| Grid/Intermediate/precipitationUncal | Grid/Intermediate/precipitationUncal | mm/hr | float32 | -9999.900390625 | N/A | N/A | N/A |
| Grid/lat | Grid/lat | degrees_north | float32 | N/A | N/A | N/A | N/A |
| Grid/lat_bnds | Grid/lat_bnds | degrees_north | float32 | N/A | N/A | N/A | N/A |
| Grid/lon | Grid/lon | degrees_east | float32 | N/A | N/A | N/A | N/A |
| Grid/lon_bnds | Grid/lon_bnds | degrees_east | float32 | N/A | N/A | N/A | N/A |
| Grid/precipitation | Grid/precipitation | mm/hr | float32 | -9999.900390625 | N/A | N/A | N/A |
| Grid/precipitationQualityIndex | Grid/precipitationQualityIndex | N/A | float32 | -9999.900390625 | N/A | N/A | N/A |
| Grid/probabilityLiquidPrecipitation | Grid/probabilityLiquidPrecipitation | percent | int16 | -9999 | N/A | N/A | N/A |
| Grid/randomError | Grid/randomError | mm/hr | float32 | -9999.900390625 | N/A | N/A | N/A |
| Grid/time | Grid/time | seconds since 1980-01-06 00:00:00 UTC | int32 | N/A | N/A | N/A | N/A |
| Grid/time_bnds | Grid/time_bnds | seconds since 1980-01-06 00:00:00 UTC | int32 | N/A | N/A | N/A | N/A |