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 Older versions will no longer be available and have been superseded by Version 07.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.
Briefly describing the Final Run, the input precipitation estimates computed from the various satellite passive microwave sensors are intercalibrated to the CORRA product (because it is presumed to be the best snapshot TRMM/GPM estimate after adjustment to the monthly GPCP SG), then "forward/backward morphed" and combined with microwave precipitation-calibrated geo-IR fields, and adjusted with seasonal GPCP SG surface precipitation data to provide half-hourly and monthly precipitation estimates on a 0.1°x0.1° (roughly 10x10 km) grid over the globe. Precipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure. The current period of record is June 2000 to the present (delayed by about 4 months).
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Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Tropical cyclone rainfall extends inland | Deng, E, Xiang, Qian, Ouyang, De-Hui, Chan, Kelvin T. F., He, Dengxin, Lin, Ning, Chan, Johnny C. L., Tu, Shifei, Chan, Pak-Wai, Liu, Zhizhao, Li, Guo-Zhi, Zhou, Shang-Qi, Dong, Yue, Ni, Yi-Qing | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Disentangling key cloud properties for precipitation retrievals from geostationary satellite data using machine learning | Choi, Hwayon, Choi, Yong-Sang, Ho, Chang-Hoi, Kim, Jinwon | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| An observational study of the modulation of the diurnal variations by the intraseasonal oscillations of the Indian summer monsoon | Misra, Vasubandhu, Jayasankar, C. B. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Performance Evaluation of a Distributed Hydrological Model Using Satellite Data over the Lake Kastoria Catchment, Greece | Papadimos, Dimitris, Papamichail, Dimitris | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| MaddenJulian Oscillation and atmospheric rivers: New insights on water source and transport for extreme rainfall over the Western US | Small, Chad A., Chen, Shuyi S., Kerns, Brandon W. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Investigating the Dharali disaster in Uttarakhand, India on August 5, 2025: perspectives from multiple Earth observation datasets | Chowdhury, Arnab, Kundal, Sahil, Behera, Indrakant, Kumar, Krishan, Sharma, Pavan, Bhardwaj, Alok | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Recent oceanic performance of GPM multisatellite precipitation estimates | Wang, Yiding, Yong, Bin, Qi, Weiqing, Wen, Yixin | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Liquid Precipitation, Wind Speed | |
| Global Teleconnections of Extreme Rainfall Events in the Yellow River Basin | Cai, Lin, Yuan, Naiming, Boers, Niklas, Kurths, Juergen | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Improvement of Google Earth Engine-Based Multi-satellite Rainfall Estimation using Rain Gauge Data in South Sulawesi | Ismail, Prayoga, Jatmiko, Retnadi Heru, Farda, Nur Mohammad, Munandar, Muhammad Arif | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Coupled tropospheric and stratospheric dynamics of Kelvin waves over | Szkolka, Wojciech, Baranowski, Dariusz B., Flatau, Maria K., Flatau, Piotr J., Marzuki, Shimomai, Toyoshi, Hashiguchi, Hiroyuki | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Convection embedded in an atmospheric river: exploring precipitation sensitivity to convective parameterizations | Luna-Nino, Rosa, Naud, Catherine M., Gershunov, Alexander, Crespo, Juan A., Posselt, Derek J., Delle Monache, Luca | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Copula-based joint distributions of rain and wind for leading edge | Visbech, Jens, Hasager, Charlotte Bay, Gocmen, Tuhfe, Rethore, Pierre-Elouan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Defining the Agricultural Wet Season in Africa Using Soil Moisture From | Chalmers, Christopher, Zhang, Yan, Xiao, Jingfeng, Li, Xing, Rigden, Angela | Brightness Temperature, Surface Soil Moisture, Soil Classification, Soil Depth, Soil Porosity, Soil Texture, Terrain Elevation, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluating multi-source precipitation datasets for hydrological | Li, Huijie, Chen, Jie, Li, Lu | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluating Precipitation Enhancement Associated with Convective Mergers in the Global Storm-resolving Models of DYAMOND Summer Phase | Tseng, Shao-Yu, Chen, Wei-Ting, Wu, Chien-Ming | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluating Seasonal Forecast Models for Cambodia's Northern Tonle Sap Basin | Brigadier, Libanda, Leak, Ngeang, Hak, Lim, Sokhom, Khoeun, Nrak, Lonh, Ilan, Ich, Rattana, Chinn | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluating Vegetation Greening and Browning across the Rio Grande Basin | Talchabhadel, Rocky, Rhodes, Edward C., Palmate, Santosh S., Kumar, Saurav | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Reflectance, Maximum/Minimum Temperature, 24 Hour Precipitation Amount, Snow Water Equivalent, Shortwave Radiation, Vapor Pressure | |
| Evaluating Environmental Moisture Relative to Tropical Deep Convective | Martinez, Giselle, Rowe, Angela K., Nunez Ocasio, Kelly M., Moon, Zachary L., Rodenkirch, Benjamin D. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluating the performance of ANN and ANFIS models for spatial | Husrevoglu, Mustafa, Gundogdu, Ismail Bulent | Surface Pressure, Heat Flux, Longwave Radiation, Shortwave Radiation, Air Temperature, Specific Humidity, Evapotranspiration, Wind Speed, Rain, Snow, Soil Moisture/Water Content, Soil Temperature, Land Surface Temperature, Snow Cover, Snow Depth, Snow Water Equivalent, Runoff, Precipitation, Precipitation Amount, Precipitation Rate | |
| Evaluation of E3SM simulated aerosols and aerosolcloud interactions across GCM and convectionpermitting scales | Huang, Meng, Ma, PoLun, Varble, Adam C., Fast, Jerome D., Hassan, Taufiq, Li, Jianfeng, Qin, Yi, Tang, Shuaiqi, Ullrich, Paul A., Yao, Yu | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation of hourly summer precipitation products over the Tibetan | Jia, Jingjing, He, Yongli, Zhang, Boyuan, Huo, Zixin, Tang, Zhen, Wang, Shanshan, Yu, Haipeng, Guan, Xiaodan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation of ten satellite-based and reanalysis precipitation datasets on a daily basis for Czechia (20012021) | Paluba, Daniel, Bliznak, Vojtech, Muller, Miloslav, Stych, Premysl | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation of the Extreme Precipitation and Runoff Flow Characteristics in a Semiarid Sub-Basin Based on Three Satellite Precipitation Products | Barraza, Rosalia Lopez, Herrera, Maria Teresa Alarcon, Celestino, Ana Elizabeth Marin, Jaquez, Armando Daniel Blanco, Cruz, Diego Armando Martinez | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Fluctuations of the 400 mm precipitation line under the influence of multiple factors | Jiang, Jie, Qin, Yaochen, Liu, Gangjun, Li, Yang, Xia, Haoming | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Formation mechanism of overshooting convection in the southwest vortex circulation under the influence of mesoscale gravity wave | Xu, Yizhou, Li, Guoping, Zhang, Xiaoyu, Dong, Yuanchang, Xie, Xin | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain |