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 Older versions will no longer be available and have been superseded by Version 07.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.
Product Summary
Citation
Citation is critically important for dataset documentation and discovery. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use and Citation Guidance.
Copy Citation
Documents
READ-ME
PI DOCUMENTATION
ANOMALIES
IMPORTANT NOTICE
Dataset Resources
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Evaluating rainfall estimates derived from soil moisture using soil hydraulic properties over the Korean Peninsula | Kim, Doyoung, Lee, Seulchan, Cho, Seongkeun, Kim, Daeha, Choi, Minha | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Land Use/Land Cover Classification | |
| 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 impacts of hydrology and pollution loadings on low dissolved oxygen in an urbanized tidal river network using modeling and monitoring | Zhang, Heng, Liu, Jiahuan, Li, Tong, Zhang, Siyu, Lin, Zhongyuan, Jia, Zhengbo, Gong, Wenping, Zhang, Guang | 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 runoff variability in transboundary basins over High Mountain Asia: Multi-dataset merging based on satellite gravimetry constraint | Jiao, Jiashuang, Pan, Yuanjin, Cui, Xiaoming, Mohasseb, Hussein A., Ding, Hao | 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 | |
| Flash Floods Impact in the Upper Citarum Watershed: A Hydrological and Hydraulic Simulation Approach | Sapan, E G A, Susanti, W D, Santosa, B H, Wardhani, F A, Widiatmoko, N, Yuvhenmindo, M R, Ridwansyah, I, Triwisesa, E, Pravitasari, A E | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Flood Rescue: An Integrated GIS and Remote Sensing-Based Decision Support System for Flood Inundation Warning and Relief | Rinku, Dhruva R., Sree, Parimi Hema, Nagajyothi, D., Kulkarni, Anita | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Hybrid FR-AHP approach for GLOF hazard assessment in the Himalayan region | Gaikwad, Deepali, Tyagi, Ankit, Tiwari, Reet Kamal | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Identifying potential for rice expansion in Burkina Faso: integrating EO and climate data for suitability mapping | Meier, Jonas, Hirner, Andreas, Akpoti, Komlavi, Hackman, Kwame, Gessner, Ursula | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impact of Dynamic Downscaling on the Simulation of Tropical Easterly | DeLaune, Connor, Misra, Vasubandhu, Jayasankar, C. B. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impact assessment of 3D-var data assimilation on simulation of tropical cyclones using WRF | Makar, Pragnya, Kumar Singh, Sanjeev, Mitra, Debashis, Kant, Yogesh | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impact of antecedent rainfall and soil saturation on widespread debris flows in the northern Western Ghats during the 2021 extreme rainfall | Islam, Sharib, Thanveer, Jiyadh, Yunus, Ali P., Beetan, Yuvika, Umrikar, Bhavana, Arya, Dhyan Singh, Siva Subramanian, Srikrishnan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Improvement of Summer Precipitation Simulation Over the Tibetan Plateau: | Zhang, Feimin, Cui, Hao, Wang, Hao | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Gridded precipitation and temperature products performance over Afghanistan: from simple bias correction to advanced data fusion | Nasimi, Mohammad Najim, Bauer-Gottwein, Peter, Boyce, Scott E., Huang, Jingshui, Disse, Markus | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| GRACE/GFO and Swarm Observation Analysis of the 2023-2024 Extreme | Zhou, Jun, Cui, Lilu, Li, Yu, Yao, Chaolong, Meng, Jiacheng, Zou, Zhengbo, Lu, Yuheng | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impacts of Global Warming on Severe Drought in Northern Taiwan: A Future | Huang, ShihMing, Lee, TsungYu, Lin, ChuanYao, Lin, YiYing, Hsu, HuangHsiung | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impacts of Northerly Low-Level Jets on Mesoscale Convective Systems East | Mu, Ye, Jones, Charles, Carvalho, Leila M. V., Kukulies, Julia, Prein, Andreas F., Xue, Lulin, Liu, Changhai | Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impacts of resolution on heavyprecipitating storms in climate model hindcasts | Wu, WenYing, Ma, HsiYen | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impacts of the Madden-Julian Oscillation on Widespread Heavy Rainfall | LopezBravo, Clemente, Vincent, Claire L., Huang, Yi, Lane, Todd P. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impacts of climate trends on the heavy precipitation event associated with Typhoon Doksuri in Northern China | Yan, Ziyu, Wang, Zhuo, Peng, Melinda | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model version 1: an attempt to resolve monsoon prediction deadlock | Krishna, R. Phani Murali, Kumar, Siddharth, Prajeesh, A. Gopinathan, Bechtold, Peter, Wedi, Nils, Roy, Kumar, Ganai, Malay, Reddy, B. Revanth, Tirkey, Snehlata, Goswami, Tanmoy, Kanase, Radhika, Sarkar, Sahadat, Deshpande, Medha, Mukhopadhyay, Parthasarathi | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Integrating remote sensing and deep learning forecasting model: A fluid-environment interface study | Hassanian, Reza, Cavallaro, Gabriele, Riedel, Morris | Reflectance, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| 20 Years of MCSs simulations over South America using a convection-permitting model | Rehbein, Amanda, Prein, Andreas F., Ambrizzi, Tercio, Ikeda, Kyoko, Liu, Changhai, Rasmussen, Roy M. | Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A dataset of tracked mesoscale precipitation systems in the tropics | Russell, James, Rajagopal, Manikandan, Veals, Peter, Skok, Gregor, Zipser, Edward, TinocoMorales, Michell | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain |