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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| Coupling of satellite-derived precipitation products with Bartlett-Lewis model to estimate intensity-frequency-duration curves for remote areas | Islam, Md. Atiqul, Yu, Bofu, Cartwright, Nick | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Do ERA5 and ERA5-land precipitation estimates outperform satellite-based precipitation products? A comprehensive comparison between state-of-the-art model ... | Xu, Jintao, Ma, Ziqiang, Yan, Songkun, Peng, Jie | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Diurnal variations of different types of cloud over the BaiuMeiyu frontal zone using retrieved cloud properties: Implication for the rainfall process | Yamashita, Takaya, Iwabuchi, Hironobu | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Deriving River Discharge Using Remotely Sensed Water Surface | Gehring, Jaclyn, Duvvuri, Bhavya, Beighley, Edward | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Forest fire action on vegetation from the perspective of trend analysis in future climate change scenarios for a Brazilian savanna region | Miranda, Jonathan da Rocha, Silva, Rosane Gomes da, Juvanhol, Ronie Silva | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Land Surface Temperature, Emissivity, Fire Ecology, Biomass Burning, Wildfires, Fire Occurrence, Burned Area, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| First Quasi-Synchronous Hurricane Quad-Polarization Observations by C-Band Radar Constellation Mission and RADARSAT-2 | Zhang, Biao, Mouche, Alexis A., Perrie, William | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Forecast Characteristics of Radar Data Assimilation Based on the Scales | Bae, Jeong-Ho, Min, Ki-Hong | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Forecasting High-Flow Discharges in a Flashy Catchment Using Multiple Precipitation Estimates as Predictors in Machine Learning Models | Zanchetta, Andre, Coulibaly, Paulin, Fortin, Vincent | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Flood forecasting with machine learning models in an operational framework | Nevo, Sella, Morin, Efrat, Gerzi Rosenthal, Adi, Metzger, Asher, Barshai, Chen, Weitzner, Dana, Voloshin, Dafi, Kratzert, Frederik, Elidan, Gal, Dror, Gideon, Begelman, Gregory, Nearing, Grey, Shalev, Guy, Noga, Hila, Shavitt, Ira, Yuklea, Liora, Royz, Moriah, Giladi, Niv, Peled Levi, Nofar, Reich, Ofir, Gilon, Oren, Maor, Ronnie, Timnat, Shahar, Shechter, Tal, Anisimov, Vladimir, Gigi, Yotam, Levin, Yuval, Moshe, Zach, Ben-Haim, Zvika, Hassidim, Avinatan, Matias, Yossi | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Geospatial modelling of rainfall erosivity in the humid tropics using remotely sensed data | Melville, Tricia, Wuddivira, Mark, Sutherland, Michael | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Global land surface evapotranspiration monitoring by ETMonitor model driven by multi-source satellite earth observations | Zheng, Chaolei, Jia, Li, Hu, Guangcheng | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Global sea surface salinity via the synergistic use of SMAP satellite and HYCOM data based on machine learning | Jang, Eunna, Kim, Young Jun, Im, Jungho, Park, Young-Gyu, Sung, Taejun | Precipitation, Surface Winds, Salinity, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Global Shallow Groundwater Patterns From Soil Moisture Satellite | Soylu, Mehmet Evren, Bras, Rafael L. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Global Tropical Precipitation Relationships to Free-Tropospheric Water Vapor Using Radio Occultations | Padulles, Ramon, Kuo, Yi-Hung, Neelin, J. David, Turk, F. Joseph, Ao, Chi O., de la Torre Juarez, Manuel | Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Filling Temporal Gaps within and between GRACE and GRACE-FO Terrestrial | Gyawali, Bimal, Ahmed, Mohamed, Murgulet, Dorina, Wiese, David N. | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation of remotely sensed global evapotranspiration data from inland river basins | Liu, Zhaofei | Aerosols, Aerosol Extinction, Aerosol Optical Depth/Thickness, Angstrom Exponent, Aerosol Particle Properties, Carbonaceous Aerosols, Dust/Ash/Smoke, Organic Particles, Sulfate Particles, Sulfur Oxides, Sulfur Compounds, Sulfate, Sulfur Dioxide, Sulfur Oxides, Particulate Matter, Dimethyl Sulfide, Black Carbon, Sea Salt, PARTICULATE MATTER (PM 2.5), PARTICULATE MATTER (PM 10), PARTICULATE MATTER (PM 1.0), Surface Pressure, Heat Flux, Longwave Radiation, Shortwave Radiation, Surface Temperature, Humidity, Evapotranspiration, Surface Winds, Rain, Precipitation Rate, Snow, Soil Moisture/Water Content, Soil Temperature, Land Surface Temperature, Snow Water Equivalent, Runoff, Precipitation, Precipitation Amount | |
| Evaluation of machine learning-based algorithms for landslide detection | Das, Raja, Wegmann, Karl W. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation of the GPM IMERG half-hourly final precipitation product in the quantification of rainfall erosivity in central Italy | Vergni, Lorenzo, Parisi, Andrea, Todisco, Francesca | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation of diverse-based precipitation data over the Amazon Region | Sapucci, Camila Ribeiro, Mayta, Victor C., da Silva Dias, Pedro Leite | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation of Terrestrial Water Storage Changes and Major Driving | Guo, Yi, Gan, Fuping, Yan, Baikun, Bai, Juan, Xing, Naichen, Zhuo, Yue | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation of Gridded Precipitation Data for Hydrologic Modeling in North-Central Texas | Ray, Ram L., Sishodia, Rajendra P., Tefera, Gebrekidan W. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Evaluation of IMERG and ERA5 PrecipitationPhase Partitioning on the Global Scale | Xiong, Wentao, Tang, Guoqiang, Wang, Tsechun, Ma, Ziqiang, Wan, Wei | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation of GPM IMERG and its constellations in extreme events over the conterminous united states | Li, Zhi, Tang, Guoqiang, Kirstetter, Pierre, Gao, Shang, Li, J.-L.F., Wen, Yixin, Hong, Yang | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Comparison between Observed and Simulated AgI Seeding Impacts in a Well-Observed Case from the SNOWIE Field Program | Xue, Lulin, Weeks, Courtney, Chen, Sisi, Tessendorf, Sarah A., Rasmussen, Roy M., Ikeda, Kyoko, Kosovic, Branko, Behringer, Dalton, French, Jeffery R., Friedrich, Katja, Zaremba, Troy J., Rauber, Robert M., Lackner, Christian P., Geerts, Bart, Blestrud, Derek, Kunkel, Melvin, Dawson, Nick, Parkinson, Shaun | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Paleoclimatic and paleoenvironmental changes in Amazonian lowlands over the last three millennia | Della Libera, Marcela Eduarda, Novello, Valdir Felipe, Cruz, Francisco William, Orrison, Rebecca, Vuille, Mathias, Maezumi, Shira Yoshi, de Souza, Jonas, Cauhy, Julio, Campos, Jose Leandro Pereira Silveira, Ampuero, Angela, Utida, Giselle, Strikis, Nicolas Misailidis, Stumpf, Cintia Fernandes, Azevedo, Vitor, Zhang, Haiwei, Edwards, R. Lawrence, Cheng, Hai | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain |