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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 Older versions will no longer be available and have been superseded by Version 07.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

Platforms
IMERG
Instruments
NOT APPLICABLE
Spatial Extent

N: 90 S: -90 E: 180 W: -180

Location
GLOBAL ,
GLOBAL LAND ,
GLOBAL OCEAN
Coordinate System
CARTESIAN
Granule Spatial Representation
CARTESIAN
Temporal Extent
1998-01-01 to Present
Data Partner
Goddard Earth Sciences Data and Information Services Center (formerly Goddard DAAC), Terrestrial Information Systems Laboratory, Earth Sciences Division, Science and Exploration Directorate, Goddard Space Flight Center, NASA (NASA/GSFC/SED/ESD/TISL/GESDI
Concept ID
C2723758340-GES_DISC
Data State
ACTIVE
Number of Files/Granules
496699
Processing Level
3
Published
Updated
Science Keywords
Precipitation ,
Precipitation Amount ,
Precipitation Rate ,
Snow ,
Rain

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

Precipitation Processing System (PPS) At NASA GSFC. (2022). GPM IMERG Early Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 [Data set]. NASA Goddard Earth Sciences Data and Information Services Center. https://doi.org/10.5067/GPM/IMERG/3B-HH-E/07 Date Accessed: 2026-05-02

Documents

SHOWING 2 OF 2

Publications Citing This Dataset

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Title Year Author Topic
Governing factors of the unprecedented extreme rainfall over Rameswaram Island S, Meenakshi, Sridharan, S. 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
Advancing Ensemble Streamflow Prediction Through Satellite-Based Peng, Kaidi, Wright, Daniel B., Derin, Yagmur, Alexander, G. Aaron, Pradhan, Ankita, Zoccatelli, Davide, Hartke, Samantha H., Li, Zhe, Tan, Jackson Evapotranspiration, Photosynthesis, Primary Production, Latent Heat Flux, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Better satellite precipitation algorithms slightly improved landslide hazard assessment Stanley, Thomas A., Sutton, Jessica R. P., Vershel, Rachel Soobitsky, Amatya, Pukar M. Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Reflectance, Total Surface Water
STREAM-Sat: A Novel Near-Realtime Quasi-Global Satellite-Only Ensemble Peng, Kaidi, Wright, Daniel B., Derin, Yagmur, Hartke, Samantha H., Li, Zhe, Tan, Jackson Atmospheric Water Vapor, Precipitation, RADAR, Precipitation Amount, Precipitation Rate, Snow, Rain
Spatial Analysis of Tropical Cyclone Yaas using Satellite Data Umakanth, N., Gogineni, Rajesh, Mohan Rao, K. Madan, Reddy, B. Revanth, Ahammad, Sk. Hasane, Rao, M.C. Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Toward the future integration of land-to-ocean observing systems to characterize organic carbon fluxes from storms Clark, J.B., Schollaert Uz, S. Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Machine-learning-based nowcasting of the Vogelsberg deep-seated landslide: why predicting slow deformation is not so easy van Natijne, Adriaan L., Bogaard, Thom A., Zieher, Thomas, Pfeiffer, Jan, Lindenbergh, Roderik C. Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Mapping the landslide susceptibility considering future land-use land-cover scenario Tyagi, Ankit, Tiwari, Reet Kamal, James, Naveen Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Moisture Sources and Pathways Determine Stable Isotope Signature of HassenruckGudipati, Hima J., Andermann, Christoff, Dee, Sylvia, Brunello, Camilla F., Baidya, Krishna Pyari, Sachse, Dirk, Meyer, Hanno, Hovius, Niels Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Near-real-time GNSS tropospheric IWV monitoring system for South America Aragon Paz, Juan Manuel, Mendoza, Luciano Pedro Oscar, Fernandez, Laura Isabel Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Ensemble precipitation estimates based on an assessment of 21 gridded precipitation datasets to improve precipitation estimations across Madagascar Ollivier, Camille C., Carriere, Simon D., Heath, Thomas, Olioso, Albert, Rabefitia, Zo, Rakoto, Heritiana, Oudin, Ludovic, Satge, Frederic Heat Flux, Air Temperature, Skin Temperature, Specific Humidity, Water Vapor, Precipitation Rate, Snow/Ice, Evaporation, Latent Heat Flux, Latent Heat Flux, Sensible Heat Flux, Diffusion, Surface Winds, Wind Speed, U/V Wind Components, Wind Stress, Wind Stress, Surface Roughness, Planetary Boundary Layer Height, Ice Fraction, Total Surface Precipitation Rate, Longwave Radiation, Shortwave Radiation, Soil Heat Budget, Soil Heat Budget, Soil Temperature, Soil Temperature, Soil Infiltration, Soil Infiltration, Soil Moisture/Water Content, Surface Soil Moisture, Root Zone Soil Moisture, Soil Moisture/Water Content, Surface Water, Runoff Rate, Average Flow, Average Flow, Precipitation, Snow Depth, Snow Melt, Snow/Ice Temperature, Leaf Area Index (LAI), Leaf Area Index (LAI), Rain, Precipitation Amount, Snow
Identification of the significant parameters in spatial prediction of landslide hazard Tyagi, Ankit, Tiwari, Reet Kamal, James, Naveen 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 Rain Estimates from Several Ground-Based Radar Networks and Satellite Products for Two Cases Observed over France in 2022 Causse, Antoine, Planche, Celine, Buisson, Emmanuel, Baray, Jean-Luc Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Prediction of the future landslide susceptibility scenario based on LULC and climate projections Tyagi, Ankit, Tiwari, Reet Kamal, James, Naveen Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Aerosolprecipitation elevation dependence over the central Himalayas using cloud-resolving WRF-Chem numerical modeling Adhikari, Pramod, Mejia, John F. Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Cloud and Precipitation Variability Associated With the Madden-Julian Shige, S., Kato, F., Aoki, S. Total Surface Precipitation Rate, RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps, Digital Elevation/Terrain Model (DEM), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Moisture sources for precipitation associated with major hurricanes during 2017 in the North Atlantic basin PerezAlarcon, Albenis, CollHidalgo, Patricia, FernandezAlvarez, Jose C., Sori, Rogert, Nieto, Raquel, Gimeno, Luis 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
Impact of transported dust aerosols on precipitation over the Nepal Himalayas using convection-permitting WRF-Chem simulation Adhikari, Pramod, Mejia, John F. Dust/Ash/Smoke, Carbonaceous Aerosols, Organic Particles, Sulfate Particles, Sulfur Oxides, Sulfur Compounds, Sulfate, Sulfur Dioxide, Sulfur Oxides, Dimethyl Sulfide, Aerosol Particle Properties, Particulate Matter, Aerosols, Surface Pressure, Pressure Thickness, Relative Humidity, Sea Salt, Black Carbon, Air Mass/Density, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Ensemble Representation of Satellite Precipitation Uncertainty Using a Nonstationary, Anisotropic Autocorrelation Model Hartke, Samantha H., Wright, Daniel B., Li, Zhe, Maggioni, Viviana, Kirschbaum, Dalia B., Khan, Sana Geopotential Height, Altitude, Surface Temperature, Skin Temperature, Upper Air Temperature, Dew Point Temperature, Air Temperature, Cloud Top Temperature, Atmospheric Winds, Surface Winds, U/V Wind Components, Upper Level Winds, U/V Wind Components, Vertical Wind Velocity/Speed, Atmospheric Pressure, Sea Level Pressure, Cloud Top Pressure, Sea Level Pressure, Surface Pressure, Specific Humidity, Total Precipitable Water, Cloud Liquid Water/Ice, Atmospheric Water Vapor, Atmospheric Ozone, Oxygen Compounds, Boundary Layer Winds, Total Ozone, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
How Well Do Multisatellite Products Capture the SpaceTime Dynamics of Precipitation? Part II: Building an Error Model through Spectral System ... Guilloteau, Clement, Foufoula-Georgiou, Efi, Kirstetter, Pierre, Tan, Jackson, Huffman, George J. Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Hydrological Evaluation of Satellite-Based Precipitation Products in Yan, Yan, Wang, Guihua, Nanding, Nergui, Chen, Weitian Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Precipitating fraction, not intensity, explains extreme coarsegrained precipitation ClausiusClapeyron scaling with sea surface temperature over tropical oceans Roca, Remy, De Meyer, Victorien, Muller, Caroline Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Pathways to Better Prediction of the MJOPart I: Effects of Model Resolution and Moist Physics on Atmospheric Boundary Layer and Precipitation Savarin, Ajda, Chen, Shuyi S. Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Pathways to Better Prediction of the MJOPart II: Impacts of AtmosphereOcean Coupling on the Upper Ocean and MJO propagation Savarin, Ajda, Chen, Shuyi S. Total Surface Precipitation Rate, Sea Surface Temperature, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
An evaluation of the performance of IMERG hourly precipitation estimates in a geographical sub-region with complex terrain and climate conditions: a case study in ... Gan, Fuwan, Chen, Shuai, Gao, Yang, Li, Yanjie Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Assessment of satellite precipitation data sets for high variability and rapid evolution of typhoon precipitation events in the Philippines Aryastana, Putu, Liu, ChianYi, JongDao Jou, Ben, Cayanan, Esperanza, Punay, Jason Pajimola, Chen, YingNong Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
A Simple Statistical Model of the Uncertainty Distribution for Daily Gridded Precipitation Multi-Platform Satellite Products Oliveira, Romulo A. J., Roca, Remy Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Surface Water Potential and Suitable Sites Identification for RWH in the Semi-Arid and Arid Watershed of Wadi Sammalus, Northeast Libya Using GIS and Remote ... Hamad, Salah, Patel, Nilanchal Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Validation of Satellite Rainfall Estimates over Equatorial East Africa Ageet, Simon, Fink, Andreas H., Maranan, Marlon, Diem, Jeremy E., Hartter, Joel, Ssali, Andrew L., Ayabagabo, Prosper Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Impacts of long-range-transported mineral dust on summertime convective cloud and precipitation: a case study over the Taiwan region Zhang, Yanda, Yu, Fangqun, Luo, Gan, Fan, Jiwen, Liu, Shuai Dust/Ash/Smoke, Carbonaceous Aerosols, Organic Particles, Sulfate Particles, Sulfur Oxides, Sulfur Compounds, Sulfate, Sulfur Dioxide, Sulfur Oxides, Dimethyl Sulfide, Aerosol Particle Properties, Particulate Matter, Aerosols, Surface Pressure, Pressure Thickness, Relative Humidity, Sea Salt, Black Carbon, Air Mass/Density, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Ranking of daily precipitation extreme events over oil pipelines in Rio Amaral, ICF, Libonati, R, Palmeira, ACPA, Ramos, AM Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Comparative climatology of outer tropical cyclone size using radial wind profiles Perez-Alarcon, Albenis, Sori, Rogert, Fernandez-Alvarez, Jose C., Nieto, Raquel, Gimeno, Luis Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Water Budgets of Tropical Cyclones through a Lagrangian Approach: A Case of Study of Hurricane Irma (2017) Perez-Alarcon, Albenis, Nieto, Raquel, Gimeno, Luis, Fernandez-Alvarez, Jose C., Coll-Hidalgo, Patricia, Sori, Rogert Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Intense and Small Freshwater Pools From Rainfall Investigated During Reverdin, G., Supply, A., Drushka, K., Thompson, E. J., Asher, W. E., Lourenco, A. Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Evaluation of GPM IMERG rainfall estimates in Singapore and assessing spatial sampling errors in ground reference Mandapaka, Pradeep V., Lo, Edmond Y. M. Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Lambada: Interactive data analytics on cold data using serverless cloud infrastructure Muller, Ingo, Marroquin, Renato, Alonso, Gustavo Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
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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 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
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