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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| Governing factors of the unprecedented extreme rainfall over Rameswaram Island | S, Meenakshi, Sridharan, S. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Brightness Temperature | |
| Heterogeneity of rainfall and associated convection in monsoon depressions over the Indian land region from long-term datasets | Goswami, Nikita, Pattnaik, Sandeep | 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, Landslides | |
| 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 | |
| Evaluating precipitation events using GPM IMERG 30-minute near real-time precipitation estimates | Sutton, Jessica R. P., Kirschbaum, Dalia, Stanley, Thomas, Orland, Elijah | Land Surface Temperature, Emissivity, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Spatial-Temporal Evaluation of Satellite-Derived Rainfall Estimations for Water Resource Applications in the Upper Congo River Basin | Boluwade, Alaba | Total Surface Precipitation Rate, Precipitation, 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 | |
| Spatial and conventional verifications of hurricanes Dorian and Fiona using the Canadian precipitation analysis & integrated multi-satellite retrievals for GPM products | Boluwade, Alaba, Farooque, Aitazaz A. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The status quo effect in the sociohydrology of floods | Mendoza Leal, Catalina, Coloma, Rocio, Ponce, Diego, Alarcon, Benjamin, Guerra, Maricarmen, Stehr, Alejandra, Carrasco, Juan Antonio, Alcayaga, Hernan, Rojas, Octavio, Link, Felipe, Link, Oscar | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The impact of Indian radar and lightning data assimilation on the shortrange forecasts of heavy rainfall events | Hari Prasad, K. B. R. R., Prasad, V. S., Sateesh, M., Amar Jyothi, K. | 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| Investigating the unprecedented summer 2022 penetration of the Indian monsoon to Iran and evaluation of global and regional model forecasts | Ghassabi, Zahra, Karami, Sara, Vazifeh, Ahad, Habibi, Maral | Atmospheric Emitted Radiation, Emissivity, Optical Depth/Thickness, Radiative Flux, Reflectance, Transmittance, Clouds, Cloud Condensation Nuclei, Cloud Droplet Concentration/Size, Cloud Liquid Water/Ice, Cloud Optical Depth/Thickness, Cloud Precipitable Water, Cloud Asymmetry, Cloud Ceiling, Cloud Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Cloud Vertical Distribution, Cloud Emissivity, Cloud Radiative Forcing, Cloud Reflectance, Cloud Types, 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 | |
| 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 | |
| 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 | |
| Comparison of Two Products of Satellite Precipitation (TRMM_3B42 v7-3-HourlyResearch Grade) and GPM-IMERG (V06-Half-HourlyEarly) in the East of Lake ... | Mahdavi, Taghi | Total Surface Precipitation Rate, 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 |
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 |