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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| Near-daily monitoring of surface temperature and channel width of the six largest Arctic rivers from space using GCOM-C/SGLI | Hori, Masahiro | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The NASA-JAXA Global Precipitation Measurement mission part II: New frontiers in precipitation science | Watters, Daniel, Battaglia, Alessandro | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Atmospheric Water Vapor | |
| The promise of a people-centred approach to floods: Types of participation in the global literature of citizen science and community-based flood risk ... | Wolff, Erich | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The Role of Mesoscale Convective Systems in Precipitation in the Tibetan | Kukulies, Julia, Chen, Deliang, Curio, Julia | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Brightness Temperature | |
| The residence time of water vapour in the atmosphere | Gimeno, Luis, Eiras-Barca, Jorge, Duran-Quesada, Ana Maria, Dominguez, Francina, van der Ent, Ruud, Sodemann, Harald, Sanchez-Murillo, Ricardo, Nieto, Raquel, Kirchner, James W. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The Impact of Groundwater Variability on Mangrove Greenness in Karimunjawa National Park based on Remote Sensing Study | Prihantono, J, Adi, N S, Nakamura, T, Nadaoka, K | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The Influence of Climate Change on River Corridors in Drylands: The Case of the Tehuacan-Cuicatlan Biosphere Reserve | Sedeno-Diaz, Jacinto Elias, Lopez-Lopez, Eugenia | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The applicability of using NARX neural network to forecast GRACE terrestrial water storage anomalies | Wang, Jielong, Chen, Yi | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Spatial and Time Warping for Gauge Adjustment of Rainfall Estimates | Le Coz, Camille, Heemink, Arnold, Verlaan, Martin, van de Giesen, Nick | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Spatial downscaling of IMERG considering vegetation index based on adaptive lag phase | Zeng, Zhaozhao, Chen, Haonan, Shi, Qian, Li, Jun | Land Use/Land Cover Classification, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Sensitivity of some African heavy rainfall events to microphysics and planetary boundary layer schemes: Impacts on localised storms | Meroni, Agostino N., Oundo, Kizito A., Muita, Richard, Bopape, MaryJane, Maisha, Thizwilondi R., Lagasio, Martina, Parodi, Antonio, Venuti, Giovanna | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| SMAP Salinity Retrievals near the Sea-Ice Edge Using Multi-Channel AMSR2 | Meissner, Thomas, Manaster, Andrew | Sea Surface Temperature, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| SMOS-IC data record of soil moisture and L-VOD: Historical development, applications and perspectives | Wigneron, Jean-Pierre, Li, Xiaojun, Frappart, Frederic, Fan, Lei, Al-Yaari, Amen, De Lannoy, Gabrielle, Liu, Xiangzhuo, Wang, Mengjia, Le Masson, Erwan, Moisy, Christophe | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Spatio-Temporal Dynamics of Plasmodium falciparum and Plasmodium vivax | Scully, Jenna, Mosnier, Emilie, Carbunar, Aurel, Roux, Emmanuel, Djossou, Felix, Garceran, Nicolas, Musset, Lise, Sanna, Alice, Demar, Magalie, Nacher, Mathieu, Gaudart, Jean | 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 | |
| Variability in air-pollutants, aerosols, and associated meteorology over | Sarkar, Ankan, Amal, K.K., Sarkar, Thumree, Panda, Jagabandhu, Paul, Debashis | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Validation of GPM IMERG extreme precipitation in the Peninsular Malaysia and Philippines by station and radar data | Da Silva, Nicolas A, Webber, Benjamin G M, Matthews, Adrian J, Feist, Matthew M, Stein, Thorwald H M, Holloway, Christopher E, Abdullah, Muhammad F A B | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Water conservation estimation based on time series ndvi in the yellow river basin | Zhang, Yangchengsi, Du, Jiaqiang, Guo, Long, Sheng, Zhilu, Wu, Jinhua, Zhang, Jing | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Evapotranspiration, Latent Heat Flux, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Untangling hybrid hydrological models with explainable artificial intelligence | Althoff, Daniel, Bazame, Helizani Couto, Nascimento, Jessica Garcia | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble | Althoff, Daniel, Rodrigues, Lineu Neiva, Bazame, Helizani Couto | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A closer look at factors governing landslide recovery time in post-seismic periods | Tanyas, Hakan, Kirschbaum, Dalia, Gorum, Tolga, van Westen, Cees J., Tang, Chenxiao, Lombardo, Luigi | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Data-driven and interpretable machine-learning modeling to explore the | Taconet, Paul, Porciani, Angelique, Soma, Dieudonne Diloma, Mouline, Karine, Simard, Frederic, Koffi, Alphonsine Amanan, Pennetier, Cedric, Dabire, Roch Kounbobr, Mangeas, Morgan, Moiroux, Nicolas | Land Surface Temperature, Emissivity, RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps, Digital Elevation/Terrain Model (DEM), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Data-driven landslide nowcasting at the global scale | Stanley, Thomas A., Kirschbaum, Dalia B., Benz, Garrett, Emberson, Robert A., Amatya, Pukar M., Medwedeff, William, Clark, Marin K. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Surface Soil Moisture | |
| countrywide monitoring of ground deformation using insar time series: A case study from Qatar | Emil, Mustafa Kemal, Sultan, Mohamed, Alakhras, Khaled, Sataer, Guzalay, Gozi, Sabreen, Al-Marri, Mohammed, Gebremichael, Esayas | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Daily Precipitation Frequency Distributions Impacts on Land-Surface Simulations of CONUS | Sarmiento, Daniel P., Slinski, Kimberly, McNally, Amy, Jacob, Jossy P., Funk, Chris, Peterson, Pete, Peters-Lidard, Christa D. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Earth observation using Python: A practical programming guide | Esmaili, Rebekah B. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain |