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 Older versions will no longer be available and have been superseded by Version 07.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.
Briefly describing the Final Run, the input precipitation estimates computed from the various satellite passive microwave sensors are intercalibrated to the CORRA product (because it is presumed to be the best snapshot TRMM/GPM estimate after adjustment to the monthly GPCP SG), then "forward/backward morphed" and combined with microwave precipitation-calibrated geo-IR fields, and adjusted with seasonal GPCP SG surface precipitation data to provide half-hourly and monthly precipitation estimates on a 0.1°x0.1° (roughly 10x10 km) grid over the globe. Precipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure. The current period of record is June 2000 to the present (delayed by about 4 months).
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Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Changes in clouds and the tropical circulation in global kilometer-scale simulations under different warming patterns | Tomassini, Lorenzo | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Atmospheric controls on precipitation isotopes in North China and their response to record-breaking torrential rainfall | Cai, Zhongyin, Li, Rong, Wang, Cheng, Tian, Lide | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Water vapour isotopes over West Africa as observed from space: which processes control tropospheric H2O HDO pair distributions? | Diekmann, Christopher Johannes, Schneider, Matthias, Knippertz, Peter, Trent, Tim, Boesch, Hartmut, Roehling, Amelie Ninja, Worden, John, Ertl, Benjamin, Khosrawi, Farahnaz, Hase, Frank | Hydrogen-deuterium Oxide, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Interferometric Radar Satellite and In-Situ Well Time-Series Reveal | Kakar, N., Metzger, S., Schone, T., Motagh, M., Waizy, H., Nasrat, N. A., Lazecky, M., Amelung, F., Bookhagen, B. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Interplay of freezethaw cycles and avalanche impact on glacial landslidedebris flow geohazard chain in the southeastern Tibetan Plateau | Huang, Taosheng, Wang, Tengfei, Zhang, Limin, Peng, Dalei, Shen, Ping | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Investigating Mechanisms Driving Differences in the Characteristics of | Hsu, WeiChing, Kooperman, Gabriel J., Hannah, Walter M. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Joint modulation of coastal rainfall in Northeast Australia by local and | Dao, T. L., Vincent, C. L., Huang, Y., Peatman, S. C., Soderholm, J. S., Birch, C. E., Roberts, D. S. | 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 | |
| Initial polarimetric radio occultation results from Spire's nanosatellite constellation: Independent assessment and potential applications | Padulles, Ramon, Cardellach, Estel, Paz, Antia, Burger, Thomas | Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Initial Polarimetric Radio Occultation Results from Spire's Nanosatellite Constellation: Satellite Payload, Collection, and Calibration | Talpe, Matthieu J., Nguyen, Vu A., Tomas, Sergio | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| InSAR Reveals Recovery of Stressed Aquifer Systems in Parts of Delhi | Kumar, Hrishikesh, Syed, Tajdarul Hassan, Amelung, Falk, Mirzaee, Sara, Venkatesh, A. S., Agrawal, Ritesh | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Intensifying tropical cyclones in the Arabian Sea replenish depleting aquifers | Saleh, Hassan, Sultan, Mohamed, Yan, Eugene, Save, Himanshu, Elhaddad, Hesham, Karimi, Hadi, Abdelmohsen, Karem, Emil, Mustafa K., Qamshouai, Sara Al | Terrestrial Water Storage, Ground Water, Glacier Mass Balance/Ice Sheet Mass Balance, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Influence of Soil Moisture on the Development of Organized Convective | Paccini, Laura, Schiro, Kathleen A. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Extreme Droughts in the Peruvian Amazon Region (2000-2024) | Martinez-Castro, Daniel, Takahashi, Ken, Espinoza, Jhan-Carlo, Vichot-Llano, Alejandro, Andrade, Miguel Octavio, Silva, Fey Yamina | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Extreme precipitation associated with atmospheric rivers over West Antarctic ice shelves: insights from kilometre-scale regional climate modelling | Gilbert, Ella, Pishniak, Denis, Torres, Jose Abraham, Orr, Andrew, Maclennan, Michelle, Wever, Nander, Verro, Kristiina | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Extreme Sahelian Rainfall Continues to Rise Despite Stable Storm | Spat, Dorian, Biasutti, Michela, Voigt, Aiko | Precipitation, Brightness Temperature, Geopotential Height, Altitude, Surface 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, Skin Temperature, Vertical Profiles, Ozone Profiles, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| High-resolution GNSS troposphere tomography through explainable deep learning-based downscaling framework | Haji-Aghajany, Saeid, Izanlou, Saeed, Tasan, Melika, Rohm, Witold, Kryza, Maciej | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Heavy Rainfalls Over the Western Java Region During the Cross-Equatorial | Zhao, Ning, Wu, Peiming, Moteki, Qoosaku, Manda, Atsuyoshi, Yokoi, Satoru, Mori, Shuichi | 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 | |
| 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 | |
| FloodCastBench: A large-scale dataset and foundation models for flood modeling and forecasting | Xu, Qingsong, Shi, Yilei, Zhao, Jie, Zhu, Xiao Xiang | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Fluctuations of the 400 mm precipitation line under the influence of multiple factors | Jiang, Jie, Qin, Yaochen, Liu, Gangjun, Li, Yang, Xia, Haoming | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Future soil erosion trends in Canadian agricultural lands from runoff and sustainability impacts | Amiri, Afshin, Ebtehaj, Isa, Soltani, Keyvan, Gumiere, Silvio Jose, Bonakdari, Hossein | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| IMERG-Like Precipitation Retrieval From Geo-Kompsat-2A Observations | Han, Kyung-Hoon, Jeong, Jaehoon, Hong, Sungwook | 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 |