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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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, Total Surface Precipitation Rate | |
| Comparison of flow simulations with sub-daily and daily GPM IMERG products over a transboundary Chenab River catchment | Ahmed, Ehtesham, Al Janabi, Firas, Yang, Wenyu, Ali, Akhtar, Saddique, Naeem, Krebs, Peter | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A spatio-temporal graph-guided convolutional LSTM for tropical cyclones precipitation nowcasting | Yang, Xuying, Zhang, Feng, Sun, Peng, Li, Xiaofan, Du, Zhenhong, Liu, Renyi | 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 | |
| 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, Liquid Precipitation, Precipitation Rate, Radar Reflectivity | |
| Hydrological Evaluation of Satellite-Based Precipitation Products in | Yan, Yan, Wang, Guihua, Nanding, Nergui, Chen, Weitian | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| 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, Total Surface Precipitation Rate | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| Satellite rainfall products outperform ground observations for landslide prediction in India | Brunetti, Maria Teresa, Melillo, Massimo, Gariano, Stefano Luigi, Ciabatta, Luca, Brocca, Luca, Amarnath, Giriraj, Peruccacci, Silvia | 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 | |
| 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 | |
| Evaluating forecast skills of moisture from convective-permitting WRF-ARW model during 2017 North American Monsoon season | Risanto, Christoforus Bayu, Castro, Christopher L., Moker, James M., Arellano, Avelino F., Adams, David K., Fierro, Lourdes M., Minjarez Sosa, Carlos M. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain |