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 Older versions will no longer be available and have been superseded by Version 07.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).
Product Summary
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
Documents
READ-ME
PI DOCUMENTATION
ANOMALIES
IMPORTANT NOTICE
GENERAL DOCUMENTATION
Dataset Resources
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Impact of Lightning Data Assimilation on the Short-Term Precipitation | Torcasio, Rosa Claudia, Federico, Stefano, Comellas Prat, Albert, Panegrossi, Giulia, D'Adderio, Leo Pio, Dietrich, Stefano | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impact of Microphysics on Tropical Precipitation Extremes in a Global StormResolving Model | Bao, Jiawei, Windmiller, Julia M. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impact of the MaddenJulian Oscillation on extreme precipitation over the western Maritime Continent and Southeast Asia | Da Silva, Nicolas A., Matthews, Adrian J. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Implementation of the Unified Representation of Deep Moist Convection in the CWBGFS | Su, Chun-Yian, Wu, Chien-Ming, Chen, Wei-Ting, Chen, Jen-Her | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impacts of the MJO on Rainfall at Different Seasons in Indonesia | Permana, D S, Supari | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluating Precipitation Errors Using the Environmentally Conditioned IntensityFrequency Decomposition Method | Di Luca, A., Argueso, D., Sherwood, S., Evans, J. P. | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluating South African Weather Service information on Idai tropical cyclone and KwaZulu- Natal flood events | Bopape, Mary-Jane M., Sebego, Ezekiel, Ndarana, Thando, Maseko, Bathobile, Netshilema, Masindi, Gijben, Morne, Landman, Stephanie, Phaduli, Elelwani, Rambuwani, Gift, Van Hemert, Louis, Mkhwanazi, Musa | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Downscaling of seasonal ensemble forecasts to the convectionpermitting scale over the Horn of Africa using the WRF model | Mori, Paolo, Schwitalla, Thomas, Ware, Markos Budusa, WarrachSagi, Kirsten, Wulfmeyer, Volker | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Convection-Permitting Simulations With the E3SM Global Atmosphere Model | Caldwell, P. M., Terai, C. R., Hillman, B., Keen, N. D., Bogenschutz, P., Lin, W., Beydoun, H., Taylor, M., Bertagna, L., Bradley, A. M., Clevenger, T. C., Donahue, A. S., Eldred, C., Foucar, J., Golaz, J.C., Guba, O., Jacob, R., Johnson, J., Krishna, J., Liu, W., Pressel, K., Salinger, A. G., Singh, B., Steyer, A., Ullrich, P., Wu, D., Yuan, X., Shpund, J., Ma, H.Y., Zender, C. S. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Continuous Multitrack Assimilation of Sentinel-1 Precipitable Water | Mateus, P., Miranda, P. M. A., Nico, G., Catalao, J. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| On the sensitivity of the simulated diurnal cycle of precipitation to 3-hourly radiosonde assimilation: a case study over the Western Maritime continent | Kwang Lee, Joshua Chun, Dipankar, Anurag, Huang, Xiang-Yu | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| On the vertical extending of the explosive extratropical cyclone: A case | Jiang, LiZhi, Yu, HaiGuo, Dong, Li, Fu, ShenMing, Sun, JianHua, Zheng, Fei, Yi, Kan, Ma, Hui | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Mesoscale Convective Systems Dominate the Energetics of the South Asian | Chen, Xingchao, Leung, L. Ruby, Feng, Zhe, Song, Fengfei, Yang, Qiu | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Model intercomparison of COSMO 5.0 and IFS 45r1 at kilometer-scale grid spacing | Zeman, Christian, Wedi, Nils P., Dueben, Peter D., Ban, Nikolina, Schar, Christoph | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Morphology of Rain Clusters Influencing Rainfall Intensity over Hainan | Huang, Tingting, Ding, Chenghui, Li, Weibiao, Chen, Yilun | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Multi-criteria decision based geospatial mapping of flood susceptibility | Das, Sumit, Gupta, Amitesh | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Major geomorphic events and natural hazards during monsoonal precipitation 2018 in the Kali Gandaki Valley, Nepal Himalaya | Bell, Rainer, Fort, Monique, Gotz, Joachim, Bernsteiner, Heidi, Andermann, Christoff, Etzlstorfer, Jurgen, Posch, Eva, Gurung, Narayan, Gurung, Sonam | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Machine Learning Based Soil Moisture Retrieval Algorithm and Validation at Selected Agricultural Sites Over India Using Cygnss Data | Tyagi, Shivani, Pandey, Dharmendra Kumar, Putrevu, Deepak, Srivastava, Prashant K., Misra, Arundhati | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Combining CMIP data with a regional convection-permitting model and observations to project extreme rainfall under climate change | Klein, Cornelia, Jackson, Lawrence S, Parker, Douglas J, Marsham, John H, Taylor, Christopher M, Rowell, David P, Guichard, Francoise, Vischel, Theo, Famien, Adjoua Moise, Diedhiou, Arona | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| CloudPrecipitation Hybrid Regimes and Their Projection onto IMERG Precipitation Data | Jin, Daeho, Oreopoulos, Lazaros, Lee, Dongmin, Tan, Jackson, Cho, Nayeong | Aerosol Backscatter, Aerosol Extinction, Aerosol Optical Depth/Thickness, Angstrom Exponent, Aerosol Particle Properties, Aerosol Radiance, Carbonaceous Aerosols, Cloud Condensation Nuclei, Dust/Ash/Smoke, Nitrate Particles, Organic Particles, Particulate Matter, Sulfate Particles, Trace Gases/Trace Species, Atmospheric Emitted Radiation, Emissivity, Optical Depth/Thickness, Radiative Flux, Reflectance, Transmittance, Atmospheric Stability, Humidity, Total Precipitable Water, Water Vapor Profiles, Cloud Condensation Nuclei, Cloud Droplet Concentration/Size, Cloud Liquid Water/Ice, Cloud Optical Depth/Thickness, Cloud Asymmetry, Cloud Ceiling, Cloud Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Cloud Vertical Distribution, Cloud Emissivity, Cloud Radiative Forcing, Cloud Reflectance, Rain Storms, Atmospheric Ozone, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Cold Pools Observed by Uncrewed Surface Vehicles in the Central and Eastern Tropical Pacific | Wills, Samantha M., Cronin, Meghan F., Zhang, Dongxiao | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Winter and spring atmospheric rivers in High Mountain Asia: climatology, dynamics, and variability | Nash, Deanna, Carvalho, Leila M. V., Jones, Charles, Ding, Qinghua | Geopotential Height, Atmospheric Ozone, Sea Level Pressure, Surface Pressure, Upper Air Temperature, Vertical Profiles, Air Temperature, Specific Humidity, U/V Wind Components, U/V Wind Components, Ozone Profiles, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Quantitative Analysis of the Effects of an Earthquake on Rainfall | Liu, Shuang, Hu, Kaiheng, Zhang, Qun, Zhang, Shaojie, Hu, Xudong, Tang, Desheng | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| SHARPEN: A scheme to restore the distribution of averaged precipitation fields | Tan, Jackson, Huffman, George J., Bolvin, David T., Nelkin, Eric J., Rajagopal, Manikandan | Atmospheric Water Vapor, 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 |