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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| Estimation of seasonal base flow contribution to a tropical river using stable isotope analysis | Bhagat, Himanshu, Ghosh, Prosenjit, Nagesh Kumar, D. | Discharge/Flow, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation of seventeen satellite-, reanalysis-, and gauge-based precipitation products for drought monitoring across mainland China | Wei, Linyong, Jiang, Shanhu, Ren, Liliang, Wang, Menghao, Zhang, Linqi, Liu, Yi, Yuan, Fei, Yang, Xiaoli | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation of SMAP/Sentinel 1 high-resolution soil moisture data to detect irrigation over agricultural domain | Jalilvand, Ehsan, Abolafia-Rosenzweig, Ronnie, Tajrishy, Masoud, Das, Narendra | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Brightness Temperature, SIGMA NAUGHT, Surface Soil Moisture, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation of Spatial Rainfall Products in Sparsely Gauged Region Using | Tanim, Ahad Hasan, Mullick, Md. Reaz Akter, Sikdar, Md. Soumik | Aerosol Optical Depth/Thickness, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Evaluation of precipitation datasets available on Google earth engine over India | Dubey, Saket, Gupta, Harshit, Goyal, Manish Kumar, Joshi, Nitin | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Global Patterns of Vegetation Response to Short-Term Surface Water Availability | He, Qing, Lu, Hui, Yang, Kun, Zhen, Ling, Yue, Siyu, Li, Yishan, Entekhabi, Dara | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| GRACEfully Closing the Water Balance: A Data-Driven Probabilistic Approach Applied to River Basins in Iran | Schoups, Gerrit, Nasseri, Mohsen | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| When enough is really enough? On the minimum number of landslides to build reliable susceptibility models | Titti, Giacomo, van Westen, Cees, Borgatti, Lisa, Pasuto, Alessandro, Lombardo, Luigi | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Machine Learning-Based Error Modeling to Improve GPM IMERG Precipitation Product over the Brahmaputra River Basin | Bhuiyan, Md Abul Ehsan, Yang, Feifei, Biswas, Nishan Kumar, Rahat, Saiful Haque, Neelam, Tahneen Jahan | 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 | |
| Increasing heavy rainfall events in south India due to changing land use | Boyaj, Alugula, Dasari, Hari P., Hoteit, Ibrahim, Ashok, Karumuri | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impacts of Drought on Vegetation Assessed by Vegetation Indices and | Rousta, Iman, Olafsson, Haraldur, Moniruzzaman, Md, Zhang, Hao, Liou, Yuei-An, Mushore, Terence Darlington, Gupta, Amitesh | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Poleward Shift of Atmospheric Rivers in the Southern Hemisphere in Recent Decades | Ma, Weiming, Chen, Gang, Guan, Bin | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Ranking of Daily Satellite-Derived Precipitation Extremes for the Orbig Pipeline in Rio De Janeiro | Amaral, I. C.F., Libonati, R. S., Palmeira, A. C. P. A. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Ranking of daily satellite-derived precipitation extremes for the orbig pipeline in Rio De Janeiro | Amaral, I. C.F., Libonati, R. S., Palmeira, A. C. P. A. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The physics of extreme rainfall event: An investigation with multisatellite observations and numerical simulations | Meenu, S., Gayatri, K., Malap, Neelam, Murugavel, P., Samanta, Soumya, Prabha, Thara V. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Atmospheric Water Vapor | |
| The first Met Office unified modelJULES regional atmosphere and land configuration, RAL1 | Bush, Mike, Allen, Tom, Bain, Caroline, Boutle, Ian, Edwards, John, Finnenkoetter, Anke, Franklin, Charmaine, Hanley, Kirsty, Lean, Humphrey, Lock, Adrian, Manners, James, Mittermaier, Marion, Morcrette, Cyril, North, Rachel, Petch, Jon, Short, Chris, Vosper, Simon, Walters, David, Webster, Stuart, Weeks, Mark, Wilkinson, Jonathan, Wood, Nigel, Zerroukat, Mohamed | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Stabilization of sentinel-1 sar time-series using climate and forest structure data for early tropical deforestation detection | Doblas, J., Carneiro, A., Shimabukuro, Y., SantAnna, S., Aragao, L., Pereira, F. R. S. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Savannah trees buffer herbaceous plant biomass against wild and domestic herbivores | Smith, Stuart William, Graae, Bente Jessen, Bukombe, John, Hassan, Shombe Ntaraluka, Lyamuya, Richard Daniel, Jacob Mtweve, Philipo, Treydte, Anna Christina, Speed, James David Mervyn | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Variational Retrievals of High Winds Using Uncalibrated CyGNSS | Cardellach, Estel, Nan, Yang, Li, Weiqiang, Padulles, Ramon, Ribo, Serni, Rius, Antonio | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Cross-examination of similarity, difference and deficiency of gauge, radar and satellite precipitation measuring uncertainties for extreme events using conventional ... | Li, Zhi, Chen, Mengye, Gao, Shang, Hong, Zhen, Tang, Guoqiang, Wen, Yixin, Gourley, Jonathan J., Hong, Yang | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Downscaling of SMAP Soil Moisture in the Lower Mekong River Basin | Dandridge, Chelsea, Fang, Bin, Lakshmi, Venkat | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Surface Pressure, Heat Flux, Longwave Radiation, Shortwave Radiation, Surface Temperature, Humidity, Evapotranspiration, Surface Winds, Rain, Precipitation Rate, Snow, Soil Moisture/Water Content, Soil Temperature, Land Surface Temperature, Snow Water Equivalent, Runoff, Surface Soil Moisture, Precipitation, Precipitation Amount, Emissivity | |
| Dual state/rainfall correction via soil moisture assimilation for improved streamflow simulation: evaluation of a large-scale implementation with Soil Moisture ... | Mao, Yixin, Crow, Wade T., Nijssen, Bart | Surface Pressure, Longwave Radiation, Shortwave Radiation, Surface Temperature, Evaporation, Humidity, Convection, Surface Winds, Rain, Land Surface Temperature, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Brightness Temperature, Soil Moisture/Water Content | |
| Contribution of Tropical Cyclones to Precipitation around Reclaimed | Yao, Dongxu, Song, Xianfang, Yang, Lihu, Ma, Ying | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Constraining the vertical distribution of coastal dust aerosol using OCO-2 O2 A-band measurements | Zeng, Zhao-Cheng, Chen, Sihe, Natraj, Vijay, Le, Tianhao, Xu, Feng, Merrelli, Aronne, Crisp, David, Sander, Stanley P., Yung, Yuk L. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Atmospheric Carbon Dioxide, Infrared Radiance |