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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| Enabling smart dynamical downscaling of extreme precipitation events with machine learning | Shi, Xiaoming | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Accuracy analysis of IMERG and CMORPH precipitation data over North China | Shen, L, Lin, R, Lu, L, Xu, C, Liu, Y | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| A simple method for water balance estimation based on the empirical method and remotely sensed evapotranspiration estimates | Falalakis, George, Gemitzi, Alexandra | Evapotranspiration, Latent Heat Flux, Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A study of thunderstorm features over Srikakulam region on 26th April, 2020 | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | ||
| An updated moving window algorithm for hourly-scale satellite precipitation downscaling: A case study in the Southeast Coast of China | Ma, Ziqiang, Xu, Jintao, He, Kang, Han, Xiuzhen, Ji, Qingwen, Wang, TseChun, Xiong, Wentao, Hong, Yang | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessment of rainfall influence on sentinel-1 time series on amazonian tropical forests aiming deforestation detection improvement | Doblas, J., Carneiro, A., Shimabukuro, Y., SantAnna, S., Aragao, L. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessment of rainfall influence on sentinel-1 time series on amazonian tropical forests aiming deforestation detection improvement | Doblas, J., Carneiro, A., Shimabukuro, Y., Sant'Anna, S., Aragao, L. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessing the origin of a massive cyanobacterial bloom in the Rio de la Plata (2019): Towards an early warning system | Aubriot, Luis, Zabaleta, Bernardo, Bordet, Facundo, Sienra, Daniel, Risso, Jimena, Achkar, Marcel, Somma, Andrea | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessment and comparison of five satellite precipitation products in Australia | Islam, Md. Atiqul, Yu, Bofu, Cartwright, Nick | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Approaches for evaluation of relief morphometric characteristics influence on spatial distribution of moisture in the soils of steppe part of Crimea | Dunaieva, Ielizaveta, Pashtetsk, Vladimir, Vecherkov, Valentyn, Popovych, Valentina, Melnichuk, Aleksandr, Terleev, Vitaly, Nikonorov, Aleksandr, Akimov, Luka, Topaj, Alexander | Longwave Radiation, Shortwave Radiation, Soil Heat Budget, Soil Heat Budget, Soil Temperature, Soil Temperature, Soil Infiltration, Soil Infiltration, Soil Moisture/Water Content, Surface Soil Moisture, Root Zone Soil Moisture, Soil Moisture/Water Content, Evaporation, Surface Water, Runoff Rate, Average Flow, Average Flow, Precipitation, Snow/Ice, Snow Depth, Snow Melt, Snow/Ice Temperature, Leaf Area Index (LAI), Leaf Area Index (LAI), Precipitation Amount, Precipitation Rate, Snow, Rain, Heat Flux, Surface Temperature, Evapotranspiration, Canopy Characteristics, Leaf Characteristics, Vegetation Cover, Albedo, Land Surface Temperature, Snow Water Equivalent, Runoff | |
| Bonnet Carre Spillway freshwater transport and corresponding biochemical properties in the Mississippi Bight | Parra, Sabrina M., Sanial, Virginie, Boyette, Adam D., Cambazoglu, M. Kemal, Soto, Inia M., Greer, Adam T., Chiaverano, Luciano M., Hoover, Angie, Dinniman, Michael S. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Fifty Years of Research on the Madden-Julian Oscillation: Recent | Jiang, Xianan, Adames, Angel F., Kim, Daehyun, Maloney, Eric D., Lin, Hai, Kim, Hyemi, Zhang, Chidong, DeMott, Charlotte A., Klingaman, Nicholas P. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Geo-epidemiology of malaria at the health area level, dire health district, Mali, 20132017 | Cissoko, Mady, Sagara, Issaka, Sankare, Moussa H., Dieng, Sokhna, Guindo, Abdoulaye, Doumbia, Zoumana, Allasseini, Balam, Traore, Diahara, Fomba, Seydou, Bendiane, Marc Karim, Landier, Jordi, Dessay, Nadine, Gaudart, Jean | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation of six satellite and reanalysis precipitation products using gauge observations over the yellow river basin, China | An, Yiming, Zhao, Wenwu, Li, Changjia, Liu, Yanxu | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Global Intercomparison of Atmospheric Rivers Precipitation in Remote Sensing and Reanalysis Products | Arabzadeh, Alireza, Ehsani, Mohammad Reza, Guan, Bin, Heflin, Stella, Behrangi, Ali | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| GPM-Based Multitemporal Weighted Precipitation Analysis Using | Ullah, Sana, Zuo, Zhengkang, Zhang, Feizhou, Zheng, Jianghua, Huang, Shifeng, Lin, Yi, Iqbal, Imran, Sun, Yiyuan, Yang, Ming, Yan, Lei | Terrain Elevation, Digital Elevation/Terrain Model (DEM), Topographical Relief Maps, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Grid-scale agricultural land and water management: A remote-sensing-based multiobjective approach | Tang, Yikuan, Zhang, Fan, Engel, Bernard A., Liu, Xiao, Yue, Qiong, Guo, Ping | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Mapping the daily rainfall over an ungauged tropical micro-watershed: A downscaling algorithm using GPM data | Mahmud, Mohd. Rizaludin, Mohd Yusof, Aina Afifah, Mohd Reba, Mohd Nadzri, Hashim, Mazlan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Moisture transport in observations and reanalyses as a proxy for snow accumulation in East Antarctica | Dufour, Ambroise, Charrondiere, Claudine, Zolina, Olga | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Identifying the Relationship between Precipitation and Zika Outbreaks in Argentina | Ngweta, Lilian, Bhanot, Karan, Maharaj, Ariane, Bogle, Ian, Munasinghe, Thilanka | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Improving an extreme rainfall detection system with GPM imerg data | Mazzoglio, Paola, Laio, Francesco, Balbo, Simone, Boccardo, Piero, Disabato, Franca | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Orographic effect and multiscale interactions during an extreme rainfall event | Baisya, Himadri, Pattnaik, Sandeep | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Precipitation Retrieval over the Tibetan Plateau from the Geostationary | Kolbe, Christine, Thies, Boris, Egli, Sebastian, Lehnert, Lukas, Schulz, Hans, Bendix, Jorg | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Rain and Sun Create Slippery Layers in Eastern Pacific Fresh Pool | Shcherbina, Andrey, D'Asaro, Eric, Harcourt, Ramsey | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| State of the climate in 2018 | Blunden, Jessica, Arndt, Derek S. | Heat Flux, Heat Flux, Heat Flux, Heat Flux, Longwave Radiation, Longwave Radiation, Shortwave Radiation, Shortwave Radiation, Water Vapor Tendency, Water Vapor Flux, Cloud Dynamics, Cloud Microphysics, Snow/Ice, Precipitation, Atmospheric Ozone, 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, Oxygen Compounds, Boundary Layer Winds, Total Ozone, Albedo, Anisotropy, Precipitation Amount, Precipitation Rate, Snow, Rain, Sea Ice Concentration, Brightness Temperature |