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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| On the utility of Ensemble Rainfall Forecasts over River Basins in India | Dube, Anumeha, Ashrit, Raghavendra | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| All-Sky Assimilation of GOES-16 Water Vapor Channels in Consideration of Cloud-Dependent Interchannel Observation-Error Correlations | Feng, Chengfeng, Pu, Zhaoxia | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Analyses of MODIS land cover/use and wildfires in Italian regions since 2001 | Ghaderpour, Ebrahim, Bozzano, Francesca, Scarascia Mugnozza, Gabriele, Mazzanti, Paolo | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow, Land Use/Land Cover Classification, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Emissivity, Land Surface Temperature | |
| An approach for good modeling and forecasting of sea surface salinity in | Ajibola-James, Opeyemi, Okeke, Francis I. | Surface Winds, Salinity, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Accelerating Earth Science to Action | Liu, Zhong, Wen, Yixin | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| A self-attention multisource precipitation fusion model for improving | You, Shaojie, Zhang, Xiaodan, Wang, Hongyu, Quan, Chen, Zhao, Tong, Zhang, Yongkun, Liu, Chang | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A Framework to Attribute Tropical Multiscale Precipitation Extremes to | Carenso, M., Fildier, B., Roca, R., Fiolleau, T. | Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A global near real-time dataset of Microwave Integrated Drought Index from the Fengyun-3 satellites | Zhang, Anzhi, Gao, Hao, Xu, Ronghan, Li, Xiaoqing, Zhao, Huichen, Jia, Gensuo | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow, Vegetation Water Content, Soil Moisture/Water Content, Skin Temperature, Land Use/Land Cover Classification | |
| Event-based mapping and spatial pattern analysis of landslides in parts of central Vietnam | Das, Raja, Wegmann, Karl W., Van Tien, Pham | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Explaining South Asian Monsoon Rainfall Seasonality Using a Metric of | Ferretti, Savannah L., Pritchard, Michael S., Ahmed, Fiaz, Peng, Liran, Baldwin, Jane W. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Hazard, and risk modelling of glacial lakes in the Sikkim Himalaya: | Gaikwad, Deepali, Tiwari, Reet Kamal, Kumar, Mahesh, Guha, Supratim | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Can real-time NDVI observations better constrain SMAP soil moisture | Feng, Sijia, Gao, Lun, Qiu, Jianxiu, Liu, Xiaoping, Crow, Wade T., Zhao, Tianjie, Tan, Chao, Wang, Shaohua, Wigneron, Jean-Pierre | Normalized Difference Vegetation Index (NDVI), Plant Phenology, Enhanced Vegetation Index (EVI), Vegetation Index, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Can Satellite Products Recognise Extreme Precipitation Over Southeastern | Benitez, Victoria D., Muller, Gabriela V., Doyle, Moira E., Forgioni, Fernando P., Lovino, Miguel A. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Background Wind Speeds Outweigh Urban Heat Islands in Downwind | Ding, Mingze, Zheng, XiaoTong, Li, Dan, Sun, Ting | 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, Land Use/Land Cover Classification, Precipitation, Precipitation Amount | |
| Assessment of spatial and temporal variations in precipitation using mixing methods based on multiple precipitation products on the Chinese Loess Plateau | Zhang, YuanYuan, Zhang, MingJun, Du, QinQin, Sun, MeiPing, Che, CunWei, Li, BeiBei | Surface Pressure, Heat Flux, Longwave Radiation, Shortwave Radiation, Air Temperature, Specific Humidity, Evapotranspiration, Wind Speed, Rain, Snow, Soil Moisture/Water Content, Soil Temperature, Land Surface Temperature, Snow Cover, Snow Depth, Snow Water Equivalent, Runoff, Surface Temperature, Humidity, Surface Winds, Precipitation Rate, Precipitation, Precipitation Amount, Total Surface Precipitation Rate | |
| Limited evidence that tropical inundation and precipitation powered the 20202022 methane surge | Xiong, Ying, Kort, Eric A., Bloom, A. Anthony, Gerlein-Safdi, Cynthia, Pu, Tianjiao, Bilir, Eren | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow, METHANE FLUX, Surface Water Processes/Measurements | |
| 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 | |
| Predicting oxygen-18 and deuterium over South America: local meteoric water lines for countries and biogeographical regions | Silva, Cesar de Oliveira Ferreira, Santarosa, Lucas Vituri, dos Santos, Vinicius, Manzione, Rodrigo Lilla, Gastmans, Didier | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Physical Controls on the Variability of Offshore Propagation of | Peatman, Simon C., Birch, Cathryn E., Schwendike, Juliane, Marsham, John H., Howard, Emma, Woolnough, Steven J., Mustafa, Jack M., Matthews, Adrian J. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Optimizing physical scheme selection in RegCM5 for improved air-sea | Desmet, Quentin, Herrmann, Marine, Ngo-Duc, Thanh | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Performance Evaluation of IMERG Satellite-Based Precipitation Estimates Against Rain Gauge Records in the Sebou Watershed, Morocco | El-Bouhali, Abdelaziz, El Ouazani Ech-Chahdi, Khadija, Yazami Ztait, Mohammed, Amyay, Mhamed, El Mazi, Mohamed | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Performance of datasets in hydrological simulation in various basin | Liu, Hui, Dong, Zhiqiang, Baoligao, Baiyin, Phetpaseuth, Vannaphone, Chandalasane, Thongthip, Chen, Wenxue, Mu, Xiangpeng, Liu, Dengfeng, Chen, Lajiao, Li, Xiaochen, Hu, Hongchang, Wen, Jie | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Probabilistic assessment of a road network subjected to rainfall-induced | Wu, Chenguang, Zhang, Jie, Yang, Cheng, Lu, Dagang | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Quality Assessment of Dual-Polarization C-Band SAR Data Influenced by | Chang, Jisung Geba, Kraatz, Simon, Oh, Yisok, Gao, Feng, Anderson, Martha | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Mapping the groundwater potential zones in mountainous areas of Southern China using GIS, AHP, and fuzzy AHP | Chen, Meng, Zhang, Shuangxi, Liu, Shengbo, Li, Mengkui, Zhang, Tao, Wu, Tengfei, Bu, Xiangyu | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow |