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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| Diurnal Propagation of Precipitation in Landfalling Tropical Cyclones | Zhang, Xinyan, Guo, Shan, Bao, Xuwei, Xu, Weixin | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Dry and warm conditions in Australia exacerbated by aerosol reduction in China | Gao, Jiyuan, Yang, Yang, Wang, Hailong, Wang, Pinya, Liao, Hong | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow, 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 | |
| Downscaled GRACE data reveals anthropogenic dominance in groundwater | Cui, Bochao, Xue, Dongping, Gui, Dongwei, Liu, Qi, Abd-Elmabod, Sameh Kotb., Chen, Xiaonan, Goethals, Peter, Maeyer, Philippe De | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Land Surface Temperature, Emissivity | |
| East African City Centers Show Lower PM2.5 Levels than Their Suburbs | Chua, Samuel De Xun, Oguge, Otienoh, Oliewo, Celestine Atieno, Sserunjogi, Richard, Okure, Deo, Adong, Priscilla, Manyele, Asinta, Hussein, Tareq, Yang, Yuheng, Lu, Xixi, Lehtipalo, Katrianne, Zaidan, Martha Arbayani, Petaja, Tuukka | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow, Aerosol Optical Depth/Thickness | |
| Defining the Agricultural Wet Season in Africa Using Soil Moisture From | Chalmers, Christopher, Zhang, Yan, Xiao, Jingfeng, Li, Xing, Rigden, Angela | Brightness Temperature, Surface Soil Moisture, Soil Classification, Soil Depth, Soil Porosity, Soil Texture, Terrain Elevation, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Enhancing Fine-Resolution Precipitation Estimates in Data-Scarce Regions: A Novel Spatial Downscaling and Correction Framework | Ullah, Sana, Shahzad, Naeem, Yan, Lei, Zuo, Zhengkang, Iqbal, Imran, Tareen, Mohammad Javed | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate, Surface Pressure, Heat Flux, Longwave Radiation, Shortwave Radiation, Surface Temperature, Humidity, Evapotranspiration, Surface Winds, Soil Moisture/Water Content, Soil Temperature, Land Surface Temperature, Snow Water Equivalent, Runoff | |
| Elevation-Dependency of Diurnal Variation of Precipitation in Boreal | Ma, Jiali, Yu, Yubin, Han, Wei, Zhao, Dajun, Yao, Xiuping | 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 | |
| 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 dataset of tracked mesoscale precipitation systems in the tropics | Russell, James, Rajagopal, Manikandan, Veals, Peter, Skok, Gregor, Zipser, Edward, TinocoMorales, Michell | Precipitation, 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| Changes in clouds and the tropical circulation in global kilometer-scale simulations under different warming patterns | Tomassini, Lorenzo | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Climate change impact on rainfall erosivity factor using disaggregation model under CMIP6 shared socio-economic pathways | Das, Tapasranjan, Sarma, Arup Kumar | 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, Brightness Temperature, Surface Soil Moisture | |
| 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 | |
| Evaluation of hourly summer precipitation products over the Tibetan | Jia, Jingjing, He, Yongli, Zhang, Boyuan, Huo, Zixin, Tang, Zhen, Wang, Shanshan, Yu, Haipeng, Guan, Xiaodan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Erratum: wet bias of summer precipitation in the northwestern Tibetan Plateau in ERA5 is linked to overestimated lower-level southerly wind over the Plateau | Ou, Tinghai, Chen, Deliang, Tang, Jianping, Lin, Changgui, Wang, Xuejia, Kukulies, Julia, Lai, Hui-Wen | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluating the performance of ANN and ANFIS models for spatial | Husrevoglu, Mustafa, Gundogdu, Ismail Bulent | 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, Precipitation, Precipitation Amount, Precipitation Rate | |
| Estimation of Ca<SUP>2+</SUP> wet deposition in the Northern Hemisphere | Chen, Wanying, Lu, Xingcheng, Xian, Chaofan, Sun, Xu, Chen, Yiang, Hu, Mingyun, Li, Geng, Fung, Jimmy C.H. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain |