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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| Performance of Seven Gridded Precipitation Products over Arid Central | Song, Lingling, Xu, Changchun, Long, Yunxia, Lei, Xiaoni, Suo, Nanji, Cao, Linlin | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Performance evaluation of Google Earth Engine based precipitation datasets under different climatic zones over India | Jain, Sukant, Tiwari, Varun, Thapa, Amrit, Mangla, Rohit, Jaiswal, R. K., Kumar, Vinay, Tiwari, Supriya, Tulbure, Mirela G., Galkate, Ravi, Lohani, A. K., Pandey, Kamal | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Predicting Crimean-Congo Hemorrhagic Fever Outbreaks via Multivariate Time-Series Classification of Climate Data | Harris, Jonathan, Munasinghe, Thilanka, Tubbs, Heidi, Anyamba, Assaf | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Predicting runoff series in ungauged basins of the Brazilian Cerrado biome | Althoff, Daniel, Rodrigues, Lineu Neiva, Silva, Demetrius David da | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Precipitation Retrieval from Fengyun-3D Microwave Humidity and | Liu, Kangwen, He, Jieying, Chen, Haonan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| National-scale flood risk assessment using GIS and remote sensing-based | Siam, Zakaria Shams, Hasan, Rubyat Tasnuva, Anik, Soumik Sarker, Noor, Fahima, Adnan, Mohammed Sarfaraz Gani, Rahman, Rashedur M., Dewan, Ashraf | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Optical-microwave diagnostics of agricultural land afforestation | Dmitriev, A.V., Chimitdorzhiev, T.N., Dobrynin, S.I., Khudaiberdieva, O.A., Kirbizhekova, I.I. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Monitoring precipitation from space: progress, challenges, and opportunities | Sharifi, Ehsan, Brocca, Luca | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Monitoring Lake Volume Variation from Space Using Satellite | Pham-Duc, Binh, Frappart, Frederic, Tran-Anh, Quan, Si, Son Tong, Phan, Hien, Quoc, Son Nguyen, Le, Anh Pham, Viet, Bach Do | 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 | |
| Object-Based Evaluation of Tropical Precipitation Systems in DYAMOND Simulations over the Maritime Continent | SU, Chun-Yian, CHEN, Wei-Ting, WU, Chien-Ming, MA, Hsi-Yen | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Projected Changes of Day-to-Day Precipitation and Choco Low-Level Jet Relationships over the Far Eastern Tropical Pacific and Western Colombia from Two CMIP6 GCM Models | Valencia, Juliana, Mejia, John F. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Seasonal and diurnal surface urban heat islands in China: an | Cao, Shisong, Cai, Yile, Du, Mingyi, Weng, Qihao, Lu, Linlin | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Rapid Identification and Spectral Moment Estimation of Non-Gaussian | Dong, Xichao, Hu, Jiaqi, Hu, Cheng, Chen, Zhiyang, Li, Yinghe | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Satellite remote sensing of environmental variables can predict acoustic | Gomez-Morales, Diego A., Acevedo-Charry, Orlando | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Robustness of Vegetation Optical Depth Retrievals Based on L-Band Global Radiometry | Chaparro, David, Feldman, Andrew F., Chaubell, Mario Julian, Yueh, Simon H., Entekhabi, Dara | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Surface Soil Moisture, Brightness Temperature | |
| Role of land-surface vegetation in the march of Indian monsoon onset | Chakraborty, Arindam, Samuel, Jerry B., Paleri, Anagha | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Rainfall Variability and Tidal Inundation Influences on Mangrove Greenness in Karimunjawa National Park, Indonesia | Prihantono, Joko, Nakamura, Takashi, Nadaoka, Kazuo, Wirasatriya, Anindya, Adi, Novi Susetyo | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Remote Sensing for Development of Rainfall IntensityDurationFrequency Curves at Ungauged Locations of Yangon, Myanmar | Kyaw, Aung Kyaw, Shahid, Shamsuddin, Wang, Xiaojun | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Residence, activity patterns and behaviour of the giant mottled eel | Piper, Adam, Belen, Alejandro, Villanueva, Bryan, Gollock, Matthew | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Remote Sensing-Based Evaluation of Social Distancing Effects during the COVID-19 Pandemic on NO2 Levels in the Urban Areas of Brasilia, Anapolis and Goiania | Valadao, Larissa Vieira, Tavares, Andre Silva, Sollaci, Catarina Balduino, Cunha, Christhian Santana, Mpongo, Fortunato Bernardo Zau, Baptista, Gustavo Macedo de Mello | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The Accuracy of Precipitation Forecasts at Timescales of 1-15 Days in | Gebremichael, Mekonnen, Yue, Haowen, Nourani, Vahid | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The first global soil moisture and vegetation optical depth product retrieved from fused SMOS and SMAP L-band observations | Li, Xiaojun, Wigneron, Jean-Pierre, Frappart, Frederic, Lannoy, Gabrielle De, Fan, Lei, Zhao, Tianjie, Gao, Lun, Tao, Shengli, Ma, Hongliang, Peng, Zhiqing, Liu, Xiangzhuo, Wang, Huan, Wang, Mengjia, Moisy, Christophe, Ciais, Philippe | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Brightness Temperature, Surface Soil Moisture | |
| The Effects of Hurricanes and Storms on the Composition of Dissolved Organic Matter in a Southeastern US Estuary | Medeiros, Patricia M. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Statistical Analysis of CyGNSS Speckle and Its Applications to Surface Water Mapping | Liu, B., Wan, W., Tang, Guoqiang, Li, H., Guo, Z., Chen, X., Hong, Yang | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The impacts of multi-physics parameterization on forecasting heavy rainfall induced by weak landfalling Typhoon Rumbia (2018) | Zhu, Yiting, Qiao, Fengxue, Liu, Yujia, Liang, Xin-Zhong, Liu, Qiyang, Wang, Rui, Zhang, Han | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain |