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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| A refined assessment model for landslide susceptibility under rainfall-earthquake coupling effects | Zeng, Ying, Zhang, Yingbin, Xiao, Shizhou, Liu, Jing, Yu, Qiangshan, Zhu, Hui | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| 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 | |
| Analysis of the Effect of the Fengyun-3D Satellite Microwave Humidity Sounder (MWHS-II) Data Assimilation on Typhoon YAGI Forecast | FENG, Yuxuan, HE, Jieying, MA, Gang | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessing the Effects of Climate Change and Anthropogenic Contributions in Parishan Wetland, Iran | Kazemi Garajeh, Mohammad, Valizadeh Kamran, Khalil, Feizizadeh, Bakhtiar, Ghaffari Aliabad, Omid, Saei, Mousa, Sadeqi, Amin | Land Use/Land Cover Classification, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessing the impact of climate change on rainfall-triggered landslides: a case study in California | Semnani, Shabnam J., Han, Yi, Bonfils, Celine J., White, Joshua A. | Soil Depth, Soil Horizons/Profile, Soil Water Holding Capacity, Soil Texture, Soil Classification, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Benefits of Assimilating DAWN and HALO Observations for Numerical Simulations of Tropical Convective Systems Associated with African Easterly Waves during ... | Feng, Chengfeng, Pu, Zhaoxia, Nehrir, Amin R., Bedka, Kristopher M., Doyle, James | 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 | |
| Characteristics of precipitation and wind extremes induced by extratropical cyclones in northeastern North America | Chen, TingChen, Di Luca, Alejandro | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessment of Satellite and Reanalysis Precipitation Data Using Statistical and Wavelet Analysis in Semi-Arid, Morocco | Chakri, Achraf, Laftouhi, Nour-Eddine, Zouhri, Lahcen, Ibouh, Hassan, Ibnoussina, Mounsif | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessment of water scarcity as a risk factor for cholera outbreaks | Magers, Bailey, Usmani, Moiz, Brumfield, Kyle D., Huq, Anwar, Colwell, Rita R., Jutla, Antarpreet S. | Air Temperature, Precipitation Rate, 24 Hour Maximum Temperature, 24 Hour Minimum Temperature, Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Snow, Rain | |
| 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 | |
| Integrating remote sensing and deep learning forecasting model: A fluid-environment interface study | Hassanian, Reza, Cavallaro, Gabriele, Riedel, Morris | Reflectance, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Integration of PhysicsBased and DataDriven Approaches for Landslide Susceptibility Assessment | Han, Yi, Semnani, Shabnam J. | Soil Depth, Soil Horizons/Profile, Soil Water Holding Capacity, Soil Texture, Soil Classification, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| InSAR Reveals Recovery of Stressed Aquifer Systems in Parts of Delhi | Kumar, Hrishikesh, Syed, Tajdarul Hassan, Amelung, Falk, Mirzaee, Sara, Venkatesh, A. S., Agrawal, Ritesh | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Intensifying tropical cyclones in the Arabian Sea replenish depleting aquifers | Saleh, Hassan, Sultan, Mohamed, Yan, Eugene, Save, Himanshu, Elhaddad, Hesham, Karimi, Hadi, Abdelmohsen, Karem, Emil, Mustafa K., Qamshouai, Sara Al | Terrestrial Water Storage, Ground Water, Glacier Mass Balance/Ice Sheet Mass Balance, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Influence of Soil Moisture on the Development of Organized Convective | Paccini, Laura, Schiro, Kathleen A. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Extreme local mesoscale convective systems over the South China coast | Wang, Chenli, Chen, Xingchao, Zhao, Kun | Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Gridded precipitation and temperature products performance over Afghanistan: from simple bias correction to advanced data fusion | Nasimi, Mohammad Najim, Bauer-Gottwein, Peter, Boyce, Scott E., Huang, Jingshui, Disse, Markus | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Flood Rescue: An Integrated GIS and Remote Sensing-Based Decision Support System for Flood Inundation Warning and Relief | Rinku, Dhruva R., Sree, Parimi Hema, Nagajyothi, D., Kulkarni, Anita | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Future soil erosion trends in Canadian agricultural lands from runoff and sustainability impacts | Amiri, Afshin, Ebtehaj, Isa, Soltani, Keyvan, Gumiere, Silvio Jose, Bonakdari, Hossein | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impact assessment of 3D-var data assimilation on simulation of tropical cyclones using WRF | Makar, Pragnya, Kumar Singh, Sanjeev, Mitra, Debashis, Kant, Yogesh | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impact of antecedent rainfall and soil saturation on widespread debris flows in the northern Western Ghats during the 2021 extreme rainfall | Islam, Sharib, Thanveer, Jiyadh, Yunus, Ali P., Beetan, Yuvika, Umrikar, Bhavana, Arya, Dhyan Singh, Siva Subramanian, Srikrishnan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Identifying potential for rice expansion in Burkina Faso: integrating EO and climate data for suitability mapping | Meier, Jonas, Hirner, Andreas, Akpoti, Komlavi, Hackman, Kwame, Gessner, Ursula | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impact of Dynamic Downscaling on the Simulation of Tropical Easterly | DeLaune, Connor, Misra, Vasubandhu, Jayasankar, C. B. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain |