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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| Satellite soil moisture data assimilation impacts on modeling weather variables and ozone in the southeastern USPart 1: An overview | Huang, Min, Crawford, James H., DiGangi, Joshua P., Carmichael, Gregory R., Bowman, Kevin W., Kumar, Sujay V., Zhan, Xiwu | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Seasonality and trends of drivers of mesoscale convective systems in southern West Africa | Klein, Cornelia, Nkrumah, Francis, Taylor, Christopher M., Adefisan, Elijah A. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Results of the Dragon 4 Project on New Ocean Remote Sensing Data for | Gibert, Ferran, Boutin, Jacqueline, Dierking, Wolfgang, Granados, Alba, Li, Yan, Makhoul, Eduard, Meng, Junmin, Supply, Alexandre, Vendrell, Ester, Vergely, Jean-Luc, Wang, Jin, Yang, Jungang, Xiang, Kunsheng, Yin, Xiaobin, Zhang, Xi | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Use of a Genetic Algorithm to Optimize a Numerical Weather Prediction System | Houtekamer, P. L., He, Bin, Jacques, Dominik, McTaggart-Cowan, Ron, Separovic, Leo, Vaillancourt, Paul A., Zadra, Ayrton, Deng, Xingxiu | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The Life Cycle of a Stationary Cloud Cluster during the Indian Summer Monsoon: A Microphysical Investigation Using Polarimetric C-Band Radar | Samanta, Soumya, Murugavel, P., Gurnule, Dinesh, Rao, Y. Jaya, Vivekanandan, Jothiram, Prabha, Thara V. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Towards a mechanistic understanding of precipitation over the far eastern tropical Pacific and western Colombia, one of the rainiest spots on Earth | Mejia, John F., Yepes, Johanna, Henao, Juan J., Poveda, German, Zuluaga, Manuel D., Raymond, David J., FuchsStone, Zeljka | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The diurnal cycle of precipitation according to multiple decades of global satellite observations, three CMIP6 models, and the ECMWF reanalysis | Watters, Daniel, Battaglia, Alessandro, Allan, Richard P. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Atmospheric Water Vapor, RADAR | |
| Substantial Sea Surface Temperature Cooling in the Banda Sea Associated With the MaddenJulian Oscillation in the Boreal Winter of 2015 | Pei, Suyang, Shinoda, Toshiaki, Steffen, John, Seo, Hyodae | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The NASA-JAXA Global Precipitation Measurement mission part II: New frontiers in precipitation science | Watters, Daniel, Battaglia, Alessandro | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Atmospheric Water Vapor | |
| The promise of a people-centred approach to floods: Types of participation in the global literature of citizen science and community-based flood risk ... | Wolff, Erich | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The role of geomorphology, rainfall and soil moisture in the occurrence of landslides triggered by 2018 Typhoon Mangkhut in the Philippines | Abanco, Claudia, Bennett, Georgina L., Matthews, Adrian J., Matera, Mark Anthony M., Tan, Fibor J. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The Role of Mesoscale Convective Systems in Precipitation in the Tibetan | Kukulies, Julia, Chen, Deliang, Curio, Julia | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Brightness Temperature | |
| Variability in air-pollutants, aerosols, and associated meteorology over | Sarkar, Ankan, Amal, K.K., Sarkar, Thumree, Panda, Jagabandhu, Paul, Debashis | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Two-decades of GPM IMERG early and final run products intercomparison: Similarity and difference in climatology, rates, and extremes | Li, Zhi, Tang, Guoqiang, Hong, Zhen, Chen, Mengye, Gao, Shang, Kirstetter, Pierre, Gourley, Jonathan J., Wen, Yixin, Yami, Teshome, Nabih, Soumaya, Hong, Yang | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Validation of satellite estimated convective rainfall products : A case study for the summer cyclone season of 2020 | SATEESH, M., KHADKE, CHINMAY, PRASAD, V. S., GOYAL, SUMAN | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Validation of satellite-based precipitation products from TRMM to GPM | Wang, Jianxin, Petersen, Walter A., Wolff, David B. | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A new approach for bias adjustment of IMERG remotely sensed snowfall product | Sadeghi, Leili, Saghafian, Bahram, Moazami, Saber | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| BCC-CSM2-HR: a high-resolution version of the Beijing Climate Center Climate System Model | Wu, Tongwen, Yu, Rucong, Lu, Yixiong, Jie, Weihua, Fang, Yongjie, Zhang, Jie, Zhang, Li, Xin, Xiaoge, Li, Laurent, Wang, Zaizhi, Liu, Yiming, Zhang, Fang, Wu, Fanghua, Chu, Min, Li, Jianglong, Li, Weiping, Zhang, Yanwu, Shi, Xueli, Zhou, Wenyan, Yao, Junchen, Liu, Xiangwen, Zhao, He, Yan, Jinghui, Wei, Min, Xue, Wei, Huang, Anning, Zhang, Yaocun, Zhang, Yu, Shu, Qi, Hu, Aixue | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Bias Correction Method Based on Artificial Neural Networks for Quantitative Precipitation Forecast | Fuentes-Barrios, Adrian, Sierra-Lorenzo, Maibys | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessment of WRF Model Parameter Sensitivity for High-Intensity | Chinta, Sandeep, Yaswanth Sai, J., Balaji, C. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Comparative analysis of TMPA and IMERG precipitation datasets in the arid environment of El-Qaa plain, Sinai | Morsy, Mona, Scholten, Thomas, Michaelides, Silas, Borg, Erik, Sherief, Youssef, Dietrich, Peter | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A new method for assessing satellite-based hydrological data products using water budget closure | Luo, Zengliang, Shao, Quanxi, Wan, Wei, Li, Huan, Chen, Xi, Zhu, Siyu, Ding, Xiangyi | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, 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, Air Temperature, Specific Humidity, Wind Speed, Snow Cover, Snow Depth | |
| Aplikasi Metode Nash Pada Perhitungan Limpasan Langsung Menggunakan Data Hujan GPM 3IMERGHH Studi Kasus SubDAS Winongo Hulu | Harsanto, Puji, Prihatmanti, Hanan Eko, Wisnulingga, Bayu Krisna | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Equatorial waves triggering extreme rainfall and floods in southwest Sulawesi, Indonesia | Latos, Beata, Lefort, Thierry, Flatau, Maria K., Flatau, Piotr J., Permana, Donaldi S., Baranowski, Dariusz B., Paski, Jaka A. I., Makmur, Erwin, Sulystyo, Eko, Peyrille, Philippe, Feng, Zhe, Matthews, Adrian J., Schmidt, Jerome M. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Spatio-temporal variability of monsoon precipitation and their effect on precipitation triggered landslides in relation to relief in Himalayas | Kashyap, Rahul, Pandey, Arvind Chandra, Parida, Bikash Ranjan | Terrain Elevation, Digital Elevation/Terrain Model (DEM), Topographical Relief Maps, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain |