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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| The Role of Regional OceanAtmosphere Coupling in Simulating the 2020 Extreme Mei-yu Event | Li, Kai, Zou, Liwei, Dan, Li, Zheng, Hui, Xu, Zhongfeng, Tang, Jianping, Yang, Fuqiang, Fei, Wenli, Zhang, Taotao, Shi, Chunxiang, Yang, Zong-Liang | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The Response of Tropical Cyclone Inner Core and Outer Rainband | Stansfield, Alyssa M., Rasmussen, Kristen L. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The Diurnal Variation of Elevated Convection over the Great Plains | Verevkin, Iaroslav, Folkins, Ian | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The Impacts of Rotational Mixing on the Precipitation Simulated by a | Hagos, Samson, Feng, Zhe, Varble, Adam C., Tai, ShengLun, Chen, Jingyi | Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The 2023 Turkiye-Syria earthquake disaster was exacerbated by an | Gorum, Tolga, Bozkurt, Deniz, Korup, Oliver, Istanbulluoglu, Erkan, Sen, Omer Lutfi, Ylmaz, Abdussamet, Karabacak, Furkan, Lombardo, Luigi, Guan, Bin, Tanyas, Hakan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Sedimentation in Saudi Arabia's 574 reservoirs: Nationwide assessment | Dash, Sonam S., Ivanovic, Nikola, Alharbi, Raied, Hancock, Gregory R., Wada, Yoshihide, McCabe, Matthew F., Pal, Debasish, Marttila, Hannu, Beck, Hylke E. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Spatial Analysis of Meteorological Drought in Southeast Asia and Australia Region | Yosa, I, Suwarman, R, Syahputra, M | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Simultaneous Reductions in Forest Resilience and Greening Trends in Southwest China | Wu, Huiying, Cui, Tianxiang, Cao, Lin | Land Surface Temperature, Emissivity, Albedo, Anisotropy, Reflectance, Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Sharpening Mesoscale Convective Systems Induced by Enhanced | Ding, Tian, Zhou, Tianjun, Guo, Zhun, Zou, Qian | Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Seasonal climate variations drive decoupling between the duration and | Wang, Wenjin, Qin, Li, Zhang, Tongwen, Yang, Feiyu, Cabon, Antoine, Wang, Zhou, Zhou, Peng, Zhang, Yaling, Fonti, Patrick, Huang, JianGuo | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Sensitivity to Data Choice for Index-Based Flood Insurance | Saunders, Alex, Tellman, Beth, Benami, Elinor, Anchukaitis, Kevin, Hossain, Sazzad, Bennett, Andrew, Islam, A. K. M. Saiful, Giezendanner, Jonathan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Sensitivity of a Kilometer-Scale Variable-Resolution Global Nonhydrostatic Model to Microphysics Schemes in Simulating a Mesoscale Convective System | Zhou, Yihui, Yu, Rucong, Zhang, Yi, Li, Jian, Chen, Haoming | Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Summer Mesoscale Convective Systems in Convection-Permitting Simulation | Lu, Yutong, Marsham, J. H., Tang, Jianping, Parker, D. J., Fang, Juan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Spatiotemporal bias correction of satellite precipitation products using | M R, Sneha, Nair, Archana, Somasundaram, K. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Variance-based sensitivity analysis of climate variability impact on | Xu, Yingqiang, Albalawneh, Abeer, Al-Zoubi, Maysoon, Baroud, Hiba | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, RADAR IMAGERY, Terrain Elevation, Digital Elevation/Terrain Model (DEM), Land Use/Land Cover Classification, Plant Phenology, Enhanced Vegetation Index (EVI), Vegetation Index, Normalized Difference Vegetation Index (NDVI), Topographical Relief Maps, Land Surface Temperature, Emissivity | |
| Wet and dry seasons modulate coastal coccolithophore dynamics off | Adekunbi, Falilu O., Grelaud, Michael, Langer, Gerald, Chukwu, Lucian O., Alvarez, Marta, Odunuga, Shakirudeen, Schulz, Kai G., Ziveri, Patrizia | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Water vapour isotopes over West Africa as observed from space: which processes control tropospheric H2O HDO pair distributions? | Diekmann, Christopher Johannes, Schneider, Matthias, Knippertz, Peter, Trent, Tim, Boesch, Hartmut, Roehling, Amelie Ninja, Worden, John, Ertl, Benjamin, Khosrawi, Farahnaz, Hase, Frank | Hydrogen-deuterium Oxide, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Unveiling hidden dynamics: fine-scale mapping of groundwater-dependent ecosystems using multi-source Earth observations | Liang, Yu, Cao, Chunyan, Zhu, Xiaoyu, Gao, Sicong, Zhang, Yongqiang, Ma, Xuanlong | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| A novel drought index integrating GNSS and precipitation data for drought monitoring in Brazil | Chen, Wei, Tang, Miao, Jiang, Zhongshan, Zhong, Min, Feng, Wei | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Comparative Analysis of Spatiotemporal Variability of Groundwater Storage in Iraq Using GRACE Satellite Data | Mohammed, Hanan Kaduim, Alwan, Imzahim A., Al-Khafaji, Mahmoud Saleh | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Enhanced precipitation prediction through the integration of gauge observations with satellite-based precipitation prediction models utilizing the Bayesian model ... | Binti Mahmud, Husniyah, Osawa, Takahiro | Total Surface Precipitation Rate, Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Enhancing Spatial Resolution of GPM Rainfall Data in Upper Cauvery Basin, India: Machine Learning Approach | Pradeep Kumar, G, Saicharan, Vasala, Shwetha, H. R | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Coupled tropospheric and stratospheric dynamics of Kelvin waves over | Szkolka, Wojciech, Baranowski, Dariusz B., Flatau, Maria K., Flatau, Piotr J., Marzuki, Shimomai, Toyoshi, Hashiguchi, Hiroyuki | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Disaggregating IMERG satellite precipitation over Czech Republic: an | Singh, Ujjwal, Nasreen, Sadaf, Tripathi, Gaurav, Mehrishi, Pragya, Pradhan, Rajani Kumar, Bestakova, Poppova, Singh, Vivek Vikram, Gouda, K C, Sharma, Laxmi Kant, Jalem, Kiran, Maca, Petr, Nidamanuri, Rama Rao, Raghubanshi, Akhilesh Singh, Markonis, Yannis, Oldrich, Rakovec, Hanel, Martin | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Distinct impacts of different marine heatwaves on precipitation | Zeng, Shijie, Dong, Lu, Wu, Lixin, Song, Fengfei, Zhang, Zhengguang, Jing, Zhao | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain |