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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| Synergistic mechanisms of gravity waves and windward slopes in | Xu, Yizhou, Li, Guoping, Zhang, Xiaoyu, Dong, Yuanchang, Wang, Kejun | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Smart Forecasting of Millimeterwave Window Frequency in Tropical Africa Using Machine Learning | Mukherjee, Vivekananda, Roy, Sandip, Chakraborty, Ayushi, Mondal, Bikas, Sadhukhan, Bikash, Maiti, Manabendra | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Urban Impacts on Precipitation in the Greater Ho Chi Minh City Metropolitan Area, Vietnam | Hasebe, Shun, Kusaka, Hiroyuki, Suzuki, Nobuyasu, NgoDuc, Thanh | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Urban rainfall trends in IMERG datasets | Sharma, Shankar, Evans, Jason P, Pitman, Andy J, Behrangi, Ali | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Validation of Satellite-based and Gridded Precipitation Products for Gap-filling in Precipitation Series in the Eastern Amazon | de Souza, Giselle Nerino Brito, de Franca e Silva, Julie Andrews, Ribeiro, Kaleb Lima, de Oliveira, Leonardo Ramos, da Silva, Paulo Ricardo Teixeira, Castellani, Debora Cristina, de Sousa Bueno Filho, Julio Silvio, Santiago, Alailson Venceslau, Vasconcelos, Steel Silva, Teixeira, Wenceslau Geraldes, de Araujo, Alessandro Carioca | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Widespread extreme precipitation events over Iran: Large-scale patterns and their associated global indices | Ghasemifar, Elham, Rashid, Irfan Ur, Planche, Celine, Baray, Jean-Luc, Almazroui, Mansour, Mishra, Manoranjan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Tropical TCV as a Process Diagnostic: Connecting Probability to Processes in kmScale Models Via Moisture Budget Statistics | Bassford, James, Marsham, John, Maybee, Ben, Parker, Douglas J. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| From single storms to large-scale waves: a multi-year kilometer-scale global simulation | Prein, Andreas F., Pothapakula, Praveen K., Zeman, Christian, Lalonde, Morgane, Rixen, Marius, Dipankar, Anurag, Leclair, Matthieu, Jocksch, Andreas | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Brightness Temperature | |
| Global climate modeling with improved precipitation characteristics by | Wang, Yiming, Zhang, Yi, Han, Yilun, Xue, Wei, Chen, Tianru, Zhou, Yihui, Li, Xiaohan, Chen, Haishan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Height-Dependent Sensitivity of Cloud Scales to Surface Temperature Anomaly Observed by Active Satellites | Zhang, Lijie, Li, Jiming, Cao, Zhenyu, Xu, Sihang, Xu, Qiudi, Jian, Bida, Wang, Yang, Wang, Yuan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Comprehensive Global Assessment of 24 Gridded Precipitation Datasets Across 18 428 Catchments Using Hydrological Modeling | Abbas, Ather, Yang, Yuan, Pan, Ming, Tramblay, Yves, Shen, Chaopeng, Ji, Haoyu, Gebrechorkos, Solomon H., Pappenberger, Florian, Pyo, JongCheol, Feng, Dapeng, Huffman, George, Nguyen, Phu, Massari, Christian, Brocca, Luca, Tan, Jackson, Beck, Hylke E. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Cloud-Rain Vertical Inconsistency Increases IMERG Precipitation Uncertainty | Zhang, Haoqian, Zhang, Aoqi, Chen, Yilun | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Does convection in atmospheric rivers at genesis impact precipitation | Naud, Catherine M., Luna-Nino, Rosa, Posselt, Derek J., Crespo, Juan A., Gershunov, A. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Double Low-Level Jets Over South China in the Warm Season: Diversity and Impacts | Zhou, Chunling, Chen, Guixing, Du, Yu, Su, Lin | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Disentangling key cloud properties for precipitation retrievals from geostationary satellite data using machine learning | Choi, Hwayon, Choi, Yong-Sang, Ho, Chang-Hoi, Kim, Jinwon | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| An observational study of the modulation of the diurnal variations by the intraseasonal oscillations of the Indian summer monsoon | Misra, Vasubandhu, Jayasankar, C. B. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Global Teleconnections of Extreme Rainfall Events in the Yellow River Basin | Cai, Lin, Yuan, Naiming, Boers, Niklas, Kurths, Juergen | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Spatial Classification of Diurnal Precipitation Cycle in the Tropical and Subtropical Regions Based on the Circular Statistical Analysis | Koad, Peeravit, Jandaeng, Chanankorn, Kongsen, Jongsuk, Somchuea, Sirirat | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Refine the uncertainty of GPM IMERG precipitation product accounting for the inherent error from rain gauges estimations | Li, Yue, Han, Bowei, Chen, Lin, Zhou, Renjun, Li, Rui | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Research on the response of the upper ocean to typhoons | Qi, Yu, Wang, Ying | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Precipitation Efficiency by Storm Type in km-Scale Climate Simulations and Satellite Observations | Kukulies, Julia, Prein, Andreas F., Done, James M., Stansfield, Alyssa M., RiosBerrios, Rosimar | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The MaddenJulian Oscillation and Equatorial Waves in Operational Forecasting | Peatman, Simon C., Hassim, Muhammad E. E., He, Yujia, Cheong, Wee Kiong, Moise, Aurel F., Ferrett, Samantha J., Lefort, Thierry, Nguyen, Hanh, Peyrille, Philippe, Wheeler, Matthew C., Xavier, Prince, Zhang, Chidong | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A flexible framework for precision reduction of WRF inputs and outputs to balance storage efficiency and scientific fidelity | Wu, Shang, Wong, David C., Wang, Jiandong, Jin, Yuzhi, Li, Junjun, Lu, Chunsong | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| 3D water vapor signature of tropical storms: GNSS tomography of the 10 most severe rainfall events in Hong Kong 20152025 | Mateus, P., Hui, D., Zhang, M., Fernandes, R., Miranda, P. M. A. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| 40 days and nights in the Rains: The inner life of the Atlantic ITCZ during BOWTIE | Klocke, Daniel, Wing, Allison A., Segura, Hans, Dengler, Marcus, Bell, Michael M., Ruppert, James H., George, Geet, Kalesse-Los, Heike, Nuijens, Louise, Moller, Klas Ove, Kiko, Rainer, Mohr, Wiebke, Kidane, Abiel T., Trosits, Anna, Mertz, Charlotte, Imker, Celine, Begler, Christian, Blandfort, Daniel, Colon-Burgos, Delian, Austen, Dominik, Junyent, Francesc, Schmidt, Hauke, Habib, Joelle, van der Giessen, Judith, Wieners, Karl-Hermann, Hayo, Lennea, Stelzner, Martin, Lovato, Mateo, ODriscoll, Owen, Paschou, Peristera, Henning, Philipp, Mackenzie, Rob, Wimmer, Werenfrid, Serikov, Ilya, Brugmann, Bjorn, Wu, Yuting, Stevens, Bjorn, Windmiller, Julia | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain |