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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| Mesoscale convective systems over the Amazon basin in a changing climate | Rehbein, Amanda, Ambrizzi, Tercio | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Learnings from rapid response efforts to remotely detect landslides triggered by the August 2021 Nippes earthquake and Tropical Storm Grace in Haiti | Amatya, Pukar, Scheip, Corey, Deprez, Aline, Malet, Jean-Philippe, Slaughter, Stephen L., Handwerger, Alexander L., Emberson, Robert, Kirschbaum, Dalia, Jean-Baptiste, Julien, Huang, Mong-Han, Clark, Marin K., Zekkos, Dimitrios, Huang, Jhih-Rou, Pacini, Fabrizio, Boissier, Enguerran | Terrain Elevation, RADAR IMAGERY, Topographical Relief Maps, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Implementation of a probability matching method in developing intensitydurationfrequency relationships for sub-daily durations using IMERG satellite-based data | Najafi Tireh Shabankareh, Rahim, Abedini, Mohammad Javad | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Investigating sources of variability in closing the terrestrial water balance with remote sensing | Michailovsky, Claire I., Coerver, Bert, Mul, Marloes, Jewitt, Graham | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Interannual variation of coastal upwelling around Hainan Island | Zhu, Junying, Zhou, Quanyi, Zhou, Qianqing, Geng, Xinxing, Shi, Jie, Guo, Xinyu, Yu, Yang, Yang, Ziwei, Fan, Renfu | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Intercomparison of Automated Near-Real-Time Flood Mapping Algorithms Using Satellite Data and DEM-Based Methods: A Case Study of 2022 Madagascar Flood | Li, Wenzhao, Li, Dongfeng, Fang, Zheng N. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Improving near-real-time satellite precipitation products through | Meng, Chengcheng, Mo, Xingguo, Liu, Suxia, Hu, Shi | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow, Total Surface Precipitation Rate | |
| Impact of Convection-Permitting and Model Resolution on the Simulation | Ding, Tian, Guo, Zhun, Zou, Liwei, Zhou, Tianjun | Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| IMERG Precipitation Improves the SMAP Level-4 Soil Moisture Product | Reichle, Rolf H., Liu, Qing, Ardizzone, Joseph V., Crow, Wade T., De Lannoy, Gabrielle J. M., Kimball, John S., Koster, Randal D. | Surface Soil Moisture, Soil Moisture/Water Content, Soil Temperature, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Brightness Temperature | |
| High-resolution regional climate modeling of warm-season precipitation | Liu, Heng, Liu, Xiaodong, Liu, Changhai, Yun, Yuxing | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| How well does the IMERG satellite precipitation product capture the timing of precipitation events? | Li, Runze, Guilloteau, Clement, Kirstetter, Pierre-Emmanuel, Foufoula-Georgiou, Efi | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow, Liquid Precipitation, Precipitation Rate, Radar Reflectivity | |
| Hydraulic design of granular and geocomposite drainage layers in pavements based on demand-capacity modeling | Kalore, Shubham A., Sivakumar Babu, G.L. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impact of land surface processes on convection over West Africa in convectionpermitting ensemble forecasts: A case study using the MOGREPS ensemble | Semeena, Valiyaveetil Shamsudheen, Klein, Cornelia, Taylor, Christopher M., Webster, Stuart | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impact of Soil Moisture in the Monsoon Region of South America during | Arsego, Vivian Bauce Machado, de Goncalves, Luis Gustavo Goncalves, Arsego, Diogo Alessandro, Figueroa, Silvio Nilo, Kubota, Paulo Yoshio, de Souza, Carlos Renato | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Impacts of Direct Assimilation of the FY-4A/GIIRS Long-Wave Temperature | Zhang, Lei, Niu, Zeyi, Weng, Fuzhong, Dong, Peiming, Huang, Wei, Zhu, Jia | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Improving satellite-based global rainfall erosivity estimates through | Fenta, Ayele Almaw, Tsunekawa, Atsushi, Haregeweyn, Nigussie, Yasuda, Hiroshi, Tsubo, Mitsuru, Borrelli, Pasquale, Kawai, Takayuki, Sewale Belay, Ashebir, Ebabu, Kindiye, Liyew Berihun, Mulatu, Sultan, Dagnenet, Asamin Setargie, Tadesaul, Elnashar, Abdelrazek, Panagos, Panos | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow, Condensation, Evaporation, Sublimation, Cloud Liquid Water/Ice, Cloud Precipitable Water, Liquid Water Equivalent, Heat Flux | |
| Improving Short-Term QPF Using Geostationary Satellite All-Sky Infrared | Zhang, Yunji, Chen, Xingchao, Bell, Michael M. | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland | Brieva, Carlos, Saco, Patricia M., Sandi, Steven G., Mora, Sebastian, Rodriguez, Jose F. | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Revisiting the Relationship Between Tropical Cyclone Rapid | Shi, Donglei, Chen, Guanghua, Xie, Xinru | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Review on spatial downscaling of satellite derived precipitation | Kofidou, Maria, Stathopoulos, Stavros, Gemitzi, Alexandra | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate, Droplet Size, Radar Reflectivity | |
| Resilience assessment of a highwayrailway complementary network under rainstorms | Chen, Jinqu, Liang, Cheng, Liu, Jie, Du, Bo, Yin, Yong, Peng, Qiyuan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Remote Sensing-Based Estimates of Changes in Stored Groundwater at Local Scales: Case Study for Two Groundwater Subbasins in California's Central ... | Ahamed, Aakash, Knight, Rosemary, Alam, Sarfaraz, Morphew, Michael, Susskind, Tea | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Response of xylem formation of Larix sibirica to climate change along the southern Altai Mountains, Central Asia | Wang, Wenjin, Huang, Jian-Guo, Jiang, Shaowei, Yu, Biyun, Zhou, Peng, Zhang, Yaling | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Representing the Subgrid Surface Heterogeneity of Precipitation in a | Arnold, Nathan P., Koster, Randal D., Trayanov, Atanas L. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Regional spatial patterns of three water ecosystems services | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow |