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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| Hydrological regime of Sahelian small water bodies from combined Sentinel-2 MSI and Sentinel-3 SRAL data | de Fleury, Mathilde, Kergoat, Laurent, Grippa, Manuela | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impacts of the Western Pacific and Indian Ocean warm pools on wildfires in Yunnan, Southwest China: Spatial patterns with interand intraannual variations | Ying, Lingxiao, Shen, Zehao, Guan, Pingao, Cao, Jie, Luo, Caifang, Peng, Xingzi, Cheng, Hujiao | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Increased chlorophyll-a concentration in Barra Bonita reservoir during | Jang, Matheus Tae Geun, Alcantara, Enner, Rodrigues, Thanan, Park, Edward, Ogashawara, Igor, Marengo, Jose A. | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Progress in Developing Scale-Able Approaches to Field-Scale Water Accounting Based on Remote Sensing | Vervoort, Rutger Willem, Fuentes, Ignacio, Brombacher, Joost, Degen, Jelle, Chambel-Leitao, Pedro, Santos, Flavio | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Performance evaluation, error decomposition and Tree-based Machine Learning error correction of GPM IMERG and TRMM 3B42 products in the Three Gorges ... | Lin, Qingxia, Peng, Tao, Wu, Zhiyong, Guo, Jiali, Chang, Wenjuan, Xu, Zhengguang | Terrain Elevation, Digital Elevation/Terrain Model (DEM), Topographical Relief Maps, Total Surface Precipitation Rate, Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Statistical Analysis of CyGNSS Speckle and Its Applications to Surface Water Mapping | Liu, B., Wan, W., Tang, Guoqiang, Li, H., Guo, Z., Chen, X., Hong, Yang | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Spatiotemporal Variation and Driving Analysis of Groundwater in the | Gao, Guangli, Zhao, Jing, Wang, Jiaxue, Zhao, Guizhang, Chen, Jiayue, Li, Zhiping | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Spatiotemporal variation of snow cover and its relationship with | Guo, Ruo-yu, Ji, Xuan, Liu, Chun-yu, Liu, Chang, Jiang, Wei, Yang, Lu-yi | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Solid water melt dominates the increase of total groundwater storage in the Tibetan Plateau | Zou, Yiguang, Kuang, Xingxing, Feng, Yuqing, Jiao, Jiu Jimmy, Liu, Junguo, Wang, Can, Fan, Linfeng, Wang, Qingjing, Chen, Jianxin, Ji, Fang, Yao, Yingying, Zheng, Chunmiao | Surface Pressure, Heat Flux, Longwave Radiation, Shortwave Radiation, Surface Temperature, Humidity, Evapotranspiration, Surface Winds, Rain, Precipitation Rate, Snow, Soil Moisture/Water Content, Soil Temperature, Land Surface Temperature, Snow Water Equivalent, Runoff, Ground Water, Precipitation, Precipitation Amount, Soil Heat Budget, Soil Heat Budget, Soil Temperature, Soil Infiltration, Soil Infiltration, Surface Soil Moisture, Root Zone Soil Moisture, Soil Moisture/Water Content, Evaporation, Surface Water, Total Runoff, Average Flow, Average Flow, Snow/Ice, Snow Depth, Snow Melt, Snow/Ice Temperature, Leaf Area Index (LAI), Leaf Area Index (LAI) | |
| Validation of the final monthly Integrated Multi-satellitE Retrievals for GPM (IMERG) Version 05 and Version 06 with ground-based precipitation gauge measurements ... | Eckert, Ellen, Hudak, David, Mekis, Eva, Rodriguez, Peter, Zhao, Bo, Mariani, Zen, Melo, Stella, Strong, Kimberly, Walker, Kaley A. | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Updates on CYGNSS Ocean Surface Wind Validation in the Tropics | Asharaf, Shakeel, Posselt, Derek J., Said, Faozi, Ruf, Christopher S. | Radar Cross-Section, Surface Winds, Surface Winds, SIGMA NAUGHT, Radar Reflectivity, Radar Cross-Section, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Tropical Continents Rainier Than Expected From Geometrical Constraints | Hohenegger, Cathy, Stevens, Bjorn | Total Surface Precipitation Rate, Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| A Novel Reference-Based and Gradient-Guided Deep Learning Model for Daily Precipitation Downscaling | Xiang, Li, Xiang, Jie, Guan, Jiping, Zhang, Fuhan, Zhao, Yanling, Zhang, Lifeng | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Comparison of flow simulations with sub-daily and daily GPM IMERG products over a transboundary Chenab River catchment | Ahmed, Ehtesham, Al Janabi, Firas, Yang, Wenyu, Ali, Akhtar, Saddique, Naeem, Krebs, Peter | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Cooling by Cyprus Lows of Surface and Epilimnion Water in Subtropical Lake Kinneret in Rainy Seasons | Kishcha, Pavel, Lechinsky, Yury, Starobinets, Boris | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow, Heat Flux, Air Temperature, Skin Temperature, Specific Humidity, Water Vapor, Snow/Ice, Evaporation, Latent Heat Flux, Latent Heat Flux, Sensible Heat Flux, Diffusion, Surface Winds, Wind Speed, U/V Wind Components, Wind Stress, Wind Stress, Surface Roughness, Planetary Boundary Layer Height, Ice Fraction | |
| Comparing and contrasting the performance of high-resolution precipitation products via error decomposition and triple collocation: an application to different climate ... | Ghomlaghi, Arash, Nasseri, Mohsen, Bayat, Bardia | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| An Extraordinary Dry Season Precipitation Event in the Subtropical Andes: Drivers, Impacts and Predictability | Valenzuela, Raul, Garreaud, Rene, Vergara, Ivan, Campos, Diego, Viale, Maximiliano, Rondanelli, Roberto | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| An attention mechanism based convolutional network for satellite precipitation downscaling over China | Jing, Yinghong, Lin, Liupeng, Li, Xinghua, Li, Tongwen, Shen, Huanfeng | Total Surface Precipitation Rate, Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| A review of downscaling methods of satellite-based precipitation estimates | Abdollahipour, Arman, Ahmadi, Hassan, Aminnejad, Babak | Total Surface Precipitation Rate, Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| A scoping review of flash floods in Malaysia: current status and the way forward | Maqtan, Raidan, Othman, Faridah, Wan Jaafar, Wan Zurina, Sherif, Mohsen, El-Shafie, Ahmed | Total Surface Precipitation Rate, Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Emission, transport, deposition, chemical and radiative impacts of mineral dust during severe dust storm periods in March 2021 over East Asia | Liang, Lin, Han, Zhiwei, Li, Jiawei, Xia, Xiangao, Sun, Yele, Liao, Hong, Liu, Ruiting, Liang, Mingjie, Gao, Yuan, Zhang, Renjian | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Event-Based Bias Correction of the GPM IMERG V06 Product by Random Forest Method over Mainland China | Liu, Zhenyu, Hou, Haowen, Zhang, Lanhui, Hu, Bin | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Evaluation of multisource precipitation input for hydrological modeling in an Alpine basin: a case study from the Yellow River Source Region (China) | Gu, Pengfei, Wang, Gaoxu, Liu, Guodong, Wu, Yongxiang, Liu, Hongwei, Jiang, Xi, Liu, Tao | Total Surface Precipitation Rate, Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Landslide Likelihood Prediction using Machine Learning Algorithms | Acharya, Vasundhara, Ghosh, Anindita, Kang, Inwon, Munasinghe, Thilanka, Binita, K C | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow, Landslides | |
| Insights into hydrological drought characteristics using GNSS-inferred large-scale terrestrial water storage deficits | Jiang, Zhongshan, Hsu, Ya-Ju, Yuan, Linguo, Cheng, Shuai, Feng, Wei, Tang, Miao, Yang, Xinghai | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow |