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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| 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 | |
| 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 | |
| Mesoscale Convective Systems in Africa and the Atlantic Ocean Under a Km-Scale Future Climate Scenario | Nunez Ocasio, Kelly M., Dougherty, Erin M., Moon, Zachary L. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Brightness Temperature | |
| Northward shift of transboundary air pollution pathways over the western Pacific under climate change | Cai, Ying, Michibata, Takuro, Irie, Hitoshi | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The catastrophic floods in 2008, 2010 and 2020 in western Ukraine: Hydrometeorological processes and the role of upper-level dynamics | Agayar, Ellina, Armon, Moshe, Sprenger, Michael, Wernli, Heini | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation and future changes of mesoscale convective systems over the conterminous United States in highresolution global and regional simulations | Fu, Dan, Prein, Andreas F. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Brightness Temperature | |
| Effects of Scale-Aware Convection Schemes on Typhoon Simulation across Varying Resolutions | Liu, Yanjie, Wang, Xiaocong, Liu, Yimin, Miao, Hao, Zhao, Dajun, Huang, Wei, Zhu, Xuesong, Zhao, Yaxin | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| EMMA-Tracker v1. 0: a mesoscale convective system tracker and 27-year European observational climatology | Kneidinger, David, Schaffer, Armin, Maraun, Douglas | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Enhancing Great Plains Nocturnal Precipitation and Low-Level Jets in AM4 With an Extended CLUBB Closure | Gentile, Emanuele Silvio, Larson, Vincent E., Zhao, Ming, Zarzycki, Colin, Svensson, Gunilla, Donner, Leo | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Storm-Resolving Earth: How Well Do Global Kilometer-scale Models Simulate Storms in East Asia's 2020 Record-breaking Wet Summer? | Huang, Xiaotong, Li, Puxi, Yu, Hongyong, Hu, Xuelin, Chen, Haoming, Li, Jian, Prein, Andreas F., Zhang, Yuanchun, Zhuang, Moran, Wu, Yufei, Zhou, Tianjun | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Brightness Temperature | |
| Unified Global Landslide Catalogue (UGLC): a single, standardised global-scale landslide dataset | Mancino, Saverio, Sblano, Anna, Lovergine, Francesco Paolo, Massimi, Vincenzo, Sethi, Tushar, Capolongo, Domenico, Amatulli, Giuseppe | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Quantifying the ratio of nonSynoptically forced precipitation events over CONUS using the quasigeostrophic omega equation | Gyawali, Nabindra, Rose, Brian E. J., Ferguson, Craig R. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Atmospheric Ozone, Sea Level Pressure, Surface Pressure, U/V Wind Components, U/V Wind Components, Potential Vorticity, Vertical Wind Velocity/Speed, Vertical Profiles, Upper Air Temperature, Air Temperature, Relative Humidity, Specific Humidity, Atmospheric Water Vapor, Cloud Liquid Water/Ice, Altitude, Geopotential Height, Ozone Profiles, Heat Flux, Skin Temperature, Water Vapor, Snow/Ice, Evaporation, Latent Heat Flux, Latent Heat Flux, Sensible Heat Flux, Diffusion, Surface Winds, Wind Speed, Wind Stress, Wind Stress, Surface Roughness, Planetary Boundary Layer Height, Ice Fraction, Carbon Monoxide, Tropopause, Methane, Outgoing Longwave Radiation, Humidity, Total Precipitable Water, Water Vapor Profiles, Cloud Droplet Concentration/Size, Cloud Optical Depth/Thickness, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Cloud Vertical Distribution, Cloud Types, Emissivity, Skin Temperature, Sea Surface Temperature | |
| Remote sensing-based assessment of long-term evapotranspiration and | Rodriguez-Alvarez, Jose A., Mateos, Luciano, Egea, Gregorio | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Representation of Convectively Coupled Equatorial Waves (CCEWs) During Boreal Summer in IITM-IMD GFS T1534 Model Reforecast | Himabindu, H., Mukhopadhyay, P., Deshpande, Medha, Tirkey, Snehlata, Sarkar, Sahadat, Ganai, Malay, Krishna, R. P. M. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| RFGWRK: A hybrid downscaling framework for high-resolution precipitation mapping in geohazard-prone mountainous regions | Zhang, Simin, Zheng, Zeshuang, Ding, Jun, Yang, Shengbing, Zeng, Yuan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Urbanization, air pollution, associated islands, and health hazards across six IGP cities | Mukherjee, Asmita, Panda, Jagabandhu, Roy, Debjyoti, Tom, Geo, Sarkar, Ankan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessment of Groundwater Potential in North-Central Palawan using Remote Sensing and Geophysical Analysis of Fractured Basement Aquifers | Cuevas, Joshua Godwin, Cari, John Esteban E., Principe, Jeark A., Tamondong, Ayin M., Dimalanta, Carla B., Armada, Leo T. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A hybrid machine learning model for flood prediction with recursive feature elimination informed by training performance | Gong, Liying, Woo, Wai Lok, Wu, Yue Ivan, Zheng, Xiujuan | RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps, Digital Elevation/Terrain Model (DEM), Land Use/Land Cover Classification, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| 25-years study (20002024) of extreme precipitation following heatwaves in the Middle East: Regional patterns, trends, and atmospheric drivers | Ghasemifar, Elham, Planche, Celine, Baray, Jean-Luc, Almazroui, Mansour, Rashid, Irfan Ur, Moradi, Sakine, Topuz, Muhammet | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A Synoptic-Scale Anticyclone Bridging Typhoon In-Fa (2021) and the | Liu, Jiayi, Tao, Li, Wang, Yuqing | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impacts of cold surges on the synoptic changes of the western North Pacific anticyclone in winter | Liu, Qian, Huang, Ling, Bai, Lanqiang, Chen, Guixing | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain |
Variables
The table below lists the variables contained within a single granule for this dataset. Variables often contain observed or derived geophysical measurements collected from a variety of sources, including remote sensing instruments on satellite and airborne platforms, field campaigns, in situ measurements, and model outputs. The terms variable, parameter, scientific data set, layer, and band have been used across NASA’s Earth science disciplines; however, variable is the designated nomenclature in NASA’s Common Metadata Repository (CMR). Variable metadata attributes such as Name, Description, Units, Data Type, Fill Value, Valid Range, and Scale Factor allow users to efficiently process and analyze the data. The full range of attributes may not be applicable to all variables. Additional information on variable attributes is typically available in the data, user guide, and/or other product documentation.
For questions on a specific variable, please use the Earthdata Forum.
| Name Sort descending | Description | Units | Data Type | Fill Value | Valid Range | Scale Factor | Offset |
|---|---|---|---|---|---|---|---|
| Grid/Intermediate/IRinfluence | Grid/Intermediate/IRinfluence | N/A | int16 | -9999 | N/A | N/A | N/A |
| Grid/Intermediate/IRprecipitation | Grid/Intermediate/IRprecipitation | mm/hr | float32 | -9999.900390625 | N/A | N/A | N/A |
| Grid/Intermediate/MWobservationTime | Grid/Intermediate/MWobservationTime | minutes | int16 | -9999 | N/A | N/A | N/A |
| Grid/Intermediate/MWprecipitation | Grid/Intermediate/MWprecipitation | mm/hr | float32 | -9999.900390625 | N/A | N/A | N/A |
| Grid/Intermediate/MWprecipSource | Grid/Intermediate/MWprecipSource | N/A | int16 | -9999 | N/A | N/A | N/A |
| Grid/Intermediate/precipitationUncal | Grid/Intermediate/precipitationUncal | mm/hr | float32 | -9999.900390625 | N/A | N/A | N/A |
| Grid/lat | Grid/lat | degrees_north | float32 | N/A | N/A | N/A | N/A |
| Grid/lat_bnds | Grid/lat_bnds | degrees_north | float32 | N/A | N/A | N/A | N/A |
| Grid/lon | Grid/lon | degrees_east | float32 | N/A | N/A | N/A | N/A |
| Grid/lon_bnds | Grid/lon_bnds | degrees_east | float32 | N/A | N/A | N/A | N/A |
| Grid/precipitation | Grid/precipitation | mm/hr | float32 | -9999.900390625 | N/A | N/A | N/A |
| Grid/precipitationQualityIndex | Grid/precipitationQualityIndex | N/A | float32 | -9999.900390625 | N/A | N/A | N/A |
| Grid/probabilityLiquidPrecipitation | Grid/probabilityLiquidPrecipitation | percent | int16 | -9999 | N/A | N/A | N/A |
| Grid/randomError | Grid/randomError | mm/hr | float32 | -9999.900390625 | N/A | N/A | N/A |
| Grid/time | Grid/time | seconds since 1980-01-06 00:00:00 UTC | int32 | N/A | N/A | N/A | N/A |
| Grid/time_bnds | Grid/time_bnds | seconds since 1980-01-06 00:00:00 UTC | int32 | N/A | N/A | N/A | N/A |