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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| 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 | |
| The Response of Tropical Cyclone Inner Core and Outer Rainband | Stansfield, Alyssa M., Rasmussen, Kristen L. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| 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 | |
| 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 | |
| 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 | |
| Decadal seasonal characteristics of precipitation microphysics over the Western Ghats using the space-borne precipitation radar | Kumar, Amit, Srivastava, Atul Kumar, Mehrotra, Bharat Ji, Srivastava, Manoj Kumar, Pattanaik, D.R. | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Contamination of Fusarium spp. and mycotoxins at different ear physiological stages of maize in Argentina | Arata, Agustin F., Martinez, Mauro, Pesquero, Natalia V., Cristos, Diego, Dinolfo, Maria I. | Heat Flux, Air Temperature, Skin Temperature, Specific Humidity, Water Vapor, Precipitation Rate, 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, Precipitation, Precipitation Amount, Snow, Rain | |
| Contrasts of Large-Scale Moisture and Heat Budgets between Different Sea Areas of the South China Sea and the Adjacent Land | Zhang, Chunyan, Wang, Donghai, Yao, Lebao, Wu, Zhenzhen, Ma, Qianhui, Li, Yongsheng, Wang, Peidong | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Deriving location-specific synthetic seasonal hyetographs using GPM records and comparing with SCS curves | Ram, Bhavin, Gaur, Murari Lal, Patel, Gautam R., Tiwari, M. K. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluating flood hazards in data-sparse coastal lowlands: highlighting | Seeger, Katharina, Peffekover, Andreas, Minderhoud, Philip S J, Vogel, Anissa, Bruckner, Helmut, Kraas, Frauke, Oo, Nay Win, Brill, Dominik | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Estimating Reactivation Times and Velocities of Slow-Moving Landslides | Ghaderpour, Ebrahim, Masciulli, Claudia, Zocchi, Marta, Bozzano, Francesca, Scarascia Mugnozza, Gabriele, Mazzanti, Paolo | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Freeze-thaw cycles and associated geomorphology in a post-glacial environment: current glacial, paraglacial, periglacial and proglacial scenarios at Pico de Orizaba ... | Soto, Victor, Welsh R., Carlos M., Yoshikawa, Kenji, Delgado Granados, Hugo | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| HSPEI: A 1-km spatial resolution SPEI dataset across the Chinese | Xia, Haoming, Sha, Yintao, Zhao, Xiaoyang, Jiao, Wenzhe, Song, Hongquan, Yang, Jia, Zhao, Wei, Qin, Yaochen | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow, Land Surface Temperature, Emissivity | |
| IMERG V07B and V06B: A Comparative Study of Precipitation Estimates | Rozante, Jose Roberto, Rozante, Gabriela | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Improvement of cloud microphysical parameterization and its advantages | Xu, Xiaoqi, Heng, Zhiwei, Li, Yueqing, Wang, Shunjiu, Li, Jian, Wang, Yuan, Chen, Jinghua, Zhang, Peiwen, Lu, Chunsong | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impact of Fusarium spp. on different maize commercial hybrids: disease evaluation and mycotoxin contamination | Arata, Agustin F., Martinez, Mauro, Castellari, Claudia, Cristos, Diego, Pesquero, Natalia V., Dinolfo, Maria I. | Heat Flux, Air Temperature, Skin Temperature, Specific Humidity, Water Vapor, Precipitation Rate, 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, Atmospheric Carbon Monoxide, Carbon Monoxide, Atmospheric Ozone, Precipitation, Precipitation Amount, Snow, Rain | |
| Impacts of Northward Typhoons on Autumn Haze Pollution Over North China | Lin, Haoxian, Ding, Ke, Huang, Xin, Lou, Sijia, Xue, Lian, Wang, Zilin, Ma, Yue, Ding, Aijun | 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, Dust/Ash/Smoke, Carbonaceous Aerosols, Organic Particles, Sulfate Particles, Sulfur Oxides, Sulfur Compounds, Sulfate, Sulfur Dioxide, Sulfur Oxides, Dimethyl Sulfide, Aerosol Particle Properties, Particulate Matter, Aerosols, Pressure Thickness, Sea Salt, Black Carbon, Air Mass/Density, Surface Temperature, Skin Temperature, Atmospheric Winds, Surface Winds, Atmospheric Pressure, Total Precipitable Water, Oxygen Compounds, Boundary Layer Winds, Aerosol Optical Depth/Thickness, Aerosol Backscatter, Aerosol Extinction, Angstrom Exponent, Aerosol Radiance, Cloud Condensation Nuclei, Nitrate Particles, Trace Gases/Trace Species, Atmospheric Emitted Radiation, Emissivity, Optical Depth/Thickness, Radiative Flux, Reflectance, Transmittance, Atmospheric Stability, Humidity, Water Vapor Profiles, Cloud Condensation Nuclei, Cloud Droplet Concentration/Size, Cloud Optical Depth/Thickness, Cloud Asymmetry, Cloud Ceiling, Cloud Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Cloud Vertical Distribution, Cloud Emissivity, Cloud Radiative Forcing, Cloud Reflectance, Rain Storms, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Drought timing differentiates the drought responses of vegetation growth on the Tibetan Plateau | Meng, Zekai, Wu, Xiuchen, Li, Yang, Wang, Xiaona | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Chlorophyll, Leaf Characteristics, Solar Induced Fluorescence, Reflectance, Photosynthesis, Primary Production | |
| Empirical and Machine Learning Models for Soil Erosion Risk Assessment: A Case Study of Tsageri Municipality, Georgia | Tkeshelashvili, Nika | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Classification of tropical cyclone rain patterns using convolutional autoencoder | Kim, Dasol, Matyas, Corene J. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Characteristics of Warm-Season Mesoscale Convective Systems Over the | Lu, Yutong, Tang, Jianping, Xu, Xin, Tang, Ying, Fang, Juan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Influence of an abnormally cold stratospheric polar vortex on the sub-regional PM2.5 anomaly in East Asia in March of 2021 | Cho, Jae-Hee, Kim, Hak-Sung | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Mesoscale structures in the Orinoco basin during an extreme | Martinez, J. Alejandro, Arias, Paola A., Dominguez, Francina, Prein, Andreas | Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate |