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 Older versions will no longer be available and have been superseded by Version 07.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.
Precipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure.
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Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| A stochastic framework for rainfall intensitytime scalereturn period relationships. Part : point modelling and regionalization over Greece | Iliopoulou, Theano, Koutsoyiannis, Demetris, Malamos, Nikolaos, Koukouvinos, Antonis, Dimitriadis, Panayiotis, Mamassis, Nikos, Tepetidis, Nikos, Markantonis, David | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessment of physical schemes for WRF model in convection-permitting mode over southern Iberian Peninsula | Solano-Farias, Feliciano, Garcia-Valdecasas Ojeda, Matilde, Donaire-Montano, David, Rosa-Canovas, Juan Jose, Castro-Diez, Yolanda, Esteban-Parra, Maria Jesus, Gamiz-Fortis, Sonia Raquel | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Bridging spatio-temporal discontinuities in global soil moisture mapping by coupling physics in deep learning | Wei, Zushuai, Miao, Linguang, Peng, Jian, Zhao, Tianjie, Meng, Lingkui, Lu, Hui, Peng, Zhiqing, Cosh, Michael H., Fang, Bin, Lakshmi, Venkat, Shi, Jiancheng | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Brief communication: Storm Daniel flood impact in Greece in 2023: mapping crop and livestock exposure from synthetic-aperture radar (SAR) | He, Kang, Yang, Qing, Shen, Xinyi, Dimitriou, Elias, Mentzafou, Angeliki, Papadaki, Christina, Stoumboudi, Maria, Anagnostou, Emmanouil N. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Causal analysis of unprecedented landslides during July 2021 in the Western Ghats of Maharashtra, India | Jain, Nirmala, Roy, Priyom, Martha, Tapas R., Sekhar, Nataraja P., Kumar, K. Vinod | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Case Study of Climate Change Effects on a Water Distribution System Design in Ha Leronti, Lesotho, Africa | Peterson, Sarah, Barkdoll, Brian | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Capability of satellite data to estimate observed precipitation in | Benitez, Victoria D., Forgioni, Fernando P., Lovino, Miguel A., Sgroi, Leandro, Doyle, Moira E., Muller, Gabriela V. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Atmospheric HONO emissions in China: Unraveling the spatiotemporal patterns and their key influencing factors | Gan, Cong, Li, Baojie, Dong, Jinyan, Li, Yan, Zhao, Yongqi, Wang, Teng, Yang, Yang, Liao, Hong | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Automated Flood Prediction along Railway Tracks Using Remotely Sensed Data and Traditional Flood Models | Zakaria, Abdul-Rashid, Oommen, Thomas, Lautala, Pasi | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Classification of tropical cyclone rain patterns using convolutional autoencoder | Kim, Dasol, Matyas, Corene J. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Identification precipitation threshold and resulting river discharge: an IDF-based approach in the Central Himalaya, Nepal | Adhikari, Tirtha Raj, Baniya, Binod, Tang, Qiuhong, Chen, Deliang, Talchabhadel, Rocky, Li, He, Shrestha, Suraj, Sigdel, Madan, Budhathoki, Bhumi Raj, Pradhanang, Soni M., Pradhananga, Dhiraj, Awasthi, Ram Prasad | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Hybrid Machine Learning Approach to Zero-Inflated Data Improves Accuracy | Francisco, Micanaldo Ernesto, Carvajal, Thaddeus M., Watanabe, Kozo | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Land Surface Temperature, Emissivity, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Hybrid modeling for grassland productivity prediction: A parametric and machine learning technique for grazing management with applicability to digital twin decision ... | Paruelo, Jose M., Texeira, Marcos, Tomasel, Fernando | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Integrated Python and GIS approach for Geomorphometric investigation of Man River Basin, Western Madhya Pradesh, India | Rathore, Ankit Kailashi, Khan, Shafia, Verma, Pramod K | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Low Water Maps of the Groundwater Table in the Inner Niger Delta Using Multisatellite Datasets Over 2000-2022 | Normandin, Cassandra, Frappart, Frederic, Bourrel, Luc, Bonnet, Marie-Paule, Wigneron, Jean-Pierre | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Landscape and meteorological determinants of malaria vectors' presence | Taconet, Paul, Zogo, Barnabas, Ahoua Alou, Ludovic P., Amanan Koffi, Alphonsine, Dabire, Roch Kounbobr, Pennetier, Cedric, Moiroux, Nicolas | RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps, Digital Elevation/Terrain Model (DEM), Land Surface Temperature, Emissivity, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Land and Atmospheric Drivers of the 2023 Flood in India | Kushwaha, Anuj Prakash, Solanki, Hiren, Vegad, Urmin, Mahto, Shanti Shwarup, Mishra, Vimal | Population Density, Land Use/Land Cover Classification, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Identifying thresholds of time-lag and accumulative effects of extreme precipitation on major vegetation types at global scale | Liu, Min, Wang, Hao, Zhai, Huiliang, Zhang, Xiaochong, Shakir, Muhammad, Ma, Jianying, Sun, Wei | Total Surface Precipitation Rate, Land Use/Land Cover Classification, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Imbalance in lake variability but not embodying driving factors on the Qinghai-Tibetan Plateau calls on heterogeneous lake management | Leng, Xuejing, Feng, Xiaoming, Feng, Yu, Sun, Chuanlian, Liu, Xiaochi, Zhang, Yu, Zhou, Chaowei, Wang, Yunqiang, Fu, Bojie | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| IMERG V07B and V06B: A Comparative Study of Precipitation Estimates | Rozante, Jose Roberto, Rozante, Gabriela | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Inter-product biases in extreme precipitation duration and frequency across China | Lu, Jiayi, Wang, Kaicun, Wu, Guocan, Ye, Aizhong, Mao, Yuna | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Intensification of daily tropical precipitation extremes from more | Bao, Jiawei, Stevens, Bjorn, Kluft, Lukas, Muller, Caroline | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Influence of typhoons on the spatiotemporal variation in rainfall erosivity in the Pearl River Basin | Cao, Zhen, Zhu, Dayun, Li, Ronghan, Wu, Zhigao, Fu, Linjing, Zhao, Yingshan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Incorporating return period in the assessment of rainfall erosivity of | Das, Tapasranjan, Sarma, Arup Kumar | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Enhancing Extreme Precipitation Forecasts through Machine Learning | Shen, Wenqi, Chen, Siqi, Xu, Jianjun, Zhang, Yu, Liang, Xudong, Zhang, Yong | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain |