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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| Toward the future integration of land-to-ocean observing systems to characterize organic carbon fluxes from storms | Clark, J.B., Schollaert Uz, S. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Using the observed variations of the start date of the rainy season over Central America for its reliable seasonal outlook | Rodgers, Joanna, Misra, Vasubandhu, Jayasankar, C. B. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Utilization of satellite data for landslide disaster mitigation in | Lestiana, Hilda, Sukristiyanti, Sukristiyanti, Fajar Saputra, Okta, Agus Ahmid, Deden, Iqbal, Prahara, Joko Trilaksono, Nurjanna, Tohari, Adrin, Saepuloh, Asep | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Using high resolution climate models to explore future changes in post-tropical cyclone precipitation | Bower, Erica, Reed, Kevin A | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Validation of CRU TS v4.08, ERA5-Land, IMERG v07B, and MSWEP v2.8 Precipitation Estimates Against Observed Values over Pakistan | Abbas, Haider, Song, Wenlong, Wang, Yicheng, Xiang, Kaizheng, Chen, Long, Feng, Tianshi, Linghu, Shaobo, Alam, Muneer | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Vertical structures and microphysical characteristics of summer precipitation in North China detected by GPM-DPR | Li, Donghuan, Qi, Youcun, Li, Huiqi | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Verification of forecasted precipitation from Tropical Storm Hermine in the Canary Islands | QuinteroPlaza, David, GonzalezAleman, Juan Jesus, AlejoHerrera, Cristo Jose, SuarezMolina, David, GonzalezAlejandre, Cesar, PenateDe La Rosa, Irene Soledad | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Uncertainty estimation of machine learning spatial precipitation predictions from satellite data | Papacharalampous, Georgia, Tyralis, Hristos, Doulamis, Nikolaos, Doulamis, Anastasios | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Upgrade and extension of LSA-SAF land surface albedo archive from EPS Metop/AVHRR: description and quality assessment | Delmotte, Anthea, Juncu, Daniel, Ceamanos, Xavier, Trigo, Isabel F., Gomes, Sandra | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Tracking the water storage and runoff variations in the Parana basin via GNSS measurements | Qiu, Keshan, You, Wei, Jiang, Zhongshan, Tang, Miao | 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, Precipitation, Precipitation Amount | |
| Wind Shear Effects in ConvectionPermitting Models Influence MCS Rainfall and Forcing of Tropical Circulation | Maybee, Ben, Marsham, John H., Klein, Cornelia M., Parker, Douglas J., Barton, Emma J., Taylor, Christopher M., Lewis, Huw, Sanchez, Claudio, Jones, Richard W., Warner, James | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Wildfire-smoke-precipitation interactions in Siberia: Insights from a regional model study | Konovalov, Igor B., Golovushkin, Nikolai A., Beekmann, Matthias | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Forel-Ule index extraction and spatiotemporal variation from MODIS imagery in the Bohai Sea of China | Wang, Lin, Meng, Qinghui, Wang, Xiang, Chen, Yanlong, Zhao, Sufang, Wang, Xinxin | 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 | |
| A dryness index TSWDI based on land surface temperature, sun-induced chlorophyll fluorescence, and water balance | Liu, Ying, Yu, Xiangyu, Dang, Chaoya, Yue, Hui, Wang, Xu, Niu, Hongbo, Zu, Pengju, Cao, Manhong | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A framework for multi-sensor satellite data to evaluate crop production losses: the case study of 2022 Pakistan floods | Qamer, Faisal Mueen, Abbas, Sawaid, Ahmad, Bashir, Hussain, Abid, Salman, Aneel, Muhammad, Sher, Nawaz, Muhammad, Shrestha, Sravan, Iqbal, Bilal, Thapa, Sunil | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A Cloud-Dependent 1DVAR Precipitation Retrieval Algorithm for FengYun-3D Microwave Soundings: A Case Study in Tropical Cyclone Mekkhala | Xu, Jintao, Ma, Ziqiang, Hu, Hao, Weng, Fuzhong | Atmospheric Water Vapor, Precipitation, RADAR, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A 6-year cycle in the Earth system | Pfeffer, Julia, Cazenave, Anny, Rosat, Severine, Moreira, Lorena, Mandea, Mioara, Dehant, Veronique, Coupry, Benjamin | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A hydraulic model of the Amur River informed by ICESat-2 elevation | Bauer-Gottwein, Peter, Zakharova, Elena, Coppo Frias, Monica, Ranndal, Heidi, Nielsen, Karina, Christoffersen, Linda, Liu, Jun, Jiang, Liguang | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Animal-sourced foods production and early childhood nutrition: Panel data evidence in central Madagascar | Ramahaimandimby, Zoniaina, Shiratori, Sakiko, Rafalimanantsoa, Jules, Sakurai, Takeshi | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Application of Machine Learning and Remote Sensing for Gap-filling Daily Precipitation Data of a Sparsely Gauged Basin in East Africa | Faramarzzadeh, Marzie, Ehsani, Mohammad Reza, Akbari, Mahdi, Rahimi, Reyhane, Moghaddam, Mohammad, Behrangi, Ali, Klove, Bjorn, Haghighi, Ali Torabi, Oussalah, Mourad | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| April 2022 Floods over East Coast South Africa: Interactions between a | Thoithi, Wanjiru, Blamey, Ross C., Reason, Chris J. C. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Anopheles sampling collections in the health districts of Korhogo (Cote dIvoire) and Diebougou (Burkina Faso) between 2016 and 2018 | Taconet, Paul, Zogo, Barnabas, Soma, Dieudonne Diloma, Ahoua Alou, Ludovic P., Mouline, Karine, Dabire, Roch Kounbobr, Amanan Koffi, Alphonsine, Pennetier, Cedric, Moiroux, Nicolas | Land Surface Temperature, Emissivity, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| AI-based estimation of precipitation in the Tibetan Plateau using multi-frame FY-4A satellite data | Liu, Nianqing, Ren, Suling, Jiang, Jianying, Qin, Danyu, Han, Bowei | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Aerosol sensitivity simulations over East Asia in a | Li, Shuping, Srland, Silje Lund, Wild, Martin, Schar, Christoph | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A satellite-observation based study on responses of clouds to aerosols | Panda, Jagabandhu, Kant, Sunny, Sarkar, Ankan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain |