N: 90 S: -90 E: 180 W: -180
Description
Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.
The Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high resolution of 0.1° every half-hour beginning 2000. It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) mission, using the GPM Core Observatory satellite as the standard to combine precipitation observations from an international constellation of satellites using advanced techniques. IMERG can be used for global-scale applications as well as over regions with sparse or no reliable surface observations. The fine spatial and temporal resolution of IMERG data allows them to be accumulated to the scale of the application for increased skill. IMERG has three Runs with varying latencies in response to a range of application needs: rapid-response applications (Early Run, 4-h latency), same/next-day applications (Late Run, 14-h latency), and post-real-time research (Final Run, 3.5-month latency). While IMERG strives for consistency and accuracy, satellite estimates of precipitation are expected to have lower skill over frozen surfaces, complex terrain, and coastal zones. As well, the changing GPM satellite constellation over time may introduce artifacts that affect studies focusing on multi-year changes.
This dataset is the GPM Level 3 IMERG Late Daily 10 x 10 km (GPM_3IMERGDL) derived from the half-hourly GPM_3IMERGHHL. The derived result represents a Late expedited estimate of the daily mean precipitation rate in mm/day. The dataset is produced by first computing the mean precipitation rate in (mm/hour) in every grid cell, and then multiplying the result by 24. This minimizes the possible dry bias in versions before "07", in the simple daily totals for cells where less than 48 half-hourly observations are valid for the day. The latter under-sampling is very rare in the combined microwave-infrared (and rain gauge in the final) dataset, variable "precipitation", and appears in higher latitudes. Thus, in most cases users of global "precipitation" data will not notice any difference. This correction, however, is noticeable in the high-quality microwave retrieval, variable "MWprecipitation", where the occurrence of less than 48 valid half-hourly samples per day is very common. The counts of the valid half-hourly samples per day have always been provided as a separate variable, and users of daily data were advised to pay close attention to that variable and use it to calculate the correct precipitation daily rates. Starting with version "07", this is done in production to minimize possible misinterpretations of the data. The counts are still provided in the data, but they are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so.
The latency of the derived Late daily product is a couple of minutes after the last granule of GPM_3IMERGHHL for the UTC data day is received at GES DISC. Since the target latency of GPM_3IMERGHHL is 14 hours, the daily should appear no earlier than 14 hours after the closure of the UTC day. For information on the original data (GPM_3IMERGHHL), please see the Documentation (Related URL).
The daily mean rate (mm/day) is derived by first computing the mean precipitation rate (mm/hour) in a grid cell for the data day, and then multiplying the result by 24. Thus, for every grid cell we have
Pdaily_mean = SUM{Pi 1[Pi valid]} / Pdaily_cnt 24, i=[1,Nf]
Where:
Pdaily_cnt = SUM{1[Pi valid]}
Pi - half-hourly input, in (mm/hr)
Nf - Number of half-hourly files per day, Nf=48
1[.] - Indicator function; 1 when Pi is valid, 0 otherwise
Pdaily_cnt - Number of valid retrievals in a grid cell per day.
Grid cells for which Pdaily_cnt=0, are set to fill value in the Daily files.
Note that Pi=0 is a valid value.
Pdaily_cnt are provided in the data files as variables "precipitation_cnt" and "MWprecipitation_cnt", for correspondingly the microwave-IR-gauge and microwave-only retrievals. They are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so.
There are various ways the daily error could be estimated from the source half-hourly random error (variable "randomError"). The daily error provided in the data files is calculated in a fashion similar to the daily mean precipitation rate. First, the mean of the squared half-hourly "randomError" for the day is computed, and the resulting (mm^2/hr) is converted to (mm^2/day). Finally, square root is taken to get the result in (mm/day):
Perr_daily = { SUM{ (Perr_i)^2 1[Perr_i valid] ) } / Ncnt_err 24}^0.5, i=[1,Nf]
Ncnt_err = SUM( 1[Perr_i valid] )
where:
Perr_i - half-hourly input, "randomError", (mm/hr)
Perr_daily - Magnitude of the daily error, (mm/day)
Ncnt_err - Number of valid half-hour error estimates
Again, the sum of squared "randomError" can be reconstructed, and other estimates can be derived using the available counts in the Daily files.
Product Summary
Citation
Citation is critically important for dataset documentation and discovery. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use and Citation Guidance.
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READ-ME
PI DOCUMENTATION
ANOMALIES
IMPORTANT NOTICE
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| An empirical approach for developing functions for the vulnerability of roads to tropical cyclones | Zhu, Jiatong, Liu, Kai, Wang, Ming, Xu, Wei, Liu, Mengting, Zheng, Jianchun | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Global and regional patterns of soil nitrous acid emissions and their acceleration of rural photochemical reactions | Wu, Dianming, Zhang, Jingwei, Wang, Mengdi, An, Junling, Wang, Ruhai, Haider, Haroon, XuRi, Huang, Ye, Zhang, Qiang, Zhou, Feng, Tian, Hanqin, Zhang, Xiuying, Deng, Lingling, Pan, Yuepeng, Chen, Xi, Yu, Yuanchun, Hu, Chunsheng, Wang, Rui, Song, Yaqi, Gao, Zhiwei, Wang, Yue, Hou, Lijun, Liu, Min | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Global sea surface salinity via the synergistic use of SMAP satellite and HYCOM data based on machine learning | Jang, Eunna, Kim, Young Jun, Im, Jungho, Park, Young-Gyu, Sung, Taejun | Precipitation, Surface Winds, Salinity, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Global land surface evapotranspiration monitoring by ETMonitor model driven by multi-source satellite earth observations | Zheng, Chaolei, Jia, Li, Hu, Guangcheng | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Brief communication: Western Europe flood in 2021mapping agriculture flood exposure from synthetic aperture radar (SAR) | He, Kang, Yang, Qing, Shen, Xinyi, Anagnostou, Emmanouil N. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Simulation and Analysis of Mesoscale Convective Systems (MCSs) Leading to a Severe Flood Over Iran | Ahmadloo, Masoomeh, Gharaylou, Maryam, Farahani, Majid M., Pegahfar, Nafiseh | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The first global soil moisture and vegetation optical depth product retrieved from fused SMOS and SMAP L-band observations | Li, Xiaojun, Wigneron, Jean-Pierre, Frappart, Frederic, Lannoy, Gabrielle De, Fan, Lei, Zhao, Tianjie, Gao, Lun, Tao, Shengli, Ma, Hongliang, Peng, Zhiqing, Liu, Xiangzhuo, Wang, Huan, Wang, Mengjia, Moisy, Christophe, Ciais, Philippe | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), 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 | |
| Estimation of seasonal base flow contribution to a tropical river using stable isotope analysis | Bhagat, Himanshu, Ghosh, Prosenjit, Nagesh Kumar, D. | Discharge/Flow, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Fire prediction with logistic regression on territory of Bosnia and Herzegovina | Musabasic, Mursel, Music, Denis, Babovic, Elmir | 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, Tropopause, Surface Pressure, Upper Air Temperature, Total Precipitable Water, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Cloud Vertical Distribution, Emissivity, Sea Surface Temperature, Skin Temperature, Carbon Monoxide, Geopotential Height, Humidity, Water Vapor Profiles, Cloud Liquid Water/Ice, Outgoing Longwave Radiation, Methane, Atmospheric Ozone, Precipitation, Precipitation Amount, Snow, Rain | |
| A record-breaking trans-Atlantic African dust plume associated with atmospheric circulation extremes in June 2020 | Pu, Bing, Jin, Qinjian | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Predicting Flood Property Insurance Claims over CONUS, Fusing Big Earth Observation Data | Yang, Qing, Shen, Xinyi, Yang, Feifei, Anagnostou, Emmanouil N., He, Kang, Mo, Chongxun, Seyyedi, Hojjat, Kettner, Albert J., Zhang, Qingyuan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Linking Soil Erosion Modeling to Landscape Patterns and Geomorphometry: An Application in Crete, Greece | Brini, Imen, Alexakis, Dimitrios D., Kalaitzidis, Chariton | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Implementation of a proactive system to monitor Aedes aegypti populations using open access historical and forecasted meteorological data | Aguirre, Exequiel, Andreo, Veronica, Porcasi, Ximena, Lopez, Laura, Guzman, Claudio, Gonzalez, Patricia, Scavuzzo, Carlos M. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Performance evaluation of GPM-IMERG early and late rainfall estimates over Lake Hawassa catchment, Rift Valley Basin, Ethiopia | Kawo, Nafyad Serre, Hordofa, Aster Tesfaye, Karuppannan, Shankar | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| SMOS-IC data record of soil moisture and L-VOD: Historical development, applications and perspectives | Wigneron, Jean-Pierre, Li, Xiaojun, Frappart, Frederic, Fan, Lei, Al-Yaari, Amen, De Lannoy, Gabrielle, Liu, Xiangzhuo, Wang, Mengjia, Le Masson, Erwan, Moisy, Christophe | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Time-variations of zeroth-order vegetation absorption and scattering at L-band | Baur, Martin J., Jagdhuber, Thomas, Feldman, Andrew F., Chaparro, David, Piles, Maria, Entekhabi, Dara | Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Dry Post Wintertime Mass Surveillance Unearths a Huge Burden of P. vivax, and Mixed Infection with P. vivax P. falciparum, a Threat to Malaria Elimination, in ... | Bhowmick, Ipsita Pal, Nirmolia, Tulika, Pandey, Apoorva, Subbarao, Sarala K., Nath, Aatreyee, Senapati, Susmita, Tripathy, Debabrata, Pebam, Rocky, Nag, Suman, Roy, Rajashree, Dasgupta, Dipanjan, Debnath, Jayanta, Gogoi, Kongkona, Gogoi, Karuna, Borah, Lakhyajit, Chanda, Rajdeep, Borgohain, Arup, Mog, Chelapro, Sarkar, Ujjwal, Gogoi, Phiroz, Debnath, Bishal, Debbarma, Jyotish, Ranjan Bhattacharya, Dibya, Joshi, Pyare Lal, Kaur, Harpreet, Narain, Kanwar | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessment of the GPM IMERG and CHIRPS precipitation estimations for the steppe part of the Crimea | Popovych, Victor, Dunaieva, Ielizaveta | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Brief communication: Hurricane Dorian: Automated near-real-time mapping of the unprecedented flooding in the Bahamas using synthetic aperture radar | Cerrai, Diego, Yang, Qing, Shen, Xinyi, Koukoula, Marika, Anagnostou, Emmanouil N. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessing the origin of a massive cyanobacterial bloom in the Rio de la Plata (2019): Towards an early warning system | Aubriot, Luis, Zabaleta, Bernardo, Bordet, Facundo, Sienra, Daniel, Risso, Jimena, Achkar, Marcel, Somma, Andrea | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| An investigation into the impact of reservoir management Kerala floods 2018: A case study of the Kakki reservoir | Ryan, Ciaran, Trigg, Mark A., Adarsh, S | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Estimation of relative canopy absorption and scattering at L-, C-and X-bands | Baur, Martin J., Jagdhuber, Thomas, Feldman, Andrew F., Akbar, Ruzbeh, Entekhabi, Dara | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Analysis of an extreme weather event in a hyper-arid region using WRF-Hydro coupling, station, and satellite data | Wehbe, Youssef, Temimi, Marouane, Weston, Michael, Chaouch, Naira, Branch, Oliver, Schwitalla, Thomas, Wulfmeyer, Volker, Zhan, Xiwu, Liu, Jicheng, Al Mandous, Abdulla | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation and intercomparison of GPM-IMERG and TRMM 3B42 daily precipitation products over Greece | Kazamias, Anastasios-Petros, Sapountzis, Marios, Lagouvardos, Kostas | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain |