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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| Satellite-Derived, Smartphone-Delivered Geospatial Cholera Risk | Nusrat, Farah, Akanda, Ali S., Islam, Abdullah, Aziz, Sonia, Pakhtigian, Emily L., Boyle, Kevin, Hanifi, Syed Manzoor Ahmed | Population Density, Land Surface Temperature, Emissivity, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Severe winter conditions in the Citlaltepetl-Cofre de Perote mountain | Soto, Victor, Travieso-Bello, Ana C., Soto-Gomez, Nadia L., Welsh-Rodriguez, Carlos M. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Spatial and conventional verifications of hurricanes Dorian and Fiona using the Canadian precipitation analysis & integrated multi-satellite retrievals for GPM products | Boluwade, Alaba, Farooque, Aitazaz A. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Vegetation Water Content Retrieval from Spaceborne GNSS-R and | Zhang, Yongfeng, Bu, Jinwei, Zuo, Xiaoqing, Yu, Kegen, Wang, Qiulan, Huang, Weimin | Land Use/Land Cover Classification, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Using CYGNSS and L-band Radiometer Observations to Retrieve Surface Water Fraction: A Case Study of the Catastrophic Flood of 2022 in Pakistan | Ma, Zhongmin, Zhang, Shuangcheng, Liu, Qi, Feng, Yanming, Guo, Qinyu, Zhao, Hebin, Feng, Yuxuan | Total Surface Precipitation Rate, Reflectance, Radar Cross-Section, Radar Reflectivity, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Uncertainty estimation of hydrological modelling using gridded precipitation as model inputs in the Gandaki River Basin | Zeng, Qiang, Zhao, Qiang, Luo, Yang-Tao, Ma, Shun-Gang, Kang, You, Li, Yu-Qiong, Chen, Hua, Xu, Chong-Yu | 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 | |
| A two-stage model for spatial downscaling of daily precipitation data | Lei, Weihao, Qin, Huawang, Hou, Xiaoyang, Chen, Haoran | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Advancing the seasonal outlook of the wet seasons of Florida | Misra, Vasubandhu, Jayasankar, C. B. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evolutionary trends and analysis of the driving factors of Ulva prolifera green tides: A study based on the random forest algorithm and multisource remote sensing images | Hou, Wenlong, Chen, Jinyue, He, Maoxia, Ren, Shilong, Fang, Lei, Wang, Chongyang, Jiang, Peng, Wang, Wanting | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The Impact of Urbanization-Induced Land Use Change on Land Surface Temperature | Halefom, Afera, He, Yan, Nemoto, Tatsuya, Feng, Lei, Li, Runkui, Raghavan, Venkatesh, Jing, Guifei, Song, Xianfeng, Duan, Zheng | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Ensemble precipitation estimates based on an assessment of 21 gridded precipitation datasets to improve precipitation estimations across Madagascar | Ollivier, Camille C., Carriere, Simon D., Heath, Thomas, Olioso, Albert, Rabefitia, Zo, Rakoto, Heritiana, Oudin, Ludovic, Satge, Frederic | 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, Total Surface Precipitation Rate, Longwave Radiation, Shortwave Radiation, Soil Heat Budget, Soil Heat Budget, Soil Temperature, Soil Temperature, Soil Infiltration, Soil Infiltration, Soil Moisture/Water Content, Surface Soil Moisture, Root Zone Soil Moisture, Soil Moisture/Water Content, Surface Water, Runoff Rate, Average Flow, Average Flow, Precipitation, Snow Depth, Snow Melt, Snow/Ice Temperature, Leaf Area Index (LAI), Leaf Area Index (LAI), Rain, Precipitation Amount, Snow | |
| Merging satellite and gauge-measured precipitation using LightGBM with an emphasis on extreme quantiles | Tyralis, Hristos, Papacharalampous, Georgia, Doulamis, Nikolaos, Doulamis, Anastasios | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Accuracy of satellite precipitation products in data-scarce Inner Tibetan Plateau comprehensively evaluated using a novel ground observation network | Liu, Zhaofei | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessment of five SMAP soil moisture products using ISMN ground-based measurements over varied environmental conditions | Yi, Chuanxiang, Li, Xiaojun, Zeng, Jiangyuan, Fan, Lei, Xie, Zhiqing, Gao, Lun, Xing, Zanpin, Ma, Hongliang, Boudah, Antoine, Zhou, Hongwei, Zhou, Wenjun, Sheng, Ye, Dong, Tianxiang, Wigneron, Jean-Pierre | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Comparison of tree-based ensemble algorithms for merging satellite and earth-observed precipitation data at the daily time scale | Papacharalampous, Georgia, Tyralis, Hristos, Doulamis, Anastasios, Doulamis, Nikolaos | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Occurrence of heavy precipitation influenced by solar wind high-speed streams through vertical atmospheric coupling | Prikryl, Paul, Rusin, Vojto | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Indirect Response of the Temperature, Humidity, and Rainfall on the Spread of COVID-19 over the Indian Monsoon Region | Mehta, Sanjay Kumar, Ananthavel, Aravindhavel, Reddy, T. V. Ramesh, Ali, Saleem, Mehta, Shyam Bihari, Kakkanattu, Sachin Philip, Purushotham, Pooja, Betsy, K. B. | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Reliability Assessment of Road Network to Precipitation Based on Historical Recorded Disruptions | Qiao, Ningning, Liu, Kai, Wang, Ming, Ni, Xiaoyong, Yang, Yongsheng | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The Effect of the Cordillera Mountain Range on Tropical Cyclone Rainfall in the Northern Philippines | Racoma, Bernard Alan B., Holloway, Christopher E., Schiemann, Reinhard K. H., Feng, Xiangbo, Bagtasa, Gerry | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation of remotely sensed global evapotranspiration data from inland river basins | Liu, Zhaofei | Aerosols, Aerosol Extinction, Aerosol Optical Depth/Thickness, Angstrom Exponent, Aerosol Particle Properties, Carbonaceous Aerosols, Dust/Ash/Smoke, Organic Particles, Sulfate Particles, Sulfur Oxides, Sulfur Compounds, Sulfate, Sulfur Dioxide, Sulfur Oxides, Particulate Matter, Dimethyl Sulfide, Black Carbon, Sea Salt, PARTICULATE MATTER (PM 2.5), PARTICULATE MATTER (PM 10), PARTICULATE MATTER (PM 1.0), 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 | |
| A Central Asia hydrologic monitoring dataset for food and water security applications in Afghanistan | McNally, Amy, Jacob, Jossy, Arsenault, Kristi, Slinski, Kimberly, Sarmiento, Daniel P., Hoell, Andrew, Pervez, Shahriar, Rowland, James, Budde, Mike, Kumar, Sujay, Peters-Lidard, Christa, Verdin, James P. | Surface Pressure, Heat Flux, Longwave Radiation, Shortwave Radiation, Air Temperature, Specific Humidity, Evapotranspiration, Wind Speed, Rain, Snow, Soil Moisture/Water Content, Soil Temperature, Land Surface Temperature, Snow Cover, Snow Depth, Snow Water Equivalent, Runoff, RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps, Digital Elevation/Terrain Model (DEM), Reflectance, Total Surface Water, Brightness Temperature, SIGMA NAUGHT, Precipitation, Precipitation Amount, Precipitation Rate | |
| Disentangling error structures of precipitation datasets using decision trees | Sui, Xinxin, Li, Zhi, Tang, Guoqiang, Yang, Zong-Liang, Niyogi, Dev | 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, Land Use/Land Cover Classification, Precipitation, Precipitation Amount, Snow, Rain | |
| Effect of dust on rainfall over the Red Sea coast based on WRF-Chem model simulations | Parajuli, Sagar P., Stenchikov, Georgiy L., Ukhov, Alexander, Mostamandi, Suleiman, Kucera, Paul A., Axisa, Duncan, Gustafson Jr., William I., Zhu, Yannian | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Areal Precipitation Coverage Ratio for Enhanced AI Modelling of Monthly Runoff: A New Satellite Data-Driven Scheme for Semi-Arid Mountainous Climate | Hosseini, Seyyed Hasan, Hashemi, Hossein, Fakheri Fard, Ahmad, Berndtsson, Ronny | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Evapotranspiration, Latent Heat Flux, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain |