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 Older versions will no longer be available and have been superseded by Version 07.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 Final Daily 10 x 10 km (GPM_3IMERGDF) derived from the half-hourly GPM_3IMERGHH. The derived result represents the Final 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 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 Final Daily product depends on the delivery of the IMERG Final Half-Hourly product GPM_IMERGHH. Since the latter are delivered in a batch, once per month for the entire month, with up to 4 months latency, so will be the latency for the Final Daily, plus about 24 hours. Thus, e.g. the Dailies for January can be expected to appear no earlier than April 2.
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
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Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Random Forest-Based Landslide Risk Assessment for Mountain Roads Under Extreme Rainfall: Implications for Infrastructure Resilience | Li, Renfei, Li, Jun, Zhou, Yang, Han, Dingding, Sun, Dongcang, Cui, Yingchen, Wang, Modi, Li, Mingliang | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Precipitation on the Manus Island of Papua New Guinea and ice cloud radiative effects in the surrounding atmosphere in boreal winters | Ren, Tong, Yang, Ping, Loeb, Norman G., Smith, William L., Minnis, Patrick | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| PrecipitationDriven Thickening and WindInduced Erosion of the Ocean Barrier Layer Under Tropical Cyclones | Wu, Dijia, Wang, Chunzai, Sun, Jia, Yan, Youfang | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| State-of-the-art hydrological datasets exhibit low water balance consistency globally | Huang, Hao, Liu, Junguo, Chen, Aifang, Ruiz-Vasquez, Melissa, Orth, Rene | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Vegetation Index, Landscape Patterns, Evapotranspiration, Photosynthesis, Primary Production, Latent Heat Flux | |
| Roles of Surface Latent Heat Flux and Gravity Waves in Offshore MCS Development in the Coastal Eastern Tropical Pacific | Hu, Jingyi, Chen, Xingchao, Peng, ChinHsuan, Leung, L. Ruby | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Observational characteristics of cloud-radiation-precipitation during 2019 drought period in Yunnan of Southwest China | Tan, Yuwei, Zhou, Xinqiang, Chen, Bing, Li, Jiandong, Lin, Guo, Liu, Xiaohong, Luo, Tao | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| On the Representation of Convectively Coupled Kelvin Waves in Operational Forecast Models: An Object-Tracking Perspective | Lawton, Quinton A., Rios-Berrios, Rosimar, Judt, Falko, Magnusson, Linus, Kohler, Martin | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Moisture build-up and thermodynamic processes in precipitation regimes during the southwest monsoon over a tropical coastal region | Andrews, Anusha, Resmi, E.A., Sumesh, R.K., Sunil, Sneha, Aswini, A.R., Sukumar, Nita, Kumar, Sumit, Sabarinath, A., Charan Teja, Tejavath, Jash, Dharmadas | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Meteorological ingredients of heavy precipitation and subsequent lake-filling episodes in the northwestern Sahara | Rieder, Joelle C., Aemisegger, Franziska, Dente, Elad, Armon, Moshe | Infrared Radiance, REFLECTED INFRARED, Visible Radiance, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Mechanisms behind the longdistance diurnal offshore precipitation propagation in northwestern South America | Hu, Jingyi, Chen, Xingchao | Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Investigating the Use of Terrain-Following Coordinates in AI-Driven Precipitation Forecasts | Sha, Yingkai, Schreck, John S., Chapman, William, Gagne, David John | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Integration of PhysicsBased and DataDriven Approaches for Landslide Susceptibility Assessment | Han, Yi, Semnani, Shabnam J. | Soil Depth, Soil Horizons/Profile, Soil Water Holding Capacity, Soil Texture, Soil Classification, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Identifying the hotspot regions of emerging triple risk due to pre-monsoon convective storms over Kerala, India | E. K., Krishna Kumar, S., Abhilash, C. S., Abhiram Nirmal, Prabath H., Kurup | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Brightness Temperature, Atmospheric Water Vapor | |
| How Do Different Precipitation Products Perform in a Dry-Climate Region? | Brobst-Whitcomb, Noelle, Maggioni, Viviana | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Surface Pressure, Heat Flux, Longwave Radiation, Shortwave Radiation, Surface Temperature, Humidity, Evapotranspiration, Surface Winds, Soil Moisture/Water Content, Soil Temperature, Land Surface Temperature, Snow Water Equivalent, Runoff, Ground Water | |
| High-resolution precipitation downscaling in mainland Southeast Asia: A novel integration of BMA and U-Net CNN | Amnuaylojaroen, Teerachai | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| How meteorological conditions influence aerosol-cloud interactions under | Zhao, Jianqi, Ma, Xiaoyan, Quaas, Johannes, Yang, Tong | Aerosol Backscatter, Aerosol Extinction, Aerosol Optical Depth/Thickness, Angstrom Exponent, Aerosol Particle Properties, Aerosol Radiance, Carbonaceous Aerosols, Dust/Ash/Smoke, Nitrate Particles, Organic Particles, Particulate Matter, Sulfate Particles, Optical Depth/Thickness, Radiative Flux, Reflectance, Atmospheric Emitted Radiation, Emissivity, Transmittance, Clouds, Cloud Condensation Nuclei, Cloud Droplet Concentration/Size, Cloud Liquid Water/Ice, Cloud Optical Depth/Thickness, Cloud Precipitable Water, Cloud Asymmetry, Cloud Ceiling, Cloud Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Cloud Vertical Distribution, Cloud Emissivity, Cloud Radiative Forcing, Cloud Reflectance, Cloud Types, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| How much did it rain? A comparison of four datasets for TC Beryl (2012) | Tiwari, Alka, Cherkauer, Keith | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Hydrological seasonality is a major driver of ecosystem metabolism in tropical nonwadeable rivers | Castillo, Maria M., Ulseth, Amber J., JarquinSanchez, Aaron, AlvarezMerino, Arturo, Capps, Krista A. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impact of orographic drag schemes on East Asia rainfall | Xie, Jinbo, Zhang, Minghua | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impacts of Global Warming on Severe Drought in Northern Taiwan: A Future | Huang, ShihMing, Lee, TsungYu, Lin, ChuanYao, Lin, YiYing, Hsu, HuangHsiung | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Improved global estimates of terrestrial evapotranspiration using the MODIS and VIIRS sensors | Endsley, K. Arthur, Zhao, Maosheng, Kimball, John S., Albrethsen, Tyler, Devadiga, Sadashiva | Albedo, Anisotropy, Land Use/Land Cover Classification, RADAR IMAGERY, Terrain Elevation, Digital Elevation/Terrain Model (DEM), Reflectance, Topographical Relief Maps, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar) | |
| Production viability index for annual agricultural crops | Ferreira, Fernanda Laurinda Valadares, Rodrigues, Lineu Neiva | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Remote Sensing Improves Multi-Hazard Flooding and Extreme Heat Detection | Preisser, Matthew, Passalacqua, Paola | Carbon Monoxide, Geopotential Height, Tropopause, Methane, Atmospheric Ozone, Surface Pressure, Outgoing Longwave Radiation, Air Temperature, Upper Air Temperature, Humidity, Total Precipitable Water, Water Vapor, Water Vapor Profiles, Cloud Liquid Water/Ice, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Cloud Vertical Distribution, Emissivity, Skin Temperature, Sea Surface Temperature, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Over 60% precipitation transformed into terrestrial water storage in global river basins from 2002 to 2021 | Zhong, Yulong, Tian, Baoming, Kim, Hyunglok, Yuan, Xing, Liu, Xinyue, Zhu, Enda, Wu, Yunlong, Wang, Lunche, Wang, Lizhe | 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, Terrestrial Water Storage, Ground Water, Glacier Mass Balance/Ice Sheet Mass Balance, Precipitation, Precipitation Amount, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| Reactive nitrogen in and around the northeastern and mid-Atlantic US: sources, sinks, and connections with ozone | Huang, Min, Carmichael, Gregory R., Bowman, Kevin W., De Smedt, Isabelle, Colliander, Andreas, Cosh, Michael H., Kumar, Sujay V., Guenther, Alex B., Janz, Scott J., Stauffer, Ryan M., Thompson, Anne M., Fedkin, Niko M., Swap, Robert J., Bolten, John D., Joseph, Alicia T. | Atmospheric Carbon Monoxide, Peroxyacetyl Nitrate, Atmospheric Ozone, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Brightness Temperature, Surface Soil Moisture |