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 Older versions will no longer be available and have been superseded by Version 07.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
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Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Prediction of lightning activity over Bangladesh using diagnostic and explicit lightning parameterizations of WRF model | Paramanik, Maruf Md Rabbani, Rabbani, Khan Md Golam, Imran, Ashik, Islam, Md Jafrul, Syed, Ishtiaque M. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Sedimentary biomarkers of human presence and taro cultivation reveal early horticulture in Remote Oceania | Camperio, Giorgia, Ladd, S. Nemiah, Prebble, Matiu, Lloren, Ronald, Argiriadis, Elena, Nelson, Daniel B., Krentscher, Christiane, Dubois, Nathalie | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Analysis of the causes of extreme precipitation in major cities of Peninsular India using remotely sensed data | Kotrike, Tharani, Keesara, Venkata Reddy, Sridhar, Venkataramana | Trace Gases/Trace Species, Atmospheric Pressure Measurements, Surface Pressure, Atmospheric Stability, Surface Temperature, Air Temperature, Boundary Layer Temperature, Dew Point Temperature, Maximum/Minimum Temperature, Skin Temperature, Vertical Profiles, Humidity, Total Precipitable Water, Water Vapor, Condensation, Water Vapor Profiles, Rain Storms, Atmospheric Ozone, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Anticipating the impact of glaciers, landslides and extreme weather events on vulnerable hydropower projects and the development of an integrated multi-hazard warning system (IMWS) | Kumar, Amit, Sain, Kalachand, Kumar, Krishna, Patidar, Pawan, Meenakshi, Reza, Arshad, Verma, Akshaya, Mishra, Aditya | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Analysis of an anthropogenically-induced landslide with emphasis on geological precursors | Apon, Mehilo, Notoka, K., Chang, C.Nokendangba, Ezung, Meripeni, Thong, Glenn T., Walling, Temsulemba | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| An intercomparison of four gridded precipitation products over Europe using an extension of the three-cornered-hat method | Lledo, Llorenc, Haiden, Thomas, Chevallier, Matthieu | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A Theory of Maximum Entropy Production and Its Application to Microwave | Wang, J., Cho, K., NegronJuarez, R. I., Colliander, A., Caravasi, E. C., Revilla, N. S. | Reflectance, Soil Moisture/Water Content, Soil Temperature, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Brightness Temperature, Surface Soil Moisture | |
| First observational investigation on the temporal trends of Vertical Total Electron Content (VTEC) over an equatorial station: Discerning the impacts of Mora and Ockhi Two tropical cyclones in 2017 | Chowdhury, Swati, Subrahamanyam, D. Bala, Choudhary, R.K. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Exploring the potential of deep learning for streamflow forecasting: A comparative study with hydrological models for seasonal and perennial rivers | Izadi, Ardalan, Zarei, Nastaran, Reza Nikoo, Mohammad, Al-Wardy, Malik, Yazdandoost, Farhad | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Extreme Precipitation Over the Southern Slope of the Tibetan Plateau and | Na, Ying, Lu, Riyu, Fu, Qiang, Leung, L. Ruby | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Himalayan Re-gridded and Observational Experiment (HiROX): Part IDevelopment | Yadav, Bankim C, Thayyen, Renoj J, Jain, Kamal, Dimri, Ashok Priyadarshan | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Cloud Liquid Water/Ice | |
| Himalayan Re-gridded and Observational Experiment (HiROX): Part IIApplication | Yadav, Bankim C, Thayyen, Renoj J, Jain, Kamal, Dimri, Ashok Priyadarshan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Exploratory Data Analysis of Climatic Trends in the Koyna Biodiversity Hotspot | Patil, Yashraj, Ramachandran, Harikrishnan, Gupta, Harshita, Ganeshi, Naresh, Janney, Dorian | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Exploring aerosolcloud interactions in liquid-phase clouds over eastern China and its adjacent ocean using the WRF-ChemSBM model | Zhao, Jianqi, Ma, Xiaoyan, Quaas, Johannes, Jia, Hailing | 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 | |
| Global convection-permitting model improves subseasonal forecast of plum | Gu, Jun, Zhao, Chun, Xu, Mingyue, Feng, Jiawang, Li, Gudongze, Zhao, Yongxuan, Hao, Xiaoyu, Chen, Junshi, An, Hong | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Global scale assessment of urban precipitation anomalies | Sui, Xinxin, Yang, Zong-Liang, Shepherd, Marshall, Niyogi, Dev | Population, Urban Areas, Land Use/Land Cover Classification, Emissivity, Land Surface Temperature, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Aerosol Extinction, Aerosol Optical Depth/Thickness | |
| Expanding the simulation of East Asian super dust storms: physical transport mechanisms impacting the western Pacific | Kong, Steven Soon-Kai, Ravindra Babu, Saginela, Wang, Sheng-Hsiang, Griffith, Stephen M., Chang, Jackson Hian-Wui, Chuang, Ming-Tung, Sheu, Guey-Rong, Lin, Neng-Huei | 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 | |
| Long-term regional air pollution characteristics in and around Hyderabad, India: Effects of natural and anthropogenic sources | Jayachandran, V., Rao, T. Narayana | Nitrogen Dioxide, Aerosol Extinction, Aerosol Optical Depth/Thickness, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Long-range transport of air pollutants increases the concentration of hazardous components of PM in northern South America | Velasquez-Garcia, Maria P., Hernandez, K. Santiago, Vergara-Correa, James A., Pope, Richard J., Gomez-Marin, Miriam, Rendon, Angela M. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Influences of Graupel Microphysics on CMA-GFS Simulation of Summer Regional Precipitation | Li, Zhe, Liu, Qijun, Ma, Zhanshan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Investigating North Sea Precipitation Variability: Implications for Offshore Wind Energy Siting and Condition Assessments | Ivanova, Tsvetelina, Porchetta, Sara, Buckingham, Sophia, Helsen, Jan, Van Beeck, Jeroen, Munters, Wim | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Integrating satellite and reanalysis precipitation products for SWAT hydrological simulation in the Jing River Basin, China | Zhang, Yangkai, Gao, Yang, Xu, Liujia, Liu, Zhengguang, Wu, Lei | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Lagged Response of MJO Convection and Precipitation to Solar Ultraviolet | Hoopes, C. A., Hood, L. L., Galarneau, T. J. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Influence of river runoff and precipitation on the seasonal and interannual variability of sea surface salinity in the eastern North Tropical Atlantic | Thouvenin-Masson, Clovis, Boutin, Jacqueline, Echevin, Vincent, Lazar, Alban, Vergely, Jean-Luc | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain |