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 Older versions will no longer be available and have been superseded by Version 07.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
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.
Copy Citation
Documents
READ-ME
PI DOCUMENTATION
ANOMALIES
IMPORTANT NOTICE
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Assessing the influence of El Nino on the California precipitation | Chavda, Digant, Li, Jingjing, Farahmand, Alireza | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Comparative analysis of cloud properties over drought- and flood-prone regions of western India using machine learning techniques | Mevada, Niyati, Srivastava, Rohit | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Coastal Supra-Permafrost Aquifers of the Arctic and Their Significant | Demir, Cansu, McClelland, James W., Bristol, Emily, Charette, Matthew A., Cardenas, M. Bayani | 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, Ground Water, Precipitation, Precipitation Amount | |
| Evaluation of IMERG climate trends over land in the TRMM and GPM eras | Zhu, Siyu, Li, Zhi, Chen, Mengye, Wen, Yixin, Liu, Zhong, Huffman, George J, Tsoodle, Theresa E, Ferraro, Sebastian C, Wang, Yuzhou, Hong, Yang | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| 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 | |
| Diurnal Vegetation Moisture Cycle in the Amazon and Response to Water | Asgarimehr, Milad, Entekhabi, Dara, Camps, Adriano | Brightness Temperature, SIGMA NAUGHT, Soil Moisture/Water Content, Radar Cross-Section, Radar Reflectivity, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Surface Soil Moisture, SENSOR COUNTS | |
| Environmental effects following a seismic sequence: the 2019 CotabatoDavao del Sur (Philippines) earthquakes | Ferrario, M. F., Perez, J. S., Dizon, M., Livio, F., Rimando, J., Michetti, A. M. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Escalating rainstorm-induced flood risks in the Yellow River Basin, China | Hu, Lei, Zhang, Qiang, Singh, Vijay P, Wang, Gang, He, Changyuan, Zhao, Jiaqi | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Comprehensive quantitative assessment of the performance of fourteen satellite precipitation products over Chinese mainland | Zhu, Shengli, Liu, Zhaofei | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Contrasting responses of vegetation productivity to intraseasonal | Harris, Bethan L., Quaife, Tristan, Taylor, Christopher M., Harris, Phil P. | Photosynthesis, Primary Production, Vegetation Productivity, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Contrasts of Large-Scale Moisture and Heat Budgets between Different Sea Areas of the South China Sea and the Adjacent Land | Zhang, Chunyan, Wang, Donghai, Yao, Lebao, Wu, Zhenzhen, Ma, Qianhui, Li, Yongsheng, Wang, Peidong | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Contribution of Western Arabian Sea Tropical Cyclones to Rainfall in the | Camberlin, P., Assowe Dabar, O., Pohl, B., Mohamed Waberi, M., Hoarau, K., Planchon, O. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Convergence of beta diversity in river macroinvertebrates following repeated summer floods | 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 | |
| Atmospheric River Rapids and Their Role in the Extreme Rainfall Event of | Francis, Diana, Fonseca, Ricardo, Bozkurt, Deniz, Nelli, Narendra, Guan, Bin | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Autocorrelation-A Simple Diagnostic for Tropical Precipitation | Spat, Dorian, Biasutti, Michela, Schuhbauer, David, Voigt, Aiko | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessing the regional climate response to different Hengduan Mountains geometries with a highresolution regional climate model | Xiang, Ruolan, Steger, Christian R., Li, Shuping, Pellissier, Loic, Srland, Silje Lund, Willett, Sean D., Schar, Christoph | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessment of 30 gridded precipitation datasets over different climates on a country scale | Araghi, Alireza, Adamowski, Jan F. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Calibration of the SMAP soil moisture retrieval algorithm to reduce bias over the Amazon rainforest | Cho, Kyeungwoo, Negron-Juarez, Robinson, Colliander, Andreas, Cosio, Eric G., Salinas, Norma, de Araujo, Alessandro, Chambers, Jefferey Q., Wang, Jingfeng | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Causal analysis of unprecedented landslides during July 2021 in the Western Ghats of Maharashtra, India | Jain, Nirmala, Roy, Priyom, Martha, Tapas R., Sekhar, Nataraja P., Kumar, K. Vinod | 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 | |
| Data fusion of satellite imagery and downscaling for generating highly fine-scale precipitation | Zhang, Xiang, Song, Yu, Nam, Won-Ho, Huang, Tailai, Gu, Xihui, Zeng, Jiangyuan, Huang, Shuzhe, Chen, Nengcheng, Yan, Zhao, Niyogi, Dev | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Non-negligible clear-sky biases of satellite thermal infrared observations for analyzing surface urban heat island intensity: A case study in China | Ma, Jin, Zhou, Ji, Zhang, Tao, Tang, Wenbin, Liao, Yangsiyu, Yang, Miao | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Albedo, Anisotropy, Land Surface Temperature, Emissivity, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Air Temperature, 24 Hour Maximum Temperature, 24 Hour Minimum Temperature, Land Use/Land Cover Classification | |
| Multidimensional forecasting of precipitation and potential | Mendez Vallejo, Carlos Andres, Lilla Manzione, Rodrigo | 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, Precipitation, Precipitation Amount, Precipitation Rate |