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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| The Effects of Hurricanes and Storms on the Composition of Dissolved Organic Matter in a Southeastern US Estuary | Medeiros, Patricia M. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Reinterpreting Precipitation Stable Water Isotope Variability in the Andean Western Cordillera Due To SubSeasonal Moisture Source Changes and SubCloud ... | Welp, Lisa R., Olson, Elizabeth J., Valdivia, Adriana Larrea, Larico, Juan Reyes, Arhuire, Efrain Palma, Paredes, Lino Morales, DeGraw, Jonathan T., Michalski, Greg M. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Streamflow Prediction in Highly Regulated, Transboundary Watersheds | Du, Tien L. T., Lee, Hyongki, Bui, Duong D., Graham, L. Phil, Darby, Stephen D., Pechlivanidis, Ilias G., Leyland, Julian, Biswas, Nishan K., Choi, Gyewoon, Batelaan, Okke, Bui, Thao T. P., Do, Son K., Tran, Tinh V., Nguyen, Hoa Thi, Hwang, Euiho | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Spatio-temporal assessment of rainfall erosivity in Ecuador based on | Delgado, Daniel, Sadaoui, Mahrez, Ludwig, Wolfgang, Mendez, Williams | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Spatiotemporal dependence of soil moisture and precipitation over India | Manoj J, Ashish, Guntu, Ravi Kumar, Agarwal, Ankit | 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 | |
| Spatiotemporal estimation of 6-hour high-resolution precipitation across | Zhou, Siqin, Wang, Yuan, Yuan, Qiangqiang, Yue, Linwei, Zhang, Liangpei | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| RecordBreaking Precipitation in Indonesia's Capital of Jakarta in Early January 2020 Linked to the Northerly Surge, Equatorial Waves, and MJO | Lubis, Sandro W., Hagos, Samson, Hermawan, Eddy, Respati, Muhamad R., Ridho, Ainur, Risyanto, Paski, Jaka A. I., Muhammad, Fadhlil R., Siswanto, Ratri, Dian Nur, Setiawan, Sonny, Permana, Donaldi S. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Robustness of Vegetation Optical Depth Retrievals Based on L-Band Global Radiometry | Chaparro, David, Feldman, Andrew F., Chaubell, Mario Julian, Yueh, Simon H., Entekhabi, Dara | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Surface Soil Moisture, Brightness Temperature | |
| Satellite remote sensing of environmental variables can predict acoustic | Gomez-Morales, Diego A., Acevedo-Charry, Orlando | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Satelliteobserved vegetation responses to intraseasonal precipitation variability | Harris, Bethan L., Taylor, Christopher M., Weedon, Graham P., Talib, Joshua, Dorigo, Wouter, van der Schalie, Robin | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Which Rainfall Errors Can Hydrologic Models Handle? Implications for | Stephens, C. M., Pham, H. T., Marshall, L. A., Johnson, F. M. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The world's second-largest, recorded landslide event: Lessons learnt from the landslides triggered during and after the 2018 Mw 7.5 Papua New Guinea ... | Tanyas, Hakan, Hill, Kevin, Mahoney, Luke, Fadel, Islam, Lombardo, Luigi | RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps, Digital Elevation/Terrain Model (DEM), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The sensitivity of the West African monsoon circulation to intraseasonal soil moisture feedbacks | Talib, Joshua, Taylor, Christopher M., Klein, Cornelia, Harris, Bethan L., Anderson, Seonaid R., Semeena, Valiyaveetil S. | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The Skills of Medium-Range Precipitation Forecasts in the Senegal River | Gebremichael, Mekonnen, Yue, Haowen, Nourani, Vahid, Damoah, Richard | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A Novel Reference-Based and Gradient-Guided Deep Learning Model for Daily Precipitation Downscaling | Xiang, Li, Xiang, Jie, Guan, Jiping, Zhang, Fuhan, Zhao, Yanling, Zhang, Lifeng | 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 | |
| Comparing and contrasting the performance of high-resolution precipitation products via error decomposition and triple collocation: an application to different climate ... | Ghomlaghi, Arash, Nasseri, Mohsen, Bayat, Bardia | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Air Quality Simulation with WRF-Chem Over Southeastern Brazil, Part I: Model Description and Evaluation Using Ground-Based and Satellite Data | Rojas Benavente, Noelia, Vara-Vela, Angel Liduvino, Nascimento, Janaina P., Rojas Acuna, Joel, Santos Damascena, Aline, Andrade, Maria de Fatima, Akemi Yamasoe, Marcia | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Hanging glacier avalanche (RaunthigadRishiganga) and debris flow disaster on 7 February 2021, Uttarakhand, India: a preliminary assessment | Thayyen, Renoj J., Mishra, P. K., Jain, Sanjay K., Wani, John Mohd, Singh, Hemant, Singh, Mritunjay K., Yadav, Bankim | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Hydrological modeling using remote sensing precipitation data in a Brazilian savanna basin | Junqueira, Rubens, Viola, Marcelo R., Amorim, Jhones da S., Camargos, Carla, de Mello, Carlos R. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Filling Temporal Gaps within and between GRACE and GRACE-FO Terrestrial | Gyawali, Bimal, Ahmed, Mohamed, Murgulet, Dorina, Wiese, David N. | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| EVALUATION OF ERA5 AND IMERG PRECIPITATION DATA FOR RISK ASSESSMENT OF WATER CYCLE VARIABLES OF A LARGE RIVER BASIN IN SOUTH ASIA USING SATELLITE DATA AND ARCHIMEDEAN COPULAS | Barma, Surajit Deb, Uttarwar, Sameer Balaji, Barane, Prathamesh, Bhat, Nagaraj, Mahesha, Amai | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation of Global Forecast System (GFS) Medium-Range Precipitation Forecasts in the Nile River Basin | Yue, Haowen, Gebremichael, Mekonnen, Nourani, Vahid | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluating effectiveness of mitigation measures for large debris flows in Wenchuan, China | He, Jian, Zhang, Limin, Fan, Ruilin, Zhou, Shengyang, Luo, Hongyu, Peng, Dalei | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluating performance of 20 global and quasi-global precipitation | Degefu, Mekonnen Adnew, Bewket, Woldeamlak, Amha, Yosef | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate |