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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| Upperlevel midlatitude troughs in boreal winter have an amplified lowlatitude linkage over Africa | Ward, Neil, Fink, Andreas H., Keane, Richard J., Parker, Douglas J. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Water isotopic characterisation of the cloud-circulation coupling in the North Atlantic trades-Part 1: A process-oriented evaluation of COSMOiso simulations with EUREC4A observations | Villiger, Leonie, Dutsch, Marina, Bony, Sandrine, Lothon, Marie, Pfahl, Stephan, Wernli, Heini, Brilouet, Pierre-Etienne, Chazette, Patrick, Coutris, Pierre, Delanoe, Julien, Flamant, Cyrille, Schwarzenboeck, Alfons, Werner, Martin, Aemisegger, Franziska | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Wet and Dry Cold Surges over the Maritime Continent | Tan, I., Reeder, M. J., Singh, M. S., Birch, C. E., Peatman, S. C. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| WarmSeason Afternoon Precipitation Peak in the Central Bay of Bengal: ProcessOriented Diagnostics | Peng, ChinHsuan, Chen, Xingchao | Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Verification of a global weather forecasting system for decision-making in farming over Africa | Kartsios, Stergios, Pytharoulis, Ioannis, Karacostas, Theodore, Pavlidis, Vasileios, Katragkou, Eleni | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The Representation of Soil Moisture-Atmosphere Feedbacks across the | Talib, Joshua, Muller, Omar V., Barton, Emma J., Taylor, Christopher M., Vidale, Pier Luigi | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The Influence of Convective Aggregation on the Stable Isotopic | Galewsky, Joseph, Schneider, Matthias, Diekmann, Christopher, Semie, Addisu, Bony, Sandrine, Risi, Camille, Emanuel, Kerry, Brogniez, Helene | Atmospheric Water Vapor, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The Role of Surface Fluxes in MJO Propagation through the Maritime Continent | Hudson, Justin, Maloney, Eric | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The role of drought conditions on the recent increase in wildfire | Zubieta, Ricardo, Ccanchi, Yerson, Martinez, Alejandra, Saavedra, Miguel, Norabuena, Edmundo, Alvarez, Sigrid, Ilbay, Mercy | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Strengthening cold wakes lead to decreasing trend of tropical cyclone rainfall rates relative to background environmental rainfall rates | Ma, Zhanhong, Lin, Yanluan, Fei, Jianfang, Zheng, Yunxia, Chu, Wenchao, Ye, Hexin | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Tropopause, Surface Pressure, Air Temperature, Upper Air Temperature, Total Precipitable Water, Water Vapor, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Cloud Vertical Distribution, Emissivity, Sea Surface Temperature, Skin Temperature, Carbon Monoxide, Geopotential Height, Humidity, Water Vapor Profiles, Cloud Liquid Water/Ice, Outgoing Longwave Radiation, Methane, Atmospheric Ozone, Atmospheric Water Vapor | |
| Estimation of subsurface salinity and analysis of Changjiang diluted water volume in the East China Sea | Kim, So-Hyun, Shin, Jisun, Kim, Dae-Won, Jo, Young-Heon | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation and Comparison of Six High-Resolution Daily Precipitation Products in Mainland China | Wu, Xiaoran, Zhao, Na | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A DownscalingMerging Scheme for Monthly Precipitation Estimation with High Resolution Based on CBAM-ConvLSTM | Tian, Bingru, Chen, Hua, Yan, Xin, Sheng, Sheng, Lin, Kangling | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Deep learning-based multi-source precipitation merging for the Tibetan Plateau | Nan, Tianyi, Chen, Jie, Ding, Zhiwei, Li, Wei, Chen, Hua | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Detection of spatial and temporal precipitation patterns using remotely sensed data in the Paranapanema River Basin, Brazil from 2000 to 2021 | Manzione, Rodrigo Lilla | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Climatology and characteristics of rapidly intensifying tropical cyclones over the North Indian Ocean | Kranthi, Ganadhi Mano, Deshpande, Medha, Sunilkumar, Khadgarai, Emmanuel, Rongmie, Ingle, Sopan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Droplet Size, Radar Reflectivity, Atmospheric Water Vapor, RADAR | |
| Generating high-resolution climatological precipitation data using SinGAN | Wang, Yang, Karimi, Hassan A. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Fragility Analysis Based on Damaged Bridges during the 2021 Flood in | Pucci, Alessandro, Eickmeier, Daniel, Sousa, Helder S., Giresini, Linda, Matos, Jose C., Holst, Ralph | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| FIM-based DSInSAR method for mapping and monitoring of reservoir bank landslides: an application along the Lancang River in China | Hu, Jiyuan, Wu, Wenhao, Motagh, Mahdi, Qin, Fen, Wang, Jiayao, Pan, Shangyi, Guo, Jiming, Zhang, Chunyu | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Flood detection using Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage and extreme precipitation data | Zhang, Jianxin, Liu, Kai, Wang, Ming | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluating a global soil moisture dataset from a multitask model (GSM3 v1. 0) with potential applications for crop threats | Liu, Jiangtao, Hughes, David, Rahmani, Farshid, Lawson, Kathryn, Shen, Chaopeng | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Land Surface Temperature, Emissivity, Surface Pressure, Heat Flux, Longwave Radiation, Shortwave Radiation, Surface Temperature, Humidity, Evapotranspiration, Surface Winds, Soil Moisture/Water Content, Soil Temperature, Snow Water Equivalent, Runoff, Brightness Temperature, Surface Soil Moisture, Vegetation Water Content, Skin Temperature, Albedo, Anisotropy, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| Evaluation of runoff estimation from GRACE coupled with different | Alghafli, Khaled, Ali, Awad M., Shi, Xiaogang, Sloan, William, Obeid, Ali A.A., Shamsudduha, Mohammad | Terrestrial Water Storage, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Everything hits at once: How remote rainfall matters for the prediction of the 2021 North American heat wave | Oertel, A., Pickl, M., Quinting, J. F., Hauser, S., Wandel, J., Magnusson, L., Balmaseda, M., Vitart, F., Grams, C. M. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A spatial and temporal analysis of commercialized NTFP production in four administrative regions in Myanmar | Chew, Wei Chuang, Okuda, Toshinori, Mon, Su Myat, Mandal, Mohammad Shamim Hasan, Shigematsu, Chihomi, Shin, Thant, Thant, Aye Mya | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A twenty-year dataset of soil moisture and vegetation optical depth from AMSR-E/2 measurements using the multi-channel collaborative algorithm | Hu, Lu, Zhao, Tianjie, Ju, Weimin, Peng, Zhiqing, Shi, Jiancheng, Rodriguez-Fernandez, Nemesio J., Wigneron, Jean-Pierre, Cosh, Michael H., Yang, Kun, Lu, Hui, Yao, Panpan | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Land Use/Land Cover Classification, Vegetation Water Content, Soil Moisture/Water Content, Skin Temperature |