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 Older versions will no longer be available and have been superseded by Version 07.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 Early Daily 10 x 10 km (GPM_3IMERGDE) derived from the half-hourly GPM_3IMERGHHE. The derived result represents an early (expedited) 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 in the final) 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 Early daily product is a couple of minutes after the last granule of GPM_3IMERGHHE for the UTC data day is received at GES DISC. Since the target latency of GPM_3IMERGHHE is 4 hours, the daily should appear about 4 hours after the closure of the UTC day. For information on the original data (GPM_3IMERGHHE), please see the Documentation (Related URL).
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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READ-ME
PI DOCUMENTATION
ANOMALIES
IMPORTANT NOTICE
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Assessing the risk posed by flash floods to the transportation network in southwestern China | He, Yufeng, Xiong, Junnan, Cheng, Weiming, Yang, Jiawei, He, Wen, Yong, Zhiwei, Duan, Yu, Liu, Jun, Yang, Gang, Wang, Nan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Dynamic Relationship Study between the Observed Seismicity and | Nath, Biswajit, Singh, Ramesh P., Gahalaut, Vineet K., Singh, Ajay P. | 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 | |
| Flood modeling through remote sensing datasets such as LPRM soil moisture and GPM-IMERG precipitation: A case study of ungauged basins across Morocco | Ouaba, Mounir, Saidi, Mohamed Elmehdi, Alam, Md Jobair Bin | Skin Temperature, Soil Moisture/Water Content, Vegetation Water Content, Surface Pressure, Heat Flux, Longwave Radiation, Shortwave Radiation, Surface Temperature, Humidity, Evapotranspiration, Surface Winds, Rain, Precipitation Rate, Snow, Soil Temperature, Land Surface Temperature, Snow Water Equivalent, Runoff, Precipitation, Precipitation Amount | |
| 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 | |
| Performance of the Global Forecast System's medium-range precipitation forecasts in the Niger river basin using multiple satellite-based products | Yue, Haowen, Gebremichael, Mekonnen, Nourani, Vahid | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| QRF4PNRT Probabilistic Postprocessing of Nearrealtime Satellite Precipitation Estimates using Quantile Regression Forests | Zhang, Yuhang, Ye, Aizhong, Nguyen, Phu, Analui, Bita, Sorooshian, Soroosh, Hsu, Kuolin | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The Accuracy of Precipitation Forecasts at Timescales of 1-15 Days in | Gebremichael, Mekonnen, Yue, Haowen, Nourani, Vahid | 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 | |
| Towards a novel high-spatial-resolution satellite rainfall product from C-band SAR Sentinel-1 over Central South Korea based on a bottom-up approach | Nguyen, Hoang Hai, Choi, Sunghwa, Lee, Dalgeun, Shin, Daeyun | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Are environmental pollution and biodiversity levels associated to the spread and mortality of COVID-19? A four-month global analysis | Fernandez, Daniel, Gine-Vazquez, Iago, Liu, Ivy, Yucel, Recai, Nai Ruscone, Marta, Morena, Marianthi, Garcia, Victor Gerardo, Haro, Josep Maria, Pan, William, Tyrovolas, Stefanos | Air Temperature, Precipitation Rate, 24 Hour Maximum Temperature, 24 Hour Minimum Temperature, Precipitation, Precipitation Amount, Snow, Rain | |
| A satellite-based investigation into the algae bloom variability in large water supply urban reservoirs during COVID-19 lockdown | Alcantara, Enner, Coimbra, Keyla, Ogashawara, Igor, Rodrigues, Thanan, Mantovani, Jose, Rotta, Luiz Henrique, Park, Edward, Fernandes Cunha, Davi Gasparini | 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 | |
| Assessment of WRF Model Parameter Sensitivity for High-Intensity | Chinta, Sandeep, Yaswanth Sai, J., Balaji, C. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Landslide geometry and activity in Villa de la Independencia (Bolivia) revealed by InSAR and seismic noise measurements | Song, Chuang, Yu, Chen, Li, Zhenhong, Pazzi, Veronica, Del Soldato, Matteo, Cruz, Abel, Utili, Stefano | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Dynamics of Amphan Cyclone and associated changes in ocean, land meteorological and atmospheric parameters | CHAUHAN, AKSHANSHA, KUMAR, RAJESH, DASH, PRASANJIT, SINGH, RAMESH P. | Atmospheric Ozone, 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 | |
| Performance evaluation of GPM-IMERG early and late rainfall estimates over Lake Hawassa catchment, Rift Valley Basin, Ethiopia | Kawo, Nafyad Serre, Hordofa, Aster Tesfaye, Karuppannan, Shankar | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| WRF GrayZone Simulations of Precipitation Over the MiddleEast and the UAE: Impacts of Physical Parameterizations and Resolution | Taraphdar, Sourav, Pauluis, Olivier M., Xue, Lulin, Liu, Changhai, Rasmussen, Roy, Ajayamohan, R. S., Tessendorf, Sarah, Jing, Xiaoqin, Chen, Sisi, Grabowski, Wojciech W. | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The record 2017 flood in South Asia: State of prediction and performance of a data-driven requisitely simple forecast model | Palash, Wahid, Akanda, Ali Shafqat, Islam, Shafiqul | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain |