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 Older versions will no longer be available and have been superseded by Version 07.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
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.
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READ-ME
PI DOCUMENTATION
ANOMALIES
IMPORTANT NOTICE
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Evaluating impacts of hydrology and pollution loadings on low dissolved oxygen in an urbanized tidal river network using modeling and monitoring | Zhang, Heng, Liu, Jiahuan, Li, Tong, Zhang, Siyu, Lin, Zhongyuan, Jia, Zhengbo, Gong, Wenping, Zhang, Guang | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation of GPM-IMERG V07 Precipitation Data Against In-Situ Measurements in a Semi-Arid Region of Turkiye | Alsenjar, Omar, Cetin, Mahmut | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impact assessment of 3D-var data assimilation on simulation of tropical cyclones using WRF | Makar, Pragnya, Kumar Singh, Sanjeev, Mitra, Debashis, Kant, Yogesh | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impact of oleoresin harvesting on the reproductive phenology of<i> | da Costa, Patricia, Castilho, Carolina Volkmer de, Cito, Artur Camurca, Barbosa, Reinaldo Imbrozio, Kaminski, Paulo Emilio, Martins, Karina, de Oliveira Wadt, Lucia Helena | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Monitoring Water From Space: An Illustration in Death Valley, California | Buzzanga, B., Hamlington, B. D., Bekaert, D. P. S., Pavelsky, T., Handwerger, A., Bonnema, M., Lee, C. | Land Use/Land Cover, MECHANICAL DISTURBANCE, DISTURBANCE, Rivers/Streams, Surface Water Processes/Measurements, Lakes/Reservoirs, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Multi-Day Extreme Precipitation Caused Major Floods in India During | Chuphal, Dipesh Singh, Malik, Iqura, Singh, Rajesh, Vangala, Gayathri, Niranjannaik, M., Vegad, Urmin, Dilip K, Nandana, Mukhopadhyay, Parthasarathi, Selvan, J. P., Kapadia, Vivek, Mishra, Vimal | Vegetation Water Content, Soil Moisture/Water Content, Skin Temperature, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Operational high-resolution Global Forecast System (GFS) T1534 model fidelity in capturing the monsoon onset over Kerala | Sarkar, Sahadat, Narbar, Sanya | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Seasonal and Interannual Variability of Particulate Organic Carbon in | Jia, Yijia, Wang, Zhenyan, Song, Xinling, Li, Wenjian, Zhao, Meihan, Fu, Yujie | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Unraveling Atmosphere and Surface Boundary Interactions Behind Extreme | Chrysanti, Asrini, Son, Sangyoung | Terrain Elevation, Digital Elevation/Terrain Model (DEM), Topographical Relief Maps, Sea Surface Temperature, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessing causal drivers of model-based cyanobacterial blooms along the South-East coast of India | Budakoti, Sachin | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Dynamics of land, ocean, and atmospheric parameters associated with Tauktae cyclone | Kumar, Rajesh, Pippal, Prity Singh, Chauhan, Akshansha, Singh, Ramesh P., Kumar, Ramesh, Singh, Atar, Singh, Jagvir | Atmospheric Ozone, Sea Level Pressure, Surface Pressure, U/V Wind Components, U/V Wind Components, Potential Vorticity, Vertical Wind Velocity/Speed, Vertical Profiles, Upper Air Temperature, Air Temperature, Relative Humidity, Specific Humidity, Atmospheric Water Vapor, Cloud Liquid Water/Ice, Altitude, Geopotential Height, Ozone Profiles, 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 | |
| Monitoring Cover Crop Biomass in Southern Brazil Using Combined | Breunig, Fabio Marcelo, Dalagnol, Ricardo, Galvao, Lenio Soares, Bispo, Polyanna da Conceicao, Liu, Qing, Berra, Elias Fernando, Gaida, William, Liesenberg, Veraldo, Sampaio, Tony Vinicius Moreira | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Air Temperature, 24 Hour Maximum Temperature, 24 Hour Minimum Temperature | |
| Spatial and conventional verifications of hurricanes Dorian and Fiona using the Canadian precipitation analysis & integrated multi-satellite retrievals for GPM products | Boluwade, Alaba, Farooque, Aitazaz A. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Predictors of disease outbreaks at continental-scale in the African region: Insights and predictions with geospatial artificial intelligence using earth observations and ... | Pezanowski, Scott, Koua, Etien Luc, Okeibunor, Joseph C, Gueye, Abdou Salam | 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, Precipitation, Precipitation Amount | |
| Evaluation of IMERG for GPM satellite-based precipitation products for extreme precipitation indices over Turkiye | Aksu, Hakan, Taflan, Gaye Yesim, Yaldiz, Sait Genar, Akgul, Mehmet Ali | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Characterizing the 2022 Extreme Drought Event over the Poyang Lake Basin | Liu, Sulan, Wu, Yunlong, Xu, Guodong, Cheng, Siyu, Zhong, Yulong, Zhang, Yi | 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, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Precipitation, Precipitation Amount | |
| NASA's Global Precipitation Measurement Mission: Leveraging Stakeholder Engagement & Applications Activities to Inform Decision-making | Portier, Andrea, Kirschbaum, Dalia, Gebremichael, Mekonnen, Kemp, Eric, Kumar, Sujay, Llabres, Iker, Snodgrass, Eric, Wegiel, Jerry | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Accuracy of satellite precipitation products in data-scarce Inner Tibetan Plateau comprehensively evaluated using a novel ground observation network | Liu, Zhaofei | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Technical note: NASAaccess - a tool for access, reformatting, and visualization of remotely sensed earth observation and climate data | Mohammed, Ibrahim Nourein, Bustamante, Elkin Giovanni Romero, Bolten, John Dennis, Nelson, Everett James | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Modeling Lightning Activity in the Third Pole Region: Performance of a km-Scale ICON-CLM Simulation | Singh, Prashant, Ahrens, Bodo | 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 | |
| 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 | |
| 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 | |
| Assessing Carbon Properties in Coastal Waters with a New Observing System Testbed | Clark, J. Blake, Uz, Stephanie Schollaert, Tsontos, Vardis, Huang, Thomas, Scott, Joel, Rogers, Laura | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain |
Variables
The table below lists the variables contained within a single granule for this dataset. Variables often contain observed or derived geophysical measurements collected from a variety of sources, including remote sensing instruments on satellite and airborne platforms, field campaigns, in situ measurements, and model outputs. The terms variable, parameter, scientific data set, layer, and band have been used across NASA’s Earth science disciplines; however, variable is the designated nomenclature in NASA’s Common Metadata Repository (CMR). Variable metadata attributes such as Name, Description, Units, Data Type, Fill Value, Valid Range, and Scale Factor allow users to efficiently process and analyze the data. The full range of attributes may not be applicable to all variables. Additional information on variable attributes is typically available in the data, user guide, and/or other product documentation.
For questions on a specific variable, please use the Earthdata Forum.
| Name Sort descending | Description | Units | Data Type | Fill Value | Valid Range | Scale Factor | Offset |
|---|---|---|---|---|---|---|---|
| lat | lat | degrees_north | float64 | N/A | N/A | N/A | N/A |
| lon | lon | degrees_east | float32 | N/A | N/A | N/A | N/A |
| MWprecipitation | MWprecipitation | mm/day | float32 | -9999.900390625 | N/A | N/A | N/A |
| MWprecipitation_cnt | MWprecipitation_cnt | count | int8 | N/A | N/A | N/A | N/A |
| MWprecipitation_cnt_cond | MWprecipitation_cnt_cond | count | int8 | N/A | N/A | N/A | N/A |
| precipitation | precipitation | mm/day | float32 | -9999.900390625 | N/A | N/A | N/A |
| precipitation_cnt | precipitation_cnt | count | int8 | N/A | N/A | N/A | N/A |
| precipitation_cnt_cond | precipitation_cnt_cond | count | int8 | N/A | N/A | N/A | N/A |
| probabilityLiquidPrecipitation | Probability of liquid precipitation estimated with a diagnostic parameterization using ancillary data. 0=missing values; 1=likely solid; 100=likely liquid or no precipitation. Screen by positive precipitation or precipitation_cnt_cond to locate meaningful probabilities. | percent | int8 | N/A | N/A | N/A | N/A |
| randomError | randomError | mm/day | float32 | -9999.900390625 | N/A | N/A | N/A |
| randomError_cnt | randomError_cnt | count | int8 | N/A | N/A | N/A | N/A |
| time | time | days since 1980-01-06 00:00:00Z | float64 | N/A | N/A | N/A | N/A |
| time_bnds | time_bnds | days since 1980-01-06 00:00:00Z | float64 | N/A | N/A | N/A | N/A |