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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| Tree species diversity drives the land surface phenology of seasonally | Godlee, J. L., Ryan, C. M., Siampale, A., Dexter, K. G. | Land Use/Land Cover Classification, Plant Phenology, Enhanced Vegetation Index (EVI), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Tropical cyclone-driven rainfall in the northeast Indian Ocean and | Krishnaja, P.B., Akhila, R.S., Kuttippurath, J., Sunanda, N. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The Conditional Bias of Extreme Precipitation in Multi-Source Merged | Kang, Xiaoqi, Dong, Jianzhi, Crow, Wade T., Wei, Lingna, Zhang, Huiwen | Heat Flux, Air Temperature, Skin Temperature, Specific Humidity, Water Vapor, Precipitation Rate, Snow/Ice, Evaporation, Latent Heat Flux, Latent Heat Flux, Sensible Heat Flux, Diffusion, Surface Winds, Wind Speed, U/V Wind Components, Wind Stress, Wind Stress, Surface Roughness, Planetary Boundary Layer Height, Ice Fraction, Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Snow, Rain | |
| Urban development pattern's influence on extreme rainfall occurrences | Yang, Long, Yang, Yixin, Shen, Ye, Yang, Jiachuan, Zheng, Guang, Smith, James, Niyogi, Dev | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Warming-induced soil moisture stress threatens food security in India | Kashyap, Rahul, Kuttippurath, Jayanarayanan | Population Estimates, Socioeconomics, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Unraveling the Sensitivity and Response of Ecosystems to Rising Moisture Stress in India | Kashyap, Rahul, Kuttippurath, Jayanarayanan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Uncertainty estimation of hydrological modelling using gridded precipitation as model inputs in the Gandaki River Basin | Zeng, Qiang, Zhao, Qiang, Luo, Yang-Tao, Ma, Shun-Gang, Kang, You, Li, Yu-Qiong, Chen, Hua, Xu, Chong-Yu | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Understanding tropical cyclone persistence over land: a case study of cyclone Gulab | Navale, Ashish, Karthikeyan, L | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Understanding the spatiotemporal variability of tropical orographic rainfall using convective plume buoyancy | Nicolas, Quentin, Boos, William R. | Atmospheric Water Vapor, Precipitation, RADAR, Precipitation Rate, Precipitation Amount, 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 | |
| Characterizing oceanographic conditions near Coiba Island and Pacific Panama using 20 years of satellite-based wind stress, SST and chlorophyll-a measurements | Crawford, Greg, Mepstead, Matthew, Diaz-Ferguson, Edgardo | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Heat Flux, Air Temperature, Skin Temperature, Specific Humidity, Water Vapor, Snow/Ice, Evaporation, Latent Heat Flux, Latent Heat Flux, Sensible Heat Flux, Diffusion, Surface Winds, Wind Speed, U/V Wind Components, Wind Stress, Wind Stress, Surface Roughness, Planetary Boundary Layer Height, Ice Fraction, Sea Surface Temperature, Sea Surface Temperature | |
| Assessment of meteorological parameters on air pollution variability over Delhi | Garsa, Kalpana, Khan, Abul Amir, Jindal, Prakhar, Middey, Anirban, Luqman, Nadeem, Mohanty, Hitankshi, Tiwari, Shubhansh | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Surface Pressure, Air Temperature, Specific Humidity, Surface Winds, Wind Speed, Geopotential Height, Heat Flux, Skin Temperature, Water Vapor, Snow/Ice, Evaporation, Latent Heat Flux, Latent Heat Flux, Sensible Heat Flux, Diffusion, Surface Winds, U/V Wind Components, Wind Stress, Wind Stress, Surface Roughness, Planetary Boundary Layer Height, Ice Fraction | |
| Bias Analysis in the Simulation of the Western North Pacific Tropical Cyclone Characteristics by Two High-Resolution Global Atmospheric Models | Liu, Qiyang, Qiao, Fengxue, Yu, Yongqiang, Zhu, Yiting, Zhao, Shuwen, Liu, Yujia, Jiang, Fulin, Hu, Xinyu | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Browning of vegetation in efficient carbon sink regions of India during | Kashyap, Rahul, Kuttippurath, Jayanarayanan, Kumar, Pankaj | Photosynthesis, Primary Production, Vegetation Productivity, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| CA-discharge: Geo-Located Discharge Time Series for Mountainous Rivers in Central Asia | Marti, Beatrice, Yakovlev, Andrey, Karger, Dirk Nikolaus, Ragettli, Silvan, Zhumabaev, Aidar, Wakil, Abdul Wakil, Siegfried, Tobias | RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps, Digital Elevation/Terrain Model (DEM), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Albedo, Snow Cover | |
| Comprehensive analysis of droughts over the Middle East using IMERG data over the past two decades (20012020) | Ghasemifar, Elham, Sonboli, Zahra, Hedayatizade, Mahin | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven ... | Zhang, Yuhang, Ye, Aizhong, Analui, Bita, Nguyen, Phu, Sorooshian, Soroosh, Hsu, Kuolin, Wang, Yuxuan | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| 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 | |
| Characteristics and Variability of Precipitation Across Different Sectors of an Extra-Tropical Cyclone: A Case Study Over the High-Latitudes of the Southern Ocean | Truong, S. C. H., Siems, S. T., May, P. T., Huang, Y., Vignon, E., Gevorgyan, A. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Automatic detection of rainfall at hourly time scales from mooring near-surface salinity in the eastern tropical Pacific | Chkrebtii, Oksana A., Bingham, Frederick M. | Fresh Water Flux, TEMPERATURE PROFILES, Surface Winds, Salinity, HUMIDITY, SURFACE AIR TEMPERATURE, Conductivity, Heat Flux, Water Temperature, Water Pressure, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Climatology of Rossby Wave Breaking over the subtropical Indian region | Thomas, Biyo, Kunchala, Ravi Kumar, Singh, Bhupendra Bahadur, Kumar, Kondapalli Niranjan | 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 | |
| Contributions from climate variation and human activities to flow regime change of Tonle Sap Lake from 2001 to 2020 | Morovati, Khosro, Tian, Fuqiang, Kummu, Matti, Shi, Lidi, Tudaji, Mahmut, Nakhaei, Pouria, Alberto Olivares, Marcelo | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Delineation of most favorable winds for southwest monsoon rainfall along Kerala coast | V. B., Adith, A. Can, Aftab, DMello, Joshua | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Demographic Evaluation and Parametric Assessment of Air Pollutants over | Khan, Abul Amir, Garsa, Kalpana, Jindal, Prakhar, Devara, Panuganti C. S., Tiwari, Shubhansh, Sharma, P. B. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain |