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 Older versions will no longer be available and have been superseded by Version 07.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 |
|---|---|---|---|
| Evaluating global precipitation datasets over Sicily: From daily estimates to extreme events | Yldz, Mehmet Berkant, Di Nunno, Fabio, de Marinis, Giovanni, Granata, Francesco | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Investigation of the role of model integration time step size in numerical simulation of Kerala heavy rainfall events in a cloud resolving scale | Chanchal, K. M., Singh, Kuvar Satya, Nayak, Sridhara | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Daily rainfall variability controls humid heatwaves in the global tropics and subtropics | Jackson, Lawrence S., Birch, Cathryn E., Chagnaud, Guillaume, Marsham, John H., Taylor, Christopher M. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Diagnosing aerosolmeteorological interactions on snow within Earth system models: a proof-of-concept study over High Mountain Asia | Roychoudhury, Chayan, He, Cenlin, Kumar, Rajesh, Arellano Jr., Avelino F. | Reflectance, Dust/Ash/Smoke, Carbonaceous Aerosols, Organic Particles, Sulfate Particles, Sulfur Oxides, Sulfur Compounds, Sulfate, Sulfur Dioxide, Sulfur Oxides, Dimethyl Sulfide, Aerosol Particle Properties, Particulate Matter, Aerosols, Surface Pressure, Pressure Thickness, Relative Humidity, Sea Salt, Black Carbon, Air Mass/Density, Aerosol Concentration, Deposition, Longwave Radiation, Shortwave Radiation, Atmospheric Radiation, Radiative Flux, Radiative Forcing, Surface Radiative Properties, Albedo, Emissivity, Cloud Properties, Cloud Fraction, Cloud Optical Depth/Thickness, Skin Temperature, Skin Temperature, Sea Surface Skin Temperature, Land Surface Temperature, Snow Cover, Aerosol Extinction, Aerosol Optical Depth/Thickness, Angstrom Exponent, PARTICULATE MATTER (PM 2.5), PARTICULATE MATTER (PM 1.0), PARTICULATE MATTER (PM 10), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Atmospheric Ozone, Sea Level Pressure, U/V Wind Components, U/V Wind Components, Potential Vorticity, Vertical Wind Velocity/Speed, Vertical Profiles, Upper Air Temperature, Air Temperature, Specific Humidity, Atmospheric Water Vapor, Cloud Liquid Water/Ice, Altitude, Geopotential Height, Ozone Profiles, Surface Temperature, Dew Point Temperature, Cloud Top Temperature, Atmospheric Winds, Surface Winds, Upper Level Winds, Atmospheric Pressure, Cloud Top Pressure, Sea Level Pressure, Total Precipitable Water, Oxygen Compounds, Boundary Layer Winds, Albedo, Snow Depth, Snow Water Equivalent, Heat Flux, Water Vapor, Snow/Ice, Evaporation, Latent Heat Flux, Latent Heat Flux, Sensible Heat Flux, Diffusion, Surface Winds, Wind Speed, Wind Stress, Wind Stress, Surface Roughness, Planetary Boundary Layer Height, Ice Fraction, Soil Heat Budget, Soil Heat Budget, Soil Temperature, Soil Temperature, Soil Infiltration, Soil Infiltration, Soil Moisture/Water Content, Surface Soil Moisture, Root Zone Soil Moisture, Soil Moisture/Water Content, Surface Water, Runoff Rate, Average Flow, Average Flow, Snow Depth, Snow Melt, Snow/Ice Temperature, Leaf Area Index (LAI), Leaf Area Index (LAI) | |
| Diurnal Cycle of Summer Precipitation Over Mainland Southeast Asia | Lai, HuiWen, Ou, Tinghai, Dai, Aiguo, Chen, Xingchao, Chen, Aifang | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Downscaled GRACE data reveals anthropogenic dominance in groundwater | Cui, Bochao, Xue, Dongping, Gui, Dongwei, Liu, Qi, Abd-Elmabod, Sameh Kotb., Chen, Xiaonan, Goethals, Peter, Maeyer, Philippe De | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Land Surface Temperature, Emissivity | |
| Drivers and impacts of westerly moisture transport events in East Africa | Peal, Robert, Collier, Emily | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Deformation Slope Extraction and Influencing Factor Analysis Using LT-1 | Liu, Jielin, Xu, Chong, Zhao, Binbin, Yang, Zhi, Liu, Yi, Zhang, Sihang, Kong, Xiaoang, Lan, Qiongqiong, Xu, Wenbin, Qi, Wenwen | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Detection of the 2022 extreme drought over the Yangtze River basin using | Wei, Linyong, Jiang, Shanhu, Ren, Liliang, Hua, Zulin, Zhang, Linqi, Duan, Zheng | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Advancing Tropical Cyclone Rainfall Simulation and Projection With EddyResolving Climate Models | Wang, Baiping, Ma, Xiaohui, Ma, Weiwei, Wu, Lixin | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A New Approach to Identifying and Analyzing Precipitation Events and Their Typical Lifecycles Over Conterminous United States | Zhu, Siyu, Tang, Guoqiang, Yan, Songkun, Du, Yu, Xu, Yue, Zhang, Mofan, Chen, Mengye, Li, Huan, Hong, Yang | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A novel matrix for landslide hazard identification combining remote sensing observation and geomorphological interpretation | Liu, Wangcai, Zhang, Yi, Chen, Guan, Yang, Yanzhong, Chang, Jing, Li, Yuanxi, Wu, Xiang, Meng, Xingmin | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A multi-method and multi-duration trend analysis of temperature and precipitation in Istanbul, Turkey, by using meteorological records, MERRA-2 reanalysis, and IMERG estimations | Sam, Sina, Ozger, Mehmet | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A global dataset of remote sensing-based soil critical point and permanent wilting point | Xu, Yawei, He, Qing, Lu, Hui, Yang, Kun, Entekhabi, Dara, Short Gianotti, Daniel J. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Land Use/Land Cover Classification, Plant Phenology, Enhanced Vegetation Index (EVI), Brightness Temperature, Soil Moisture/Water Content | |
| An upgraded high-precision gridded precipitation dataset for the Chinese mainland considering spatial autocorrelation and covariates | Hu, Jinlong, Miao, Chiyuan, Su, Jiajia, Zhang, Qi, Gou, Jiaojiao, Sun, Qiaohong | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessing the Standardized Precipitation Index Utilizing Satellite-Based and Reanalyzed Precipitation Products in Semi-Arid Region, Iran | ShirmohammadiAliakbarkhani, Zahra, Akbari, Abolghasem, Rajabi Jaghargh, Majid, Cox, Jonathan Peter | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Assessment of the WRF model configuration optimization in predicting the heavy rainfall over urban city Bhubaneswar, India | Boyaj, Alugula, Karrevula, N. R., Swain, Madhusmita, Sinha, P., Nadimpalli, Raghu, Islam, Sahidul, Vinoj, V., Khare, Manoj, Niyogi, Dev, Mohanty, U. C. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessing the impact of climate change on rainfall-triggered landslides: a case study in California | Semnani, Shabnam J., Han, Yi, Bonfils, Celine J., White, Joshua A. | Soil Depth, Soil Horizons/Profile, Soil Water Holding Capacity, Soil Texture, Soil Classification, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessing antecedent climatic and hydrological conditions and anthropogenic impacts to drive catastrophic flooding in the northeastern United States | Aryal, Aashutosh, Lakshmi, Venkataraman | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Snow Cover | |
| Analysis of tropical cyclone driven rainfall in the Arabian Sea and its coastal regions for the past two decades (20002020) | Akhila, R. S., Kuttippurath, J., Chakraborty, A. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Asymmetric spatial anomaly patterns of precipitation between light and very heavy precipitation over Indian urban agglomerations | Kalamalla, Lokesh, Satyanarayana, A. N. V. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Land Use/Land Cover Classification | |
| Cascading Spatial Scales in the Hydrological Cycle Over Africa | Zhang, Yan, Rigden, Angela | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Brightness Temperature, Surface Soil Moisture | |
| Climate Change Is Altering Ecosystem Water Use Efficiency in | Green, Tristan, Salvucci, Guido, Friedl, Mark A. | Land Use/Land Cover Classification, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Evapotranspiration, Photosynthesis, Primary Production, Latent Heat Flux, Plant Phenology | |
| Comprehending dust aerosol impacts on cloud and rainfall distribution during a 'dust-rain'storm through WRF-Chem simulations | Sarkar, Ankan, Panda, Jagabandhu | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Aerosol Extinction, Aerosol Optical Depth/Thickness, Aerosol Optical Depth/Thickness, Aerosol Optical Depth/Thickness | |
| Extreme local mesoscale convective systems over the South China coast | Wang, Chenli, Chen, Xingchao, Zhao, Kun | Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain |