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 Older versions will no longer be available and have been superseded by Version 07.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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Documents
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ANOMALIES
IMPORTANT NOTICE
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| A Machine Learning approach for Total Water storage anomaly eXtension back to 1980 (ML-TWiX) | Saemian, Peyman, Tourian, Mohammad J., Douch, Karim, Foster, James, Gou, Junyang, Wiese, David, AghaKouchak, Amir, Sneeuw, Nico | Sea Surface Height, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Analysis of the November 2023 to March 2024 Marine Heatwave in the Java Sea Using Satellite and Profiling Floats Data | Wulansari, Willy, Widada, Sugeng, Maslukah, Lilik, Wirasatriya, Anindya, Surendra, Oky | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Assessing Flash Drought Development and Propagation Across the Contiguous United States Using Remote Sensing | Zeraati, Masoud, Farahmand, Alireza, Seager, Richard, Fowler, Hayley J., Madani, Nima, Parazoo, Nicholas, Manning, Colin, White, Christopher J., Wen, Yixin, Mehran, Ali, AghaKouchak, Amir | Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Evapotranspiration, Photosynthesis, Primary Production, Latent Heat Flux, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Carbon Monoxide, Geopotential Height, Tropopause, Methane, Atmospheric Ozone, Surface Pressure, Outgoing Longwave Radiation, Air Temperature, Upper Air Temperature, Humidity, Total Precipitable Water, Water Vapor, Water Vapor Profiles, Cloud Liquid Water/Ice, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Cloud Vertical Distribution, Emissivity, Skin Temperature, Sea Surface Temperature, Heat Flux, Longwave Radiation, Shortwave Radiation, Surface Temperature, Surface Winds, Soil Moisture/Water Content, Soil Temperature, Land Surface Temperature, Snow Water Equivalent, Runoff | |
| Daily and Monthly Scale Comparisons of Three Gridded Precipitation Datasets over the British Columbia Province, Canada | Ogawa, Riki, Iseri, Yoshihiko, Kavvas, M. Levent, Duren, Angela M. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Event-Based Verification of IMERG Precipitation Estimates over Complex Terrain in the Southern Appalachian Mountains | Major, Dylan, Prat, Olivier P., Nelson, Brian R., Miller, Douglas K., Petkovic, Veljko, Arulraj, Malarvizhi, Ferraro, Ralph | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Exploration of the spatiotemporal evolution characteristics of atmospheric rivers in East Asia in the past decade based on a multidimensional adaptive atmospheric river identification algorithm | Zhu, Tingting, Liu, Wenchao, Li, Wenhao, Shum, C.K., Zhang, Shengkai, Zhang, Yu | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Flood pulse monitoring in wetlands with multi-temporal Sentinel-1 interferometric coherence data: Application to the Okavango Delta (Botswana) | Gaudare, Louis, Corgne, Samuel, Jolivet, Marc, Dauteuil, Olivier, Doubre, Cecile, Wolski, Piotr, Grandin, Raphael, Doin, Marie-Pierre, Durand, Philippe | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| High predictability potential of highly synchronized widespread floods in monsoon regions | Zhang, Jianxin, Liu, Kai, Wang, Ming, Li, Kaiwen, Cai, Fenying, Ludescher, Josef, Kurths, Jurgen, Marwan, Norbert | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Long-term assessment of cyclonic disturbances over the North Indian Ocean (18912019) using a cloud-based platform with special reference to cyclone Fani | Chatterjee, Soumen, Biswas, Biplab | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Milk Matters: Enhancing Early Childhood Nutrition Through Dairy in Central Madagascar | Ramahaimandimby, Zoniaina, Shiratori, Sakiko, Rafalimanantsoa, Jules, Sakurai, Takeshi | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Salinity-induced global pattern of atmospheric water constraints on mangrove photosynthetic activity revealed by time series Sentinel-2 data | Liu, Yanjie, Deng, Yueting, Luo, Hui, Chen, Nengwang, Chen, Yougan, Jin, Zhenong, Wang, Xu, Zhang, Hongsheng, Zhu, Xudong | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Quantifying Historical and Future Surface Soil Moisture Drying Using Deep Learning and Remote Sensing | Bo, Yong, Li, Xueke, Liu, Kai, Wang, Shudong, Tang, Qiuhong, Jiang, Yelin, Li, Zhengqiang, Lu, Shanlong, Wang, Litao, Feng, Chenglian, Zhou, Zhan, Zhou, Guangsheng | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Role of Arabian Sea warm pool and atmospheric instability in triggering a monsoonal MCC over Peninsular India | Jose, Subin, Jayachandran, V., Pradeep, Nandana S | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| The Impact of the MJO on Climate in Hawai 'i | Nash, Audrey A., Torri, Giuseppe | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| PhysicsConstrained Network for Enhanced ExtendedRange Precipitation Forecasting in East Asia | Wang, Yudan, Chen, Huimin, Wu, Hao, Liu, Jane, Yuan, Huiling, Cao, Shuya, Wang, Tijian, Zhuang, Bingliang | RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps, Digital Elevation/Terrain Model (DEM), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks | Shinde, Rajat, Ankur, Kumar, Phillips, Christopher E., Gupta, Aman, Pfreundschuh, Simon, Roy, Sujit, Kirkland, Sheyenne, Gaur, Vishal, Kolluru, Venkatesh, Lin, Amy, Trital, Prajun, Sheshadri, Aditi, Nair, Udaysankar, Maskey, Manil, Ramachandran, Rahul | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Variations in land-atmosphere coupling during drought-heatwave events | Yoon, Donghyuck, Chen, Jan-Huey, Hsu, Hsin, Findell, Kirsten L. | 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 | |
| Tropical Cyclone Intensity Sensitivity to Sea Surface Temperature and Mixed Layer Depth | Wellmeyer, Evan David, Ricchi, Antonio, Ferretti, Rossella | 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 | |
| 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 | |
| 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 | |
| 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 |
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 |