N: 50 S: -50 E: 180 W: -180
Description
TMPA (3B42_Daily) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG dataset (doi: 10.5067/GPM/IMERGDF/DAY/06).
This daily accumulated precipitation product is generated from the research-quality 3-hourly TRMM Multi-Satellite Precipitation Analysis TMPA (3B42). It is produced at the NASA GES DISC, as a value added product. Simple summation of valid retrievals in a grid cell is applied for the data day. The result is given in (mm). The beginning and ending time for every daily granule are listed in the file global attributes, and are taken correspondingly from the first and the last 3-hourly granules participating in the aggregation. Thus the time period covered by one daily granule amounts to 24 hours, which can be inspected in the file global attributes.
Counts of valid retrievals for the day are provided for every variable, making it possible to compute conditional and unconditional mean precipitation for grid cells where less than 8 retrievals for the day are available.
Efforts have been made to make the format of this derived product as similar as possible to the new Global Precipitation Measurement CF-compliant file format.
The information provided here on the TRMM mission, and on the original 3-hr 3B42 product, remain relevant for this derived product. Note, however, this product is in netCDF-4 format.
The following describes the derivation in more details.
The daily accumulation is derived by summing valid retrievals in a grid cell for the data day. Since the 3-hourly source data are in mm/hr, a factor of 3 is applied to the sum. Thus, for every grid cell we have
Pdaily = 3 SUM{Pi 1[Pi valid]}, i=[1,Nf]
Pdaily_cnt = SUM{1[Pi valid]}
where:
Pdaily - Daily accumulation (mm)
Pi - 3-hourly input, in (mm/hr)
Nf - Number of 3-hourly files per day, Nf=8
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.
On occasion, the 3-hourly source data have fill values for Pi in a very few grid cells. The total accumulation for such grid cells is still issued, inspite of the likelihood that thus resulting accumulation has a larger uncertainty in representing the "true" daily total. These events are easily detectable using "counts" variables that contain Pdaily_cnt, whereby users can screen out any grid cells for which
Pdaily_cnt less than Nf.
There are various ways the accumulated daily error could be estimated from the source 3-hourly error. In this release, the daily error provided in the data files is calculated as follows. First, squared 3-hourly errors are summed, and then square root of the sum is taken. Similarly to the precipitation, a factor of 3 is finally applied:
Perr_daily = 3 { SUM[ (Perr_i 1[Perr_i valid])^2 ] }^0.5 , i=[1,Nf]
Ncnt_err = SUM( 1[Perr_i valid] )
where:
Perr_daily - Magnitude of the daily accumulated error power, (mm)
Ncnt_err - The counts for the error variable
Thus computed Perr_daily represents the worst case scenario that assumes the error in the 3-hourly source data, which is given in mm/hr, accumulates first within the 3-hour period of the source data, and then continues to accumulate during the day. These values, however, can easily be converted to root mean square error estimate of the rainfall rate:
rms_err = { (Perr_daily/3) ^2 / Ncnt_err }^0.5 (mm/hr)
This estimate assumes that the error given in the 3-hourly files is representative of the error of the rainfall rate (mm/hr) within the 3-hour window of the files, and it is random throughout the day. Note, this should be interpreted as the error of the rainfall rate (mm/hr) for the day, not the daily accumulation.
Product Summary
Citation
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Documents
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| A Skillful Prediction of Monsoon Intraseasonal Oscillation Using Deep Learning | Anirudh, K. M., Raj, Prasang, Sandeep, S., Kodamana, Hariprasad, Sabeerali, C. T. | Total Surface Precipitation Rate | |
| A multi-task deep learning model for bias correction and merging of precipitation data in the Lancang-Mekong River Basin | Jiao, Yuxin, Hsu, Kuolin, Li, Jinyang, Duan, Xingwu | Total Surface Precipitation Rate | |
| Assessment of water scarcity as a risk factor for cholera outbreaks | Magers, Bailey, Usmani, Moiz, Brumfield, Kyle D., Huq, Anwar, Colwell, Rita R., Jutla, Antarpreet S. | Air Temperature, Precipitation Rate, 24 Hour Maximum Temperature, 24 Hour Minimum Temperature, Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Snow, Rain | |
| Anomalous oceanic moisture supply conceals expected stable water isotopic depletion during monsoon extreme rain events in Kerala, India | Lekshmy, P.R., Christy, Angel Anita, Krishnadas, Sreeshma, Midhun, M., Thirumalai, Kaustubh, Yadava, Madhusudan G., Kumar, Sanjeev, Mohankumar, K. | Total Surface Precipitation Rate | |
| Exploring the Impact of Optimization Techniques on Streamflow Prediction | Reddy, Nagireddy Masthan, Srivastav, Roshan | Total Surface Precipitation Rate | |
| Evaluation of gridded precipitation datasets over Iran | Najafi, Mohammad Saeed, Alizadeh, Omid, Sauter, Tobias | Total Surface Precipitation Rate | |
| Future MJO Change and Its Impact on Extreme Precipitation and | Wang, Jiabao, DeFlorio, Michael J., Kim, Hyemi, Guirguis, Kristen, Gershunov, Alexander | Total Surface Precipitation Rate | |
| GIRAFE v1: a global climate data record for precipitation accompanied by | Konrad, Hannes, Roca, Remy, Niedorf, Anja, Finkensieper, Stephan, Schroder, Marc, Cloche, Sophie, Panegrossi, Giulia, Sano, Paolo, Kidd, Christopher, Juca Oliveira, Romulo Augusto, Fennig, Karsten, Sikorski, Thomas, Lemoine, Madeleine, Hollmann, Rainer | Atmospheric Water Vapor, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate, Brightness Temperature | |
| Evaluasi Data Hujan Berbasis Satelit untuk Menentukan Debit Aliran Masuk Waduk Selorejo Menggunakan Model HBV-96 | Hidayat, Ivana Nathalia, Yudianto, Doddi, Sanjaya, Stephen | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Water level estimation at 10 virtual stations in the Yarlung Zangbo River based on Jason-2/3 satellite altimeter data | Mao, Qiongyao, Qiao, Gang | Total Surface Precipitation Rate | |
| Understanding the performance of global precipitation products for hydrological modeling in the data-scarce morphologically complex central Himalayan region | Sandilya, Sneha, Singh, Sunayana, Kumar, Sonu, Rajput, Jitendra | Total Surface Precipitation Rate | |
| Spatially Specific Responses of Precipitation 18O to Monsoon Depression Activities Over the Southern Tibetan Plateau | Wang, Shangjie, Tian, Lide, Yang, Yun, Cai, Zhongyin, Liu, Feng, Li, Shijie, Wu, Bin | Total Surface Precipitation Rate | |
| Mixing of Rain and River Water in the Bay of Bengal From Basin-Scale | Jarugula, Sreelekha, Sengupta, Debasis, Shroyer, Emily, Papa, Fabrice | Total Surface Precipitation Rate | |
| Insights into the Australian mid-Holocene climate using downscaled climate models | Lowry, Andrew L., McGowan, Hamish A. | Total Surface Precipitation Rate | |
| Integrated Multisource Data Assimilation and NSGA-II Multiobjective Optimization Framework for Streamflow Simulations | Bahrami, Maziyar, Talebbeydokhti, Nasser, Rakhshandehroo, Gholamreza, Nikoo, Mohammad Reza, Alamdari, Nasrin | Total Surface Precipitation Rate | |
| Influence of Aqueous-Phase Chemistry on the Concentrations of | Cho, JaeHee, Kim, HakSung | Total Surface Precipitation Rate | |
| Identifying thresholds of time-lag and accumulative effects of extreme precipitation on major vegetation types at global scale | Liu, Min, Wang, Hao, Zhai, Huiliang, Zhang, Xiaochong, Shakir, Muhammad, Ma, Jianying, Sun, Wei | Total Surface Precipitation Rate, Land Use/Land Cover Classification, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Impact of Deforestation in the Maritime Continent on the MaddenJulian Oscillation | Chang, Chiung-Wen June, Lo, Min-Hui, Tseng, Wan-Ling, Tsai, Yu-Cian, Yu, Jia-Yuh | Total Surface Precipitation Rate | |
| Precipitation Anomalies and Trends Estimated via Satellite Rainfall Products in the Cananeia-Iguape Coastal System, Southeast Region of Brazil | Baratto, Jakeline, de Bodas Terassi, Paulo Miguel, de Beserra de Lima, Nadia Gilma, Galvani, Emerson | Total Surface Precipitation Rate, 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 | |
| Precipitation Characteristics of Easterly Waves Across the Global | Hollis, Margaret A., Stachnik, Justin P., LewisMerritt, Carrie, McCrary, Rachel R., Martin, Elinor R. | Total Surface Precipitation Rate, 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, Upper Air Temperature, Temperature Tendency, Atmospheric Ozone, Sea Level Pressure, Surface Pressure, U/V Wind Components, Potential Vorticity, Vertical Wind Velocity/Speed, Vertical Profiles, Relative Humidity, Atmospheric Water Vapor, Cloud Liquid Water/Ice, Altitude, Geopotential Height, Ozone Profiles, Precipitation, Precipitation Amount, Snow, Rain | |
| Prediction of Tropical Cyclogenesis Based on Machine Learning Methods | Loi, Chi Lok, Wu, ChunChieh, Liang, YuChiao | Total Surface Precipitation Rate | |
| Radiative and Chemical Effects of Non-Homogeneous Methane on Terrestrial | Zhang, Qian, Wang, Tijian, Wu, Hao, Qu, Yawei, Xie, Min, Li, Shu, Zhuang, Bingliang, Li, Mengmeng, Kilifarska, Natalya Andreeva | Total Surface Precipitation Rate | |
| The relationship between the tropopause folds and deep convective activities over the Tibetan Plateau | Wang, Yan, Tian, Hongying, Chao, Luyao, Tu, Xiaoxu, Jiang, Jiaying, Luo, Jiali | Total Surface Precipitation Rate | |
| Spatial-Temporal Evaluation of Satellite-Derived Rainfall Estimations for Water Resource Applications in the Upper Congo River Basin | Boluwade, Alaba | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Towards an advanced representation of precipitation over Morocco in a | Balhane, Saloua, Cheruy, Frederique, Driouech, Fatima, El Rhaz, Khalid, Idelkadi, Abderrahmane, Sima, Adriana, Vignon, Etienne, Drobinski, Philippe, Chehbouni, Abdelghani | Total Surface Precipitation Rate |