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 |
|---|---|---|---|
| Delineation of management zones in agricultural fields using covercrop biomass estimates from PlanetScope data | Breunig, Fabio Marcelo, Galvao, Lenio Soares, Dalagnol, Ricardo, Dauve, Carlos Eduardo, Parraga, Adriane, Santi, Antonio Luiz, Della Flora, Diandra Pinto, Chen, Shuisen | Total Surface Precipitation Rate | |
| Effects of the North Atlantic Subtropical High on summertime precipitation organization in the southeast United States | Nieto Ferreira, Rosana, Rickenbach, Thomas M. | Total Surface Precipitation Rate | |
| Diagnosis of meteorological factors associated with recent extreme rainfall events over Burundi | Nkunzimana, Athanase, Bi, Shuoben, Alriah, Mohamed Abdallah Ahmed, Zhi, Tang, Kur, Ngong Awan Daniel | Total Surface Precipitation Rate | |
| Evaluation of the TRMM 3B42 product for extreme precipitation analysis over southwestern Iran | Keikhosravi Kiany, Mohammad Sadegh, Masoodian, Seyed Abolfazl, Balling Jr, Robert C., Montazeri, Majid | Total Surface Precipitation Rate | |
| Estimation of vertical heat diffusivity at the base of the mixed layer in the Bay of Bengal | Girishkumar, M. S., Ashin, K., McPhaden, M. J., Balaji, B., Praveenkumar, B. | Total Surface Precipitation Rate | |
| Dryland precipitation climatology from satellite observations | Morin, Efrat, Marra, Francesco, Armon, Moshe | Total Surface Precipitation Rate | |
| Accuracy analysis of IMERG and CMORPH precipitation data over North China | Shen, L, Lin, R, Lu, L, Xu, C, Liu, Y | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| A New and Simplified Approach for Estimating the Daily River Discharge | Zeng, Tian, Wang, Lei, Li, Xiuping, Song, Lei, Zhang, Xiaotao, Zhou, Jing, Gao, Bing, Liu, Ruishun | Total Surface Precipitation Rate | |
| Water Storage Variations Over Bangladesh | Khaki, Mehdi | Total Surface Precipitation Rate | |
| Suitability of TRMM products with different temporal resolution (3-hourly, daily, and monthly) for rainfall erosivity estimation | Li, Xianghu, Li, Zhen, Lin, Yaling | Total Surface Precipitation Rate | |
| UAV-based evaluation of morphological changes induced by extreme rainfall events in meandering rivers | Akay, Semih Sami, Ozcan, Orkan, Sanl, Fusun Balk, Gorum, Tolga, Sen, Omer Lutfi, Bayram, Bulent | Total Surface Precipitation Rate | |
| Satellite-derived GRACE groundwater storage variation in complex aquifer system in India | Singh, Leelambar, Saravanan, Subbarayan | Total Surface Precipitation Rate | |
| Role of Intra-Seasonal Variability in the Indian Summer Monsoon on the Hydration and Dehydration of the Upper Troposphere | Uma, K. N., Mohan, T. S., Das, Subrata Kumar | Total Surface Precipitation Rate | |
| Precipitation probability and its future changes from a global cloudresolving model and CMIP6 simulations | Na, Ying, Fu, Qiang, Kodama, Chihiro | Total Surface Precipitation Rate | |
| Remote sensing of sea surface salinity variability in the South China Sea | Yi, Daling Li, Melnichenko, Oleg, Hacker, Peter, Potemra, James | Total Surface Precipitation Rate | |
| Local human movement patterns and land use impact exposure to zoonotic malaria in Malaysian Borneo | Fornace, Kimberly M, Alexander, Neal, Abidin, Tommy R, Brock, Paddy M, Chua, Tock H, Vythilingam, Indra, Ferguson, Heather M, Manin, Benny O, Wong, Meng L, Ng, Sui H, Cox, Jon, Drakeley, Chris | Total Surface Precipitation Rate, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Terrain Elevation, Digital Elevation/Terrain Model (DEM), Topographical Relief Maps | |
| Modeling the effects of land cover change on sediment concentrations in a gold-mined Amazonian basin | Abe, Camila Andrade, Lobo, Felipe Lucia, Novo, Evlyn Marcia Leao de Moraes, Costa, Maycira, Dibike, Yonas | Deforestation, Land Use/Land Cover Classification, Biomass, Forest Composition/Vegetation Structure, Leaf Area Index (LAI), Litter Characteristics, Plant Characteristics, Vegetation Species, Carbon, Nitrogen, Soil Bulk Density, Soil Moisture/Water Content, Soil Texture, Canopy Characteristics, Total Surface Precipitation Rate | |
| Quantifying the Congruence between Air and Land Surface Temperatures for | Singh, Shaktiman, Bhardwaj, Anshuman, Singh, Atar, Sam, Lydia, Shekhar, Mayank, Martin-Torres, F. Javier, Zorzano, Maria-Paz | Land Surface Temperature, Emissivity, Total Surface Precipitation Rate | |
| Assessment of satellite-based precipitation measurement products over the hot desert climate of Egypt | Nashwan, Mohamed Salem, Shahid, Shamsuddin, Wang, Xiaojun | Condensation, Evaporation, Sublimation, Cloud Liquid Water/Ice, Cloud Precipitable Water, Liquid Water Equivalent, Precipitation Rate, Rain, Heat Flux, Total Surface Precipitation Rate | |
| A double instrumental variable method for geophysical product error estimation | Dong, Jianzhi, Crow, Wade T., Duan, Zheng, Wei, Lingna, Lu, Yang | Total Surface Precipitation Rate | |
| Evaluation of satellite-based rainfall estimates in the lower mekong river basin (southeast asia) | Dandridge, Chelsea, Lakshmi, Venkat, Bolten, John, Srinivasan, Raghavan | Total Surface Precipitation Rate | |
| Development of a system for drought monitoring and assessment in South Asia | Amarnath, Giriraj, Pani, Peejush, Alahacoon, Niranga, Chockalingam, Jeganathan, Mondal, Saptarshi, Matheswaran, Karthikeyan, Sikka, Alok, Rao, K.V., Smakhtin, Vladimir | Total Surface Precipitation Rate | |
| Influence of atmospheric rivers in the occurrence of devastating flood associated with extreme precipitation events over Chennai using different reanalysis data sets | Dhana Lakshmi, D., Satyanarayana, A.N.V. | Total Surface Precipitation Rate | |
| Drought and urbanization: The case of the Philippines | Porio, Emma, Dator-Bercilla, Jessica, Narisma, Gemma, Cruz, Faye, Yulo-Loyzaga, Antonia | Total Surface Precipitation Rate | |
| Bayesian Multi-Scale Spatio-Temporal Modeling of Precipitation in the | Christensen, Michael F., Heaton, Matthew J., Rupper, Summer, Reese, C. Shane, Christensen, William F. | Total Surface Precipitation Rate |