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 |
|---|---|---|---|
| The CO2 record at the Amazon Tall Tower Observatory: A new opportunity to study processes on seasonal and interannual scales | Botia, Santiago, Komiya, Shujiro, Marshall, Julia, Koch, Thomas, Gakowski, Micha, Lavric, Jost, GomesAlves, Eliane, Walter, David, Fisch, Gilberto, Pinho, Davieliton M., Nelson, Bruce W., Martins, Giordane, Luijkx, Ingrid T., Koren, Gerbrand, Florentie, Liesbeth, Carioca de Araujo, Alessandro, Sa, Marta, Andreae, Meinrat O., Heimann, Martin, Peters, Wouter, Gerbig, Christoph | Total Surface Precipitation Rate | |
| STUDI PENGELOLAAN AIR HUJAN DALAM RANGKA PENGEMBANGAN TAMAN KONSERVASI DI KOTA SAWAHLUNTO, SUMATERA BARAT | Kent, Steven, Yudianto, Doddi, Fitriana, Finna | Total Surface Precipitation Rate | |
| Weathertypeconditioned calibration of Tropical Rainfall Measuring Mission precipitation over the South Pacific Convergence Zone | Mirones, Oscar, Bedia, Joaquin, FernandezGranja, Juan A., Herrera, Sixto, Van Vloten, Sara O., Pozo, Andrea, Cagigal, Laura, Mendez, Fernando J. | Total Surface Precipitation Rate | |
| Tropical Continents Rainier Than Expected From Geometrical Constraints | Hohenegger, Cathy, Stevens, Bjorn | Total Surface Precipitation Rate, Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Enhanced Chlorophyll-a in the Coastal Waters near the Eastern Guangdong during the Downwelling Favorable Wind Period | Yang, Chaoyu, Ye, Haibin | Total Surface Precipitation Rate | |
| Effects of Improved Simulation of Precipitation on Evapotranspiration and its Partitioning over Land | Cui, Zeyu, Wang, Yong, Zhang, Guang J., Yang, Mengmiao, Liu, Jane, Wei, Linyi | Total Surface Precipitation Rate | |
| Evaluation of GPM-IMERG rainfall estimates at multiple temporal and spatial scales over Greece | Kazamias, Anastasios-Petros, Sapountzis, Marios, Lagouvardos, Konstantinos | Total Surface Precipitation Rate | |
| Daily precipitation dataset at 0.1 for the Yarlung Zangbo River basin from 2001 to 2015 | Zhao, Keke, Peng, Dingzhi, Gu, Yu, Pang, Bo, Zhu, Zhongfan | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Deep learning based short-range forecasting of Indian summer monsoon rainfall using earth observation and ground station datasets | Kumar, Bipin, Abhishek, Namit, Chattopadhyay, Rajib, George, Sandeep, Singh, Bhupendra Bahadur, Samanta, Arya, Patnaik, B.S.V., Gill, Sukhpal Singh, Nanjundiah, Ravi S., Singh, Manmeet | Total Surface Precipitation Rate | |
| Aerosols over East and South Asia: Type Identification, Optical | Liu, Yushan, Yi, Bingqi | Total Surface Precipitation Rate | |
| Assessment of satellite precipitation products at different time scales over a cyclone prone coastal river basin in India | Setti, Sridhara, Yumnam, Karisma, Rathinasamy, Maheswaran, Agarwal, Ankit | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Analysis of hydrological impacts caused by climatic and anthropogenic changes in Upper Grande River Basin, Brazil | Melo, Pamela A., Alvarenga, Livia A., Tomasella, Javier, de Mello, Carlos R., Martins, Minella A., Coelho, Gilberto | 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, Atmospheric Radiation, Longwave Radiation, Shortwave Radiation, Radiative Flux, Radiative Forcing, Surface Radiative Properties, Albedo, Emissivity, Cloud Properties, Cloud Fraction, Cloud Optical Depth/Thickness, Skin Temperature, Sea Surface Skin Temperature | |
| Complexities of Extreme Rainfall in the Philippines | Olaguera, Lyndon Mark P., Cruz, Faye Abigail T., Dado, Julie Mae B., Villarin, Jose Ramon T. | Total Surface Precipitation Rate | |
| Extra predictability from a seamless approach for Asian summer monsoon | Li, Xiaojing, Tang, Youmin, Song, Xunshu | Total Surface Precipitation Rate | |
| Evaluation of IMERG and ERA5 precipitation products over the Mongolian | Xin, Ying, Yang, Yaping, Chen, Xiaona, Yue, Xiafang, Liu, Yangxiaoyue, Yin, Cong | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Evaluation of the accuracy of seven gridded satellite precipitation products over the Godavari River basin, India | Reddy, N. M., Saravanan, S. | Total Surface Precipitation Rate, Land Use/Land Cover Classification | |
| Hydrological modeling using remote sensing precipitation data in a Brazilian savanna basin | Junqueira, Rubens, Viola, Marcelo R., Amorim, Jhones da S., Camargos, Carla, de Mello, Carlos R. | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Impact of meteorological parameterization schemes on CTM model simulations | Srivastava, Nishi, Blond, Nadege | Total Surface Precipitation Rate | |
| Water vapor monitoring with IGS RTS and GPT3/VMF3 functions over Turkey | Tunal, Engin | Total Surface Precipitation Rate, 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 | |
| The Spatial-Temporal Characteristics of Soil Moisture and Its Persistence over Australia in the Last 20 Years | Cai, Jiangtao, Chen, Tiexi, Yan, Qingyun, Chen, Xin, Guo, Renjie | Total Surface Precipitation Rate, 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, Ground Water | |
| Rising ecosystem water demand exacerbates the lengthening of tropical dry seasons | Xu, Hao, Lian, Xu, Slette, Ingrid J., Yang, Hui, Zhang, Yuan, Chen, Anping, Piao, Shilong | Total Surface Precipitation Rate | |
| PhenologyGross Primary Productivity (GPP) Method for Crop Information Extraction in Areas Sensitive to Non-Point Source Pollution and Its Influence on ... | Li, Mengyao, Wu, Taixia, Wang, Shudong, Sang, Shan, Zhao, Yuting | Total Surface Precipitation Rate | |
| SAR based flood risk analysis: A case study Kerala flood 2018 | Pramanick, Niloy, Acharyya, Rituparna, Mukherjee, Sandip, Mukherjee, Sudipta, Pal, Indrajit, Mitra, Debashis, Mukhopadhyay, Anirban | Total Surface Precipitation Rate | |
| Regime shifts of the wet and dry seasons in the tropics under global warming | Guo, Jinyuan, Hu, Shujuan, Guan, Yuping | Total Surface Precipitation Rate | |
| Hurricanes as an enabler of Amazon fires | Tsui, Enoch Yan Lok, Toumi, Ralf | Total Surface Precipitation Rate |