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 |
|---|---|---|---|
| Robust effects of cloud superparameterization on simulated daily rainfall intensity statistics across multiple versions of the C ommunity E arth S ystem M odel | Kooperman, Gabriel J., Pritchard, Michael S., Burt, Melissa A., Branson, Mark D., Randall, David A. | Total Surface Precipitation Rate | |
| Precipitation intensity required for landslide initiation in Rwanda | Piller, Angela | Total Surface Precipitation Rate | |
| Assimilation of TRMM Precipitation into a Hydrological Model of a Southern Andes Watershed | Alarcon, Vladimir J., Alcayaga, Hernan, Alvarez, Enrique | Total Surface Precipitation Rate | |
| Cholera transmission in Ouest Department of Haiti: dynamic modeling and the future of the epidemic | Kirpich, Alexander, Weppelmann, Thomas A., Yang, Yang, Ali, Afsar, Morris, J. Glenn, Longini, Ira M. | Total Surface Precipitation Rate | |
| Coupling remote sensing bio-optical and three-dimensional hydrodynamic modeling to study the phytoplankton dynamics in a tropical hydroelectric reservoir | Curtarelli, M.P., Ogashawara, I., Alcantara, E.H., Stech, J.L. | Total Surface Precipitation Rate | |
| Evolution, properties, and spatial variability of MJO convection near and off the equator during DYNAMO | Xu, Weixin, Rutledge, Steven A., Schumacher, Courtney, Katsumata, Masaki | Total Surface Precipitation Rate | |
| Increased Isolation Frequency of Toxigenic Vibrio cholerae O1 from Environmental Monitoring Sites in Haiti | Alam, Meer T., Weppelmann, Thomas A., Longini, Ira, De Rochars, Valery Madsen Beau, Morris, John Glenn, Ali, Afsar | Total Surface Precipitation Rate | |
| How well do gridded datasets of observed daily precipitation compare over Australia? | Contractor, Steefan, Alexander, Lisa V., Donat, Markus G., Herold, Nicholas | Total Surface Precipitation Rate | |
| Midlatitude cyclones in the southeastern United States: frequency and structure differences by cyclogenesis region | Nieto Ferreira, Rosana, Earl Hall, Linwood | Total Surface Precipitation Rate | |
| Systematic evaluation of satellite-based rainfall products over the Brahmaputra basin for hydrological applications | Bajracharya, Sagar Ratna, Palash, Wahid, Shrestha, Mandira Singh, Khadgi, Vijay Ratan, Duo, Chu, Das, Partha Jyoti, Dorji, Chhimi | Total Surface Precipitation Rate | |
| Vertical structure and physical processes of the MaddenJulian Oscillation: Biases and uncertainties at short range | Xavier, Prince K., Petch, Jon C., Klingaman, Nicholas P., Woolnough, Steve J., Jiang, Xianan, Waliser, Duane E., Caian, Mihaela, Cole, Jason, Hagos, Samson M., Hannay, Cecile, Kim, Daehyun, Miyakawa, Tomoki, Pritchard, Michael S., Roehrig, Romain, Shindo, Eiki, Vitart, Frederic, Wang, Hailan | Total Surface Precipitation Rate | |
| The influence of the spectral truncation on the simulation of waves in the tropical stratosphere | Krismer, T. R., Giorgetta, M. A., von Storch, J. S., Fast, I. | Total Surface Precipitation Rate | |
| Spatial downscaling of monthly TRMM precipitation based on EVI and other geospatial variables over the Tibetan Plateau from 2001 to 2012 | Shi, Yuli, Song, Lei | Total Surface Precipitation Rate | |
| Refinement of SMOS multiangular brightness temperature toward soil moisture retrieval and its analysis over reference targets | Zhao, Tianjie, Shi, Jiancheng, Bindlish, Rajat, Jackson, Thomas J., Kerr, Yann H., Cosh, Michael H., Cui, Qian, Li, Yunqing, Xiong, Chuan, Che, Tao | Total Surface Precipitation Rate | |
| Anomalous features of mid-tropospheric CO2 during Indian summer monsoon drought years | Tiwari, Yogesh K., Revadekar, J.V., Ravi Kumar, K. | Total Surface Precipitation Rate | |
| Cloudaerosol coupled index in estimating the break phase of Indian summer monsoon | Chaudhuri, Sutapa, Pal, Jayanti | Total Surface Precipitation Rate | |
| Comparison of rainfall estimations by TRMM 3B42, MPEG and CFSR with ground-observed data for the Lake Tana basin in Ethiopia | Worqlul, A. W., Maathuis, B., Adem, A. A., Demissie, S. S., Langan, S., Steenhuis, T. S. | Total Surface Precipitation Rate | |
| AIRS retrieved CO2 and its association with climatic parameters over India during 20042011 | Kumar, K. Ravi, Revadekar, J.V., Tiwari, Yogesh K. | Total Surface Precipitation Rate | |
| Description and basic evaluation of Beijing Normal University Earth system model (BNU-ESM) version 1 | Ji, D., Wang, L., Feng, J., Wu, Q., Cheng, H., Zhang, Q., Yang, J., Dong, W., Dai, Y., Gong, D., Zhang, R.-H., Wang, X., Liu, J., Moore, J. C., Chen, D., Zhou, M. | Carbon, Nitrogen, Soil Water Holding Capacity, Soil Bulk Density, Soil Chemistry, Soil Classification, Soil Moisture/Water Content, Soil Horizons/Profile, Total Surface Precipitation Rate | |
| Modeling the Influence of Environment and Intervention onCholera in Haiti | Tennenbaum, Stephen, Freitag, Caroline, Roudenko, Svetlana | Total Surface Precipitation Rate | |
| Validation of Extreme Rainfall Estimation in an Urban Area derived from Satellite Data: A Case Study on the Heavy Rainfall Event in July, 2011 | Yoon, Sun-Kwon, Park, Kyung-Won, Kim, Jong Pil, Jung, Il-Won | Total Surface Precipitation Rate | |
| Subtropical cyclones over the southwestern South Atlantic: Climatological aspects and case study | Gozzo, Luiz Felippe, da Rocha, Rosmeri P., Reboita, Michelle S., Sugahara, Shigetoshi | Total Surface Precipitation Rate | |
| Transport of short-lived climate forcers/pollutants (SLCF/P) to the Himalayas during the South Asian summer monsoon onset | Cristofanelli, P, Putero, D, Adhikary, B, Landi, T C, Marinoni, A, Duchi, R, Calzolari, F, Laj, P, Stocchi, P, Verza, G, Vuillermoz, E, Kang, S, Ming, J, Bonasoni, P | Aerosol Optical Depth/Thickness, Atmospheric Ozone, Reflectance, Total Surface Precipitation Rate, Nitrogen Dioxide | |
| The Use of Remote Sensing Data in a Colombian Andean Basin for Risk Analysis | Ocampo Lopez, Olga Lucia, Velez Upegui, Jorge Julian | Total Surface Precipitation Rate | |
| Time-lagged impact of spring sensible heat over the Tibetan Plateau on the summer rainfall anomaly in East China: case studies using the WRF model | Wang, Ziqian, Duan, Anmin, Wu, Guoxiong | Total Surface Precipitation Rate |