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 Hemispheric and Seasonal Comparison of Tropospheric to Mesospheric GravityWave Propagation | Alexandre, D., Thurairajah, B., England, S. L., Cullens, C. Y. | Total Surface Precipitation Rate | |
| A Geomorphic Approach for Identifying Flash Flood Potential Areas in the East Rapti River Basin of Nepal | Pangali Sharma, Til Prasad, Zhang, Jiahua, Khanal, Narendra Raj, Prodhan, Foyez Ahmed, Nanzad, Lkhagvadorj, Zhang, Da, Nepal, Pashupati | Total Surface Precipitation Rate, RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps, Digital Elevation/Terrain Model (DEM) | |
| Assessment of groundwater recharge along the Guarani aquifer system outcrop zone in Sao Paulo State (Brazil): an important tool towards integrated management | Santarosa, Lucas Vituri, Gastmans, Didier, Sitolini, Tatiana Penteado, Kirchheim, Roberto Eduardo, Betancur, Sebastian Balbin, de Oliveira, Marcelo E. Dias, Campos, Jose Claudio Viegas, Manzione, Rodrigo Lilla | 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, Total Surface Precipitation Rate | |
| Forest Fire Risk Prediction from Satellite Data with Convolutional | Santopaolo, Alessandro, Saif, Syed Saad, Pietrabissa, Antonio, Giuseppi, Alessandro | Land Surface Temperature, Fire Occurrence, Surface Thermal Properties, THERMAL ANOMALIES, Total Surface Precipitation Rate, Emissivity, Fire Ecology, Biomass Burning, Wildfires, Burned Area, Terrain Elevation, RADAR IMAGERY, Topographical Relief Maps | |
| Heterogeneous Trends of Precipitation Extremes in Recent Two Decades over East Africa | Mtewele, Zacharia Florence, Xu, Xiyan, Jia, Gensuo | Total Surface Precipitation Rate | |
| Hydro-climatology study of the Ogooue River basin using hydrological modeling and satellite altimetry | Bogning, Sakaros, Frappart, Frederic, Paris, Adrien, Blarel, Fabien, Nino, Fernando, Saux Picart, Stephane, Lanet, Pauline, Seyler, Frederique, Mahe, Gil, Onguene, Raphael, Bricquet, Jean-Pierre, Etame, Jacques, Paiz, Marie-Claire, Braun, Jean-Jacques | Total Surface Precipitation Rate | |
| Impact of climate change on cotton production in Bangladesh | Nadiruzzaman, Md, Rahman, Mahjabeen, Pal, Uma, Croxton, Simon, Rashid, Md Bazlur, Bahadur, Aditya, Huq, Saleemul | Total Surface Precipitation Rate | |
| WRF GrayZone Simulations of Precipitation Over the MiddleEast and the UAE: Impacts of Physical Parameterizations and Resolution | Taraphdar, Sourav, Pauluis, Olivier M., Xue, Lulin, Liu, Changhai, Rasmussen, Roy, Ajayamohan, R. S., Tessendorf, Sarah, Jing, Xiaoqin, Chen, Sisi, Grabowski, Wojciech W. | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Subsurface oceanic structure associated with atmospheric convectively coupled equatorial Kelvin waves in the eastern Indian Ocean | Azaneu, Marina, Matthews, Adrian J., Baranowski, Dariusz B. | Total Surface Precipitation Rate | |
| Time-lag correlations between atmospheric anomalies and earthquake events in Iran and the surrounding Middle East region (19802018) | Mansouri Daneshvar, Mohammad Reza, Freund, Friedemann T., Ebrahimi, Majid | Total Surface Precipitation Rate, Air Temperature, Precipitation Rate, 24 Hour Maximum Temperature, 24 Hour Minimum Temperature | |
| The North Equatorial Countercurrent and the Zonality of the Intertropical Convergence Zone | Sun, Zhikuo, Lu, Jianhua | Total Surface Precipitation Rate | |
| Seasonal variability of freshwater plumes in the eastern Gulf of Guinea as inferred from satellite measurements | Houndegnonto, O. J., Kolodziejczyk, N., Maes, C., Bourles, B., DaAllada, C. Y., Reul, N. | Total Surface Precipitation Rate | |
| Projected drought conditions by CMIP6 multimodel ensemble over Southeast Asia | Supharatid, S., Nafung, J. | Total Surface Precipitation Rate | |
| Rainfall variability over the Indus, Ganga, and Brahmaputra river basins: A spatio-temporal characterisation | Patel, Akansha, Goswami, Ajanta, Dharpure, Jaydeo K., Thamban, Meloth | Total Surface Precipitation Rate | |
| Performance evaluation of high-resolution satellite products in estimating rainfall condition over West Borneo | Nauval, Fadli, Sinatra, Tiin, Awaludin, Asif, Fatria, Dita | Total Surface Precipitation Rate | |
| MaddenJulian oscillation influence on sub-seasonal rainfall variability on the west of South America | Recalde-Coronel, G. Cristina, Zaitchik, Benjamin, Pan, William K. | Total Surface Precipitation Rate | |
| Intraseasonal predictions for the south american rainfall dipole | Diaz, Nicolas, Barreiro, Marcelo, Rubido, Nicolas | Total Surface Precipitation Rate | |
| Influence of digital elevation models on the simulation of rainfall-induced landslides in the hillslopes of Guwahati, India | Sarma, Chiranjib Prasad, Dey, Arindam, Krishna, A. Murali | Terrain Elevation, Digital Elevation/Terrain Model (DEM), Topographical Relief Maps, RADAR IMAGERY, Total Surface Precipitation Rate | |
| Role of equatorial waves and convective gravity waves in the 2015/16 quasi-biennial oscillation disruption | Kang, Min-Jee, Chun, Hye-Yeong, Garcia, Rolando R. | Atmospheric Ozone, Sea Level Pressure, Surface Pressure, Pressure Thickness, U/V Wind Components, U/V Wind Components, Potential Vorticity, Vertical Wind Velocity/Speed, Vertical Profiles, Upper Air Temperature, Air Temperature, Relative Humidity, Specific Humidity, Atmospheric Water Vapor, Cloud Liquid Water/Ice, Cloud Fraction, Altitude, Geopotential Height, Ozone Profiles, Total Surface Precipitation Rate, Precipitation | |
| Retrieval of daily reference evapotranspiration for croplands in South Korea using machine learning with stellite images and numerical weather prediction data | Kim, Nari, Kim, Kwangjin, Lee, Soobong, Cho, Jaeil, Lee, Yangwon | Total Surface Precipitation Rate, Evapotranspiration, Photosynthesis, Primary Production, Latent Heat Flux, Land Use/Land Cover Classification, Photosynthetically Active Radiation, Leaf Area Index (LAI), Leaf Characteristics, Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| The record 2017 flood in South Asia: State of prediction and performance of a data-driven requisitely simple forecast model | Palash, Wahid, Akanda, Ali Shafqat, Islam, Shafiqul | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Synchronization of extreme rainfall during the Australian summer monsoon: Complex network perspectives | Cheung, Kevin K. W., Ozturk, Ugur | Total Surface Precipitation Rate | |
| Assessing the effect of spatial resolution on the delineation of management zones for smallholder farming in southern Brazil | Breunig, Fabio Marcelo, Galvao, Lenio Soares, Dalagnol, Ricardo, Santi, Antonio Luiz, Della Flora, Diandra Pinto, Chen, Shuisen | Total Surface Precipitation Rate | |
| Appraisal of hydro-meteorological factors during extreme precipitation event: case study of Kedarnath cloudburst, Uttarakhand, India | Pratap, Shailendra, Srivastava, Prashant K., Routray, Ashish, Islam, Tanvir, Mall, Rajesh Kumar | Total Surface Precipitation Rate | |
| Constrained Data Assimilation Filtering | Khaki, Mehdi | Total Surface Precipitation Rate |