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
Citation is critically important for dataset documentation and discovery. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use and Citation Guidance.
Copy Citation
Documents
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| A multi-scale investigation of rainfall drivers over Metro Manila, Philippines using empirical orthogonal function analysis | Llorin, Alyssa Gewell A., Dairaku, Koji | Total Surface Precipitation Rate | |
| AdriE: a high-resolution ocean model ensemble for the Adriatic Sea under severe climate change conditions | Bonaldo, Davide, Carniel, Sandro, Colucci, Renato R., Denamiel, Clea, Pranic, Petra, Raicich, Fabio, Ricchi, Antonio, Sangelantoni, Lorenzo, Vilibic, Ivica, Vitelletti, Maria Letizia | Total Surface Precipitation Rate | |
| Achieving water budget closure through physical hydrological process modelling: insights from a large-sample study | Zheng, Xudong, Liu, Dengfeng, Huang, Shengzhi, Wang, Hao, Meng, Xianmeng | Total Surface Precipitation Rate | |
| 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 | |
| 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 | |
| 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 | |
| Distinct Intraseasonal Oscillation Modes Over the Tropical Indo-Pacific | Li, Yiran, Hu, Haibo, Liu, Fei, Patterson, Matthew, Yang, XiuQun, Lu, Kecheng, Mao, Kefeng, Wang, Ziyi, Wang, Rongrong | Total Surface Precipitation Rate | |
| Deep learning based bias correction of TRMM precipitation estimates using IMD-gridded precipitation as ground observation | Mishra Sharma, Sumanta Chandra, Mitra, Adway | Total Surface Precipitation Rate | |
| Evaluation of gridded precipitation datasets over Iran | Najafi, Mohammad Saeed, Alizadeh, Omid, Sauter, Tobias | Total Surface Precipitation Rate | |
| 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 | |
| Identifying Optimal Reanalysis and Remote Sensing Data Combinations for | Pan, Suli, Ma, Di, Gu, Haiting, Xu, Chao, Zhou, Xiaojie, Zhu, Qiang | 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 | |
| Identifying Saharan dust driven export of biogenic material in the ultraoligotrophic eastern Mediterranean Sea | van Boxtel, Anouk P. E., Stuut, Jan-Berend W., Peterse, Francien | Total Surface Precipitation Rate, Sea Surface Temperature, Aerosol Backscatter, Aerosol Extinction, Aerosol Optical Depth/Thickness, Angstrom Exponent, Aerosol Particle Properties, Aerosol Radiance, Carbonaceous Aerosols, Cloud Condensation Nuclei, Dust/Ash/Smoke, Nitrate Particles, Organic Particles, Particulate Matter, Sulfate Particles, Trace Gases/Trace Species, Atmospheric Emitted Radiation, Emissivity, Optical Depth/Thickness, Radiative Flux, Reflectance, Transmittance, Atmospheric Stability, Humidity, Total Precipitable Water, Water Vapor Profiles, Cloud Condensation Nuclei, Cloud Droplet Concentration/Size, Cloud Liquid Water/Ice, Cloud Optical Depth/Thickness, Cloud Asymmetry, Cloud Ceiling, Cloud Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Cloud Vertical Distribution, Cloud Emissivity, Cloud Radiative Forcing, Cloud Reflectance, Rain Storms, Atmospheric Ozone | |
| Hydroclimatic Drivers of Dissolved Organic Carbon in Asia's Major Rivers | Septiani, Retno W., Park, JiHyung, Bogard, Matthew J., Cardace, Dawn, Akanda, Ali S. | Carbon Monoxide, Geopotential Height, Tropopause, Methane, Atmospheric Ozone, Surface Pressure, Outgoing Longwave Radiation, Air Temperature, Upper Air Temperature, Humidity, Total Precipitable Water, Water Vapor, Water Vapor Profiles, Cloud Liquid Water/Ice, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Cloud Vertical Distribution, Emissivity, Skin Temperature, Sea Surface Temperature, Total Surface Precipitation Rate, Precipitation Rate, 24 Hour Maximum Temperature, 24 Hour Minimum Temperature, Land Surface/Agriculture Indicators, Drought Indices, Satellite Soil Moisture Index | |
| 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 | |
| Exploring the Impact of Optimization Techniques on Streamflow Prediction | Reddy, Nagireddy Masthan, Srivastav, Roshan | 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 | |
| Insights into the terminal pleistocene climate of Australia from high resolution climate modelling | Lowry, Andrew L., McGowan, Hamish A. | Total Surface Precipitation Rate | |
| Lack of influence of cross-equatorial ocean heat transport on the | Saran, Rajendran, Sandeep, S. | Total Surface Precipitation Rate | |
| Refining remote sensing precipitation datasets in the South Pacific with an adaptive multi-method calibration approach | Mirones, Oscar, Bedia, Joaquin, Herrera, Sixto, Iturbide, Maialen, Bano Medina, Jorge | Total Surface Precipitation Rate | |
| Recent forest loss in the Brazilian Amazon causes substantial reductions in dry season precipitation | Liu, Yu, Spracklen, Dominick V., Parker, Douglas J., Holden, Joseph, Ge, Jun, Guo, Weidong | Total Surface Precipitation Rate, Albedo, Anisotropy, Reflectance, Land Use/Land Cover Classification, Evapotranspiration, Latent Heat Flux | |
| Relationship between latent and radiative heating fields of tropical cloud systems using synergistic satellite observations | Chen, Xiaoting, Stubenrauch, Claudia J., Mandorli, Giulio | Total Surface Precipitation Rate, Heat Flux | |
| Multifractality of climate networks | Thomas, Adarsh Jojo, Kurths, Jurgen, Schertzer, Daniel | Total Surface Precipitation Rate | |
| Understanding nocturnally-driven extreme precipitation events over Lake Victoria in a convection-permitting model | Glazer, Russell H., Coppola, Erika, Giorgi, Filippo | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A Mechanism for the Summer Monsoon Precipitation Variability Over | Singh, Rahul, Sandeep, S. | Total Surface Precipitation Rate |
Variables
The table below lists the variables contained within a single granule for this dataset. Variables often contain observed or derived geophysical measurements collected from a variety of sources, including remote sensing instruments on satellite and airborne platforms, field campaigns, in situ measurements, and model outputs. The terms variable, parameter, scientific data set, layer, and band have been used across NASA’s Earth science disciplines; however, variable is the designated nomenclature in NASA’s Common Metadata Repository (CMR). Variable metadata attributes such as Name, Description, Units, Data Type, Fill Value, Valid Range, and Scale Factor allow users to efficiently process and analyze the data. The full range of attributes may not be applicable to all variables. Additional information on variable attributes is typically available in the data, user guide, and/or other product documentation.
For questions on a specific variable, please use the Earthdata Forum.
| Name Sort descending | Description | Units | Data Type | Fill Value | Valid Range | Scale Factor | Offset |
|---|---|---|---|---|---|---|---|
| HQprecipitation | HQprecipitation | mm | float32 | -9999.900390625 | N/A | N/A | N/A |
| HQprecipitation_cnt | HQprecipitation_cnt | count | int8 | N/A | N/A | N/A | N/A |
| IRprecipitation | IRprecipitation | mm | float32 | -9999.900390625 | N/A | N/A | N/A |
| IRprecipitation_cnt | IRprecipitation_cnt | count | int8 | N/A | N/A | N/A | N/A |
| lat | lat | degrees_north | float32 | N/A | N/A | N/A | N/A |
| lon | lon | degrees_east | float32 | N/A | N/A | N/A | N/A |
| precipitation | precipitation | mm | float32 | -9999.900390625 | N/A | N/A | N/A |
| precipitation_cnt | precipitation_cnt | count | int8 | N/A | N/A | N/A | N/A |
| randomError | randomError | mm | float32 | -9999.900390625 | N/A | N/A | N/A |
| randomError_cnt | randomError_cnt | count | int8 | N/A | N/A | N/A | N/A |