N: 50 S: -50 E: 180 W: -180
Description
TMPA (3B42) 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/IMERG/3B-HH/06).
This dataset was the output from the TMPA (TRMM Multi-satellite Precipitation Analysis) Algorithm. It provides precipitation estimates in the TRMM regions that have the (nearly-zero) bias of the ”TRMM Combined Instrument” precipitation estimate and the dense sampling of high-quality microwave data with fill-in using microwave-calibrated infrared estimates. The granule temporal coverage is 3 hours.
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.
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Documents
READ-ME
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Does vertical wind shear increase tropical cyclone rain? | Lau, King Heng, Toumi, Ralf | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Multiday precipitation extremes are projected to become less likely in southern Pakistan | Dollan, Ishrat J., Maggioni, Viviana, Araujo, Diogo, Bhuiyan, Soelem Aafnan, Nair, Anju Vijayan, Nikolopoulos, Efthymios I. | Air Temperature, Precipitation Amount, Atmospheric Water Vapor, Precipitation, Total Surface Precipitation Rate | |
| A hybrid machine learning model for flood prediction with recursive feature elimination informed by training performance | Gong, Liying, Woo, Wai Lok, Wu, Yue Ivan, Zheng, Xiujuan | RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps, Digital Elevation/Terrain Model (DEM), Land Use/Land Cover Classification, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Event-Based Verification of IMERG Precipitation Estimates over Complex Terrain in the Southern Appalachian Mountains | Major, Dylan, Prat, Olivier P., Nelson, Brian R., Miller, Douglas K., Petkovic, Veljko, Arulraj, Malarvizhi, Ferraro, Ralph | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Total Surface Precipitation Rate | |
| Prior weight dilution in Bayesian model averaging for groundwater | Hu, Haoxin, Zeng, Xiankui, Wu, Jichun, Wang, Dong | Snow Cover, Total Surface Precipitation Rate | |
| Low level jet controlled dynamical and thermodynamical regimes of the diurnal cycle of rainfall over the western ghats | Utkarsh, Verma, Pokhrel, Samir, Sahoo, Patita Kalyana, Choudhury, B Abida, Yashas, Shivamurthy, Chaudhari, Hemantkumar S, Konwar, Mahen, Saha, Subodh K | Total Surface Precipitation Rate | |
| Integrated Spatio-Hydrological Modeling for Hydro-Power Management in North East India | Kurbah, Shanbor, Barman, Diganta, Arjun, B. M., Das, Ranjit | Total Surface Precipitation Rate, Cloud Liquid Water/Ice, Precipitation Amount, Precipitation Rate | |
| Integrating CHIRPS rainfall and FJ Mock model for inflow estimation and water availability analysis in reservoir | Febrianti, D T, Bisri, M, Sisinggih, D, Aina, C | Total Surface Precipitation Rate | |
| Improving thermodynamic nudging in the E3SM Atmosphere Model version 2 (EAMv2): strategy and hindcast skills on weather systems | Zhang, Shixuan, Leung, L. Ruby, Harrop, Bryce E., Bora, Aniruddha, Karniadakis, George, Shukla, Khemraj, Zhang, Kai | Total Surface Precipitation Rate | |
| Mean biases dominate CMIP6 model deficiencies in simulating heavy rainfall during monsoon intraseasonal oscillation | Shi, Qingjian, Qin, Jianhuang, Li, Baosheng | Total Surface Precipitation Rate | |
| Satellite-based Rainfall Datasets: A Global Systematic Review of Applications, Accuracy, and Research Gaps | Conte, Luiza Chiarelli, Tassi, Rutineia, Bayer, Debora Missio | Total Surface Precipitation Rate | |
| Runoff Modeling in Northern Tianshan Glacial Basins Based on Multi-Source Precipitation Products | He, Jing, Zhang, Haoran, Guo, Chunmei, Huang, Tianyu, Wang, Chubo, Zhou, Qixiang, Song, Libing | Total Surface Precipitation Rate | |
| Improvement of Google Earth Engine-Based Multi-satellite Rainfall Estimation using Rain Gauge Data in South Sulawesi | Ismail, Prayoga, Jatmiko, Retnadi Heru, Farda, Nur Mohammad, Munandar, Muhammad Arif | Total Surface Precipitation Rate, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| GIS-integrated 2D HEC-RAS modeling approach for assessing dam-break floods and their socio-economic consequences in urban arid environment | Khan, Mohd Yawar Ali, ElKashouty, Mohamed, Refadah, Samyah Salem | Total Surface Precipitation Rate | |
| Remote Sensing of the North Eastern Himalayan Ecosystem | Total Surface Precipitation Rate | ||
| Iron isotope insights into equatorial Pacific biogeochemistry | Camin, Capucine, Labatut, Marie, Pradoux, Catherine, Murray, James W., Lacan, Francois | Total Surface Precipitation Rate | |
| Limitations of satellite precipitation products in capturing extreme precipitation events: An object-based evaluation over China | Gu, Hongji, Shen, Dingtao, Chen, Jinming, Zhang, Chunxiao, Xiao, Shuting, Yu, Fei | Total Surface Precipitation Rate | |
| Synoptic flow patterns and key factors controlling rapid intensification onset of tropical cyclones along the China coast | Li, Xiaomeng, Zhan, Ruifen, Wang, Yuqing, Yan, Fengxia | Total Surface Precipitation Rate | |
| Decadal precipitation variability and atmospheric brown cloud effects: a multi-platform assessment over the Indian subcontinent | Jangid, Manish, Dass, Avinash | Total Surface Precipitation Rate, Aerosols, Aerosol Extinction, Aerosol Optical Depth/Thickness, Angstrom Exponent, Aerosol Particle Properties, Carbonaceous Aerosols, Dust/Ash/Smoke, Organic Particles, Sulfate Particles, Sulfur Oxides, Sulfur Compounds, Sulfate, Sulfur Dioxide, Sulfur Oxides, Particulate Matter, Dimethyl Sulfide, Black Carbon, Sea Salt, PARTICULATE MATTER (PM 2.5), PARTICULATE MATTER (PM 1.0), PARTICULATE MATTER (PM 10), 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, Skin Temperature, Sea Surface Skin Temperature | |
| Enhancing Satellite Precipitation Product Accuracy for Hydrological and Environmental Analysis in Semi-Arid Urban Regions | Heir, A. Vaezi, Salehi, E., Zand, A. Daryabeigi, Bidhendi, G. Nabi | Total Surface Precipitation Rate | |
| Evaluating and enhancing the performance of satellite precipitation products by considering uncertainty in rain gauge observations | Wei, Tai, Zhong, Xian-Ci, Gao, Yang | Total Surface Precipitation Rate | |
| Are urban impacts on heavy rainfall amplified in mountainous regions? | Urdiales-Flores, Diego, Koukoula, Marika, Prein, Andreas F, Jan de Vries, Andries, Mariethoz, Gregoire, Bendix, Jorg, Dominguez, Francina, Celleri, Rolando, Peleg, Nadav | Total Surface Precipitation Rate | |
| Artificial neural network-based forecasting of SPI for extreme event analysis in the Upper Sao Francisco sub-basin | Santos, Celso Augusto Guimaraes, de Medeiros Miranda, Vanessa Negreiros, Brasil Neto, Reginaldo Moura, de Farias, Camilo Allyson Simoes, da Silva, Richarde Marques | Total Surface Precipitation Rate | |
| A Comprehensive review of global precipitation Products: Availability and application for flood inundation mapping | Nikam, Vikrant, Patil, Mangal, Wandre, Sarika, Nikam, Bhaskar, Nandgude, Sachin, Ghatge, Jayant | Total Surface Precipitation Rate | |
| A Dual-TransUNet Deep Learning Framework for Multi-Source Precipitation Merging and Improving Seasonal and Extreme Estimates | Ye, Yuchen, Qi, Zixuan, Li, Shixuan, Qi, Wei, Cai, Yanpeng, Yuan, Chaoxia | 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 |
|---|---|---|---|---|---|---|---|
| Grid/HQprecipitation | Grid/HQprecipitation | mm/hr | float32 | -9999.900390625 | N/A | N/A | N/A |
| Grid/IRprecipitation | Grid/IRprecipitation | mm/hr | float32 | N/A | N/A | N/A | N/A |
| Grid/precipitation | Grid/precipitation | mm/hr | float32 | N/A | N/A | N/A | N/A |
| Grid/relativeError | Grid/relativeError | mm/hr | float32 | N/A | N/A | N/A | N/A |
| Grid/satObservationTime | Grid/satObservationTime | minutes | int8 | N/A | N/A | N/A | N/A |
| Grid/satPrecipitationSource | Grid/satPrecipitationSource | N/A | int16 | N/A | N/A | N/A | N/A |
| nlat | nlat | degrees_north | float32 | N/A | N/A | N/A | N/A |
| nlon | nlon | degrees_east | float32 | N/A | N/A | N/A | N/A |