Questa raccolta non viene più aggiornata. Vedi
IMERG mensile
Questo algoritmo del set di dati unisce i dati delle microonde di più satelliti,
tra cui SSMI, SSMIS, MHS, AMSU-B e AMSR-E, ciascuno inter-calibrato con lo
strumento combinato TRMM.
L'algoritmo 3B43 viene eseguito una volta al mese di calendario per produrre il singolo campo di stima del tasso di precipitazione e di stima dell'errore di precipitazione RMS (3B43) combinando le stime IR/di alta qualità unite ogni 3 ore (3B42) con l'analisi mensile accumulata del pluviometro del Global Precipitation Climatology Centre (GPCC).
Tutti i set di dati sulle precipitazioni globali hanno un'origine dati di calibrazione,
necessaria per controllare le differenze di distorsione tra i satelliti
che contribuiscono. I dati multisatellitari vengono mediati su scala mensile e
combinati con l'analisi mensile
delle precipitazioni superficiali del Global Precipitation Climatology Centre (GPCC). In ogni caso, i dati multisatellitari
vengono aggiustati alla media su vasta area dell'analisi dei dati di misurazione, se disponibili
(principalmente sulla terraferma), e poi combinati con l'analisi dei dati di misurazione utilizzando una
semplice ponderazione della varianza dell'errore casuale stimato inverso. Le regioni con una scarsa copertura
di stazioni di misurazione, come l'Africa centrale e gli oceani, hanno una ponderazione maggiore
sull'input satellitare.
Adler, R.F., G.J. Huffman, A. Chang, R. Ferraro, P. Xie, J. Janowiak,
B. Rudolf, U. Schneider, S. Curtis, D. Bolvin, A. Gruber, J. Susskind,
P. Arkin, E.J. Nelkin, 2003: The Version 2 Global Precipitation
Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present).
J. Hydrometeor., 4(6), 1147-1167.
Huffman, G.J., 1997: Estimates of Root-Mean-Square Random Error
for Finite Samples of Estimated Precipitation, J. Appl. Meteor.,
1191-1201.
Huffman, G.J., 2012: Algorithm Theoretical Basis Document (ATBD)
Version 3.0 for the NASA Global Precipitation Measurement (GPM)
Integrated Multi-satellitE Retrievals for GPM (I-MERG). GPM Project,
Greenbelt, MD, 29 pp.
Huffman, G.J., R.F. Adler, P. Arkin, A. Chang, R. Ferraro, A.
Gruber, J. Janowiak, A. McNab, B. Rudolph e U. Schneider, 1997:
The Global Precipitation Climatology Project (GPCP) Combined Precipitation
Dataset, Bul. Amer. Meteora. Soc., 78, 5-20.
Huffman, G.J., R.F. Adler, D.T. Bolvin, G. Gu, E.J. Nelkin, K.P.
Bowman, Y. Hong, E.F. Stocker, D.B. Wolff, 2007: The TRMM Multi-satellite
Precipitation Analysis: Quasi-Global, Multi-Year, Combined-Sensor
Precipitation Estimates at Fine Scale. J. Hydrometeor., 8(1), 38-55.
Huffman, G.J., R.F. Adler, M. Morrissey, D.T. Bolvin, S. Curtis,
R. Joyce, B McGavock, J. Susskind, 2001: Global Precipitation at
One-Degree Daily Resolution from Multi-Satellite Observations. J.
Hydrometeor., 2(1), 36-50.
Huffman, G.J., R.F. Adler, B. Rudolph, U. Schneider e P. Keehn,
1995: Global Precipitation Estimates Based on a Technique for Combining
Satellite-Based Estimates, Rain Gauge Analysis, and NWP Model Precipitation
Information, J. Clim., 8, 1284-1295.
Questa raccolta non viene più aggiornata. Consulta IMERG monthly Questo set di dati unisce algoritmicamente i dati delle microonde di più satelliti, tra cui SSMI, SSMIS, MHS, AMSU-B e AMSR-E, ciascuno inter-calibrato con lo strumento combinato TRMM. L'algoritmo 3B43 viene eseguito una volta al mese di calendario per produrre il tasso di precipitazione e l'RMS stimati migliori…
[null,null,[],[[["\u003cp\u003eThe TRMM 3B43V7 dataset provides monthly precipitation estimates from 1998 to 2019, derived from multiple satellite data sources.\u003c/p\u003e\n"],["\u003cp\u003eThis dataset is no longer updated and users are directed to the IMERG monthly dataset for more current precipitation data.\u003c/p\u003e\n"],["\u003cp\u003ePrecipitation estimates are generated by merging microwave and infrared data, calibrated using rain gauge analysis primarily over land.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset is available at a spatial resolution of 27830 meters and includes bands for precipitation, relative error, and gauge weighting.\u003c/p\u003e\n"],["\u003cp\u003eTRMM 3B43V7 data is in the public domain and freely accessible for use and distribution.\u003c/p\u003e\n"]]],[],null,["# TRMM 3B43: Monthly Precipitation Estimates\n\nDataset Availability\n: 1998-01-01T00:00:00Z--2019-12-01T00:00:00Z\n\nDataset Provider\n:\n\n\n [NASA GES DISC at NASA Goddard Space Flight Center](https://doi.org/10.5067/TRMM/TMPA/MONTH/7)\n\nCadence\n: 1 Month\n\nTags\n:\n[climate](/earth-engine/datasets/tags/climate) [geophysical](/earth-engine/datasets/tags/geophysical) [jaxa](/earth-engine/datasets/tags/jaxa) [nasa](/earth-engine/datasets/tags/nasa) [precipitation](/earth-engine/datasets/tags/precipitation) [rainfall](/earth-engine/datasets/tags/rainfall) [trmm](/earth-engine/datasets/tags/trmm) [weather](/earth-engine/datasets/tags/weather) \n\n#### Description\n\n**This collection is no longer being updated. See\n[IMERG monthly](https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_MONTHLY_V06)**\n\nThis dataset algorithmically merges microwave data from multiple satellites,\nincluding SSMI, SSMIS, MHS, AMSU-B and AMSR-E, each inter-calibrated to the\nTRMM Combined Instrument.\n\nAlgorithm 3B43 is executed once per calendar month to produce the single,\nbest-estimate precipitation rate and RMS precipitation-error estimate field\n(3B43) by combining the 3-hourly merged high-quality/IR estimates (3B42)\nwith the monthly accumulated Global Precipitation Climatology Centre (GPCC)\nrain gauge analysis.\n\nAll of the global precipitation datasets have some calibrating data source,\nwhich is necessary to control bias differences between contributing\nsatellites. The multi-satellite data are averaged to the monthly scale and\ncombined with the Global Precipitation Climatology Centre's (GPCC) monthly\nsurface precipitation gauge analysis. In each case the multi-satellite data\nare adjusted to the large-area mean of the gauge analysis, where available\n(mostly over land), and then combined with the gauge analysis using a\nsimple inverse estimated-random-error variance weighting. Regions with poor\ngauge coverage, like central Africa and the oceans, have a higher weighting\non the satellite input.\n\nSee the [algorithm description](https://trmm.gsfc.nasa.gov/3b43.html)\nand the [file specification](https://pps.gsfc.nasa.gov/Documents/filespec.TRMM.V7.pdf)\nfor details.\n\n### Bands\n\n\n**Pixel Size**\n\n27830 meters\n\n**Bands**\n\n| Name | Units | Min | Max | Pixel Size | Description |\n|--------------------------|-------|---------|---------|------------|-----------------------------------------------------------|\n| `precipitation` | mm/hr | 0\\* | 6.73\\* | meters | Merged microwave/IR precipitation estimate |\n| `relativeError` | mm/hr | 0.001\\* | 16.36\\* | meters | Merged microwave/IR precipitation random error estimate |\n| `gaugeRelativeWeighting` | % | 0\\* | 100\\* | meters | Relative weighting of the rain gauges used in calibration |\n\n\\* estimated min or max value\n\n### Terms of Use\n\n**Terms of Use**\n\nThis dataset is in the public domain and is available\nwithout restriction on use and distribution. See [NASA's\nEarth Science Data \\& Information Policy](https://www.earthdata.nasa.gov/engage/open-data-services-and-software/data-and-information-policy)\nfor additional information.\n\n### Citations\n\nCitations:\n\n- Adler, R.F., G.J. Huffman, A. Chang, R. Ferraro, P. Xie, J. Janowiak,\n B. Rudolf, U. Schneider, S. Curtis, D. Bolvin, A. Gruber, J. Susskind,\n P. Arkin, E.J. Nelkin, 2003: The Version 2 Global Precipitation\n Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present).\n J. Hydrometeor., 4(6), 1147-1167.\n- Huffman, G.J., 1997: Estimates of Root-Mean-Square Random Error\n for Finite Samples of Estimated Precipitation, J. Appl. Meteor.,\n 1191-1201.\n- Huffman, G.J., 2012: Algorithm Theoretical Basis Document (ATBD)\n Version 3.0 for the NASA Global Precipitation Measurement (GPM)\n Integrated Multi-satellitE Retrievals for GPM (I-MERG). GPM Project,\n Greenbelt, MD, 29 pp.\n- Huffman, G.J., R.F. Adler, P. Arkin, A. Chang, R. Ferraro, A.\n Gruber, J. Janowiak, A. McNab, B. Rudolph, and U. Schneider, 1997:\n The Global Precipitation Climatology Project (GPCP) Combined Precipitation\n Dataset, Bul. Amer. Meteor. Soc., 78, 5-20.\n- Huffman, G.J., R.F. Adler, D.T. Bolvin, G. Gu, E.J. Nelkin, K.P.\n Bowman, Y. Hong, E.F. Stocker, D.B. Wolff, 2007: The TRMM Multi-satellite\n Precipitation Analysis: Quasi-Global, Multi-Year, Combined-Sensor\n Precipitation Estimates at Fine Scale. J. Hydrometeor., 8(1), 38-55.\n- Huffman, G.J., R.F. Adler, M. Morrissey, D.T. Bolvin, S. Curtis,\n R. Joyce, B McGavock, J. Susskind, 2001: Global Precipitation at\n One-Degree Daily Resolution from Multi-Satellite Observations. J.\n Hydrometeor., 2(1), 36-50.\n- Huffman, G.J., R.F. Adler, B. Rudolph, U. Schneider, and P. Keehn,\n 1995: Global Precipitation Estimates Based on a Technique for Combining\n Satellite-Based Estimates, Rain Gauge Analysis, and NWP Model Precipitation\n Information, J. Clim., 8, 1284-1295.\n\n### Explore with Earth Engine\n\n| **Important:** Earth Engine is a platform for petabyte-scale scientific analysis and visualization of geospatial datasets, both for public benefit and for business and government users. Earth Engine is free to use for research, education, and nonprofit use. To get started, please [register for Earth Engine access.](https://console.cloud.google.com/earth-engine)\n\n### Code Editor (JavaScript)\n\n```javascript\nvar dataset = ee.ImageCollection('TRMM/3B43V7')\n .filter(ee.Filter.date('2018-04-01', '2018-05-01'));\nvar precipitation = dataset.select('precipitation');\nvar precipitationVis = {\n min: 0.1,\n max: 1.2,\n palette: ['blue', 'purple', 'cyan', 'green', 'yellow', 'red'],\n};\nMap.setCenter(6.746, 46.529, 3);\nMap.addLayer(precipitation, precipitationVis, 'Precipitation');\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/TRMM/TRMM_3B43V7) \n[TRMM 3B43: Monthly Precipitation Estimates](/earth-engine/datasets/catalog/TRMM_3B43V7) \nThis collection is no longer being updated. See IMERG monthly This dataset algorithmically merges microwave data from multiple satellites, including SSMI, SSMIS, MHS, AMSU-B and AMSR-E, each inter-calibrated to the TRMM Combined Instrument. Algorithm 3B43 is executed once per calendar month to produce the single, best-estimate precipitation rate and RMS ... \nTRMM/3B43V7, climate,geophysical,jaxa,nasa,precipitation,rainfall,trmm,weather \n1998-01-01T00:00:00Z/2019-12-01T00:00:00Z \n-50 -180 50 180 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [](https://doi.org/https://doi.org/10.5067/TRMM/TMPA/MONTH/7)\n- [](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/TRMM_3B43V7)"]]
