이 데이터 세트는 SSMI, SSMIS, MHS, AMSU-B, AMSR-E를 비롯한 여러 위성의 마이크로파 데이터를 알고리즘으로 병합하며, 각 데이터는 TRMM 결합 기기에 대해 상호 교정됩니다.
알고리즘 3B43은 3시간마다 병합된 고품질/IR 추정치(3B42)를 월별 누적 전 세계 강수량 기후 센터 (GPCC) 우량계 분석과 결합하여 단일의 최적 추정 강수량 및 RMS 강수량 오류 추정치 필드 (3B43)를 생성하기 위해 매월 한 번 실행됩니다.
모든 전역 강수량 데이터 세트에는 기여하는 위성 간의 편향 차이를 제어하는 데 필요한 보정 데이터 소스가 있습니다. 다중 위성 데이터는 월별 규모로 평균화되고 전 세계 강수 기후학 센터 (GPCC)의 월별 지표 강수량 측정기 분석과 결합됩니다. 각 경우에 다중 위성 데이터는 가능한 경우(주로 육지 위) 게이지 분석의 넓은 영역 평균으로 조정된 다음 간단한 역 추정 무작위 오류 분산 가중치를 사용하여 게이지 분석과 결합됩니다. 중앙아프리카, 해양 등 게이지 범위가 좋지 않은 지역은 위성 입력에 더 높은 가중치가 부여됩니다.
Adler, R.F., G.J. Huffman, A. Chang, R. Ferraro, P. Xie, J. Janowiak,
B. Rudolf, U. Schneider, S. Curtis, D. 볼빈, 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: 추정 강수량의 유한 표본에 대한 평균 제곱근 무작위 오류 추정치, J. Appl. Meteor.,
1191~1201.
Huffman, G.J., 2012: NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (I-MERG)의 알고리즘 이론적 근거 문서 (ATBD) 버전 3.0 GPM Project,
Greenbelt, MD, 29 pp.
Huffman, G.J., R.F. Adler, P. 아킨, A. Chang, R. 페라로, A.
Gruber, J. Janowiak, A. McNab, B. 루돌프, U. Schneider, 1997:
The Global Precipitation Climatology Project (GPCP) Combined Precipitation
Dataset, Bul. Amer. Meteor 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: Multi-Satellite Observations를 통한 1도 일일 해상도의 전 세계 강수량 J.
Hydrometeor., 2(1), 36-50.
Huffman, G.J., R.F. Adler, B. Rudolph, U. 슈나이더, P. Keehn, 1995: 위성 기반 추정치, 우량계 분석, NWP 모델 강수 정보를 결합하는 기법에 기반한 전역 강수량 추정치, J. Clim., 8, 1284-1295.
이 컬렉션은 더 이상 업데이트되지 않습니다. IMERG 월별 이 데이터 세트는 SSMI, SSMIS, MHS, AMSU-B, AMSR-E를 비롯한 여러 위성의 마이크로파 데이터를 알고리즘으로 병합하며, 각 데이터는 TRMM 결합 기기에 대해 상호 교정됩니다. 알고리즘 3B43은 월 1회 실행되어 단일의 최적 추정 강수량과 RMS를 생성합니다.
[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from 1998 to 2019, derived from multiple satellite data sources.\u003c/p\u003e\n"],["\u003cp\u003eThis dataset is no longer updated and users are directed to the IMERG monthly dataset for more current precipitation data.\u003c/p\u003e\n"],["\u003cp\u003ePrecipitation estimates are generated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)"]]
