0
点赞
收藏
分享

微信扫一扫

Google Earth Engine——TRMM/34B2产品包含一个网格化的、经TRMM调整的、合并的红外降水(毫米/小时)和降水误差的有效值估计,时间分辨率为3小时,空间分辨率为0.25度。


The Tropical Rainfall Measuring Mission (TRMM) is a joint mission between NASA and the Japan Aerospace Exploration Agency (JAXA) designed to monitor and study tropical rainfall. The 34B2 product contains a gridded, TRMM-adjusted, merged infrared precipitation (mm/hr) and RMS precipitation-error estimate, with a 3-hour temporal resolution and a 0.25 degree spatial resolution.

See the ​​algorithm description​​​ and the ​​file specification​​ for details.

Documentation:

  • ​​PI Documentation​​
  • ​​File Specification for TRMM Products​​
  • ​​Comparison between TRMM versions 6 and 7​​
  • ​​Readme​​
  • ​​Details of the TMPA algorithm used in this product​​
  • TRMM Data Gaps
  • ​​Transition from TMPA to IMERG​​

热带降水测量任务(TRMM)是美国航天局和日本宇宙航空研究开发机构(JAXA)的一项联合任务,旨在监测和研究热带降水。34B2产品包含一个网格化的、经TRMM调整的、合并的红外降水(毫米/小时)和降水误差的有效值估计,时间分辨率为3小时,空间分辨率为0.25度。

详见算法说明和文件说明。

文件。

PI文件

TRMM产品的文件规范

TRMM第6和第7版之间的比较

自述文件

本产品中使用的TMPA算法的细节

TRMM的数据差距

从TMPA到IMERG的过渡

Dataset Availability

1998-01-01T00:00:00 - 2019-12-31T00:00:00

Dataset Provider

​​NASA GES DISC at NASA Goddard Space Flight Center​​

Collection Snippet

​ee.ImageCollection("TRMM/3B42")​

Resolution

27830 meters

Bands Table

Name

Description

Min

Max

Units

precipitation

Merged microwave/IR precipitation estimate

0

100

mm/hr

relativeError

Merged microwave/IR precipitation random error estimate

0

100

mm/hr

satPrecipitationSource

Flag to show source of data

satPrecipitationSource Bitmask



  • Bits 0-5: Source
  • 0: No observation
  • 1: AMSU
  • 2: TMI
  • 3: AMSR
  • 4: SSMI
  • 5: SSMI/S
  • 6: MHS
  • 7: TCI
  • 30: AMSU/MHS average
  • 31: Conical scanner average
  • 50: IR


HQprecipitation

Pre-gauge-adjusted microwave precipitation estimate

0

100

mm/hr

IRprecipitation

Pre-gauge-adjusted infrared precipitation estimate

0

100

mm/hr

satObservationTime

Satellite observation time minus the time of the granule. In case of overlapping satellite observations, the two or more observation times are equal-weighting averaged.

-90

90

Minutes

使用说明:

This dataset is in the public domain and is available without restriction on use and distribution. See ​​NASA's Earth Science Data & Information Policy​​ for additional information.

引用:

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, and 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: 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, and 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

代码:

var dataset = ee.ImageCollection('TRMM/3B42')
.filter(ee.Filter.date('2018-04-01', '2018-04-10'));
var precipitation =
dataset.select(['precipitation', 'HQprecipitation', 'IRprecipitation']);
var precipitationVis = {
min: 0.0,
max: 12.0,
gamma: 5.0,
};
Map.setCenter(-79.98, 23.32, 4);
Map.addLayer(precipitation, precipitationVis, 'Precipitation');

Google Earth Engine——TRMM/34B2产品包含一个网格化的、经TRMM调整的、合并的红外降水(毫米/小时)和降水误差的有效值估计,时间分辨率为3小时,空间分辨率为0.25度。_sql

 Google Earth Engine——TRMM/34B2产品包含一个网格化的、经TRMM调整的、合并的红外降水(毫米/小时)和降水误差的有效值估计,时间分辨率为3小时,空间分辨率为0.25度。_降水_02

 Google Earth Engine——TRMM/34B2产品包含一个网格化的、经TRMM调整的、合并的红外降水(毫米/小时)和降水误差的有效值估计,时间分辨率为3小时,空间分辨率为0.25度。_分析_03

csdn

举报

相关推荐

0 条评论