A Blended SatelliteIn Situ Near-Global Surface Temperature Dataset

A Blended SatelliteIn Situ Near-Global Surface Temperature Dataset A near-global surface temperature dataset was produced by blending several sources of information. For the oceans, these include in situ and infrared satellite-derived sea surface temperatures that were already processed into a monthly product. Land data analysis uses two sources of data. The first is high quality monthly in situ reports from the Global Historical Climatologic Network with more than 1000 stations from around the world. The second source of information is the recently developed passive microwave satellite-derived land surface temperature derivation methodology described in Williams et al. These data are blended on a 1 1 grid that excludes only ice- and snow-covered regions lacking in situ observations. Available starting in January 1992 and updated 10 days after the end of the calendar month, this product is useful for monitoring regional climate anomalies and provides insights into climate variations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bulletin of the American Meteorological Society American Meteorological Society

A Blended SatelliteIn Situ Near-Global Surface Temperature Dataset

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0477
D.O.I.
10.1175/1520-0477(2000)081<2157:ABSSNS>2.3.CO;2
Publisher site
See Article on Publisher Site

Abstract

A near-global surface temperature dataset was produced by blending several sources of information. For the oceans, these include in situ and infrared satellite-derived sea surface temperatures that were already processed into a monthly product. Land data analysis uses two sources of data. The first is high quality monthly in situ reports from the Global Historical Climatologic Network with more than 1000 stations from around the world. The second source of information is the recently developed passive microwave satellite-derived land surface temperature derivation methodology described in Williams et al. These data are blended on a 1 1 grid that excludes only ice- and snow-covered regions lacking in situ observations. Available starting in January 1992 and updated 10 days after the end of the calendar month, this product is useful for monitoring regional climate anomalies and provides insights into climate variations.

Journal

Bulletin of the American Meteorological SocietyAmerican Meteorological Society

Published: Sep 25, 2000

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