Development and analysis of a long-term, global, terrestrial land surface temperature dataset based on HIRS satellite retrievals

Amanda L. Siemann, Gabriele Coccia, Ming Pan, Eric F. Wood

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

Land surface temperature (LST) is a critical state variable for surface energy exchanges as it is one of the controls on emitted radiation at Earth's surface. LST also exerts an important control on turbulent fluxes through the temperature gradient between LST and air temperature. Although observations of surface energy balance components are widely accessible from in situ stations in most developed regions, these ground-based observations are not available in many underdeveloped regions. Satellite remote sensing measurements provide wider spatial coverage to derive LST over land and are used in this study to form a high-resolution, long-term LST data product. As selected by the Global Energy and Water Exchanges project (GEWEX) Data and Assessments Panel (GDAP) for development of internally consistent datasets, the High Resolution Infrared Radiation Sounder (HIRS) data are used for the primary satellite observations because of the data record length. The final HIRS-consistent, hourly, global, 0.5° resolution LST dataset for clear and cloudy conditions from 1979 to 2009 is developed through merging the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) LST estimates with the HIRS retrievals using a Bayesian postprocessing procedure. The Baseline Surface Radiation Network (BSRN) observations are used to validate the HIRS retrievals, the CFSR LST estimates, and the final merged LST dataset. An intercomparison between the original retrievals and CFSR LST datasets, before and after merging, is also presented with an analysis of the datasets, including an error assessment of the final LST dataset.

Original languageEnglish (US)
Pages (from-to)3589-3606
Number of pages18
JournalJournal of Climate
Volume29
Issue number10
DOIs
StatePublished - Jan 1 2016

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

Keywords

  • Observational techniques and algorithms
  • Physical Meteorology and Climatology
  • Satellite observations
  • Surface temperature

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