Abstract

The European Organization for the Exploitation of Meteorological Satellites’ (EUMETSAT) Meteosat satellites provide the unique opportunity to compile a 30+ year land surface temperature (LST) climate data record. Since the Meteosat instrument on-board Meteosat 2–7 is equipped with a single thermal channel, single-channel LST retrieval algorithms are used to ensure consistency across Meteosat satellites. The present study compares the performance of two single-channel LST retrieval algorithms: (1) A physical radiative transfer-based mono-window (PMW); and (2) a statistical mono-window model (SMW). The performance of the single-channel algorithms is assessed using a database of synthetic radiances for a wide range of atmospheric profiles and surface variables. The two single-channel algorithms are evaluated against the commonly-used generalized split-window (GSW) model. The three algorithms are verified against more than 60,000 LST ground observations with dry to very moist atmospheres (total column water vapor (TCWV) 1–56 mm). Except for very moist atmospheres (TCWV > 45 mm), results show that Meteosat single-channel retrievals match those of the GSW algorithm by 0.1–0.5 K. This study also outlines that it is possible to put realistic uncertainties on Meteosat single-channel LSTs, except for very moist atmospheres: simulated theoretical uncertainties are within 0.3–1.0 K of the in situ root mean square differences for TCWV < 45 mm.

Highlights

  • Land surface temperature (LST) is an important climate state variable

  • The 2 K target accuracy (RMSD) of the LSA SAF land surface temperature (LST) dataset is reached for the majority of angles and total column water vapor (TCWV) classes for physical radiative transfer-based mono-window (PMW) and statistical mono-window model (SMW), degrading into larger errors for very moist atmospheres with high angles, i.e., for very large optical paths

  • For very moist atmospheres (TCWV > 50 mm) and high viewing angles (VZA > 55 mm), the SMW performed slightly better than the PMW

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Summary

Introduction

Land surface temperature (LST) is an important climate state variable. Precise estimates of the radiative surface skin temperature are essential to compute the surface radiative and sensible heat balance [1]. Reported accuracies are 1–2 K for GSW [12,20,21], 2.5 K or less for PMW [8,19] and 2–4 K for SMW [22] Those performance metrics from the literature cannot be compared, since they refer to different satellite sensors with distinct viewing geometries, with variations in instrument calibration and different validation data for a physical parameter (LST), which is highly variable in time and space [8,10,23]. This study is unique in that it compares PMW, SMW and GSW LST retrievals from identical satellite acquisitions with a large number of in situ measurements across different climate zones

Satellite Data
Generalized Split-Window Model
Physical Mono-Window Model
Statistical Mono-Window Model
Theoretical Uncertainty Characterization
Ground-Based LST Measurements
Theoretical Uncertainty Analysis
Ground-Based Validation
Gobabeb Station
Dahra Station
RMZ and Evora Stations
Conclusions
GCOS 2011 Report
Full Text
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