Meteosat Land Surface Temperature Climate Data Record: Achievable Accuracy and Potential Uncertainties

Abstract

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 OPEN ACCESSRemote Sens. 2015, 7 13140(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.

Long Term Validation of Land Surface Temperature Retrieved from MSG/SEVIRI with Continuous in-Situ Measurements in Africa

“…The stations, being part of LSA SAF's validation effort and supported by EUMETSAT, were chosen and designed to validate LST derived from MSG/SEVIRI, but are equally well suited to validate other LST products [21,22]. Figure 1 shows the stations' locations within the field of view of the Meteosat satellites: Evora (Portugal, since 2005; cork-oak trees and grass), Dahra (Senegal, since 2008; tiger bush), Gobabeb (Namibia, since 2007; gravel plain), and RMZ Farm/Farm Heimat (Namibia, since 2009; Kalahari bush).…”

Permanent Stations for Calibration/Validation of Thermal Sensors over Spain

“…While in situ measurements cover an area below 5 m 2 , satellite data has an extension of 100 meters or more. In general, as spatial resolution drops, it is more difficult to retrieve a single representative point of the measured area, and the installation of additional sensors is necessary if the surface variability is high [21]. In our case, because test sites areas have low variability, only one point was taken for the ground data.…”

Improving Land Surface Temperature Retrievals over Mountainous Regions

“…LST also partly controls the surface's turbulent heat flux, which modulates the thermodynamic structure of the atmospheric boundary layer [1]. Several studies pointed out the importance of LST in a range of applications, such as general model assessment [2,3], data assimilation [4][5][6][7][8], hydrology [9,10], and climate monitoring [11,12], among others.…”

“…Taking into account the growing interest in LST as an essential climate variable, the Satellite Application Facilities on Climate Monitoring and Land Surface Analysis (CM SAF and LSA SAF) are currently compiling a 25 year LST Climate data record (CDR) with hourly temporal and 0.05 degree spatial resolutions [11], using brightness temperatures measured by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Meteosat First Generation (MFG) and Meteostat Second Generation (MSG) satellites. The atmospheric correction makes use of profiles of water vapor and temperature extracted from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses, namely ERA-Interim (hereafter ERA-Int), a global atmospheric reanalysis from 1979, continuously updated in real time [13].…”