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Comparisons of Diurnal Variations of Land Surface Temperatures from Numerical Weather Prediction Analyses, Infrared Satellite Estimates and In Situ Measurements

L’Observatoire de Paris, Centre National de la Recherche Scientifique (CNRS), Paris Sciences et Lettres (PSL); 75014 Paris, France
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Remote Sens. 2020, 12(3), 583; https://doi.org/10.3390/rs12030583 (registering DOI)
Received: 17 December 2019 / Revised: 24 January 2020 / Accepted: 1 February 2020 / Published: 10 February 2020
(This article belongs to the Section Atmosphere Remote Sensing)
This study evaluates the diurnal cycle of Land Surface Temperature (LST) from Numerical Weather Prediction (NWP) reanalyses (ECMWF ERA5 and ERA Interim), as well as from infrared satellite estimates (ISCCP and SEVIRI/METEOSAT), with in situ measurements. Data covering a full seasonal cycle in 2010 are studied. Careful collocations and cloud filtering are applied. We first compare the reanalysis and satellite products at continental and regional scales, and then we concentrate on comparisons with the in situ observations, under a large variety of environments. SEVIRI shows better agreement with the in situ measurements than the other products, with bias often less than ±2K and correlation of 0.99. Over snow or arid surface, ISCCP tends to have more systematic errors than the other products. ERA5 agrees better to the in situ over barren land than ERA Interim, particularly at night time, thanks to the new surface model. However, over vegetated surfaces, both reanalyses tend to have higher/lower temperature at night/day time than the in situ measurements, probably related to the surface processes and its interactions with atmosphere in the NWP model.
Keywords: land surface temperature; satellite thermal infrared; seasonal and diurnal variations; in situ measurements land surface temperature; satellite thermal infrared; seasonal and diurnal variations; in situ measurements
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MDPI and ACS Style

Wang, X.; Prigent, C. Comparisons of Diurnal Variations of Land Surface Temperatures from Numerical Weather Prediction Analyses, Infrared Satellite Estimates and In Situ Measurements. Remote Sens. 2020, 12, 583.

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