Abstract
This work gives a first insight into the potential of the Weather Research and Forecasting (WRF) model to provide high-resolution vertical profiles for land surface temperature (LST) retrieval from thermal infrared (TIR) remote sensing. WRF numerical simulations were conducted to downscale NCEP Climate Forecast System Version 2 (CFSv2) reanalysis profiles, using two nested grids with horizontal resolutions of 12 km (G12) and 3 km (G03). We investigated the utility of these profiles for the atmospheric correction of TIR data and LST estimation, using the moderate resolution atmospheric transmission (MODTRAN) model and the Landsat 8 TIRS10 band. The accuracy evaluation was performed using 27 clear-sky cases over a radiosonde station in Southern Brazil. We included in the comparative analysis NASA’s Atmospheric Correction Parameter Calculator (ACPC) web-tool and profiles obtained directly from the NCEP CFSv2 reanalysis. The atmospheric parameters from ACPC, followed by those from CFSv2, were in better agreement with parameters calculated using in situ radiosondes. When applied into the radiative transfer equation (RTE) to retrieve LST, the best results (RMSE) were, in descending order: CFSv2 (0.55 K), ACPC (0.56 K), WRF G12 (0.79 K), and WRF G03 (0.82 K). Our findings suggest that there is no special need to increase the horizontal resolution of reanalysis profiles aiming at RTE-based LST retrieval. However, the WRF results were still satisfactory and promising, encouraging further assessments. We endorse the use of the well-known ACPC and recommend the NCEP CFSv2 profiles for TIR atmospheric correction and LST single-channel retrieval.
Highlights
We introduced into the moderate resolution atmospheric transmission (MODTRAN) as input, vertical profiles of pressure, air temperature, and relative humidity from: (i) SBPA radiosonde; (ii) National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) reanalysis; (iii) Weather Research and Forecasting (WRF) G12; (iv) WRF G03
With respect to mean absolute error (MAE) and root mean square error (RMSE), the best results were from Atmospheric Correction Parameter Calculator (ACPC) followed by CFSv2
This study evaluated the use of the WRF numerical model to simulate high-resolution profiles, improving horizontal, temporal, and vertical resolutions of NCEP CFSv2 reanalysis data
Summary
Land surface temperature (LST) is one of the essential climate variables (ECVs) of the Global Climate Observing System (GCOS) [1,2]. It is closely connected to Earth–atmosphere interactions, playing a pivotal role in surface energy and water balances at both local and global scales [3,4,5]. It is worth mentioning that the LST should not be confused with the near-surface air temperature (typically measured by meteorological/in situ stations); the LST refers to the so-called “skin temperature” [10]
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