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Abstract

Large spatial variations of surface temperature (>10 K) are commonly observed in mountains due to the extreme variety of slopes, altitudes, and orographic conditions. Although modeling the surface energy budget for a flat surface is a common task in meteorological and hydrological modeling, significant additional work is required to account for the modulation of the short-wave irradiance by the local slopes, shadows, reillumination between the facing slopes in both short and long waves, slope effect on turbulent fluxes, altitudinal atmospheric variations, wind flow around the relief, and surface heterogeneity. This poster presents a modeling chain to compute the surface temperature at decameter resolution. The first module is a photon transport Monte Carlo algorithm that calculates the incident, reflected and emitted radiation on every facet of the mesh describing the terrain. The second component is a surface scheme that estimates all energy fluxes, and deduces the surface temperature. An initial assessment at the Col du Lautaret, in France, shows an agreement between the simulations and local observations within 0.2^∘C in winter, and a satisfying high spatial correlation with Landsat 8/9 satellite observations. The direct effect of short-wave modulation by the slope is the main driver of the variations, during clear-sky days. The next steps include<strike>s</strike> improving the long-wave emission from the atmosphere, surface heterogeneity (snow/grass/rock), and spatial variations in the wind speed. This modeling chain will be useful to better estimate snow melt for hydrological applications, ground temperature for ecological applications, and surface-atmosphere fluxes for micro-meteorological applications.