Modelling surface temperature and radiation budget of snow-covered complex terrain

Abstract

Abstract. The surface temperature controls the temporal evolution of the snowpack, playing a key role in metamorphism and snowmelt. It shows large spatial variations in mountainous areas because the surface energy budget is affected by the topography, for instance because of the modulation of the short-wave irradiance by the local slope and the shadows and the short-wave and long-wave re-illumination of the surface from surrounding slopes. These topographic effects are often neglected in large-scale models considering the surface to be flat and smooth. Here we aim at estimating the surface temperature of snow-covered mountainous terrain in clear-sky conditions in order to evaluate the relative importance of the different processes that control the spatial variations. For this, a modelling chain is implemented to compute the surface temperature in a kilometre-wide area from local radiometric and meteorological measurements at a single station. The first component of this chain is the Rough Surface Ray-Tracing (RSRT) model. Based on a photon transport Monte Carlo algorithm, this model quantifies the incident and reflected short-wave radiation on every facet of the mesh describing the snow-covered terrain. The second component is a surface scheme that estimates the terms of the surface energy budget from which the surface temperature is eventually estimated. To assess the modelling chain performance, we use in situ measurements of surface temperature and satellite thermal observations (Landsat 8) in the Col du Lautaret area, in the French Alps. The results of the simulations show (i) an agreement between the simulated and measured surface temperature at the station for a diurnal cycle in winter within 0.2 ∘C; (ii) that the spatial variations in surface temperature are on the order of 5 to 10 ∘C in the domain and are well represented by the model; and (iii) that the topographic effects ranked by importance are the modulation of solar irradiance by the local slope, followed by the altitudinal variations in air temperature (lapse rate), the re-illumination by long-wave thermal emission from surrounding terrain, and the spectral dependence of snow albedo. The changes in the downward long-wave flux because of variations in altitude and the absorption enhancement due to multiple bounces of photons in steep terrain play a less significant role. These results show the necessity of considering the topography to correctly assess the energy budget and the surface temperature of snow-covered complex terrain.

Error and Uncertainty Degrade Topographic Corrections of Remotely Sensed Data

“…The view factor controls the re‐reflection of solar radiation that strikes the surface and the fraction of the diffuse irradiance that reaches the surface. The view factor is also important in modeling the thermal infrared radiation in the mountains (Robledano et al., 2022). The terrain geometry affects the incoming irradiance and the reflected radiation, so the errors in elevation itself are less important than errors in slope, aspect, and view factor.…”

Error and Uncertainty Degrade Topographic Corrections of Remotely Sensed Data

“…Tables 2 and 3 summarize results for all fine-and coarse-resolution datasets analyzed. The view factor is also important in modeling the thermal infrared radiation in the mountains (Robledano et al, 2022). The terrain geometry affects the incoming irradiance and the reflected radiation, so the errors in elevation itself are less important than errors in slope, aspect, and view factor.…”

Construction of a Clear‐Sky Three Dimensional Sub‐Grid Terrain Long‐Wave Radiative Effect Parameterization Scheme Under Isotropic Assumption

“…The clear‐sky SDLR over the rugged surface ($${L}_{t{\downarrow}}$$) can be calculated by the equation of Duguay (1995) and Robledano et al. (2022): $$${L}_{t{\downarrow}}=\mathit{SKV}\cdot {L}_{p{\downarrow}}+(1-SKV)\cdot {L}_{\mathrm{sur}}$$$ where SKV · L p ↓ and (1 − SKV ) · L sur are the SDLR from the atmosphere and the received long‐wave radiation emitted from the surrounding terrains, respectively. The L sur is the long‐wave radiation fluxes emitted by the surrounding terrain and calculated by $${L}_{\text{sur}}={\overline{\varepsilon }}_{\text{sur}}\sigma {\overline{T}}_{\text{sur}}^{4}$$, where $${\overline{\varepsilon }}_{\text{sur}}$$ and $${\overline{T}}_{\text{sur}}$$ are the surface emissivity ( ε s ) and temperature ( T s ) regionally averaged over the neighboring sub‐grids within the radius of 1 km.…”