Surface representation impacts on turbulent heat fluxes in the Weather Research and Forecasting (WRF) model (v.4.1.3)

Abstract

Abstract. The water and energy transfers at the interface between the Earth's surface and the atmosphere should be correctly simulated in numerical weather and climate models. This implies the need for a realistic and accurate representation of land cover (LC), including appropriate parameters for each vegetation type. In some cases, the lack of information and crude representation of the surface lead to errors in the simulation of soil and atmospheric variables. This work investigates the ability of the Weather Research and Forecasting (WRF) model to simulate surface heat fluxes in a heterogeneous area of southern France using several possibilities for the surface representation. In the control experiments, we used the default LC database in WRF, which differed significantly from the actual LC. In addition, sub-grid variability was not taken into account since the model uses, by default, only the surface information from the dominant LC category in each pixel (dominant approach). To improve this surface simplification, we designed three new interconnected numerical experiments with three widely used land surface models (LSMs) in WRF. The first one consisted of using a more realistic and higher-resolution LC dataset over the area of analysis. The second experiment aimed at investigating the effect of using a mosaic approach; 30 m sub-grid surface information was used to calculate the final grid fluxes based on weighted averages from values obtained for each LC category. Finally, in the third experiment, we increased the model stomatal conductance for conifer forests due to the large flux errors associated with this vegetation type in some LSMs. The simulations were evaluated with gridded area-averaged fluxes calculated from five tower measurements obtained during the Boundary-Layer Late Afternoon and Sunset Turbulence (BLLAST) field campaign. The results from the experiments differed depending on the LSM and displayed a high dependency of the simulated fluxes on the specific LC definition within the grid cell, an effect that was enhanced with the dominant approach. The simulation of the fluxes improved using the more realistic LC dataset except for the LSMs that included extreme surface parameters for coniferous forest. The mosaic approach produced fluxes more similar to reality and served to particularly improve the latent heat flux simulation of each grid cell. Therefore, our findings stress the need to include an accurate surface representation in the model, including soil and vegetation sub-grid information with updated surface parameters for some vegetation types, as well as seasonal and man-made changes. This will improve the modelled heat fluxes and ultimately yield more realistic atmospheric processes in the model.

Surface impacts of large offshore wind farms

“…The shape and land use of the coastlines influence the meteorology through land-atmosphere interactions at the micro-and meso-scale levels [24,33]. It is therefore important to have an accurate representation of the coastlines to predict the turbulent heat fluxes and other properties correctly [32], including timing and track of the sea breeze circulation. To this end, we modified the land use in the WRF default input, which is the 21-class moderate resolution imaging spectroradiometer (MODIS) land use database [37].…”

Advancing understanding of land–atmosphere interactions by breaking discipline and scale barriers

“…As a result, surface heterogeneities are normally calculated as an aggregate of surface properties, which could lead to misrepresentations of turbulence. 40 These inaccurate calculations of the turbulent transport of momentum, heat, moisture, and aerosols could yield erroneous calculations of cloud dynamics and physics. Turbulent-and cloud-resolving models as presented and discussed in this article are able to simulate the most energetic parts of the coupling between land-atmosphere, 35,41 and therefore, simulate the three regimens presented in Figure 2 as a continuum of linked scales.…”

“…Note that the relationship between these length scales varies during the day due to variation of radiation and turbulence that yields different conditions in the interaction between land and the atmosphere, that is, cloudy versus clear, and thermal stratification, that is, stable, unstable, and neutral 38,39 . The challenge relies on first understanding how these differences of surface heterogeneities influence the atmospheric flow and second how to represent this coupling between surface heterogeneity and flow organization in weather 40 and climate 12 models. In these models, processes related to land–atmosphere exchange and turbulence are represented in the form of parameterized expressions.…”

A Method to Improve Land Use Representation for Weather Simulations Based on High‐Resolution Data Sets—Application to Corine Land Cover Data in the WRF Model

“…Other data sets with more local coverage are Corine Land Cover (CLC; EEA, 2020) for Europe and NLCD for the United States (Homer et al., 2020). A map like CLC offers a well‐detailed description of LC, and therefore appears to be well adapted for meteorological models which resolve fine scales but since it is in a vector type, it requires a rasterization step (De Meij & Vinuesa, 2014; Golzio et al., 2021; Prósper et al., 2019; Román‐Cascón et al., 2021).…”

“…One might assume that the higher the number of classes used to represent the LC, the better the description of the surface. Nevertheless, aggregation errors could be introduced by having a large number of classes as suggested by Román-Cascón et al (2021). Figure 1 illustrates how the SLM aggregation option could generate a different output over an area according to the initial number of classes available and could lead to a wrong class choice when the class number increases.…”