This paper presents a novel methodology for mesoscale-to-microscale downscaling of near-surface wind fields. The model chain consists on the Weather Research and Forecast mesoscale model and the Alya-CFDWind microscale model (assuming neutral stability). The downscaling methodology combines precomputed microscale simulations with a mesoscale forecast using a domain segmentation technique and transfer functions. As a result, the downscaled wind field preserves the mesoscale pattern but, at the same time, incorporates local mesoscale subgrid terrain effects, particularly at valleys and channelling zones. The methodology has been validated over a 9-month period on a very complex terrain site instrumented with a dense observational network of meteorological masts. With respect to mesoscale results, the global skills of the downscaled wind at masts improve for wind direction and remain similar for wind velocity.However, a substantial improvement occurs under stable and neutral conditions and for high wind velocity regimes.
KEYWORDScomplex terrain, downscaling, high-resolution, near-surface winds, transfer functions, wind forecast
INTRODUCTIONNear-surface wind fields are typically obtained from mesoscale Numerical Weather Prediction (NWP) models that describe the physics and dynamics of the atmosphere in the mesoscale range, ie, covering phenomena with characteristic dimensions spanning from several hundreds down to few kilometres (at the edge of the meso-scale, eg, Orlanski 1 ). Operational configurations of mesoscale NWP models use horizontal grid resolutions from tens to ∼ 1 km. These terrain discretizations are often insufficient to capture flow effects over complex terrains, where the near-surface winds are strongly influenced by mesoscale subgrid topographic features. However, high-resolution (tens to hundreds of metres) near-surface wind fields can be important in applications where microscale topographic features and terrain roughness exert a major control on the wind flow. Some of these applications include wind resource evaluation, wind power forecast, or simulation of wind-driven hazardous phenomena such as wildfire spreading or atmospheric dispersion of pollutants and toxic substances. In these cases, some mesoscale-to-microscale downscaling strategy turns necessary.Traditionally, high-resolution near-surface microscale winds have been obtained by means of mass-consistent diagnostic models (eg, Kitada et al and Homicz 2,3 ). These models enforce the conservation of mass and, in some cases, include also additional parameterisations to approximate (not solve) microscale effects such as wind channelling and thermal slope flows. In this case, the downscaling strategy consists on obtaining an initial guess wind field from a mesoscale NWP model, which is then projected over the finer complex terrain grid applying some divergence minimisation procedure (eg, Wagenbrenner et al 4 ). This methodology works well in simple cases but fails in representing phenomena such as recirculation behind obstacles, vortex...