Abstract
Abstract. Recent decades have witnessed a drastic increase in the fidelity of numerical weather prediction (NWP) modeling. Currently, both research-grade and operational NWP models regularly perform simulations with horizontal grid spacings as fine as 1 km. This migration towards higher resolution potentially improves NWP model solutions by increasing the resolvability of mesoscale processes and reducing dependency on empirical physics parameterizations. However, at the same time, the accuracy of high-resolution simulations, particularly in the atmospheric boundary layer (ABL), is also sensitive to orographic forcing which can have significant variability on the same spatial scale as, or smaller than, NWP model grids. Despite this sensitivity, many high-resolution atmospheric simulations do not consider uncertainty with respect to selection of static terrain height data set. In this paper, we use the Weather Research and Forecasting (WRF) model to simulate realistic cases of lower tropospheric flow over and downstream of mountainous islands using the default global 30 s United States Geographic Survey terrain height data set (GTOPO30), the Shuttle Radar Topography Mission (SRTM), and the Global Multi-resolution Terrain Elevation Data set (GMTED2010) terrain height data sets. While the differences between the SRTM-based and GMTED2010-based simulations are extremely small, the GTOPO30-based simulations differ significantly. Our results demonstrate cases where the differences between the source terrain data sets are significant enough to produce entirely different orographic wake mechanics, such as vortex shedding vs. no vortex shedding. These results are also compared to MODIS visible satellite imagery and ASCAT near-surface wind retrievals. Collectively, these results highlight the importance of utilizing accurate static orographic boundary conditions when running high-resolution mesoscale models.
Highlights
Parallel computing platforms enable regional-scale numerical weather prediction (NWP) models1 to be integrated with fine-scale grid spacings, down to approximately 1 km horizontally
Overall fidelity, offered by finer-resolution models as it pertains to orographic flows, mesoscale NWP models are still constrained by multiple factors such as necessary physics parameterizations (Doyle et al, 2013; Draxl et al, 2014)
With the SRTM30-based and GMTED30-based elevation, the increased topographic steepness along with the layer of stable stratification beneath the maximum height of the island caused much of the flow to split and pass around the lateral flanks of Gran Canaria (GC)
Summary
Parallel computing platforms enable regional-scale numerical weather prediction (NWP) models to be integrated with fine-scale grid spacings, down to approximately 1 km horizontally. Aside from under-resolved terrain height in modeled grids, which is essentially an oversimplification of the source terrain height data, we show in this paper that uncertainty in source terrain height data sets themselves can be significant enough to result in fundamental differences in simulated orographic flow mechanics. This finding illustrates that the sensitivity of NWP models can be more complex than first-order biases recently documented by Teixeira et al (2014). Our results demonstrate that selection of terrain height source data can, in some cases, be critical to successfully capturing the fundamental mechanics of mesoscale orographic wakes
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