A spatial evaluation of high-resolution wind fields from empirical and dynamical modeling in hilly and mountainous terrain

Abstract

Abstract. Empirical high-resolution surface wind fields, automatically generated by a weather diagnostic application, the WegenerNet Wind Product Generator (WPG), were intercompared with wind field analysis data from the Integrated Nowcasting through Comprehensive Analysis (INCA) system and with regional climate model wind field data from the Consortium for Small Scale Modeling Model in Climate Mode (CCLM). The INCA analysis fields are available at a horizontal grid spacing of 1 km × 1 km, whereas the CCLM fields are from simulations at a 3 km × 3 km grid. The WPG, developed by Schlager et al. (2017, 2018), generates diagnostic fields on a high-resolution grid of 100 m × 100 m, using observations from two dense meteorological station networks: the WegenerNet Feldbach Region (FBR), located in a region predominated by a hilly terrain, and its Alpine sister network, the WegenerNet Johnsbachtal (JBT), located in a mountainous region. The wind fields of these different empirical–dynamical modeling approaches were intercompared for thermally induced and strong wind events, using hourly temporal resolutions as supplied by the WPG, with the focus on evaluating spatial differences and displacements between the different datasets. For this comparison, a novel neighborhood-based spatial wind verification methodology based on fractions skill scores (FSSs) is used to estimate the modeling performances. All comparisons show an increasing FSS with increasing neighborhood size. In general, the spatial verification indicates a better statistical agreement for the hilly WegenerNet FBR than for the mountainous WegenerNet JBT. The results for the WegenerNet FBR show a better agreement between INCA and WegenerNet than between CCLM and WegenerNet wind fields, especially for large scales (neighborhoods). In particular, CCLM clearly underperforms in the case of thermally induced wind events. For the JBT region, all spatial comparisons indicate little overlap at small neighborhood sizes, and in general large biases of wind vectors occur between the regional climate model (CCLM) and analysis (INCA) fields and the diagnostic (WegenerNet) reference dataset. Furthermore, grid-point-based error measures were calculated for the same evaluation cases. The statistical agreement, estimated for the vector-mean wind speed and wind directions again show better agreement for the WegenerNet FBR than for the WegenerNet JBT region. A combined examination of all spatial and grid-point-based error measures shows that CCLM with its limited horizontal resolution of 3 km × 3 km, and hence too smoothed an orography, is not able to represent small-scale wind patterns. The results for the JBT region indicate significant biases in the INCA analysis fields, especially for strong wind speed events. Regarding the WegenerNet diagnostic wind fields, the statistics show acceptable performance in the FBR and somewhat overestimated wind speeds for strong wind speed events in the Enns valley of the JBT region.

WegenerNet high-resolution weather and climate data from 2007 to 2020

“…Gridded heat index (apparent temperature) fields are generated from L2 temperature and humidity fields using an equation developed by Schoen (2005),…”

“…The dew point therein is calculated using an equation based on the Magnus-Tetens formula (Barenbrug, 1974;Schoen, 2005):…”

The performance of GRAMM-SCI and WRF in simulating the surface-energy budget and thermally driven winds in an Alpine valley

“…Overall, most model evaluation studies are restricted to readily available quantities such as specific humidity, radiation, wind, and temperature (Tomasi et al 2017;Jiménez-Esteve et al 2018). A number of studies have evaluated the performance of mesoscale models in simulating flow fields in the Alps (Giovannini et al 2014;Gsella et al 2014;Cantelli et al 2017;Schmidli et al 2018;Schlager et al 2019;Oettl 2021a), but comparisons with observations of all surface-energy fluxes are still scarce in the scientific literature (one exception being Sun et al 2017), especially in mountainous terrain and with respect to small-scale spatial variations. The above studies have highlighted some challenges in simulating the near-surface atmosphere over mountainous terrain.…”

Verification of Near Surface Wind Patterns in Germany using Clear Air Radar Echoes

“…used for wind verification by Zschenderlein et al (2019)) conveniently provide highly resolved, gap-free data but the realism of the underlying model would have to be verified against some other data beforehand. Interpolated station data (for example the VERA analysis used within MesoVICT) are generally too coarsely resolved to represent structures on the scale of single kilometers, denser station networks such as the WegenerNet data-set used by Schlager et al (2019) are rare. Bousquet et al (2008) and Beck et al (2014) use Multi-Doppler wind retrievals from the French national radar network to verify wind predictions from the AROME model.…”