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.
Highlights
Surface wind is often regarded as one of the most difficult meteorological variables to model, over areas of complex terrain like the Alps (Whiteman, 2000; Sfetsos, 2002; Abdel-Aal et al, 2009; Gómez-Navarro et al, 2015)
The data acquired from the two WegenerNet regions Feldbach Region (FBR) and JBT are automatically quality controlled and processed by the WegenerNet Processing System (WPS), consisting of four subsystems (Kirchengast et al, 2014): the Command Receive Archiving System transfers raw measurement data via wireless transmission to the WegenerNet database in Graz, the Quality Control System checks the data quality, the Data Product Generator (DPG) generates regular station time series and gridded fields of weather and climate products, and the Visualization and Information System offers the data to users via the WegenerNet data portal
The Integrated Nowcasting through Comprehensive Analysis (INCA) and the WegenerNet wind fields show a similar distribution with generally low wind speeds and prevailing southerly wind directions. The intercomparison of these INCA data with WegenerNet data for this event shows the largest wind fractions skill score (WFSS) values for all neighborhood sizes, which indicates a good overlap of the wind classes (Fig. 2e, INCAvsWN_therm_FBR)
Summary
Surface wind is often regarded as one of the most difficult meteorological variables to model, over areas of complex terrain like the Alps (Whiteman, 2000; Sfetsos, 2002; Abdel-Aal et al, 2009; Gómez-Navarro et al, 2015). Innovation in computer sciences, new methods in weather analysis or nowcasting models, advanced software architectures used in regional climate models (RCMs), and the growing power of computers in the meantime led to highly resolved outputs from such models at the 1 km scale (Awan et al, 2011; Suklitsch et al, 2011; Prein et al, 2013b; Prein et al, 2015; Leutwyler et al, 2016; Kendon et al, 2017) These models, contain various limitations and sources of uncertainties. Our primary motivation for this study is to explore and provide improved insight by careful intercomparisons of the relative performance strength and weakness of empirical and dynamical wind field modeling at high spatial resolution over complex terrain where actual wind station observations will generally be available at sparse station density.
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