
Generation of High-Resolution Wind Fields from the WegenerNet Dense Meteorological Station Network in Southeastern Austria
Author(s) -
Christoph Schlager,
Gottfried Kirchengast,
Jürgen Fuchsberger
Publication year - 2017
Publication title -
weather and forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.393
H-Index - 106
eISSN - 1520-0434
pISSN - 0882-8156
DOI - 10.1175/waf-d-16-0169.1
Subject(s) - wind speed , environmental science , meteorology , weather station , automatic weather station , wind direction , maximum sustained wind , temporal resolution , numerical weather prediction , geography , wind gradient , physics , quantum mechanics
An operational weather diagnostics application for automatic generation of wind fields in near–real time from observations delivered by the high-density WegenerNet meteorological station network in the Feldbach region of Austria is introduced. The purpose of the application is to empirically provide near-surface wind fields of very high spatial and temporal resolution for evaluating convection-permitting climate models as well as investigating weather and climate variability on a local scale. The diagnostic California Meteorological Model (CALMET) is used as the core tool. This model computes 3D wind fields based on observational weather data, a digital elevation model, and land-use categories. The application first produces the required input files from the WegenerNet stations and subsequently runs the CALMET model based on this input. In a third step the modeled wind fields are stored in the WegenerNet data archives every 30 min with a spatial resolution of 100 m × 100 m, while also generating averaged weather and climate products during postprocessing. The performance of the modeling against station observations, for which wind speeds were classified into weak and strong wind speeds, is evaluated and reasonably good results were found for both wind speed classes. The statistical agreement for the vector-mean wind speed is slightly better for weak wind speeds than for strong ones while the difference between modeled and observed wind directions is smaller for strong wind speeds than for weak ones. The application is also a valuable tool for other high-density networks.