MetGIS™: combination of Meteorological and Geographic Information Systems to produce high resolution mountain weather forecasts
Article first published online: 30 JAN 2012
© 2012 Royal Meteorological Society
Volume 20, Issue 3, pages 371–378, September 2013
How to Cite
Spreitzhofer, G., Sperka, S. and Steinacker, R. (2013), MetGIS™: combination of Meteorological and Geographic Information Systems to produce high resolution mountain weather forecasts. Met. Apps, 20: 371–378. doi: 10.1002/met.1299
- Issue published online: 5 SEP 2013
- Article first published online: 30 JAN 2012
- Manuscript Accepted: 22 DEC 2011
- Manuscript Revised: 15 NOV 2011
- Manuscript Received: 23 MAR 2011
- mesoscale model;
- fresh snow forecast;
MetGIS™ is an innovative Java-based, combined Meteorological and Geographic Information System, with a specific emphasis on snow and mountain weather. This constantly upgraded prediction scheme has been developed within the framework of a number of interdisciplinary international research projects. A principal focus of the system is the automated production of high-resolution, downscaled forecast maps of meteorological parameters such as precipitation, fresh snow amounts, the snow limit, the form of precipitation, wind and air temperature.
The geographic part of the system includes topographies relying on data bases such as SRTM (Shuttle Radar Topographic Mission) and representations of roads, rivers, railway lines, political borders and cities. On top of these, partly linked to terrain features, down-scaled meteorological information can be visualized in a variety of display styles. Meteorological forecast data of any numerical model with common output data formats can be used as a starting point for the downscaling procedures. Currently, the real-time output of the GFS (Global Forecast System of the US National Weather Service) is used as a base for MetGIS™ forecasts. Verification results are quite encouraging so far. Mean absolute errors are in the range of 1.3–3 °C for 36 h temperature forecasts, and around 80% of the 24 h forecasts predicted correctly, if the precipitation will be below or above 1 mm.