This is a cache of https://www.uibk.ac.at/en/acinn/research/atmospheric-dynamics/projects/s-mos/. It is a snapshot of the page at 2024-11-30T05:33:18.929+0100.
S-MOS – Universität Innsbruck

Second Generation Model Output Statistics (S-MOS)

 

S-MOS

 

The project will develop an objective weather forecasting system for temperature, humidity, wind, precipitation, sunshine duration, and specific weather conditions that affect fruit and wine growing and dairy farming in South Tyrol and implement it operationally at the weather forecast office in South Tyrol.

It aims at improving and developing systems that automatically postprocess output from numerical weather prediction (NWP) models and project it onto point locations as an additional support to human forecasters. The big step forward through this project will be a thoroughly evaluated operational second-generation MOS (Model Output Statistics) with the latest statistical methods used in meteorological applications and newly developed methods in the framework of the project, which also integrates information about the uncertainty of the forecast.

The benefits from using the latest NWP model versus one with long-term reforecast will be investigated. Different methods will be rigorously evaluated using a set of verification measures and compared to the results from a first generation MOS system.

Project

Project Leader:
Georg MAYR

Members:
Dabernig Markus
Gebetsberger Manuel

External Members:
Co-PI: Zeileis Achim (Department of Statistics, University of Innsbruck)

Funding Agencies:
Autonome Provinz Bozen – Abteilung Bildungsförderung, Universität und Forschung ORBZ110725

Project Duration:
02/12/2013 to 30/11/2016

Publications

2017

Dabernig, M., G. J. Mayr, J. W. Messner, and A. ZeileisSpatial ensemble post-processing with standardized anomaliesQuarterly Journal of the Royal Meteorological Society143http://onlinelibrary.wiley.com/doi/10.1002/qj.2975/full

2016

Dabernig, M., G. J. Mayr, J. W. ; Messner, and A. ZeileisSpatial Ensemble Post-Processing with Standardized AnomalieWorking Papers in Economics and Statistics - University of Innsbruck

Google Scholar

BibTex


 

Nach oben scrollen