Statistical single-station short-term forecasting of temperature and probability of precipitation: Area interpolation and NWP combination
Raible CC, Bischof G, Fraedrich K, Kirk E
WEATHER AND FORECASTING
14 (2): 203-214 APR 1999


Abstract:
Two statistical single-station short-term forecast schemes are introduced and applied to real-time weather prediction. A multiple regression model (R model) predicting the temperature anomaly and a multiple regression Markov model (M model) forecasting the probability of precipitation are shown. The following forecast experiments conducted for central European weather stations are analyzed: (a) The single-station performance of the statistical models, (b) a linear error minimizing combination of independent forecasts of numerical weather prediction and statistical models, and (c) the forecast representation for a region deduced by applying a suitable interpolation technique. This leads to an operational weather forecasting system for the temperature anomaly and the probability of precipitation; the statistical techniques demonstrated provide a potential for future applications in operational weather forecasts.

Keywords:
Verification, statistical single-station forecasting

Addresses:
Raible CC, Bischof G, Fraedrich K, Kirk E, Univ Hamburg, Inst Meteorol, Bundesstr 55, D-20146 Hamburg, Germany

Reprints:
Raible CC, Climate and Environmental Physics, Physics Institute, University of Bern, Sidlerstrasse 5, CH-3012 Bern, Switzerland, raible@climate.unibe.ch