Open AccessCalibrated Precipitation Forecasts for a Limited-Area Ensemble Forecast System Using Reforecasts
Author(s) -
Felix Fundel,
André Walser,
Mark A. Liniger,
C. Frei,
Christof Appenzeller
Publication year - 2010
Publication title -
monthly weather review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.862
H-Index - 179
eISSN - 1520-0493
pISSN - 0027-0644
DOI - 10.1175/2009mwr2977.1
Subject(s) - quantitative precipitation forecast , precipitation , environmental science , calibration , probabilistic logic , forecast skill , meteorology , climatology , lead time , forecast verification , consensus forecast , ensemble forecasting , scale (ratio) , computer science , forecast period , weather forecasting , return period , econometrics , statistics , mathematics , geography , geology , artificial intelligence , economics , operations management , cartography , archaeology , flood myth , cash flow , accounting , cash flow statement
The calibration of numerical weather forecasts using reforecasts has been shown to increase the skill of weather predictions. Here, the precipitation forecasts from the Consortium for Small Scale Modeling Limited Area Ensemble Prediction System (COSMO-LEPS) are improved using a 30-yr-long set of reforecasts. The probabilistic forecasts are calibrated on the exceedance of return periods, independently from available observations. Besides correcting for systematic model errors, the spatial and temporal variability in the amplitude of rare precipitation events is implicitly captured when issuing forecasts of return periods. These forecast products are especially useful for issuing warnings of upcoming events. A way to visualize those calibrated ensemble forecasts conveniently for end users and to present verification results of the return period–based forecasts for Switzerland is proposed. It is presented that, depending on the lead time and return period, calibrating COSMO-LEPS with reforecasts increases the precipitation forecast skill substantially (about 1 day in forecast lead time). The largest improvements are achieved during winter months. The reasonable choice of the length of the reforecast climatology is estimated for an efficient use of this computational expensive calibration method.