Predictive skill of teleconnection patterns in twentieth century seasonal hindcasts and their relationship to extreme winter temperatures in Europe

European winter weather is dominated by several low-frequency teleconnection patterns, the main ones being the North Atlantic Oscillation, East Atlantic, East Atlantic/Western Russia and Scandinavian patterns. We analyze the century-long ERA-20C reanalysis and ASF-20C seasonal hindcast datasets and find that these patterns are subject to decadal variability and fluctuations in predictive skill. Using indices for determining periods of extreme cold or warm temperatures, we establish that the teleconnection patterns are, for some regions, significantly correlated or anti-correlated to cold or heat waves. The seasonal hindcasts are however only partly able to capture these relationships. There do not seem to be significant changes to the observed links between large-scale circulation patterns and extreme temperatures between periods of higher and lower predictive skill

Ensemble model output statistics for wind vectors

A bivariate ensemble model output statistics (EMOS) technique for the postprocessing of ensemble forecasts of two-dimensional wind vectors is proposed, where the postprocessed probabilistic forecast takes the form of a bivariate normal probability density function. The postprocessed means and variances of the wind vector components are linearly bias-corrected versions of the ensemble means and ensemble variances, respectively, and the conditional correlation between the wind components is represented by a trigonometric function of the ensemble mean wind direction. In a case study on 48-hour forecasts of wind vectors over the North American Pacific Northwest with the University of Washington Mesoscale Ensemble, the bivariate EMOS density forecasts were calibrated and sharp, and showed considerable improvement over the raw ensemble and reference forecasts, including ensemble copula coupling

Statistical post-processing of weather forecast ensembles: obtaining optimal deterministic and probabilistic predictions at multiple time scales

Weather forecasts are produced by complex numerical models, issued to end users and then updated after a certain period of time, usually at least several hours. During this time, it might become obvious that the current forecasts are somehow flawed and of little use. Nonetheless, they are not changed until being replaced by a new batch from the most recent run of the model. This work proposes a new statistical post-processing method, Rapid Adjustment of Forecast Trajectories, that improves the quality of predictions even after they have been issued and thus increases their potential value to customers. The inherent correlation between errors at different forecast times allows for adjustments being applied to future predictions based on very recent observations. Thus, both fast-developing and systematic forecast errors can be corrected in a flexible and swift manner. It complements other, conventional statistical post-processing and results in a significant gain in forecast quality. This novel technique can be applied to any forecast time range, from a few hours to several days and weeks, while being very economical and versatile