Content area
Full text
1. Introduction
Forecasts from numerical weather prediction (NWP) models are subject to errors from several sources, including initial conditions, lateral boundary conditions, and the models’ formulations (e.g., numerical truncation and parameterization of unresolved physical processes). Errors in initial conditions are sometimes addressed through the use of an ensemble of initial conditions (e.g., Magnusson et al. 2008; Bowler 2006; Kalnay 2003). For limited-area models, errors in lateral boundary conditions are handled by using perturbed boundary conditions (Saito et al. 2012). On the other hand, model errors are addressed through the use of accurate numerical schemes, a stochastic kinetic energy backscatter scheme (Berner et al. 2011), and stochastic physics (Buizza et al. 1999). All of these approaches improve NWP model forecasts. However, forecasting near-surface variables (e.g., 10-m wind speed) remains a challenge as a result of a poor representation of atmospheric boundary layer processes (e.g., stable stratification) in NWP models.
Additional increases in the accuracy of NWP forecasts can be achieved through postprocessing methods (e.g., Glahn and Lowry 1972; Kalman 1960; Crochet 2004; Leith 1978; Stensrud and Skindlov 1996). An objective of a postprocessing method is to improve the accuracy of the forecast by reducing both systematic and random errors, while preserving or improving the correlation with observations (Wilks 2006).
Analog-based methods are one means of postprocessing. They have been applied, for instance, to long-range weather prediction (Bergen and Harnack 1982), short-term visibility and mesoscale transport forecasts (Esterle 1992; Carter and Keisler 2000), and medium-range precipitation predictions (Hamill et al. 2006). Recently, Delle Monache et al. (2011) introduced two new analog-based methods. Both chose analogs by calculating a Euclidean distance between a current forecast and a history of forecasts at an individual observing site. From there, the first method (herein abbreviated AN) forms an ensemble from the observations that correspond to the best analogs. A deterministic forecast is simply the weighted mean of the ensemble, but the distribution can also be used to produce probabilistic forecasts (Delle Monache et al. 2013). In a second, variant method (herein abbreviated ANKF), a Kalman filter predictor–corrector algorithm (e.g., Bozic 1994; Roeger et al. 2001; Delle Monache et al. 2006, 2008) is applied to the analogs arranged into a series that is rank ordered by descending distance to the current forecast....