Combined Kalman Filter and Universal Kriging to Improve Storm Wind Speed Predictions for the Northeastern United States ALEXANDER SAMALOT,MARINA ASTITHA, AND JAEMO YANG University of Connecticut, Civil and Environmental Engineering, Storrs, Connecticut GEORGE GALANIS Hellenic Naval Academy, Section of Mathematics, Mathematical Modeling and Applications Laboratory, Piraeus, Greece (Manuscript received 17 April 2018, in final form 25 March 2019) ABSTRACT The scope of this study is to assess a combination of well-known techniques for bias reduction and spatial interpolation in an attempt to improve wind speed prediction for storms on a gridded domain. This is ac- complished by implementing Kalman filter (KF) for bias reduction and universal kriging (UK) for spatial interpolation as postprocessing steps for the Weather Research and Forecasting (WRF) Model. It is shown that for surface wind speed, a linear KF is adequate for eliminating systematic model errors with the available storm history. KF-estimated wind speed biases at station locations are then interpolated across the model domain using UK. The combined KF–UK approach improves the wind speed forecast median bias by 55% and RMSE by 15% (bulk statistics), while benefits obtained at station-specific locations can reach maximum improvements of 72% for RMSE and 100% for bias. Contingency statistics that inform on model performance over four categories of wind speed magnitude reveal that calm/moderate winds are successfully corrected but strong/gale winds cannot be adequately corrected by the combination of KF and UK, which is a disadvantage for improving prediction of severe storm conditions. 1. Introduction Despite constant improvement, numerical prediction of atmospheric processes, and especially extreme storm conditions, is affected by imperfect initial and boundary conditions, surface properties (topography, vegetation, soil moisture, and soil type), subgrid-scale phenomena, and planetary boundary layer representation (Pleim 2007; Mass et al. 2008; Hu et al. 2010; Wyszogrodzki et al. 2013; Frediani et al. 2016). Extreme storms are storms charac- terized by intense precipitation and pressure gradients and strong winds such as nor’easters, tropical storms, blizzards with strong sustained winds, thunderstorms as well as frontal systems that bring similar types of weather patterns. Accurate prediction of such storms is needed especially for the northeastern United States because of the impacts of strong wind speeds on the infrastructure and the environ- ment, such as power outages caused by falling trees/ branches and/or pole failure, among others. There has been a long history of using statistical postprocessing techniques to improve Numerical Weather Prediction (NWP) such as modified Taylor–Kriging, kriging and vector autoregressive models, ensemble mod- eling, block-regression (Hamill 1999; Galanis et al. 2006; Louka et al. 2008; Roux et al. 2009; Cheng et al. 2013; Tse et al. 2014; Olaofe 2017, Liu et al. 2010; Fan et al. 2015; Scheuerer and Moller 2015; Zamo et al. 2016), mean bias removal (Hacker and Rife 2007; Wilczak et al. 2006), model output statistics (MOS) (Wilks and Hamill 2007; Glahn et al. 2009; Muller 2011), and Kalman filter (KF) (McCollor and Stull 2008; Rincon et al. 2010; Delle Monache et al. 2006, 2008, 2011). All of the mentioned studies deal with correc- tions of NWP models but not specifically on extreme storms, which is the main goal of this work. The main objective is to assess the temporal and spatial error characteristics of the Weather Research and Forecasting (WRF) Model wind speed predictions for 107 storms that have impacted the northeastern United States, using a combination of KF for systematic error removal and universal kriging (UK) for spatial interpolation. KF has proven to be an effective tool for Corresponding author: Marina Astitha, marina.astitha@ uconn.edu JUNE 2019 SAMALOT ET AL. 587 DOI: 10.1175/WAF-D-18-0068.1 Ó 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 02/18/22 04:10 PM UTC