Daniel Hodyss

I design and build statistical modelling systems for large dimensional problems in the geosciences. This includes both state estimation as well as predictive/forecast models from both a deterministic as well as probabilistic perspective. Please see the following selected articles for further information: Hodyss, D., 2012: Accounting for skewness in ensemble data assimilation. Mon. Wea. Rev., 140, 2346-2358. Hodyss, D et al., 2014: Inducing tropical cyclones to undergo Brownian motion: A comparison between Itô and Stratonovich in a numerical weather prediction model. Mon. Wea. Rev. 142, 1982-1996. Hodyss, D., E. Satterfield, et al., 2016: Inaccuracies with multimodel postprocessing methods involving weighted, regression-corrected forecasts. Mon. Wea. Rev., 144, 1649-1668. Hodyss, D., W.F. Campbell, J. Whitaker, 2016: Observation-dependent posterior inflation for the ensemble Kalman filter. Mon. Wea. Rev., 144, 2667-2684. Hodyss, D., J. Anderson, N. Collins, W. Campbell, A. Reinecke, 2017: Quadratic Polynomial Regression using Serial Observation Processing: Implementation within DART. Mon. Wea. Rev., DOI: /10.1175/MWR-D-17-0089.1