Date of Award
8-1-2023
Language
English
Document Type
Master's Thesis
Degree Name
Master of Science (MS)
College/School/Department
Department of Atmospheric and Environmental Sciences
Dissertation/Thesis Chair
Paul E Roundy
Committee Members
Ryan D Torn
Keywords
Empirical Orthogonal Functions, Numerical Weather Prediction, Statistical Postprocessing
Subject Categories
Atmospheric Sciences
Abstract
Model prediction for extended range forecasts is fraught with shortcomings that generate forecast error and decrease skill. Biases in Numerical Weather Prediction (NWP) models generate patterns of error that grow larger with time. These inherent biases systematically alter the modeled positioning of atmospheric features that emerge as synoptic or subseasonal error. While individual weather events and anomalous patterns will generate model noise, it is computationally possible to separate the signal behind this error from noise. For 200 hPa Geopotential Height (Z200), systematic biases in model forecasts often manifest themselves as errors in atmospheric wave patterns in the high latitudes, including inaccurate forecasts of Rossby wave phase speed in the polar jet. Once the predicted structure of the wave pattern in the jet is out of phase with verification, error anomalies grow rapidly with lead time, decreasing the quality of deterministic and ensemble model forecasts entering the long range and subseasonal timescales.
Recommended Citation
Stikeleather, William David, "The Development Of A Statistical Postprocessing Algorithm By Two-Step Space-Time Eof Analysis" (2023). Legacy Theses & Dissertations (2009 - 2024). 3248.
https://scholarsarchive.library.albany.edu/legacy-etd/3248
