Mississippi State University Scholars Junction Theses and Dissertations Theses and Dissertations 1-1-2014 Using Teleconnection Indices to Predict Tornado Outbreak Frequency in the Us Kent Harris Sparrow Follow this and additional works at: https://scholarsjunction.msstate.edu/td Recommended Citation Sparrow, Kent Harris, "Using Teleconnection Indices to Predict Tornado Outbreak Frequency in the Us" (2014). Theses and Dissertations. 4912. https://scholarsjunction.msstate.edu/td/4912 This Graduate Thesis - Open Access is brought to you for free and open access by the Theses and Dissertations at Scholars Junction. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of Scholars Junction. For more information, please contact [email protected]. Automated Template C: Created by James Nail 2013V2.1 Using teleconnection indices to predict tornado outbreak frequency in the US By Kent Sparrow A Thesis Submitted to the Faculty of Mississippi State University in Partial Fulfillment of the Requirements for the Degree of Master of Science in Geosciences in the Department of Geosciences Mississippi State, Mississippi May 2014 Copyright by Kent Sparrow 2014 Using teleconnection indices to predict tornado outbreak frequency in the US By Kent Sparrow Approved: ____________________________________ Andrew E. Mercer (Major Professor) ____________________________________ Jamie Dyer (Committee Member) ____________________________________ Grady Dixon (Committee Member) ____________________________________ Michael E. Brown (Graduate Coordinator) ____________________________________ Dean's Name Dean College's Name Name: Kent Sparrow Date of Degree: May 17, 2014 Institution: Mississippi State University Major Field: Geosciences Major Professor: Andrew E. Mercer Title of Study: Using teleconnection indices to predict tornado outbreak frequency in the US Pages in Study: 71 Candidate for Degree of Master of Science The goal of this study is to improve seasonal tornado outbreak forecasting by creating a statistical model that forecasts tornado outbreak frequency in the US using teleconnection indices as predictors. For this study, a tornado outbreak is defined as more than 6 tornado reports associated with a single synoptic system and an event N15 rating index of 0.5 or higher. The tornado outbreak season is confined to all months after February for a given calendar year. Monthly teleconnection indices are derived from a rotated principal component analysis (RPCA) of the geopotential height fields. Various regression techniques were trained with a sample of monthly teleconnection indices, tested on new data, and optimized to achieve the highest predictive skill. The outcome of this study could potentially allow forecasters the ability to predict tornado outbreak potential on a climatological scale with months of lead-time, allowing for better preparation strategies for tornado outbreak seasons. ACKNOWLEDGEMENTS This material is based upon work supported by the National Science Foundation under Grant No. DGE-0947419 at Mississippi State University. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflt the views of the National Science Foundation. ii TABLE OF CONTENTS ACKNOWLEDGEMENTS ................................................................................................ ii LIST OF TABLES ...............................................................................................................v LIST OF FIGURES ........................................................................................................... vi CHAPTER I. INTRODUCTION .............................................................................................1 II. LITERATURE REVIEW ..................................................................................3 2.1 Synoptic-Scale Patterns Conducive for Tornado Outbreaks. ................3 2.2 Teleconnections .....................................................................................4 2.2.1 ENSO pattern in the SST field .........................................................5 2.2.2 The Arctic Oscillation (AO) in the 1000-hPa Geopotential Height Field .....................................................................................7 2.3 Common Teleconnection Patterns in the 500-hP Geopotential Height Field ...........................................................................................9 2.3.1 North Atlantic Oscillation (NAO) .................................................10 2.3.2 Pacific/North American Pattern (PNA) .........................................12 2.3.3 West Pacific/North Pacific Oscillation (WP/NPO) .......................14 III. DATA AND METHODS ................................................................................16 3.1 Data Description ..................................................................................16 3.1.1 Outbreak Data ................................................................................16 3.1.2 Geopotential Height Data ..............................................................19 3.2 RPCA Methods ....................................................................................20 3.2.1 Teleconnections in the Geopotential Height Fields .......................20 3.3 Assessing Covariation with Linear Regression and SVRs ..................28 3.3.1 Linear Regression ..........................................................................28 3.3.2 SVRs ..............................................................................................30 IV. RESULTS ........................................................................................................32 4.1 PCA results ..........................................................................................32 4.1.1 January 500-hPa PCA ....................................................................32 4.1.2 February 500-hPa PCA ..................................................................43 iii 4.1.3 January and February 1000-hPa heights PCA ...............................53 4.2 Regression Results ...............................................................................56 4.2.1 Standard Linear Regression Results ..............................................56 4.2.2 Stepwise Regression Results..........................................................56 4.2.3 SVR Results ...................................................................................60 V. DISCUSSION/CONCLUSION .......................................................................63 REFERENCES ..................................................................................................................68 iv LIST OF TABLES 4.1 Stepwise Regression ........................................................................................58 v LIST OF FIGURES 2.1 Niño 3.4 Region .................................................................................................6 2.2 Niño 3.4 Index ...................................................................................................6 2.3 AO Loading Pattern and Index ..........................................................................8 2.4 The NAO loading pattern (top) and time series (bottom) displayed in Mercer and Richman (2012). ...........................................................................11 2.5 The PNA loading pattern (top) and time series displayed in Richman and Mercer (2012)............................................................................................13 2.6 The WP/NPO pattern displayed in Richman and Mercer (2012). ...................15 3.1 Graph of major tornado outbreaks ...................................................................19 3.2 The NCEP/NCAR reanalysis grid (a) and the interpolated Fibonacci grid (b) (from Richman and Mercer 2012). .....................................................20 3.3 Scree Test .........................................................................................................23 3.4 January Scree Test ...........................................................................................25 3.5 February Scree Test .........................................................................................26 3.6 January AO Scree Test.....................................................................................27 3.7 February AO Scree Test...................................................................................28 4.1 The WP/NPO January Pattern .........................................................................33 4.2 The WP/NPO RPC score time series ...............................................................34 4.3 SZW January Pattern .......................................................................................34 4.4 The January SZW RPC score time series ........................................................35 4.5 The NA January pattern...................................................................................36 4.6 The January NA RPC score time series ...........................................................36 vi 4.7 The January EA pattern ...................................................................................37 4.8 The January EA RPC score time series ...........................................................37 4.9 The January NAO pattern ................................................................................38 4.10 The January