Spatial Analysis to Predict Minimum Temperature and Initial Spring Freeze in the Southeast United States

• Activity Number: 195
• Type: Contributed
• Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Meagan Gentry*+ and Austin John
• Companies: NC State University and NC State University
• Keywords: spatial ; meteorology ; probability ; regression ; climate

Abstract

The goal of this paper is to provide a high-resolution map of daily predicted minimum temperatures over the Southeast US. From these predicted temperatures, we can determine the first date of spring freeze in any location on the map, which is valuable to agricultural industry among other things. First, we introduce a spatial regression model on the daily minimum temperatures recorded by the gauge stations in West Virginia, North Carolina, and Tennessee. The spatial model not only incorporates certain climatic and geographical variables, but also considers the spatial dependency among locations. By the same procedure, we also apply spatial models for longer time periods (weekly, monthly, and seasonal), in order to see the variation in models from time to time. We utilize simple spatial kriging to predict the daily minimum temperature for each point on the high-resolution map. The probability of the minimum temperature lower than 28 Fahrenheit can be estimated for each location over time under assumption of normality. The map of these probabilities is useful in determining the expected first spring freezes date for different locations.