| Summary: | In Chapter 7, annual counts were used to create rate models, and in Chapter 8, lifetime maximum winds were used to create intensity models. In this chapter, we show you how to use cyclone track data together with climate field data to create spatial models. Spatial models make use of location information in data. Geographic coordinates locate the hurricane’s center on the surface of the earth and wind speed provides an attribute. Spatial models make use of location separate from attributes. Given a common spatial framework, these models can accommodate climate data including indices (e.g., North Atlantic Oscillation) and fields (e.g., sea-surface temperature). Here, we show you how to create a spatial framework for combining hurricane data with climate data. The method tessellates the basin with hexagons and populates them with local cyclone and climate information (Elsner et al., 2012). In Chapter 5, you learned how to create a spatial data frame using functions from the sp package (Bivand et al., 2008). Let us review. Here you are interested in wind speeds along the entire track for all tropical storms and hurricanes during the 2005 North Atlantic season. You begin by creating a data frame from the best.use.RData file, where you subset on year and wind speed and convert the speed to meters per second. . . . > load("best.use.RData") > W.df = subset(best.use, Yr==2005 & WmaxS >= 34 + & Type=="*") > W.df$WmaxS = W.df$WmaxS * .5144 . . . The asterisk for Type indicates a tropical cyclone as opposed to a tropical wave or extratropical cyclone. The number of rows in your data frame is the total number of cyclone hours (3,010), and you save this by typing . . . > ch = nrow(W.df) . . . Next, assign the lon and lat columns as spatial coordinates using the coordinates function (sp). Finally, make a copy of your data frame, keeping only the spatial coordinates and the wind speed columns.
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