EastAuClimate          package:latticeExtra          R Documentation

_C_l_i_m_a_t_e _o_f _t_h_e _E_a_s_t _C_o_a_s_t _o_f _A_u_s_t_r_a_l_i_a

_D_e_s_c_r_i_p_t_i_o_n:

     A set of climate statistics for 16 coastal locations along Eastern
     Australia. These sites were chosen to be approximately equally
     spaced to cover the whole eastern coast of Australia. For each
     site, climate statistics were calculated for the standard 30-year
     period 1971-2000. Only sites with nearly-complete data were
     chosen.

_U_s_a_g_e:

     data(EastAuClimate)

_F_o_r_m_a_t:

     A data frame with the following 10 variables and 5 items of
     metadata for each of 16 sites.

     '_S_u_m_m_e_r_M_a_x_T_e_m_p' average daily maximum air temperature (degrees C)
          in February. 

     '_S_u_m_m_e_r_M_i_n_T_e_m_p' average daily minimum air temperature (degrees C)
          in February. 

     '_W_i_n_t_e_r_M_a_x_T_e_m_p' average daily maximum air temperature (degrees C)
          in July. 

     '_W_i_n_t_e_r_M_i_n_T_e_m_p' average daily minimum air temperature (degrees C)
          in July. 

     '_S_u_m_m_e_r_R_a_i_n' median total precipitation in February (mm/month). 

     '_W_i_n_t_e_r_R_a_i_n' median total precipitation in July (mm/month). 

     '_M_e_a_n_A_n_n_R_a_i_n' average total amount of precipitation recorded in a
          year (mm/year). 

     '_R_a_i_n_D_a_y_s' average number of days in a year with at least 1 mm of
          precipitation. 

     '_C_l_e_a_r_D_a_y_s' average number of clear days in a year. This statistic
          is derived from cloud cover observations, which are measured
          in oktas (eighths). A clear day is recorded when the mean of
          the 9 am and 3 pm cloud observations is less than or equal to
          2 oktas. 

     '_C_l_o_u_d_y_D_a_y_s' average number of clear days in a year. A cloudy day
          is recorded when the mean of the 9 am and 3 pm cloud
          observations is greater than or equal to 6 oktas. 

     '_I_D' BOM Site number. 

     '_L_a_t_i_t_u_d_e' Site latitude (degrees North). 

     '_L_o_n_g_i_t_u_d_e' Site longitude (degrees East). 

     '_E_l_e_v_a_t_i_o_n' Site elevation (m). 

     '_S_t_a_t_e' Australian state: TAS = Tasmania, VIC = Victoria, NSW =
          New South Wales, QLD = Queensland. 

     The row names of the data frame give the location names. Note:
     these are not the official names of the climate stations.

_S_o_u_r_c_e:

     Sites were chosen by hand from maps on the Bureau of Meteorology
     website. The data were extracted manually from web pages under
     <URL: http://www.bom.gov.au/climate/averages/> and processed to
     extract a subset of statistics. - by Felix Andrews felix@nfrac.org

     Bureau of Meteorology, Commonwealth of Australia. Product
     IDCJCM0026 Prepared at Wed 31 Dec 2008.

     Definitions of statistics adapted from <URL:
     http://www.bom.gov.au/climate/cdo/about/about-stats.shtml>

_E_x_a_m_p_l_e_s:

     data(EastAuClimate)

     ## Compare the climates of state capital cities
     EastAuClimate[c("Hobart", "Melbourne", "Sydney", "Brisbane"),]

     ## A function to plot maps (a Lattice version of maps::map)
     lmap <-
        function(database = "world", regions = ".", exact = FALSE,
                 boundary = TRUE, interior = TRUE, projection = "",
                 parameters = NULL, orientation = NULL,
                 aspect = "iso", type = "l",
                 par.settings = list(axis.line = list(col = "transparent")),
                 xlab = NULL, ylab = NULL, ...)
     {
        theMap <- map(database, regions, exact = exact,
                      boundary = boundary, interior = interior,
                      projection = projection, parameters = parameters,
                      orientation = orientation, plot = FALSE)
        xyplot(y ~ x, theMap, type = type, aspect = aspect,
               par.settings = par.settings, xlab = xlab, ylab = ylab,
               default.scales = list(draw = FALSE), ...)
     }

     ## Plot the sites on a map of Australia
     if (require("maps")) {
       lmap(regions = c("Australia", "Australia:Tasmania"),
            exact = TRUE, xlim = c(130, 170),
            panel = function(...) {
               panel.xyplot(...)
               with(EastAuClimate, {
                 panel.points(Longitude, Latitude, pch = 16)
                 txt <- row.names(EastAuClimate)
                 i <- c(3, 4)
                 panel.text(Longitude[ i], Latitude[ i], txt[ i], pos = 2)
                 panel.text(Longitude[-i], Latitude[-i], txt[-i], pos = 4)
               })
            })
     }

     ## Average daily maximum temperature in July (Winter).
     xyplot(WinterMaxTemp ~ Latitude, EastAuClimate, aspect = "xy",
            type = c("p", "a"), ylab = "Temperature (degrees C)")

     ## (Make a factor with levels in order - by coastal location)
     siteNames <- factor(row.names(EastAuClimate),
                levels = row.names(EastAuClimate))
     ## Plot temperature ranges (as bars), color-coded by RainDays
     segplot(siteNames ~ WinterMinTemp + SummerMaxTemp, EastAuClimate,
             level = RainDays, sub = "Color scale: number of rainy days per year",
             main = paste("Typical temperature range and wetness",
                "of coastal Australian cities", sep = "\n"))

     ## Show Winter and Summer temperature ranges separately
     segplot(Latitude ~ WinterMinTemp + SummerMaxTemp, EastAuClimate,
        main = "Average daily temperature ranges \n of coastal Australian sites",
        ylab = "Latitude", xlab = "Temperature (degrees C)",
        par.settings = simpleTheme(lwd = 3, alpha = 0.5),
        key = list(text = list(c("July (Winter)", "February (Summer)")),
                   lines = list(col = c("blue", "red"))),
        panel = function(x, y, z, ..., col) {
           with(EastAuClimate, {
              panel.segplot(WinterMinTemp, WinterMaxTemp, z, ..., col = "blue")
              panel.segplot(SummerMinTemp, SummerMaxTemp, z, ..., col = "red")
           })
        })

     ## Northern sites have Summer-dominated rainfall;
     ## Southern sites have Winter-dominated rainfall.
     xyplot(SummerRain + WinterRain ~ Latitude, EastAuClimate,
            type = c("p", "a"), auto.key = list(lines = TRUE),
            ylab = "Rainfall (mm / month)")

     ## Clear days are most frequent in the mid latitudes.
     xyplot(RainDays + CloudyDays + ClearDays ~ Latitude, EastAuClimate,
            type = c("p", "a"), auto.key = list(lines = TRUE),
            ylab = "Days per year")

