Changes between Version 3 and Version 4 of udg/ecoms/RPackage/examples/verification


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Timestamp:
May 12, 2016 4:59:25 PM (6 years ago)
Author:
juaco
Comment:

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  • udg/ecoms/RPackage/examples/verification

    v3 v4  
    146146}}}
    147147
     148
    148149[[Image(image-20160512-161841.png)]]
    149150
    150 
    151 
    152 
    153 
     151The results reveal a significant cold bias of the CFSv2 model predictions.
     152
     153
     154=== Correlation
     155
     156We follow a similar approach to compute the ensemble mean forecast correlation against the verifying observations:
     157
     158
     159{{{#!text/R
     160corr <- veriApply("EnsCorr",
     161                  fcst = mn.tx.forecast$Data,
     162                  obs = mn.tx.obsintp$Data,
     163                  ensdim = 1, tdim = 2)
     164
     165fields::image.plot(tx.forecast$xyCoords$x,
     166                   tx.forecast$xyCoords$y,
     167                   t(corr),
     168                   asp = 1, xlab = "", ylab = "",
     169                   main = "Mean tmax correlation - JJA")
     170
     171downscaleR:::draw.world.lines()
     172
     173}}}
     174
     175
     176[[Image(image-20160512-162800.png)]]
     177
     178We find that the ensemble mean summer forecasts for 1991-2000 correlate well with the verifying observations over the north-western sector of the analysis area, but the forecasts do not skilfully represent year-to-year variability over the Iberian Peninsula and the Mediterranean area.
     179
     180
     181=== Ranked probability skill score (RPSS)
     182
     183We next illustrate the ranked probability skill score (RPSS). Here we use the RPSS for tercile forecasts, that is probability forecasts for the three categories colder than average, average, and warmer than average. In order to convert observations and forecast in probabilities for the three categories, we have to add an additional argument `prob` to the `veriApply` function with the quantile boundaries for the categories chosen. In this case, to indicate that validation is performed on the terciles, we use the value `prob=c(1/3,2/3)`, as indicated next:
     184
     185{{{#!text/R
     186rpss <- veriApply("EnsRpss",
     187                  fcst = mn.tx.forecast$Data,
     188                  obs = mn.tx.obsintp$Data,
     189                  prob = c(1/3,2/3),
     190                  ensdim = 1, tdim = 2)
     191}}}
     192
     193In this case, the output is a list consisting of two components: The first one is the RPSS lon-lat matrix, as in the previous examples. The second one, provides the standard error, useful to calculate the significance of the score at each particular grid point (at the 95% c.i. in this example):
     194
     195
     196{{{#!text/R
     197# RPSS map
     198fields::image.plot(tx.forecast$xyCoords$x,
     199                   tx.forecast$xyCoords$y,
     200                   t(rpss$rpss),
     201                   asp = 1, xlab = "", ylab = "", main = "tmax RPSS - JJA")
     202downscaleR:::draw.world.lines()
     203
     204# Compute significant points and collocate spatially:
     205sig.i <- rpss$rpss > rpss$rpss.sigma*qnorm(0.95)
     206lons <- rep(mn.tx.obsintp$xyCoords$x, each = length(mn.tx.obsintp$xyCoords$y))
     207lats <- rep(mn.tx.obsintp$xyCoords$y, length(mn.tx.obsintp$xyCoords$x))
     208points(lons[sig.i], lats[sig.i], pch = 19)
     209
     210}}}
     211
     212[[Image(image-20160512-165909.png)]]
     213
     214
     215== Acknowledgements
     216
     217These examples have been prepared by Jonas Bhend (**Meteo Swiss**), in collaboration with the **Santander Met Group**.
     218
     219== Package versions and session info
     220
     221{{{#!text/R
     222print(sessionInfo(), locale = FALSE)
     223
     224R version 3.3.0 (2016-05-03)
     225## Platform: x86_64-pc-linux-gnu (64-bit)
     226## Running under: Ubuntu 14.04.4 LTS
     227
     228## attached base packages:
     229## [1] stats     graphics  grDevices utils     datasets  methods   base     
     230
     231## other attached packages:
     232## [1] downscaleR_1.0-1        easyVerification_0.2.0  loadeR.ECOMS_1.0-0      loadeR_1.0-0            loadeR.java_1.1-0     
     233## [6] rJava_0.9-8             SpecsVerification_0.4-1
     234
     235## loaded via a namespace (and not attached):
     236##  [1] Rcpp_0.12.4       devtools_1.10.0   maps_3.1.0        MASS_7.3-44       evd_2.3-2         munsell_0.4.3     colorspace_1.2-6
     237##  [8] lattice_0.20-33   pbapply_1.1-3     plyr_1.8.3        fields_8.4-1      tools_3.3.0       CircStats_0.2-4   parallel_3.3.0   
     238## [15] grid_3.3.0        spam_1.3-0        dtw_1.18-1        digest_0.6.9      abind_1.4-3       akima_0.5-12      bitops_1.0-6     
     239## [22] RCurl_1.95-4.8    memoise_1.0.0     sp_1.2-3          proxy_0.4-15      scales_0.4.0      boot_1.3-17       verification_1.42
     240
     241}}}