#
#In all following examples, forecasts of the Nino-3.4 index are evaluated
#
#----------------------
#Example 1: Dichotomous observations, dichotomous forecasts
# ---------------------
#Load set of dichotomous observations and dichotomous forecasts
data(cnrm.nino34.dd)
obsv = cnrm.nino34.dd$obsv
fcst = cnrm.nino34.dd$fcst
#Calculate skill score
afc(obsv, fcst, obsv.type="d", fcst.type="d")
# ---------------------
#Example 2: Dichotomous observations, (ordinal) polychotomous forecasts
# ---------------------
#Load set of dichotomous observations and polychotomous forecasts (4 categories)
data(cnrm.nino34.dm)
obsv = cnrm.nino34.dm$obsv
fcst = cnrm.nino34.dm$fcst
#Calculate skill score
afc(obsv, fcst, obsv.type="d", fcst.type="m", m=4)
# ---------------------
#Example 3: Dichotomous observations, probabilistic forecasts
# ---------------------
#Load set of dichotomous observations and probabilistic forecasts
data(cnrm.nino34.dp)
obsv = cnrm.nino34.dp$obsv
fcst = cnrm.nino34.dp$fcst
#Calculate skill score
afc(obsv, fcst, obsv.type="d", fcst.type="p")
# ---------------------
#Example 4: Dichotomous observations, continuous forecasts
# ---------------------
#Load set of dichotomous observations and continuous forecasts
data(cnrm.nino34.dc)
obsv = cnrm.nino34.dc$obsv
fcst = cnrm.nino34.dc$fcst
#Calculate skill score
afc(obsv, fcst, obsv.type="d", fcst.type="c")
# ---------------------
#Example 5: Dichotomous observations, ensemble forecasts
# ---------------------
#Load set of dichotomous observations and 9-member ensemble forecasts
data(cnrm.nino34.de)
obsv = cnrm.nino34.de$obsv
fcst = cnrm.nino34.de$fcst
#Calculate skill score
afc(obsv, fcst, obsv.type="d", fcst.type="e")
# ---------------------
#Example 6: Polychotomous (ordinal) observations, polychotomous (ordinal) forecasts
# ---------------------
#Load set of polychotomous observations (4 categories) and polychotomous forecasts (4 categories)
data(cnrm.nino34.mm)
obsv = cnrm.nino34.mm$obsv
fcst = cnrm.nino34.mm$fcst
#Calculate skill score
afc(obsv, fcst, obsv.type="m", fcst.type="m", m=4, m2=4)
# ---------------------
#Example 7: Polychotomous (ordinal) observations, probabilistic forecasts forecasts
# ---------------------
#Load set of polychotomous observations (4 categories) and probabilistic forecasts
data(cnrm.nino34.mp)
obsv = cnrm.nino34.mp$obsv
fcst = cnrm.nino34.mp$fcst
#Calculate skill score
afc(obsv, fcst, obsv.type="m", fcst.type="p", m=4)
# ---------------------
#Example 8: Polychotomous (ordinal) observations, continuous forecasts
# ---------------------
#Load set of polychotomous observations (4 categories) and continuous forecasts
data(cnrm.nino34.mc)
obsv = cnrm.nino34.mc$obsv
fcst = cnrm.nino34.mc$fcst
#Calculate skill score
afc(obsv, fcst, obsv.type="m", fcst.type="c", m=4)
# ---------------------
#Example 9: Polychotomous (ordinal) observations, ensemble forecasts
# ---------------------
#Load set of polychotomous observations (4 categories) and 9-member ensemble forecasts
data(cnrm.nino34.me)
obsv = cnrm.nino34.me$obsv
fcst = cnrm.nino34.me$fcst
#Calculate skill score
afc(obsv, fcst, obsv.type="m", fcst.type="e", m=4)
# ---------------------
#Example 10: Polychotomous (nominal) observations, polychotomous (nominal) forecasts
# ---------------------
#Load set of polychotomous observations (4 categories) and polychotomous forecasts (4 categories)
data(cnrm.nino34.mm)
obsv = cnrm.nino34.mm$obsv
fcst = cnrm.nino34.mm$fcst
#Calculate skill score
afc(obsv, fcst, obsv.type="n", fcst.type="n", m=4)
# ---------------------
#Example 11: Polychotomous (nominal) observations, probabilistic forecasts
# ---------------------
#Load set of polychotomous observations (4 categories) and probabilistic forecasts
data(cnrm.nino34.mp)
obsv = cnrm.nino34.mp$obsv
fcst = cnrm.nino34.mp$fcst
#Calculate skill score
afc(obsv, fcst, obsv.type="n", fcst.type="p", m=4)
# ---------------------
#Example 12: Continuous observations, continuous forecasts
# ---------------------
#Load set of continuous observations and continuous forecasts
data(cnrm.nino34.cc)
obsv = cnrm.nino34.cc$obsv
fcst = cnrm.nino34.cc$fcst
#Calculate skill score
afc(obsv, fcst, obsv.type="c", fcst.type="c")
# ---------------------
#Example 13: Continuous observations, ensemble forecasts
# ---------------------
#Load set of continuous observations and 9-member ensemble forecasts
data(cnrm.nino34.ce)
obsv = cnrm.nino34.ce$obsv
fcst = cnrm.nino34.ce$fcst
#Calculate skill score
afc(obsv, fcst, obsv.type="c", fcst.type="e")
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