clim <- s2dv::Clim(ts_temp$exp, ts_temp$obs, time_dim = "sdate",
dat_dim = c("dat", "member"))
ano_exp <- s2dv::Ano(ts_temp$exp, clim$clim_exp)
ano_obs <- s2dv::Ano(ts_temp$obs, clim$clim_obs)
corr_ano <- s2dv::Corr(s2dv::MeanDims(ano_exp, 'member'), ano_obs,
time_dim = 'sdate', dat_dim = 'dat')
input_cor <- array(dim = c(dat = 1, 3, time = 5))
input_cor[, 1, ] <- corr_ano$conf.lower[, 1, 1, ]
input_cor[, 2, ] <- corr_ano$corr[, 1, 1, ]
input_cor[, 3, ] <- corr_ano$conf.upper[, 1, 1, ]
rms_ano <- s2dv::RMS(s2dv::MeanDims(ano_exp, 'member'), ano_obs,
time_dim = 'sdate', dat_dim = 'dat')
input_rms <- array(dim = c(dat = 1, 3, time = 5))
input_rms[, 1, ] <- rms_ano$conf.lower[, 1, 1, ]
input_rms[, 2, ] <- rms_ano$rms[, 1, 1, ]
input_rms[, 3, ] <- rms_ano$conf.upper[, 1, 1, ]
Viz2VarsVsLTime(input_cor, input_rms,
toptitle = "Time correlation and RMSE with ERA5",
ytitle = "K", title_scale = 0.7,
monini = 11, freq = 1, limits = c(-1, 5),
listexp = c('SEAS5'), listvars = c('Corr', 'RMSE'),
fileout = NULL)
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