WEACLIM

WEACLIM analyses, transforms and generate daily time series (of rainfall) for downscaling studies

Description

This toolbox has been developped primarily to study daily rainfall time series. There are three general drivers used to generate daily time series using K Nearest Analog, Weather Classification and Local Scaling approaches (knn_analog_model, weather_type_model and local_scaling). It includes also simple statistical tests (for correlation and probability transition) and also analysis of intra-seasonal variability of rainfall time series. Are also included simple univariate stochastic weather generators for simulating time series.

• Required Products: Signal Processing Toolbox; Statistics Toolbox
• MATLAB release: MATLAB 6.0 (R12)

Other Files

replace_missing.m,
roc.m,
rocscore.m,
rps.m,
scale_mean_var.m,
search_mode.m,
select_time_area.m,
shiftgrid.m,
stan.m,
subseas.m,
swg.m,
swg_cluster.m,
swg_gamma.m,
swg_mixexp.m,
swg_mos_cca.m,
swg_weibull.m,
test_markov_chain.m,
weather_type_model.m,
content.m,
anomaly.m,
bp.m,
chi2.m,
choose_area_gridded.m,
choose_date_daily.m,
choose_date_monthly.m,
classe.m,
copy.m,
corr_mc.m,
cv.m,
decalage.m,
distance_euclid.m,
dof.m,
dof_comb.m,
dof_comb_red.m,
ebisuzaki.m,
filtrage.m,
find_analog.m,
find_spell.m,
fliplonlat.m,
inverse_fisherz.m,
inverse_log_odd.m,
inverse_persist.m,
knn_analog_model.m,
lepscont.m,
local_scaling.m,
log_odd.m,
markov_chain_1.m,
mixed_exp_rnd.m,
mixed_expfit.m,
mos_cca.m,
nancov.m,
nandof.m,
nanstan.m,
padd.m,
persist.m,
ponder.m,
prob_wet_dry.m,
proj.m