SWM

Stochastic Weather Model (SWM) code

Download ERA5 data across required region from https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=form
Select Total Precipitation from Wind, Pressure and Precipitation, and continous hourly data for as many years as feasible
(recommend 40+)
Put this data in a folder called "data" in the same folder as the rest of the .R files

Open SWM_v0.R

Set number of runs required (nruns)
Set rain tolerance (rain_tol)
Set desired output start time (ts)
Set desired output duration (td)

Run all code from within SWM_v0.R

step1_convert.R

Reads and processes ERA5 data in the data folder

step2_build_weather_blocks.R

Builds blocks of wet and dry according to rain_tol

step3_random_weather_count.R

Creates stochastic rain array (simulated data)

step4_write_rain_netcdf.R

Writes this rain array out as a netcdf

The above produces nruns of simulated data.

Statistical analyses - all written as standalone checks

check1_students_t_test.R

Monthly means and variance (ANOVA) between real and simulated data
Includes Shapiro-Wilks tests for normality
Includes Bartlett (normal) and Levene (not normal) tests for ANOVA

check2_tukey.R

Tukey's Honest Significant Difference test to determine whether source (real or simulated) is a significant factor in the prediction of total monthly rainfall

check3_acf_daily_cummulative.R

Temporal trends on daily timescale using autocorrelation function

check3_acf_monthly_cummulative.R

Temporal trends on monhtly timescale using autocorrelation function

check4_bootstrap.R

Compares empirical CDFs for simulated data with that for the real data Build 95th percentile envelopes using nruns = 95