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Run_programs_temp.R
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Run_programs_temp.R
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################################### Script for calculating predictability of extremes #######################
################################### Created by: Mingkai Jiang #######################
################################### First created on: 2015-03-10 #######################
################################### Modified on: 2017-06-08 #######################
##############################################################################################################
##############################################################################################################
##############################################################################################################
#### Prepare all functions and coordinate files
### clear workspace
rm(list=ls())
### Calls script containing all necessary functions
source("R/prepare_R.R")
### Get SCCS coordinates and Station ID Files
corDF <- read.csv("input/weight_dis_ht.csv")
### Get GHCN station list
gDF <- read.csv("input/ghcnd-stations.csv")
### Get growing season information
growDF <- read.csv("input/PlantSeasonality.csv")
### Process growing season data to have single entry for each SCCS society
growDF <- growing_season_single_entry(growDF)
### Obtain SCCS based GHCN stations that are closest to the SCCS point
stationDF <- select_5_ghcn_stations(corDF, gDF, growDF)
### Obtain GHCN station list to process
station.list <- c(stationDF$ghcn1,stationDF$ghcn2,stationDF$ghcn3,
stationDF$ghcn4,stationDF$ghcn5)
##############################################################################################################
#### select on SCCS sites based on their information sheet
### Step 1:
### Convert all files from .dly to .csv format, removed data quality flag
ConvertFiles_Temp(sourceDir = "data/ghcnd_all/ghcnd_all/",
stations = station.list,
destDir = "data/ghcnd_selected_tmin")
### Step 2:
### Restructure the files to continuous days, added leap years
### Replacing old with new files
ReStructureFile(sourceDir = "data/ghcnd_selected_tmin", destDir = "data/restructured_tmin")
### Step 3:
### Check year range quality - only include data with > 10 yrs of data
### - and data with < 50% missing values
station.list.upd <- Missing_check(station.list,
sourceDir = "data/restructured_tmin",
destDir = "data/ghcnd_gap_filled_tmin")
## update stationDF
stationDF.upd <- Update_station_list_5_stations(station.list.upd, stationDF)
### Step 4:
### Gap filling 1. - use statistical correlation among 9 stations to gap fill all 9 stations
### - excluding big chunk of missing data which will be filled later use a different function
## the following sites are problematic, so excluded in the first run
## problem sites: 0 (non-NA) cases: non-missing data do not overlap across all sites
## dim(X) must hvae a postive length: two sites overlapping problem
stationDF2 <- stationDF.upd[-c(14,21,27,28,29,42,51,56,58,64,65,66,71,72,76),]
Gap_Fill_5_stations(stationDF2,
sourceDir = "data/ghcnd_gap_filled_tmin",
destDir = "data/ghcnd_gap_filled_tmin")
# Fill all remaining stations using data within the station
### Step 5:
### Gap filling 2. - use same period in other years to fill big chunk of missing data
### - and the remaining unfilled sites
Gap_Fill_within_station_5_stations(station.list.upd,
sourceDir = "data/ghcnd_gap_filled_tmin",
destDir = "data/ghcnd_gap_filled_tmin")
### Step 6:
### Check year range quality - only include data with > 10 yrs of data
### commented out because Missing_check is doing this
YrRange10(sourceDir = "data/ghcnd_gap_filled_tmin")
### Step 7:
### Update stationDF2 to remove all removed stations from this list
### and add the growing season information
final_station_DF <-Final_station_list_5_stations(sourceDir = "data/ghcnd_gap_filled_tmin",
stationDF.upd,
outname="tmin")
##############################################################################################################
#### Compute indices
### Step 1:
### Calculate threshold based index fd at seasonal timestep
ThrIndS_temp_ann(sourceDir = "data/ghcnd_gap_filled_tmin", destDir = "data/indices/ThrIndS_temp")
### Step 2:
### Calculate consecutive days indices
consecutive_day_indices_tmin(final_station_DF,
sourceDir = "data/ghcnd_gap_filled_tmin", destDir = "data/indices/CSDI")
### Calculate consecutive days indices for hunter/gatherer societies, i.e. sites without any plant grow information
consecutive_day_indices_hunter_gatherer_tmin(final_station_DF,
sourceDir = "data/ghcnd_gap_filled_tmin",
destDir = "data/indices/CSDI_hunter_gatherer")
### Calculate annual consecutive day indices, regardless of the societies
CSDI_annual(sourceDir = "data/ghcnd_gap_filled_tmin", destDir = "data/indices/annual_consecutive_tmin")
##############################################################################################################
#### Calculate whole year range predictability
##Calculate fd predictability
fd_pred(sourceDir = "data/indices/ThrIndS_temp", destDir = "data/predictability")
##Calculate tmin predictability
tmin_pred(sourceDir = "data/indices/ThrIndS_temp", destDir = "data/predictability")
## Calculate CSDI consecutive predictability
consec_cold_pred(sourceDir = "data/indices/CSDI", destDir = "data/predictability")
## Calculate CSDI consecutive predictability for hunter and gatherer societies
consec_cold_pred_hunter_gatherer(sourceDir = "data/indices/CSDI_hunter_gatherer", destDir = "data/predictability")
##############################################################################################################
### End
rm(list=ls())