FORTRAN: G95 (GCC 4.0.3 (g95 0.94!))
1. Stations were chosen with less than 10% missing data for the period of 1895-2011.
2. For each station, the accumulated precipitation values for 2-day periods were determined and then ranked for the entire period of record.
3. For each station, the top N values were identified, where N equals the number of years of available data, divided by 5. These events were considered extreme.
4. For each station, the number of extreme events in each year was computed.
5. Grid box (1 degree by 1 degree) average values were calculated for each year, by averaging the values for each station available in that grid box.
6. For each region, for each year, we calculated the average number of events were calculated as the arithmetic average of all of the grid box values in each region.
7. For each region, a trend was calculated using ordinary least squares regression applied to regional time series of average number of events.
8. The change was calculated as the percentage difference between the end points of the trend line. The end points are 1901 and 2016. The percentage was calculated with respect to the overall mean of the time series.
cssr-40f6-4004-bc96-process
CS_observed changes in heavy precip_Figure_7_4.png;
stations18952011-90_new.prcp;
getprcpextremes_Apr25_2017_CSSR.f95;
prcpextremes_2dy_5yr_18952016_CSSR.txt;
gridprcpextremes_regions_2dy_5yr_1895_2016_CSSR.txt;
gridprcpextremes_regions_2dy_5yr_1895_2016_CSSR.xlsx;
gridaverage_regions_updatedUSW_Aug19_2016.f95
Red Hat Enterprise Linux Server release 7.3