Key points of post
· Far more rain fell between 6 and 6.59 am and between 11 and 11.59pm
It has been just over a month and
a half since we got the Weather Station here at WorldFish up and running and we
thought it would fun to have a quick look at our first complete month of data,
ie. data collected at 5 minute intervals for all of September (2014) typically
one of the wettest months in Penang. Remember that a couple of months ago we looked at the frequency distribution of rainfall events by hour of day based on data from Bayan Lepas International
airport.
After aggregating of the rainfall
data, we looked at the frequency distribution of rainfall events by hour of day,
and also by the day of week and we noticed some features we thought were worth sharing. One might be forgiven for
assuming that there would not be too much variation in the amount of
rain that falls either during a specific hour of the day, or a specific day of
the week. However, the rainfall data captured here at the WorldFish office at
Batu Maung show that certain times of day and days of the week saw significantly more rainfall. In September 2014, we saw much more rain between 6 and 6.59am (65.9 mm) and between 11
and 11.59pm (48.5 mm). The rain falling
at these times contributed over 30% of
the rainfall for that entire month (354.5 mm) with 19% of the total monthly rainfall
falling between 6 and 6.59am. Bizarrely it was also much more likely to rain on Wednesdays (102.6mm) and Saturdays
(106.2mm's). To put this into perspective over 60% of September 2014’s rain fell on either Saturday or Wednesday!
The plots also show that there
was very little rainfall on Fridays during September 2014 (5.6 mm). Interestingly north-east Penang Island experienced
a once-in-40-year rainfall event recently (137 mm's in a few hours) on FRIDAY
night (3 October 2014). At the same time, however, very little rain was
recorded at our PWS at Worldfish. Rainfall events can thus be extremely
localized indicating the importance of establishing well-distributed and
densely placed weather stations for studying weather and climate.
Granted, the rainfall data we looked
at only covers a short period of time and
the large variations could be pure coincidence. Conversely, these could also
indicate consistent patterns or trends with scientific explanations possible (lunar
periodicity, seasonal effects?). We intend
to keep an eye on these features of the data and it will be interesting to see
what emerges when time periods are
examined.
As usual, the R code used the produce the plots in this post is provided below. The data is pulled from Weather Underground using the 'weatherData' package.
## Load required libraries library(weatherData) library(ggplot2) library(scales) library(plyr) library(reshape2) library(lubridate) library(zoo) # Get data for PWS using weatherData package pws <- getWeatherForDate("IPENANGB2", "2014-09-01","2014-09-30", station_type = "id",opt_detailed=T, opt_custom_columns=T, custom_columns=c(1,2,6,7,10,13)) # Rename columns colnames(pws)<-c("time","time1","tempc","wdd","wspd","prcp","rain") ## Adding date columns pws$time<-as.POSIXct(pws$time1,format="%Y-%m-%d %H:%M:%S",tz="Australia/Perth") pws$year <- as.numeric(format(pws$time,"%Y")) pws$date <-as.Date(pws$time,format="%Y-%m-%d",tz="Australia/Perth") pws$year <- as.numeric(as.POSIXlt(pws$date)$year+1900) pws$month <- as.numeric(as.POSIXlt(pws$date)$mon+1) pws$monthf <- factor(pws$month,levels=as.character(1:12),labels=c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"),ordered=TRUE) pws$weekday <- as.POSIXlt(pws$date)$wday pws$weekdayf <- factor(pws$weekday,levels=rev(0:6),labels=rev(c("Mon","Tue","Wed","Thu","Fri","Sat","Sun"))) pws$yearmonth <- as.yearmon(pws$date) pws$yearmonthf <- factor(pws$yearmonth) pws$week <- as.numeric(format(as.Date(pws$date),"%W")) pws$weekf<- factor(pws$week) pws$jday<-yday(pws$date) pws$hour <- as.numeric(format(strptime(pws$time, format = "%Y-%m-%d %H:%M"),format = "%H")) pws$min <- as.numeric(format(strptime(pws$time, format = "%Y-%m-%d %H:%M"),format = "%M")) # Remove duplicate values # Function to compute consecutive differences dif <- function(x) c(diff(x), NA) # Apply the function to each individual pws<-ddply(pws, 'jday', transform, actrain = dif(rain)) pws$actrain[pws$actrain<0]<- 0 pws$actrain[is.na(pws$actrain)] <- 0 # Summarize rainfall data by hour and by day rd <- ddply(pws,.(weekday,weekdayf),summarize, raintot = sum(actrain,na.rm=T)) rh <- ddply(pws,.(hour),summarize, raintot = sum(actrain,na.rm=T)) # Plot rainfall by hour h <- ggplot(rh,aes(hour,raintot,fill=raintot))+geom_bar(stat="identity")+ geom_text(aes(label=round(raintot,3)), vjust = -0.4, size = 2.5)+ ggtitle("Total rainfall by hour for September 2014 - WorldFish Weather Station\n")+ scale_fill_continuous(low='grey90', high='steelblue') + theme_bw()+ xlab("\nHour of Day")+ ylab("Total Precipitation (mm)\n")+ theme(axis.text.x = element_text(size=10), legend.position="none", panel.background=element_blank(), axis.title.x=element_text(size=10,colour="grey20"), axis.title.y=element_text(size=10,colour="grey20"), panel.grid.major = element_line(colour = "grey",size=0.25,linetype='longdash'), axis.text.x=element_text(size=10,colour="grey20",face="bold"), plot.title = element_text(lineheight=1, face="bold",size = 13, colour = "grey20")) h # Plot rainfall by Week Day d <- ggplot(rd,aes(weekday,raintot,fill=raintot))+geom_bar(stat="identity")+ geom_text(aes(label=round(raintot,3)), vjust = -0.4, size = 2.5)+ ggtitle("Total rainfall by day for September 2014 - WorldFish Weather Station\n")+ scale_fill_continuous(low='grey90', high='steelblue') + theme_bw()+ xlab("\nDay of Week")+ ylab("Total Precipitation (mm)\n")+ scale_x_continuous(breaks=c(0,1,2,3,4,5,6),labels=c("Mon","Tue","Wed","Thu","Fri","Sat","Sun"))+ theme(axis.text.x = element_text(size=10), legend.position="none", panel.background=element_blank(), axis.title.x=element_text(size=10,colour="grey20"), axis.title.y=element_text(size=10,colour="grey20"), panel.grid.major = element_line(colour = "grey",size=0.25,linetype='longdash'), axis.text.x=element_text(size=10,colour="grey20",face="bold"), plot.title = element_text(lineheight=1, face="bold",size = 13, colour = "grey20")) d # Save plots to png ggsave(h,file="WF-PWS-RFbyHr.png",width=7,height=5,dpi=400,type="cairo") ggsave(d,file="WF-PWS-RFbyDay.png",width=7,height=5,dpi=400,type="cairo")
