Rainfall event frequencies recorded by the Worldfish PWS in September 2014

Key points of post

·         Wednesday and Saturdays were far wetter.
·         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")

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Weather station at WorldFish HQ goes live

Worldfish HQ in Penang now has a weather monitoring station which is transmitting data live every five minutes to the internet where it is stored and served by Weather Underground (http://www.wunderground.com/personal-weather-station/dashboard?ID=IPENANGB2#history). 

Data and graphical output can be downloaded and examined by anyone in the world with an internet connection for free.   This is a really brilliant Citizen Science project, useful for both professionals and amateur enthusiasts, and we have been surprised at how simple it is to set up.  So far the majority of the weather stations on the Weather Underground network are in developed countries, but with the help of the CGIAR (Consultative Group on International Agricultural Research) of which we are a part, we think the network can expand increasingly into developing countries.  The next step for us is to establish weather stations at WorldFish offices worldwide (Zambia, Australia, Malawi, Bangladesh, Solomon Islands, Cambodia, Philippines, Myanmar and Timor-Leste).  From our headquarters in these countries we hope to expand the network out into the farming and fishing communities that we serve.

One substantial barrier for understanding the impact of climate variability and change on food production in developing countries is the lack of fine-scale spatial data, especially out in the sticks (boondocks). We think that Citizen Science projects like this can really help food producers, from farmers to fishers, smooth out risk from weather variability (perhaps using finance products like Index-Based Insurance).

If you are interested in setting up a weather station and linking it to Weather Underground please don’t hesitate to get in touch with us if you need any advice or further information.

The weather station we are using was bought in Penang (http://www.lacrossetechnology.com/2810/index.php) and installed very quickly by local contractors.  It consists of a temperature and humidity sensor, a self-emptying rain gauge and a solar powered anemometer (measures wind speed & direction).  All are linked wirelessly to a central control panel installed conveniently in our office.  Registration with Weather Underground is quick and straightforward allowing users to upload their weather data to the world-wide-web for free.

If you want to examine the data we are collecting at Worldfish HQ in more detail (see figure which shows weather variability at our office on August 26^th 2014)  you can use R (http://cran.r-project.org/) as outlined below.

Anemometer, wind vane and the tipping bucket self-emptying rain gauge

Temperature and Humidity Sensor (within radiation shield) outside the WorldFish NRM office

Control panel inside the WorldFish NRM office that collects
data from the sensors and transmits to a CPU via a USB dongle

# Code to plot weather variables from WorldFish Penang PWS linked via Weather Underground (Station: IPENANGB2)
 
# Load required libraries
 
library(weatherData)
library(ggplot2)
library(mgcv)
library(scales)
library(plyr)
library(reshape2)
library(gridExtra)
library(lubridate)
library(weathermetrics)
 
station.id="IPENANGB2"
date="2014-08-26"
 
# Get detailed weather station data
 
wd<-getDetailedWeather(station.id,date,station_type = "id", opt_all_columns=T)
 
str(wd)
 
# Rename columns
 
colnames(wd)<-c("time","time1","tempc","dewpc","slp","wd","wdd","wspd","guspd","hum","prcp","conditions",
                 "cc","rain","software","date_utc","unk")
 
wd$time<-as.POSIXct(wd$time1,format="%Y-%m-%d %H:%M:%S")
 
wd$rain[wd$rain < 0 ] <- NA
 
# Plot temperatures
 
dt_p <- ggplot(wd, aes(time, tempc)) + 
        xlab("Time") + ylab(as.expression(expression( paste("Temperature (", degree,"C)","\n")))) + 
        geom_line(colour="red",size=0.5) +
        geom_point(colour="red",size=1)+
        theme_bw() +
        ggtitle(paste('Plot of weather variables for',station.id, 'on',strftime(date,"%d-%b-%Y"),'\n\n',"Temperature\n")) +
        scale_x_datetime(labels = date_format("%I:%M:%p"),breaks = date_breaks("4 hours"))+
        theme(plot.title = element_text(lineheight=1.2, face="bold",size = 16, colour = "grey20"),
        panel.border = element_rect(colour = "black",fill=F,size=1),
        panel.grid.major = element_line(colour = "grey",size=0.25,linetype='longdash'),
        panel.grid.minor = element_blank(),
        panel.background = element_rect(fill = "ivory",colour = "black"))
 
dt_p
 
# Plot windspeed
 
wd$wspd[wd$wspd=="Calm"] <- 0
 
wd$wspd<-as.numeric(wd$wspd)
 
winds <- subset(melt(wd[,c("time","wspd")], id = "time"), value > 0)
 
dws_p <- ggplot(winds, aes(time, value))+
         geom_bar(stat="identity",col="limegreen",width=.5)+
         geom_point(data=wd,aes(time,guspd),col="seagreen4",size=1.5,shape=1)+        
         scale_x_datetime(labels = date_format("%I:%M:%p"),breaks = date_breaks("4 hours"))+
         ylab("km/hour\n")+
         xlab("Time")+
         scale_y_continuous(limits=c(0,20))+
         theme_bw()+
         theme(plot.title = element_text(lineheight=1.2, face="bold",size = 16, colour = "grey20"),
         panel.border = element_rect(colour = "black",fill=F,size=1),
         panel.grid.major = element_line(colour = "grey",size=0.25,linetype='longdash'),
         panel.grid.minor = element_blank(),
         panel.background = element_rect(fill = "ivory",colour = "black"))+        
         ggtitle("Wind and gust speed\n")
 
dws_p
 
# Plot wind vectors
 
wd$u <- (-1 * wd$wspd) * sin((wd$wdd) * pi / 180.0)
wd$v <- (-1 * wd$wspd) * cos((wd$wdd) * pi / 180.0)
dw = subset(wd, u != 0 & v != 0)
 
v_breaks = pretty_breaks(n = 5)(min(dw$v):max(dw$v))
v_labels = abs(v_breaks)
 
dwd_p <-  ggplot(data = dw, aes(x = time, y = 0)) +
          theme_bw() +
          theme(plot.margin = unit(c(0, 1, 0.5, 0.5), 'lines')) +
          geom_segment(aes(xend = time + u*360, yend = v), arrow = arrow(length = unit(0.15, "cm")), size = 0.5) + 
          geom_point(data = subset(dw, !is.na(u)), alpha = 0.5,size=1) +
          scale_x_datetime(name="Time",labels = date_format("%I:%M:%p"),breaks = date_breaks("4 hours")) +
          ggtitle("Wind vectors\n")+
          scale_y_continuous(name = "Wind vectors (km/h)\n", labels = v_labels, breaks = v_breaks)+
          theme(plot.title = element_text(lineheight=1.2, face="bold",size = 16, colour = "grey20"),
          panel.border = element_rect(colour = "black",fill=F,size=1),
          panel.grid.major = element_line(colour = "grey",size=0.25,linetype='longdash'),
          panel.grid.minor = element_blank(),
          panel.background = element_rect(fill = "ivory",colour = "black"))
 
dwd_p
 
# Plot Sea Level Pressure
 
wd$slp <- as.numeric(as.character(wd$slp))
 
dslp_p <- ggplot(wd, aes(time, slp)) + 
          geom_point(col="purple4",size=1) + 
          geom_step(col="purple",size=0.5)+
          scale_x_datetime(labels = date_format("%I:%M:%p"),breaks = date_breaks("4 hours"))+
          ggtitle("Sea Level Pressure\n")+
          ylab("hPa\n\n")+
          xlab("Time")+
          theme_bw()+ 
          theme(plot.title = element_text(lineheight=1.2, face="bold",size = 16, colour = "grey20"),
          panel.border = element_rect(colour = "black",fill=F,size=1),
          panel.grid.major = element_line(colour = "grey",size=0.25,linetype='longdash'),
          panel.grid.minor = element_blank(),
          panel.background = element_rect(fill = "ivory",colour = "black"))
 
dslp_p
 
 
# Plot Relative Humidity
 
dh_p <-  ggplot(wd, aes(time, hum)) + 
         geom_point(colour="royalblue4",size=1) +
         geom_step(col="royalblue",size=0.5)+
         xlab("Time") + ylab("Relative Humidity (%)\n") + 
         ggtitle("Relative humidity\n")+
         theme_bw() +
         scale_x_datetime(labels = date_format("%I:%M:%p"),breaks = date_breaks("4 hours"))+
         theme(plot.title = element_text(lineheight=1.2, face="bold",size = 16, colour = "grey20"),
         panel.border = element_rect(colour = "black",fill=F,size=1),
         panel.grid.major = element_line(colour = "grey",size=0.25,linetype='longdash'),
         panel.grid.minor = element_blank(),
         panel.background = element_rect(fill = "ivory",colour = "black"))
 
dh_p
 
# Plot Precipitation
 
dr_p <- ggplot(wd, aes(time, prcp)) + 
        xlab("Time") + ylab("Rainfall (mm)") + 
        geom_bar(stat="identity",colour="darkcyan",size=0.5,fill="darkcyan") +
        #geom_point(colour="darkcyan",size=2)+
        theme_bw() +
        ggtitle("Precipitation\n")+
        scale_x_datetime(labels = date_format("%I:%M:%p"),breaks = date_breaks("4 hours"))+
        theme(plot.title = element_text(lineheight=1.2, face="bold",size = 16, colour = "grey20"),
        panel.border = element_rect(colour = "black",fill=F,size=1),
        panel.grid.major = element_line(colour = "grey",size=0.25,linetype='longdash'),
        panel.grid.minor = element_blank(),
        panel.background = element_rect(fill = "ivory",colour = "black"))
 
dr_p
 
# Plot Cumulative Precipitation
 
dcr_p <- ggplot(wd, aes(time, rain)) + 
         xlab("Time") + ylab("Rainfall (mm)") + 
         geom_line(colour="darkcyan",size=0.5) +
         geom_point(colour="darkcyan",size=1)+
         theme_bw() +
         ggtitle("Cumulative Precipitation\n")+
         scale_x_datetime(labels = date_format("%I:%M:%p"),breaks = date_breaks("4 hours"))+
         theme(plot.title = element_text(lineheight=1.2, face="bold",size = 16, colour = "grey20"),
         panel.border = element_rect(colour = "black",fill=F,size=1),
         panel.grid.major = element_line(colour = "grey",size=0.25,linetype='longdash'),
         panel.grid.minor = element_blank(),
         panel.background = element_rect(fill = "ivory",colour = "black"))
 
dcr_p
 
 
# Calculate Heat Index using weathermetrics
 
wd$hi <- heat.index(t = wd$tempc,rh = wd$hum,temperature.metric = "celsius",output.metric = "celsius",round = 2)
 
# Plot Heat Index
 
dhi_p <- ggplot(wd, aes(time, hi)) + 
         xlab("Time") + ylab(as.expression(expression(paste("Temperature (", degree,"C)",)))) + 
         geom_line(colour="red",size=0.5) +
         geom_point(colour="red",size=1)+
         theme_bw() +
         ggtitle("Heat Index\n") +
         scale_x_datetime(labels = date_format("%I:%M:%p"),breaks = date_breaks("4 hours"))+
         scale_y_continuous(breaks=c(25,30,35,40,45))+
         theme(plot.title = element_text(lineheight=1.2, face="bold",size = 16, colour = "grey20"),
         panel.border = element_rect(colour = "black",fill=F,size=1),
         panel.grid.major = element_line(colour = "grey",size=0.25,linetype='longdash'),
         panel.grid.minor = element_blank(),
         panel.background = element_rect(fill = "ivory",colour = "black"))
 
dhi_p
 
# Export weather variables plot to png file
 
plot.name1 <- paste(station.id,"_WU_",date,".png",sep="")
 
png(plot.name1,width = 12, height = 25, units = "in",
     bg = "white", res = 500, family = "", restoreConsole = TRUE,
     type = "cairo")
 
grid.draw(rbind(ggplotGrob(dt_p),ggplotGrob(dh_p),ggplotGrob(dr_p),ggplotGrob(dcr_p),ggplotGrob(dslp_p),ggplotGrob(dws_p),ggplotGrob(dwd_p),ggplotGrob(dhi_p),size="first"))
 
dev.off()

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