Key points of post
- Weather Underground combines a website, database and a global network of airport and Personal Weather Stations maintained largely by amateurs (http://www.wunderground.com/).
- The network is growing fast, and the data can be accessed freely.
- The utility of Weather Underground is illustrated below using an example from one day’s weather in Penang, Malaysia.
Recently we’ve discovered Weather Underground (http://www.wunderground.com/) where you can access data from a global network of weather stations. Weather Underground is a ‘citizen science’ website specializing in the collection and dissemination of meteorological data. It promotes a Personal Weather Station network whereby subscribers buy their own weather station, install it in their garden or wherever, and then plug into the global network via the internet. The data they collect are then automatically stored in the ‘cloud’ where they can be accessed, analyzed and visualized by anyone with a computer. Most Personal Weather Stations are in the United States, but other countries are catching up fast. There are three on Penang Island so far; one at Dalat International School, another in Tanjung Tokong, and the International Airport at Bayan Lepas is also connected.
The data collected via the Personal
Weather Stations can be imported automatically into our software of choice, R,
(http://cran.r-project.org/) where they can be analyzed using the extensive
statistical and data-visualisation tools available.
As an example of the type of data
available on Weather Underground, we downloaded and plotted data for a single
day, collected at Bayan Lepas International Airport on 2nd June
2014. Data for six important
meteorological parameters were selected
and have been plotted below at half-hourly intervals.
Sometimes it can seem that weather in
Penang is rather uniform throughout the day (ie. very hot), but this is
actually not the case. Starting at
midnight, Air Temperatures were ~ 27°C until 8am when they rose rapidly to peak
between 1.30 and 3.30pm (best time for a siesta). After that temperature fell slowly, changing
little between 6pm and 10pm remaining at 29°C. In the afternoon
of 2nd June 2014 the wind speed increased from the south-west, after
blowing very lightly from the north all night.
The rise in wind-speed during the day was related to a fall in
barometric pressure, and this is of course the ‘South West Monsoon’ wind. By dusk the wind speed fell away again. ‘Heat-index’ is a measure of the comfort a
human is likely to experience, combining measures of temperature and humidity.
On 2nd June this was clearly, directly related to the overall
ambient temperature (top graph versus bottom) but this is not always the case.
Note: as usual the R-code for accessing Weather Underground and creating
the graphs is given below. Readers can use this to plot data for their own
location, dependent on the proximity of a Personal Weather Station.
We have used the 'weatherData' package developed by Ram Narasimhan to fetch the data from Weather Underground. The package can be installed directly from the CRAN site of from his site at http://ram-n.github.io/weatherData/
You can also get more information about the package from the github site that goes into further detail with examples and some of the other additional functions that come with it.
## Load required libraries library(weatherData) library(ggplot2) library(mgcv) library(scales) library(plyr) library(reshape2) library(gridExtra) library(lubridate) library(weathermetrics) ### Find required 4 letter station code using function below # Example getStationCode("Penang") or getStationCode("Honiara") getStationCode("Penang") station.id="WMKP" date="2014-06-02" s<-getStationCode(station.id) s1<-(strsplit(s,split= " "))[[1]] station.name<-paste(s1[4],s1[5]) # Get detailed weather station data wd<-getDetailedWeather(station.id,date, opt_all_columns=T) str(wd) # Rename columns colnames(wd)<-c("time","mytime","tempc","dewpc","hum","slp","vsb","wd","wspd","guspd","prcp","events", "conditions","wdd","date_utc") ## 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=3)+ theme_bw() + ggtitle(paste('Plot of weather variables for',station.name,"(",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_point(col="seagreen",size=3)+ 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(0,30))+ stat_smooth(aes(group = 1), col="seagreen4",method = "loess",span=0.3,se=T,size=1.2)+ 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 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(0.25, "cm")), size = 0.75) + geom_point(data = subset(dw, !is.na(u)), alpha = 0.5,size=3) + 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=3) + stat_smooth(span = 0.3,method="loess",col="purple",size=1.2)+ 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_step(colour="royalblue",size=0.5) + geom_point(colour="royalblue4",size=3) + xlab("Time") + ylab("Relative Humidity (%)\n") + ggtitle("Relative humidity\n")+ geom_smooth(aes(group = 1), col="royalblue",method = "loess",span=0.3,se=T,size=1.2)+ 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 ## Calculate Heat Index using weathermetrics package 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=3)+ 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 plot.name1 <- paste(station.id,"_WU_",date,".png",sep="") tiff(plot.name1,width = 13, height = 18, units = "in", compression = "lzw",bg = "white", res = 600, family = "", restoreConsole = TRUE, type = "cairo") grid.draw(rbind(ggplotGrob(dt_p),ggplotGrob(dh_p),ggplotGrob(dslp_p),ggplotGrob(dws_p),ggplotGrob(dwd_p),ggplotGrob(dhi_p),size="first")) dev.off()
