This blog is run by Jason Jon Benedict and Doug Beare to share insights and developments on open source software that can be used to analyze patterns and trends in in all types of data from the natural world. Jason currently works as a geospatial professional based in Malaysia. Doug lives in the United Kingdom and is currently Director of Globefish Consultancy Services which provides scientific advice to organisations that currently include the STECF [Scientific, Technical and Economic Committee for Fisheries, https://stecf.jrc.europe.eu/] and ICCAT, https://www.iccat.int/en/

Thursday, 28 August 2014

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 26th 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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