Weather Forecast from MET Office

February 16, 2019

(This article was first published on R tutorial for Spatial Statistics, and kindly contributed to R-bloggers)

This is another function I wrote to access the MET office API and obtain a 5-day ahead weather forecast:

METDataDownload <- function(stationID, product, key){
library("RJSONIO") #Load Library
connectStr <- paste0("",stationID,"?res=",product,"&key=",key)

con <- url(connectStr)
data.json <- fromJSON(paste(readLines(con), collapse=""))

LocID <- data.json$SiteRep$DV$Location$`i`
LocName <- data.json$SiteRep$DV$Location$name
Country <- data.json$SiteRep$DV$Location$country
Lat <- data.json$SiteRep$DV$Location$lat
Lon <- data.json$SiteRep$DV$Location$lon
Elev <- data.json$SiteRep$DV$Location$elevation

Details <- data.frame(LocationID = LocID,
LocationName = LocName,
Country = Country,
Lon = Lon,
Lat = Lat,
Elevation = Elev)
param <-"rbind",data.json$SiteRep$Wx$Param)

if(product == "daily"){
dates <- unlist(lapply(data.json$SiteRep$DV$Location$Period, function(x){x$value}))
DayForecast <-"rbind", lapply(data.json$SiteRep$DV$Location$Period, function(x){x$Rep[[1]]}))
NightForecast <-"rbind", lapply(data.json$SiteRep$DV$Location$Period, function(x){x$Rep[[2]]}))
colnames(DayForecast)[ncol(DayForecast)] <- "Type"
colnames(NightForecast)[ncol(NightForecast)] <- "Type"

ForecastDF <- plyr::rbind.fill.matrix(DayForecast, NightForecast) %>%
as_tibble() %>%
mutate(Date = as.Date(rep(dates, 2))) %>%
mutate(Gn = as.numeric(Gn),
Hn = as.numeric(Hn),
PPd = as.numeric(PPd),
S = as.numeric(S),
Dm = as.numeric(Dm),
FDm = as.numeric(FDm),
W = as.numeric(W),
U = as.numeric(U),
Gm = as.numeric(Gm),
Hm = as.numeric(Hm),
PPn = as.numeric(PPn),
Nm = as.numeric(Nm),
FNm = as.numeric(FNm))

} else {
dates <- unlist(lapply(data.json$SiteRep$DV$Location$Period, function(x){x$value}))
Forecast <-"rbind", lapply(lapply(data.json$SiteRep$DV$Location$Period, function(x){x$Rep}), function(x){"rbind",x)}))
colnames(Forecast)[ncol(Forecast)] <- "Hour"

DateTimes <- seq(ymd_hms(paste0(as.Date(dates[1])," 00:00:00")),ymd_hms(paste0(as.Date(dates[length(dates)])," 21:00:00")), "3 hours")

if(nrow(Forecast) extra_lines <- length(DateTimes)-nrow(Forecast)
for(i in 1:extra_lines){
Forecast <- rbind(rep("0", ncol(Forecast)), Forecast)

ForecastDF <- Forecast %>%
as_tibble() %>%
mutate(Hour = DateTimes) %>%
filter(D != "0") %>%
mutate(F = as.numeric(F),
G = as.numeric(G),
H = as.numeric(H),
Pp = as.numeric(Pp),
S = as.numeric(S),
T = as.numeric(T),
U = as.numeric(U),
W = as.numeric(W))


list(Details, param, ForecastDF)


The API key can be obtained for free at this link:

Once we have an API key we can simply insert the station ID and the type of product we want to obtain the forecast. We can select between two products: daily and 3hourly

To obtain the station ID we need to use another query and download an XML with all stations names and ID:


url = paste0("",key)
XML_StationList <- read_xml(url)

write_xml(XML_StationList, "StationList.xml")

This will save an XML, which we can then open with a txt editor (e.g. Notepad++).

The function can be used as follows:

METDataDownload(stationID=3081, product="daily", key)

It will return a list with 3 elements:

  1. Station info: Name, ID, Lon, Lat, Elevation
  2. Parameter explanation
  3. Weather forecast: tibble format
I have not tested it much, so if you find any bug you are welcome to tweak it on GitHub:

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