Weather Forecast from MET Office

February 16, 2019
By

(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
library("plyr")
library("dplyr")
library("lubridate")
connectStr <- paste0("http://datapoint.metoffice.gov.uk/public/data/val/wxfcs/all/json/",stationID,"?res=",product,"&key=",key)

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

#Station
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)
#Parameters
param <- do.call("rbind",data.json$SiteRep$Wx$Param)

#Forecast
if(product == "daily"){
dates <- unlist(lapply(data.json$SiteRep$DV$Location$Period, function(x){x$value}))
DayForecast <- do.call("rbind", lapply(data.json$SiteRep$DV$Location$Period, function(x){x$Rep[[1]]}))
NightForecast <- do.call("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$value}))
Forecast <- do.call("rbind", lapply(lapply(data.json$SiteRep$DV$Location$Period, function(x){x$Rep}), function(x){do.call("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:
https://www.metoffice.gov.uk/datapoint/api

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:

library(xml2)

url = paste0("http://datapoint.metoffice.gov.uk/public/data/val/wxfcs/all/daily/sitelist?key=",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:
https://github.com/fveronesi/METOfficeForecast

To leave a comment for the author, please follow the link and comment on their blog: R tutorial for Spatial Statistics.

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