Use Case - Ogimet database

Ogimet - download and visualize wind patterns over Svalbard

1. Downloading hourly data from the Ogimet repository for the defined time frame (2018/01/01-2018/12/31); chosen station: Svalbard Lufthavn
2. Using external package ‘openair’ to visualize the downloaded results

library(climate)
# downloading data
df <- meteo_ogimet(interval = "hourly",
                     date = c("2018-01-01", "2018-12-31"), 
                     station = "01008")

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# loading external packages:
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(openair) # external package for plotting wind roses

# converting wind direction from character into degress required by most 
wdir <- data.frame(ddd = c("CAL","N","NNE","NE","ENE","E","ESE","SE","SSE",
                           "S","SSW","SW","WSW","W","WNW","NW","NNW"),
                   dir = c(NA, 0:15 * 22.5), stringsAsFactors = FALSE)
# changing date column to the format required by openair package:
df$Date <- as.POSIXct(df$Date, tz = "UTC")
df$date <- df$Date
df <- left_join(df, wdir)

## Joining with `by = join_by(ddd)`

df$ws <- df$ffkmh / 3.6 # conversion to m/s from km/h
df$gust <- df$Gustkmh / 3.6 # conversion to m/s from km/h
windRose(mydata = df, ws = "ws", wd = "dir", type = "season", paddle = FALSE, 
         main = "Svalbard Lufthavn (2018)", ws.int = 3, dig.lab = 3, layout = c(4, 1))

# do we miss any data?
summaryPlot(df[ ,c("date", "TC", "ws", "gust")])

# which sectors are responsible for warm/cold air mass advection:
polarPlot(df, pollutant = "TC", x = "ws", wd = "dir", k = 50, force.positive = FALSE, 
          type = "season", layout = c(4, 1), resolution = "fine",  normalise = FALSE)