In bczernecki/climate: Interface to Download Meteorological (and Hydrological) Datasets

knitr::opts_chunk$set(echo = TRUE)

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")

# loading external packages:
library(dplyr)
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)

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)

bczernecki/climate documentation built on April 12, 2025, 9:44 a.m.