Introduction

Rainfall patterns play a crucial role in understanding Ireland’s climate, which is known for its variability and abundant precipitation. This project examines monthly rainfall trends from four prominent weather stations across the country: Belfast, Dublin Airport, University College Galway, and Cork Airport. These stations represent a diverse geographical spread, capturing rainfall characteristics across northern, eastern, western, and southern Ireland.

Using an interactive dygraph, we visualize rainfall data from January 1850 to December 2014, providing a dynamic tool for exploring temporal trends. The dygraph allows users to analyze both seasonal variations and long-term trends while offering a RangeSelector control, enabling simultaneous adjustments of the time window for all four stations. This feature is particularly useful for comparing rainfall patterns across regions over specific periods, shedding light on the spatial and temporal variability of rainfall in Ireland.


Data Overview

The rainfall dataset encompasses over a century and a half of monthly rainfall measurements from multiple weather stations across Ireland, spanning from January 1850 to December 2014. It serves as a valuable resource for analysing historical precipitation patterns and understanding how they differ across regions and time periods.

The dataset contains the following key columns:

This project focuses on the four selected stations: Belfast, Dublin Airport, University College Galway, and Cork Airport, chosen for their geographical diversity and significance in representing Ireland’s climate regions.


Explanation of the dygraph’s code

# Loading Libraries and Data

library(dygraphs)

library(xts)

library(dplyr)

library(sf)

library(tidyr)

library(htmlwidgets)

library(lubridate)

setwd("~/Desktop/GY672[A] -  R")

#Load rainfall data

load("rainfall.RData")

#Define the stations of interest

selected_stations <- c("Dublin Airport", "University College Galway", "Belfast", "Cork Airport")

#Filter rainfall data for the selected stations

filtered_data <- rain %>%
  filter(Station %in% selected_stations) %>%
  mutate(
    Date = make_date(Year, match(Month, month.abb), 1)
  )

#Pivot the data to wide format for dygraph

rainfall_xts <- filtered_data %>%
  select(Date, Station, Rainfall) %>%
  pivot_wider(names_from = Station, values_from = Rainfall) %>%
  as.data.frame()

#Convert the dataframe to an xts object

rainfall_xts <- xts(rainfall_xts[, -1], order.by = rainfall_xts$Date)

#Create the dygraph

# Create the dygraph
rainfall_dygraph <- dygraph(rainfall_xts, main = "Monthly Rainfall in Ireland, 1850-2014") %>%
  dyAxis("y", label = "Rainfall (mm)") %>%
  dySeries("Belfast", color = "#00BFFF") %>%
  dySeries("Dublin Airport", color = "#008B00") %>%
  dySeries("University College Galway", color = "#AB82FF") %>%
  dySeries("Cork Airport", color = "#CD2626") %>%
  dyRangeSelector()

#Render the dygraph

print(rainfall_dygraph)

#Save the dygraph as an HTML file

library(htmlwidgets)

saveWidget(rainfall_dygraph, "rainfall_dygraph.html", selfcontained = TRUE)

Interactive Dygraph

Click here to view the interactive dygraph showing monthly rainfall trends.

Observations and Patterns

Seasonal Patterns

The dygraph indicates clear seasonal fluctuations in rainfall levels. Rainfall tends to peak during the winter months (November–January), particularly in stations such as Cork Airport and University College Galway, while it drops to lower levels during the summer months (May–August).

Regional Variations

Year-to-Year Variations

The dygraph also shows notable interannual variability. While some years experience consistent rainfall across all stations, others show marked differences, such as particularly high peaks at Cork Airport. Periods of extreme rainfall are more frequent in the earlier part of the time series (pre-1900).

Conclusions

The interactive dygraph reveals distinct seasonal and regional rainfall patterns across the four stations, underscoring Ireland’s diverse precipitation characteristics. The ability to dynamically adjust the time window highlights the importance of long-term observations for identifying trends and extremes. The analysis emphasises Cork Airport’s tendency for heavy rainfall events and Dublin Airport’s drier conditions, providing valuable insights into Ireland’s climate variability.


This project is submitted as part of module GY672[A] - Analysing Spatial and Temporal Data Using R (2024-25: Semester 1), Maynooth University.