This blog provides a brief overview of the patterns identified in monthly rainfall data for four key weather stations: Belfast, Dublin Airport, Cork Airport, and University College Galway. By examining the trends and variations in precipitation, we aim to uncover insights into regional rainfall behavior over time.
The rainfall dataset used in this analysis has been generously provided by Conor Murphy and Dr. Simon Noone. It is part of a comprehensive collection from 25 weather stations across the island of Ireland, measuring precipitation levels over various time periods.
This blog will also explain the data preparation and the R code used to analyse and visualise the rainfall patterns, making the workflow reproducible and accessible for readers interested in climate data analysis.
Let´s dive in and explore the rainfall trends across these four locations. We load in the data and check the structure.
Classes 'tbl_df', 'tbl' and 'data.frame': 25 obs. of 9 variables:
$ Station : chr "Athboy" "Foulksmills" "Mullingar" "Portlaw" ...
$ Elevation : int 87 71 112 8 131 49 14 45 15 62 ...
$ Easting : num 270400 284100 241780 246600 319700 ...
$ Northing : num 261700 118400 247765 115200 186000 ...
$ Lat : num 53.6 52.3 53.5 52.3 52.9 ...
$ Long : num -6.93 -6.77 -7.37 -7.31 -6.22 -8.1 -9.06 -8 -8.29 -6.64 ...
$ County : chr "Meath" "Wexford" "Westmeath" "Waterford" ...
$ Abbreviation: chr "AB" "F" "M" "P" ...
$ Source : chr "Met Eireann" "Met Eireann" "Met Eireann" "Met Eireann" ...This initial view of the data structure shows us there are 25 observations and 9 variables across the stations, such as elevation, easting and northing,latitude and longitude, county, abbreviation and source, which for the majority is Met Eireann.
To begin, we first check whether the necessary packages dygraph for creating interactive plots and dplyr for data manipulation - are already installed. If they are not, the code automatically installs them, ensuring that everything is set up for the next steps.
We then load the packages into the R session to make them available for use.
Next up, the code takes the rainfall data and groups it by year and month, adding up the rainfall values for each combination. It then creates a time series object (rain_ts) starting in January 1850 with monthly data.
We must filter the four stations we are seeking to analyse more closely from the 25 in order to plot them. To do this we filter using the following code:
rain %>% filter(Station=="Dublin Airport") %>%
summarise(Rainfall=sum(Rainfall),.by=c(Year,Month)) %>% pull(Rainfall) %>%
ts(start=c(1850,1),freq=12) -> dub_ts
rain %>% filter(Station=="Belfast") %>%
summarise(Rainfall=sum(Rainfall),.by=c(Year,Month)) %>% pull(Rainfall) %>%
ts(start=c(1850,1),freq=12) -> bel_ts
rain %>% filter(Station=="Cork Airport") %>%
summarise(Rainfall=sum(Rainfall),.by=c(Year,Month)) %>% pull(Rainfall) %>%
ts(start=c(1850,1),freq=12) -> cork_ts
rain %>% filter(Station=="University College Galway") %>%
summarise(Rainfall=sum(Rainfall),.by=c(Year,Month)) %>% pull(Rainfall) %>%
ts(start=c(1850,1),freq=12) -> galway_ts
beldubcorkgalway_ts <- cbind(bel_ts,dub_ts,cork_ts,galway_ts)
window(beldubcorkgalway_ts,c(1850,1),c(1850,7)) bel_ts dub_ts cork_ts galway_ts
Jan 1850 115.7 75.8 155.3 108.9
Feb 1850 156.4 112.0 359.5 163.8
Mar 1850 157.2 80.3 216.2 174.9
Apr 1850 107.2 74.7 191.3 152.8
May 1850 116.2 101.1 157.0 133.1
Jun 1850 16.1 11.3 9.9 33.9
Jul 1850 72.5 58.4 174.9 111.9We can now view the time series for all four stations on the same interactive, dynamic and zoomable line plot.
beldubcorkgalway_ts %>% dygraph(width=960,height=360) %>%
dyRangeSelector()The range selector here allows us to zoom in for a look at certain time periods within the broader range of 1850-2015. Try narrowing in on the year 1900 and compare the graphs for Belfast, Dublin, Cork and Galway. Cork is recording the highest precipitation measurements, and Dublin the lowest, with all four stations depicting the same pattern in fluctuations.
We can also view the same information on four separated, interactive line plots.
library(htmltools)
browsable(tagList(
dygraph(dub_ts, main = "Dublin") %>% dyRangeSelector(),
dygraph(bel_ts, main = "Belfast") %>% dyRangeSelector(),
dygraph(cork_ts, main = "Cork") %>% dyRangeSelector(),
dygraph(galway_ts, main = "Galway") %>% dyRangeSelector()
))An analysis of the monthly rainfall data from Belfast, Dublin Airport, Cork Airport and University College Galway reveals a clear and consistent seasonal trend: Higher rainfall during winter months and lower rainfall in the summer months. This pattern holds true across all four weather stations, indicating a strong correlation in precipitation behaviour throughout the island of Ireland.
During these months, the rainfall levels peak, with all stations consistently recording their highest monthly averages. This increase in precipitation is likely due to the influence of the North Atlantic weather systems, which bring frequent storms and wet conditions to the region.
In contrast, the summer months show a marked decrease in rainfall. The data indicates relatively drier conditions, which align with the typical high-pressure systems that dominate during this time, resulting in fewer rain-bearing weather fronts.
Despite slight variations in the magnitude of rainfall between stations - such as slightly higher levels in the western station of University College Galway - the overall patterns remain consistent. This uniformity underscores the shared climatic influences affecting the island, such as prevailing westerly winds and the moderating impact of the Atlantic Ocean.
A closer inspection of the data also reveals a noticeable rise in the extremes of rainfall measurements toward the latter part of the time series. This suggests a growing variability in precipitation patterns, which may be associated with broader changes in climatic conditions.
The consistent seasonal patterns of rainfall have significant implications for various sectors, including agriculture, water resource management and urban planning. Understanding these trends is crucial for anticipating seasonal water availability and mitigating the risks associated with winter flooding or summer droughts.
In summary, the rainfall data from Belfast, Dublin Airport, Cork Airport, and University College Galway demonstrates a shared seasonal rhythm, characterised by wetter winters and drier summers. This consistency highlights the broader climatic forces shaping Ireland’s rainfall patterns, providing a valuable foundation for further analysis and climate adaptation strategies.
Thanks for reading!