# Designing an Interactive Visual Analytics System for
## 5 Interactive Precipitation Data Analysis System
### Station-Specific Analysis Interface
#### 5.1.5 Seasonal and Monthly Precipitation Analysis  
Precipitation patterns often vary significantly by month or season. As supporting seasonal and monthly precipitation analyses are important for identifying unique precipitation patterns, we added four visualizations: seasonal PCA analysis, monthly precipitation average analysis, annual precipitation cycle analysis, and monthly precipitation distribution analysis (Figure 7). The seasonal PCA analysis visualization (Figure 7A,B) utilizes principal component analysis (PCA) to identify underlying patterns in the precipitation data. PCA captures the main variations in the dataset by determining principal components that represent the directions of maximum variance. This technique is valuable for projecting high-dimensional data into a lower-dimensional space while preserving key relationships. For this analysis, we extract seven features from the precipitation data: mean precipitation volume, standard deviation of precipitation volumes, mean event duration, standard deviation of durations, temporal spread of events (standard deviation of days of the year), proportion of heavy precipitation events (using the 75 th percentile threshold), and proportion of wet days. These features characterize each season's precipitation by its volume (\(\mathcal{S}\)), event durations (\(\Delta t\)), and intensities (\(\mathcal{S}/\Delta t\)). Figure 7: Visualizations of seasonal analysis with principal component analysis (PCA) plots (**A**,**B**) and annual precipitation cycle analyses (**C**,**D**). In the visualization, the first and second principal components are used for the axes of the scatterplot, with distinct shapes representing different seasons (circle: spring, rectangle: summer, triangle: fall, and diamond: winter) and colors indicating years. An interactive color legend is designed as a gradient color bar from dark purple (earlier years) to bright yellow (recent years), allowing users to highlight specific years of interest. Figure 7B shows an example where the user has highlighted seasonal precipitation averages for 2014. PCA visualization reveals that numerous precipitation events create a cluster that appears on the left bottom of the scatterplot, with several potential outliers positioned far from this cluster. Notably, spring 2014 and fall 2013 precipitation data deviate significantly from the typical pattern, indicating anomalous behavior. This finding aligns with the result in the annual maximum precipitation analysis (see Figure 5C), denoting unusually high-intensity precipitation events during 2013 and 2014. Figure 7C,D present annual precipitation cycle visualization designed to reveal distinct seasonal patterns in the daily precipitation data. As seasonal precipitation cycle analysis is crucial for understanding flooding distribution [61], this visualization depicts mean precipitation values throughout the year, with data points scaled by sample size to indicate observation density. By default, it shows the measured daily cumulative average precipitation for all precipitation data in a selected weather station. It allows users to select data for specific years using a drop-down menu. Different color attributes are used to display seasonal precipitation data to support seasonal trend analysis. Winter precipitation patterns are analyzed in two segments (early winter: January and February, and late winter: December) to represent the cyclic nature of the annual pattern more clearly. To highlight gradual changes in precipitation throughout the year, smooth transition lines between seasons are added using cubic basis spline interpolation. Specifically, Figure 7C represents the annual cycle of 75 years of precipitation data. It shows that the precipitation intensity ranges from 0.13 to 1.46 inches, with the lowest average precipitation intensities occurring in spring and the highest in summer. It also reveals relatively stable precipitation amounts over the past 75 years.