# 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
However, when evaluating the precipitation data yearly, we found a significant precipitation intensity fluctuation in 2013 (see Figure 7D). This finding indicates a substantial deviation from historical precipitation patterns in the region compared to those observed before 2000. Monthly precipitation analysis is also supported in the interface. It provides insights into precipitation patterns at a monthly temporal scale. Figure 8A,B show monthly precipitation analysis visualization without and with error bars, respectively. It creates cumulative monthly averages. Figure 8A shows the overall monthly average across the entire precipitation data in the selected weather station. As we applied the IETD analysis, monthly average precipitation was computed on both the original hourly and IETD precipitation events data. The visualization displays the measured monthly average precipitation as a connected polyline. This representation supports a comparative understanding between raw precipitation measurements and IETD-derived precipitation events and helps users determine how the IETD analysis affects the precipitation patterns. When raw and IETD-processed precipitation patterns show substantial differences, it suggests periods of persistent rainfall characterized by short intervals between events, which the IETD analysis combines into unified precipitation events. This visualization incorporates interactive features that enable users to toggle between different temporal views, from the comprehensive multi-year average to specific yearly patterns. Measuring errors and representing them with error bars is supported by three error measures such as standard deviation (SD), standard error (SE), and confidence interval (CI). As SD shows the spread of all values, it is good for identifying how much precipitation is spread. SE shows uncertainty in the mean estimate. CI represents the range where the true mean likely falls under a confidence range (95%confidence). Figure 8B shows an example of showing SD on the selected year of 2013. The measured SD and sample size information are presented. This flexibility of controlling and changing the visualization options helps identify both long-term precipitation trends and anomalous years. Boxylots are widely used for analyzing precipitation data because they effectively reveal temporal patterns [62; 63; 64]. Thus, a boxplot visualization is added to present monthly precipitation data distributions, following the traditional boxplot design of having a box and two whiskers. All precipitation data are broken up into three quartiles (Q1, Q2, and Q3) in the box. Q1 and Q3 indicate lower and upper quartiles representing 25% and 75% of data falls below this value, respectively. Q2 represents the median value, denoting the 50 th percentile of data. It shows the interquartile range (IQR) by measuring the difference between Q3 and Q1, depicting the spread of the data. With IQR, the upper and lower whiskers are determined to represent the extension of the largest value within 1.5 \(\times\) IQR above Q3 and the smallest value within 1.5 \(\times\) IQR below Q1. The upper and lower whiskers extend to the largest and smallest values within 1.5 \(\times\) IQR from Q3 and Q1, respectively. Figure 8C shows monthly precipitation distributions at Reagan National Airport through boxplots. By default, monthly boxplots are generated by analyzing all available precipitation data. Users are allowed to change the distribution to a specific year. Figure 8D presents monthly boxplots for 2013. As discussed above, a possible weather pattern shift was observed in that weather station around 2013. The boxplot distributions reveal significantly higher precipitation volumes in March, July, and October, denoting a distinct difference from historical patterns. With the station-specific analysis interface, several precipitation data analyses can be performed, including temporal pattern analysis, trend examination, seasonality studies, and anomaly detection at a specific location.