# Designing an Interactive Visual Analytics System for
###### Abstract  
As precipitation analysis reveals critical statistical characteristics, temporal patterns, and spatial distributions of rainfall and snowfall events, it plays an important role in planning urban drainage systems, flood forecasting, hydrological modeling, and climate studies. It helps engineers design climate-resilient infrastructure capable of withstanding extreme weather events, which is becoming increasingly important as precipitation patterns change over time. With precipitation analysis, multiple valuable information can be determined, such as storm intensity, duration, and frequency. To enhance understanding of precipitation data and analysis results, researchers often use graphical representation methods to show the data in visual formats. Although existing precipitation analysis and basic visual representations are helpful, it is critical to have a comprehensive analysis and visualization system to detect significant patterns and anomalies in high-resolution temporal precipitation data more effectively. This study presents a visual analytics system enabling interactive analysis of hourly precipitation data across all U.S. states. Multiple coordinated visualizations are designed to support both single and multiple-station analysis. These visualizations allow users to examine temporal patterns, spatial distributions, and statistical characteristics of precipitation events directly within visualizations. Case studies demonstrate the usefulness of the designed system by evaluating various historical storm events. p 20  
management models [5]. Precipitation occurs as a sequence of random meteorological events characterized by varying amounts of rainfall, duration, intensity, and inter-event time [6]. Thus, the analysis their distribution over time and understanding of these rainfall characteristics are crucial for developing effective stormwater management systems [7; 8]. Across the United States, many regions are experiencing significant alterations in rainfall frequency and intensity, necessitating more sophisticated analytical approaches [9; 10]. In this paper, we present an innovative interactive visual analytics system to address the critical challenge of analyzing shifting precipitation patterns in a changing climate. The National Oceanic and Atmospheric Administration (NOAA) provides collected hourly precipitation data that serve as a critical resource for scientists, hydrologists, and climate researchers [11]. This comprehensive dataset is broadly used to analyze precipitation patterns and evidence-based decision making for water resource management. However, as no single data source has existed to provide continuous hourly precipitation records for over a century, we propose a new way to build a comprehensive dataset by integrating various data sources, addressing missing values, and resolving duplicate records. With this dataset, we designed a precipitation analysis system to enable comprehensive storm event characterization based on inter-event time definition (IETD) methodology. It provides multiple coordinated visualizations that support revealing temporal patterns, spatial distributions, and statistical characteristics of precipitation events. This approach is often referred to as coordinated multiple views (CMVs) [12] and is effective in climate data analysis [13]. The multiple coordinated visualizations enable users to make comparisons, identify correlations, and gain a more comprehensive understanding of precipitation data. This designed interactive visual analytics system is particularly valuable because it supports detailed analysis for identifying storm intensity, duration, and frequency. In addition, it can detect precipitation trends and possible anomalous precipitation events. Because of these multiple features, the system may provide critical information for designing efficient drainage systems, supporting flood forecasting, and informing hydrologic modeling through detailed precipitation analysis. It also allows users to download precipitation data for further analysis.