journal article·2025-05-13Open access

Designing an Interactive Visual Analytics System for Precipitation Data Analysis

Dong Hyun Jeong, Pradeep Behera, Bong Keun Jeong, Carlos David Luna Sangama, Bryan Higgs, Soo-Yeon Ji

Applied Sciences· Volume 15· Issue 10

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.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Remote Sensing in Agriculture · Meteorological Phenomena and Simulations · Data Visualization and Analytics

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Computer Science and Information Technology, University of the District of Columbia, Washington, DC 20008, USA

Department of Civil Engineering, University of the District of Columbia, Washington, DC 20008, USA

Department of Management and Decision Sciences, Coastal Carolina University, Conway, SC 29528, USA

Department of Computer Science, Bowie State University, Bowie, MD 20715, USA

Funding

National Science Foundation · Army Research Office

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