# Designing an Interactive Visual Analytics System for
## 2 Previous Work  
Analyzing precipitation data is essential in water systems and climate research to understand trends, patterns, and extremes of climate change. Many studies have been proposed to investigate different methodologies and applications of precipitation data analysis in different contexts. Precipitation studies encompass a wide range of topics, such as climate change, hydrometeorology, agricultural resource planning, urban flooding, and stormwater management. In this paper, we focus on three of the key areas of research in precipitation: predictive modeling, long-term pattern analysis, and event-specific analysis. The first stream of research focuses on developing models to predict future precipitation by applying machine learning and statistical methods. Researchers have attempted to generate various precipitation forecasts based on various environmental and meteorological factors. For example, [PERSON] [14] introduced a rainfall forecasting system by applying softmax regression. They analyzed historical weather data with the system to predict the probability of class membership by categorizing them into one of five classes--light, moderate, heavy, violet, or no rainfall--based on the maximum likelihood. The proposed method was tested using weather data collected from 11 cities in Myanmar for 2018 and 2022. The result showed that the system can predict an 83% accuracy rate in rainfall prediction. [PERSON] et al. [15] integrated a high-resolution weather research and forecasting (WRF) model to support pluvial flood forecasting. Various spatial resolution, cumulus parameterization schemes, and forecast lead times were evaluated and then used as input in a rainfall threshold and 1D MIKE urban drainage model for the accuracy of flood forecasts. They found that rainfall estimate and flood severity classification results differ depending on the choice of configurations, such as neighborhood size, forecast horizon, and cumulus parameter. However, in general, increasing the resolution of WRF did not improve flood forecast accuracy. This finding suggests that trade-offs should be made regarding resolution, computational demands, and forecast accuracy to improve early warning systems. [PERSON] et al. [16] introduced an innovative approach to estimating probable maximum precipitation (PMP) in the context of climate change. This methodology integrates storm transposition techniques and the pseudo-global warming method in the weather research and forecasting (WRF) model framework. Storm transposition techniques simulate how a storm might behave if it occurred in a different geographic context to assess potential impacts. The pseudo-global warming method applies projected climate change perturbations to historical atmospheric conditions so that the system can assess how storms might behave under warming scenarios. By integrating these two methods, this study aims to dynamically simulate extreme precipitation events under various climate change scenarios. Based on two case studies, the authors demonstrate that the proposed WRF-based methodology, combined with storm transposition and the pseudo-global warming method, provides a consistent framework for estimating PMP under climate change and provides actionable insights. Traditional statistical methods often struggle to capture complex, nonlinear characteristics from precipitation data. To address this challenge, [PERSON] et al. [17] investigated the application of advanced sequential deep learning models, such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, in monthly precipitation prediction using all-India monthly average precipitation for the period 1871-2016. They found that both RNN and LSTM models captured temporal patterns effectively. They also determined that the RNN model required more computational resources and had higher training complexity. As a result, the LSTM model was recommended for more efficient and accurate precipitation forecasting across various regions in India. [PERSON] et al.