An Integrated Search System for K orea Weather Data (2023)

Abstract: We introduce WeatherSearch, an integrated search system deployed at the Korea Meteorological Administration (KMA). WeatherSearch enables users to retrieve all the relevant data for weather forecasting from a massive weather database with simple natural language queries. We carefully design and conduct multiple expert surveys and interviews for template creation and apply data augmentation techniques including template filling to collect 4 million data points with minimal human labors. We then finetune mT5 on the collected dataset and achieve an average MRR of 0.66 and an average Recall of 0.82. We also discuss weather-data-specific characteristics that should be taken into account for creating such a system. We hope our paper serves as a simple and effective guideline for those designing similar systems in other regions of the world.

## introduction weather forecasting is an important task that involves predicting future weather conditions based on current and past meteorological observations. accurate weather forecasting not only impacts our daily lives but also plays a crucial role in potentially saving lives and resources during natural disasters. in the case of south korea, the diversity of weather phenomena (due to its three-sided coastline and approximately 70% of the land consisting of mountainous areas) increases the significance and challenges of weather forecasting. meteorological experts rely on two main types of data sources for weather forecasting. the first is the comprehensive meteorological information system (comis), which provides access to radar images, cloud images, satellite imagery, and other relevant data. comis has a structure similar to a typical website, featuring a hierarchical tree structure with select boxes, drop-down menus, and other interactive elements. navigating through this structure often requires multiple clicks to arrive at the final target information, and it can be time-consuming, especially when the exact location of the data is unknown. as a result, most experts tend to stick to the pages they are familiar with and rarely explore other pages and this limitation hinders the effective utilization of diverse weather data. the second data source is the korea meteorological administration (kma) database, which stores past weather observation data such as temperature, humidity, wind speed, and precipitation. the kma database contains an incredibly extensive volume of data. its one-minute interval data for 600 unique stations makes 900,000 data points per day, covering roughly 40 years since the 1980s. to retrieve the data from such a massive database, proficiency in using sql queries is necessary. however, many meteorological experts, particularly newcomers unfamiliar with weather database and senior professionals who struggle with programming languages, face difficulties in using sql queries. consequently, they result in spending a significant amount of unnecessary time constructing queries or resort to using only basic sql queries. in this paper, we introduce weathersearch, an integrated search system that allows the users to access to all the necessary weather data from the comis and the database through natural language queries. to the best of our knowledge, there are currently no existing search models specifically tailored to the meteorological field and the corresponding training data. our focus has been on constructing datasets that actively incorporate the opinions of industry experts. subsequently, using the constructed datasets, we fine-tune a pretrained mt5 model @xcite to map each natural language query to a structured form. we construct two domain-specific datasets: (1) a natural language query-sql query dataset and (2) a keyword-url dataset. to collect the sql dataset, we conduct multiple expert surveys and interviews targeting 24 experts to gather responses. based on these responses, we manually create question templates and corresponding sql query templates. subsequently, various techniques, including template filling @xcite , are applied to cover the entire scope of the database, resulting in the final dataset. the url dataset is collected by crawling all possible urls from the comis and tagging them with corresponding keywords. we preprocess the keywords to make them similar to the actual search keywords by applying useful techniques. through the deployment of weathersearch to real-world meteorological experts, we anticipate the following contributions: through the disclosure of our methodology, we hope to offer support to those seeking to create similar systems for different regions or languages. machine learning for weather machine learning techniques have been increasingly applied in the meteorological domain. several notable applications of machine learning in meteorology include weather prediction (pangu-weather (bi et al., 2022), graphcast (lam et al., 2022)), extreme weather event detection (extremeweather (racah et al., 2016), climatenet (kashinath et al., 2021)), climate modeling (metnet (sønderby et al., 2020)), and data analysis. however, a single machine learning model capable of efficiently querying vast amounts of databases and websites to quickly access weather data is currently lacking. semantic parsing semantic parsing is a fundamental task that involves mapping natural language expressions to structured representations. it encompasses various applications, including sql query generation and code generation. notable models in sql query generation include seq2sql @xcite , spider @xcite , cosql @xcite , and unite @xcite , which have demonstrated advancements in accurately generating sql queries from natural language inputs. on the other hand, in the code generation, alpha-code @xcite , synchromesh @xcite , and coderl @xcite have emerged as prominent approaches, showcasing their ability to transform natural language instructions into executable code. we apply semantic parsing to the generation of sql queries and urls (structured representation) in the weather domain. ## data collection we construct domain-specific datasets necessary to train the search system. as mentioned in §section 1, weather data comes from two different sources, and each source has its own structured query for accessing data. for this reason, we build separate datasets for each source. one is the sql dataset, which comprises pairs of natural language queries and corresponding sql queries ( §section 3.1). the other is the url dataset, which consists of pairs of natural language keywords and corresponding urls ( §section 3.2). ## conclusion we introduces weathersearch, a model specifically designed for searching on korean weather data. to develop the model, we construct two datasets tailored to the weather domain and finetune mt5 on the two datasets. these datasets effectively incorporate the expertise and feedback from the weather experts, making the model directly applicable to real-world scenarios. the experimental results demonstrate sufficiently accurate performance for deployment across various metrics. future work includes conducting human evaluation for qualitative assessment of the system. weathersearch is expected to provide valuable assistance in accessing and utilizing weather data to the weather experts, ultimately improving decisionmaking process and productivity for weather forecasting.