AI-Enhanced Meteorological Data Retrieval Systems for Improved Forecast Operations

Meteorological data are vast and complex, and their rapid and accurate retrieval is essential for forecasting operations. However, traditional systems have struggled with limited search accuracy and inefficient processing speeds, hindering effective forecast support. To address these challenges, this study developed an AI-based system capable of performing speech recognition, URL search, extreme value detection, and local forecast error analysis. In speech recognition, the Whisper-large model achieved a character error rate (CER) of 3.19%, with GPU memory usage reduced by 15.7% and inference time by 38.18%, enabling real-time processing and scalability in multi-GPU environments. The URL search systems translated natural language inputs into SQL queries and URLs, achieving a Mean Reciprocal Rank (MRR) of 0.92, thereby enhancing data retrieval precision. The extreme value detection systems utilized GPT-4-based template augmentation to expand training data by approximately 111%, significantly improving detection performance and search accuracy. For local forecast error analysis, a prototype chatbot was implemented using prompt engineering and a Text-to-SQL model, allowing for the automated identification of inconsistencies in local forecasts and streamlining the analysis process. These systems have substantially enhanced operational workflows across meteorological tasks, facilitating rapid data retrieval through voice commands, precise responses to complex queries, and real-time analytical support. Future research will focus on further refining these technologies to tackle a wider range of meteorological challenges and integrate them into global forecasting systems for enhanced accuracy and reliability.