Generating Weather Forecast Texts with Case Based Reasoning
Several techniques have been used to generate weather forecast texts. In this paper, case based reasoning (CBR) is proposed for weather forecast text generation because similar weather conditions occur over time and should have similar forecast texts. CBR-METEO, a system for generating weather forecast texts was developed using a generic framework (jCOLIBRI) which provides modules for the standard components of the CBR architecture. The advantage in a CBR approach is that systems can be built in minimal time with far less human effort after initial consultation with experts. The approach depends heavily on the goodness of the retrieval and revision components of the CBR process. We evaluated CBRMETEO with NIST, an automated metric which has been shown to correlate well with human judgements for this domain. The system shows comparable performance with other NLG systems that perform the same task.
Code (0)
등록된 구현이 없습니다.
Tasks
RetrievalText GenerationSimilar Papers 제목 키워드 기반
Weather-Informed Probabilistic Forecasting and Scenario Generation in Power Systems
The integration of renewable energy sources (RES) into power grids presents significant challenges due to their intrinsic stochasticity and uncertainty, necessitating the development of new techniques for reliable and ef…
Time SeriesUncertainty quantification for data-driven weather models
Artificial intelligence (AI)-based data-driven weather forecasting models have experienced rapid progress over the last years. Recent studies, with models trained on reanalysis data, achieve impressive results and demons…
Decision MakingUncertainty QuantificationWeather ForecastingFengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting
Kilometer-scale modeling of global atmosphere dynamics enables fine-grained weather forecasting and decreases the risk of disastrous weather and climate activity. Therefore, building a kilometer-scale global forecast mod…
Weather ForecastingZephyrus: An Agentic Framework for Weather Science
Foundation models for weather science are pre-trained on vast amounts of structured numerical data and outperform traditional weather forecasting systems. However, these models lack language-based reasoning capabilities,…
Weather ForecastingWeatherQA: Can Multimodal Language Models Reason about Severe Weather?
Severe convective weather events, such as hail, tornadoes, and thunderstorms, often occur quickly yet cause significant damage, costing billions of dollars every year. This highlights the importance of forecasting severe…
Data Integration