Drawing from a comprehensive and unique dataset encompassing both quantitative and qualitative weather risk measures, the study finds that both numerical and textual representations of weather risk can predict future credit risk, expected stock returns, and firm fundamentals. To explore the textual dimension of weather risk, this pa- per utilizes advanced natural language processing (NLP) techniques, including Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, and leverages Large Language Model (LLM) such as BERT (Bidirectional Encoder Representations from Transformers). To conduct the empirical analysis, this study utilizes Artificial Intel- ligence (AI) using TensorFlow/Keras, Deep Learning (DL), and Machine Learning (ML).