Visualizing datasets like temperature, wind, and precipitation on maps provides crucial context for understanding global weather patterns and earth surface phenomena. The National Oceanographic and Atmospheric Administration (NOAA) offers extensive observational and modeled data, but translating this information into compelling cartographic visualizations can be complex. This guide demonstrates how to leverage NOAA's data using the Felt API and Python libraries within a Jupyter notebook environment, focusing on creating visualizations such as global wind gusts from the Global Forecast System (GFS) model. The process involves accessing data via the NOAA NOMADS portal, handling data ephemeral challenges, and employing techniques like color mapping for effective visual representation. The tutorial also highlights using tools like rasterio for data handling and matplotlib for visualization, ultimately facilitating the creation of detailed maps on Felt's platform, which supports annotating atmospheric features. Despite the technical challenges, the approach aims to simplify the process of integrating weather data into visual maps, with Felt planning to enhance these capabilities in the future.