Wednesday April 26th: Creating Interactive Plots to Display Weather Data Using the Holoviz/Geoviews Libraries


Pangeo Showcase Talk by Michael Barletta at SUNY Albany
I am a senior meteorology student at SUNY Albany with a passion for creating weather plots in python.
Using interactive maps to display and analyze meteorological data is crucial for understanding weather patterns and making informed decisions. It is an avenue that generally, has been seldom explored or made widely available. Interactive maps allow users to visualize and explore data in a more engaging and interactive way, providing a better understanding of the spatial distribution of meteorological phenomena. These maps can display real-time weather data, such as temperature, precipitation, wind direction and speed, air pressure, and other important metrics. Additionally, interactive maps can incorporate various layers of information that can provide a more comprehensive view of data. Overall, the use of interactive maps for meteorological data analysis is a valuable tool for researchers, forecasters, and the general public to better understand and prepare for weather events.

Using Python and the powerful Geoviews, Datashader, Panel and Bokeh libraries, I have developed interactive maps that display various meteorological variables across the United States. The maps I created plot ASOS reports, which provide information on precipitation type (including MPING), wind gusts, precipitation, and temperature. The interactive maps I developed offer an engaging and interactive way to visualize and explore this data. One of the significant advantages of the interactivity allowed with these libraries is it allows users to go back to each hour over the last day. I utilized the Datashader library to be able to add gridded numerical model data into Geoviews as well. This allows users to be able to zoom into only areas that they’re interested in when looking at a modeled forecast. I also developed an ERA-5 reanalysis viewer, which allows users to select any date in the ERA dataset and display any variable for that day. Overall, the maps and tools I developed using Python, Geoviews, Datashader, Panel and Bokeh libraries provide a powerful and flexible platform for analyzing meteorological data. They offer an engaging and interactive way to explore meteorological variables and can be used by researchers, forecasters, and anyone interested in weather to analyze and interpret past and future data in a more meaningful way.