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Creating Wind Rose Inset Axes on an OpenStreetMap with Cartopy

• 0:00:02 Introduction: The video builds upon a previous MetPy Monday episode, focusing on visualizing wind data using wind roses plotted as insets on an OpenStreetMap.

• 0:00:21 Recap of Previous Episode: Last week, wind data from four ASOS stations was read using Pandas, and wind roses were created, revealing distinct wind patterns (southwest, north-south, north, and northwesterly) at each station.

• 0:00:49 Creating a Location Dictionary:

  • A dictionary called locations is created to store the longitude and latitude of each station.
  • The dictionary keys are station abbreviations, and the values are lists containing [longitude, latitude].
  • This is done by taking the subset of the station's data frame and retrieving only the first value by longitude and latitude.

• 0:02:37 Utilizing OpenStreetMap: OpenStreetMap (OSM), a free and open-source map, is used as the base map (similar to Google Maps).

• 0:03:24 Required Libraries:

  • cartopy.crs (as ccrs): For coordinate reference systems.
  • cartopy.io.img_tiles (as cit): To fetch map tiles from OSM.
  • matplotlib.axes._axes (as inset_axes).

• 0:04:14 Defining Map Boundaries: Minimum/maximum longitude and latitude values (minlon, maxlon, minlat, maxlat) are defined for the Denver metro area.

• 0:04:36 Creating the Map:

  • A Plate Carrée projection (ccrs.PlateCarree()) is defined.
  • A figure and main axes are created using Matplotlib, with the projection set to Plate Carrée.
  • The map extent is set using set_extent with the defined boundaries.
  • Gridlines with labels are added.
  • OSM tiles are fetched using cit.OSM() and added to the main axes using add_image. A scale of 10 is chosen for metropolitan area detail.

• 0:07:48 Adding Wind Roses as Insets:

  • A height_in_degrees variable (0.1 degrees) is defined to control the size of the wind roses.
  • A loop iterates through each station in the stations list.

• 0:08:22 Inside the Loop (Per Station):

  • Longitude and latitude are retrieved from the locations dictionary.
  • The relevant station's data is extracted from the DataFrame.
  • An inset axes (WindroseAxes) is created using inset_axes within the main axes:
    • Width and height are set to 100%.
    • The bounding box is anchored using the station's longitude and latitude, adjusted by height_in_degrees.
    • The transform is set to main_ax.transData to use the main axes' data coordinates.
    • axes_class is set to windrose.WindroseAxes.
  • The wind rose is plotted on the WindroseAxes using bar, similar to the previous episode, with red color and the station name as the title.
  • Unnecessary tick labels (left and bottom) are removed using WindroseAxes.tick_params to clean up the visualization.

• 0:12:30 Final Result: The code produces a map with wind roses plotted as insets at their respective station locations on the OpenStreetMap, providing a clear geographical context for the wind patterns.

• Key Takeaways

  • This method leverages the ability to plot a cartopy map using image tiles.
  • Combining different libraries (Pandas, MetPy, Cartopy, Matplotlib) allows for powerful and informative data visualization.
  • Inset axes are a useful technique for embedding plots within other plots, providing context and detail.
  • Open-source tools like OpenStreetMap offer readily available map data for geographical visualizations. *Careful control of axis properties and transforms is crucial for accurate placement and scaling of inset plots.