.rst .pdf

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Website

User Guide

Meteogram forecasts of:

• 1. FWI values

• 2. Associated meteorology

• 3. Station-located two-week comparison of observed vs. forecasted FWI values

• 4. Associated meteorology. Solid lines represent forecasts and dashed lines represent observed values.

• A and B can be activated by a double-click on a Desktop computer and single-click on mobile.

• C and D can be activated by selecting the “weather station” layer in the main drop-down menu and clicking on a weather station of interest.

Visualization Steps

Below are the steps to visualizing the data on a leaflet map. The data starts in python as a zarr file and is converted to a workable leafletjs formate.

1. Python’s matplotlib is used to create countourf of each forecast product.

   • reference: https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.contourf.html

   • fwf/json/colormaps.json contains the color schemes, the defined contour levels, and the max/min for each contourf plot.

2. From here geojsoncontour is used to convert the countourf plot to a geojson file.

   • The utility that does this is in within fwf/utils/geoutils.mycontourf_to_geojson

   • reference: https://github.com/bartromgens/geojsoncontour

   • Here is a snippet of the code

   Cnorm = matplotlib.colors.Normalize(vmin= vmin, vmax =vmax+1)
   contourf = plt.contourf(lngs, lats, fillarray, levels = levels, \
                           linestyles = 'None', norm = Cnorm, colors = colors, extend = 'both')
   plt.close()
   
   geojsoncontour.contourf_to_geojson(
       contourf=contourf,
       min_angle_deg=None,
       ndigits=2,
       stroke_width=None,
       fill_opacity=None,
       geojson_properties=None,
       unit='',
       geojson_filepath = f'/fwf/data/geojson/{folderdate}/{geojson_filepath}.geojson')
   

3. Now that the data is in a geojson format it could be added to a leaflet map using a variety of different leaflet extensions. However, the file size is a bit large, at this stage ~ 8 Mb. To help reduce the file size geojsons are converted to topojsons using geo2topo

   • If you quantize the geojosn to a topojson you save a lot on file size

   • I found if you use a quantization count (q) of 1e4, will reduce the geojson file by nearly an order of magnitude and doesn’t take away from the quality of the visualization on leaflet

   • reference: https://github.com/topojson/topojson-server

   • Execute geo2topo quantization on comand line

   geo2topo -q 1e4 path_to_infile/file_YYYYMMDDHH.geojson > path_to_outfile/file_YYYYMMDDHH.json
   

4. Now that the data is in a topojsons its added to leaflet using Leaflet.VectorGrid.Slicer

   • API: https://leaflet.github.io/Leaflet.VectorGrid/vectorgrid-api-docs.html

   • GitHub: https://github.com/Leaflet/Leaflet.VectorGrid

   • An example js code block snippet

   fetch(url, {cache: "default"}).then(function(response){
       return response.json();
   }).then(function(json){
       newLayer.addLayer(L.vectorGrid.slicer( json, {
           minZoom: 2,
           maxZoom: 18,
           rendererFactory: L.canvas.tile,
           vectorTileLayerStyles:{
               'FFMC': geo_json_styler18
                   }
               }
           ).setZIndex(500)
       )
   })};
   

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