Hardware-accelerated geospatial data plotting in the browser
Lots of meteorological data web sites have a model where the data live on a central server, get plotted on the server, and then the server serves static images to the client. This creates a bottleneck where adding fields and view sectors takes exponentially more processing power for the server. One way around this is to offload the plotting to the client and to have the browser plot the data on a pan-and-zoomable map. Unfortunately, in the past, this has required developing low-level plotting code, and depending on the mapping library, the performance may be poor.
autumnplot-gl provides a solution to this problem by making hardware-accelerated data plotting in the browser easy. This was designed with meteorological data in mind, but anyone wanting to contour geospatial data on a map can use autumnplot-gl.
autumnplot-gl is designed to be used with either Mapbox GL JS or MapLibre GL JS mapping libraries. If you're using webpack or another node-based build tool, you can install by running
npm i autumnplot-glAdditionally, pre-built autumnplot-gl javascript files area available here. Adding them to your page exposes the API via the apgl global variable (e.g., instead of new PlateCarreeGrid(...) in the examples, you'd call new apgl.PlateCarreeGrid(...)).
The first step in plotting data is to create a grid. Currently, the only supported grids are PlateCarree (a.k.a. Lat/Lon) and Lambert Conformal Conic.
// Create a grid object that covers the continental United States
const nx = 121, ny = 61;
const grid = new PlateCarreeGrid(nx, ny, -130, 20, -65, 55);Next, create a RawScalarField with the data. autumnplot-gl doesn't care about how data get to the browser, but it should end up in a Float32Array in row-major order with the first element being at the southwest corner of the grid. A future version might include support for reading from, say, a Zarr file. Once you have your data in that format, to create the raw data field:
// Create the raw data field
const height_field = new RawScalarField(grid, height_data);Next, to contour the field, create a Contour object and pass it some options. At this time, a somewhat limited set of options is supported, but I do plan to expand this.
// Contour the data
const height_contour = new Contour(height_field, {color: '#000000', interval: 30});Next, create the actual layer that gets added to the map. The first argument ('height-contour' here) is an id. It doesn't mean much, but it does need to be unique between the different PlotLayers you add to the map.
// Create the map layer
const height_layer = new PlotLayer('height-contour', height_contour);Finally, add it to the map. The interface for Mapbox and MapLibre are the same, at least currently, though there's nothing that says they'll stay that way in the future. Assuming you're using MapLibre:
const map = new maplibregl.Map({
container: 'map',
style: 'https://api.maptiler.com/maps/basic-v2/style.json?key=' + maptiler_api_key,
center: [-97.5, 38.5],
zoom: 4
});
map.on('load', () => {
map.addLayer(height_layer, 'railway_transit_tunnel');
});The 'railway_transit_tunnel' argument is a layer in the map style, and this means to add your layer just below that layer on the map. This usually produces better results than just blindly slapping all your layers on top of all the map (though the map style itself may require some tweaking to produce the best results).
Wind barb plotting is similar to the contours, but it requires using a RawVectorField with u and v data.
const vector_field = new RawVectorField(grid, u_data, v_data);
const barbs = new Barbs(vector_field, {color: '#000000', thin_fac: 16});
const barb_layer = new PlotLayer('barbs', barbs);
map.on('load', () => {
map.addLayer(barb_layer, 'railway_transit_tunnel');
});The wind barbs are automatically rotated based on the grid projection. Also, the density of the wind barbs is automatically varied based on the map zoom level. The 'thin_fac': 16 option means to plot every 16th wind barb in the i and j directions, and this is defined at zoom level 1. So at zoom level 2, it will plot every 8th wind barb, and at zoom level 3 every 4th wind barb, and so on. Because it divides in 2 for every deeper zoom level, 'thin_fac' should be a power of 2.
Plotting filled contours is also similar to plotting regular contours, but there's some additional steps for the color map. A couple color maps are available by default (see here for more details), but if you have the colors you want, creating your own is (relatively) painless (hopefully). First, set up the colormap. Here, we'll just use the bluered colormap included by default.
// colormaps is imported via `import {colormaps} from 'autumnplot-gl'`
const colormap = colormaps.bluered(-10, 10, 20);
const fills = new ContourFilled(height, {cmap: colormap});
const height_fill_layer = new PlotLayer('height-fill', fills);
map.on('load', () => {
map.addLayer(height_fill_layer, 'railway_transit_tunnel');
});Normally, when you have color-filled contours, you have a color bar on the plot. To create an SVG color bar:
const colorbar_svg = makeColorBar(colormap, {label: "Height Perturbation (m)",
ticks: [-10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10],
orientation: 'horizontal',
tick_direction: 'bottom'});
document.getElementById('colorbar-container').appendChild(colorbar_svg);The previous steps have gone through plotting a static dataset on a map, but in many instances, you want to view a dataset that changes, say over time. Rather than continually remove and add new layers when the user changes the time, which would get tedious, waste video RAM, and probably wouldn't perform very well, autumnplot-gl provides MultiPlotLayer, which allows the plotted data to easily and quickly change over time (or height or any other axis that might be relevant).
// Contour some data
const height_contour_f00 = new Contour(grid, height_f00);
const height_contour_f01 = new Contour(grid, height_f01);
const height_contour_f02 = new Contour(grid, height_f02);
// Create a varying map layer
const height_layer_time = new MultiPlotLayer('height-contour-time');
// Add the contoured data to it
height_layer_time.addField(height_contour_f00, '20230112_1200');
height_layer_time.addField(height_contour_f01, '20230112_1300');
height_layer_time.addField(height_contour_f02, '20230112_1400');
// Add to the map like normal
map.on('load', () => {
map.addLayer(height_layer_time, 'railway_transit_tunnel');
});The second argument to addField() is the key to associate with this field. This example uses the absolute time, but you could just as easily use 'f00', 'f01', ... or anything else that's relevant as long as it's unique. Now to set the active time (i.e., the time that gets plotted):
// Set the active field in the map layer (the map updates automatically)
height_layer.setActiveKey('20230112_1200');autumnplot-gl comes with several built-in color maps, accessible via import {colormaps} from 'autumnplot-gl'. These are basic blue/red and red/blue diverging color maps plus a selection from PivotalWeather. The blue/red and red/blue are functions that take a minimum contour level, a maximum contour level, and a number of colors. For example, this creates a blue/red colormap starting at -10, ending at 10, and with 20 colors:
const colormap = colormaps.bluered(-10, 10, 20);Here are all the colormaps available:
The above exmple uses map tiles from Maptiler. Map tiles from Maptiler or Mapbox or others are free up to a (reasonably generous) limit, but the pricing can be a tad steep after reaching the limit. The tiles from these services are extremely detailed, and really what you're paying for there is the hardware to store, process, and serve that data. While these tiles are very nice, the detail is way overkill for a lot of uses in meteorology.
So, I've created some less-detailed map tiles that are small enough that they can be hosted without dedicated hardware. However the tradeoff is that they're only useful down to zoom level 8 or 9 on the map, such that the viewport is somewhere between half a US state and a few counties in size. If that's good enough for you, then these tiles could be useful.
A few capabilities are missing from this library as of v2.0.
- Helper functions for reading from specific data formats. For instance, I'd like to add support for reading from a zarr file.
- A whole bunch of little things that ought to be fairly straightforward like tweaking the size of the wind barbs and contour thicknesses.
- Support for contour labeling. I'd like to add it, but I'm not really sure how I'd do it with the contours as I've implemented them. Any WebGL gurus, get in touch.
Even though autumnplot-gl is currently an extremely new package with relatively limited capability, I hope folks see potential and find it useful. Any contributions to fill out some missing features are welcome.