Visual tweaks

_posts/2024-03-06-Something-in-the-Wind--Weather-Forecasting-in-the-Age-of-AI.md

@@ -66,7 +66,7 @@ This type of network works well in a lot of applications, but when we’re model
 
 In mathematics and computer science, a graph is a structure made up of nodes (items or objects; data points) connected by edges. Graphs are particularly useful for modelling weather patterns around the earth because we can use them to build up a spherical surface, much like this [geodesic dome at the University of Surrey](https://artuk.org/discover/artworks/geodesic-dome-1982-272590).
 
-![Photograph of a spherical metal art sculpture]({{ site.github.url }}/pling/assets/GeodesicDome.jpg "Geodesic Dome")    ![Close-up photo of the same structure; metal rods annotated as edges; intersections annotated as nodes]({{ site.github.url }}/pling/assets/Graph.png "Annotated Dome")
+![Two photographs of a spherical metal art sculpture. The second is in close-up with metal rods annotated as edges and intersections annotated as nodes]({{ site.github.url }}/pling/assets/GeodesicDomes.png "Geodesic Domes")
 
 This represents the earth’s surface much better than a simple grid of latitude/longitude coordinates, because we’ve now directly linked each point with its closest neighbours.
 
@@ -76,6 +76,7 @@ So instead of the MLP process described above, each layer of the GNN propagates
 
 ![Diagram showing how information propagates in a Graph Neural Network]({{ site.github.url }}/pling/assets/MessagePassing.png "Message Passing Diagram")
 *How information propagates in a Graph Neural Network. **(1)** Input graph with four connected nodes. **(2)** Messages are created for each neighbouring node, combining information from the sending and receiving nodes. **(3)** Messages are received from each neighbouring node. **(4)** Messages are aggregated. **(5)** Nodes are updated using the aggregated messages.*
+{: style="font-size: 80%; text-align: center;"}
 
 So just like weather patterns propagate around the earth’s surface over time, each layer of the network allows information to propagate further and further around the graph. And just as the planet doesn’t deform (at least not significantly enough for weather forecasting), the graph’s structure is always preserved.
 
@@ -90,7 +91,8 @@ As with any machine learning approach, GraphCast had to be trained with a good d
 Start by collating recorded weather observations from around the world, going back as far as 1940. These measurements are patchy and not always accurate, so traditional NWP methods are used to essentially make “forecasts” for the past. This technique, known as Data Assimilation, fills in the gaps and irons out any problematic areas to give us a consistent, complete dataset for every point on the globe over the past 80 years.
 
 ![Two jigsaw puzzles depicting weather patterns around the earth. The first puzzle has missing pieces; the second is complete.]({{ site.github.url }}/pling/assets/ERA5.png "Data Assimilation Jigsaw")
-*Data Assimilation fills in the missing pieces to create a complete picture of the weather over the past 80 years. Image credit: Copernicus ECMWF https://youtu.be/FAGobvUGl24*
+*Data Assimilation fills in the missing pieces to create a complete picture of the weather over the past 80 years. Image credit: Copernicus ECMWF [https://youtu.be/FAGobvUGl24](https://youtu.be/FAGobvUGl24)*
+{: style="font-size: 80%; text-align: center;"}
 
 ## Changing the World, One Forecast at a Time