This is another in my series on a close examination of the GHCN V4 database of station monthly average temperatures. The previous post described a system for displaying time series graphs and numerical data for named stations (with search). This post draws attention to an older system, where a triangle mesh is shown for each month, with the reporting stations as nodes, and shaded according to temperature anomaly (TAVG) for that month. The shading is such that it is correct for each node (with complications where some are nearly coincident), so it reveals a lot about the low-level structure of he data. There is enough homogeneity that the overall pattern shows through. I had thought about replacing it with a LOESS-based map, which would actually be a lot easier, and is better for the overall picture. I'll probably put that up at some stage, but I think the low-level info of this plot justifies its place.
The page for this facility (link on right - "WebGL Map of...") is ancient, more or less for the duration of GHCN V3. The original post from 2012 is here. The plot is now based on the Moyhu WebGL facility. That means that as well as being able to drag the plot around as a trackball, you can click on the small map for quick centering. The overlay of mesh and points can be toggled off and on with the checkboxes beside the small map. The table beside that lets you choose a month. With the top bar you choose the decade, next the year, and then the month. When the right date shows below, click "Fetch and Show". When the right data is showing, the date will be on top left. There are now zoom buttons, as well as zooming but dragging vertically on the globe with right button down. The Subset button lets you speed up response by choosing a subset, but I don't think you'll need that here. You may notice that I have shifted from Rainbow to GISS-like colors and ranges.
When you click on the sphere, it shows the code name, name and anomaly of the nearest station. The anomalies are as calculated by TempLS V4 mesh, and the base period is 1961-90. The color scale is centered to the mean for the month, so it varies as you go back in time.
I'm using unadjusted data, and there are a number of stations that stick out as different. I actually developed the viewer to see what is going on there, and I'll write about that next.
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Nice. What are the meshing artifacts on the equator and ?75? N/S?
ReplyDeleteWilliam,
DeleteIt's a convex hull mesh. At the equator, four SST points can be coplanar, so the mesh can validly connect them in two ways, and it seems to alternate. The roaring forties are due to my SST culling policy, described here. I try to thin out nodes as the longitudes converge, to retain reasonably well-shaped triangles. But it does mess up the regularity of the mesh in transition regions.
The meshes come from convhulln() in the R gometry package.
Nice work as always
ReplyDeleteThanks, Steven
DeleteExcellent work, thoughtfully done and well displayed.
ReplyDeleteI did have to laugh at estimating about a third of Africa based on one temperature station ...
w.
Thanks, Willis
DeleteYes, Central Africa is a problem.
Same with much of the Arctic influenced by one station. I agree on the excellent work, great for visualizing the temperature data. Too bad most of the land masses don't look like the US.
DeleteThanks, Bryan.
DeleteOn the Arctic, it's true that one station, OSTROV_KOTELNYJ, carries a lot of weight. Of the pentagon shape covering most of that ocean, it counts for about a third in the mesh method of TempLS. It can't be more, because each triangle weights its nodes equally for that area. In the LOESS method, it gets much less, because the stations further south also get quite a lot of weight. Then it becomes important that those stations are in agreement, as they are for that region. That doesn't certify that it's right for the N Pole, but it does safeguard against an outlier reading at the weighted location.
Well Willis that's why you check the answer against data that has more stations.
DeleteThat's funny since the global temperature variability observed can be reasonably well modeled by observations from a single location at a met station in Australia.
DeleteNicely done.
ReplyDeleteThanks, John
Delete