Aug 25, 2010

Work with Web Map Service (WMS)

Web Map Service (WMS) is is a standard protocol for serving geo-referenced map images over the Internet. JPL OneEarth is used as the data source here.

All the datasets that exist on OneEarth server are documented for WMS clients in an XML Capabilities file, which contains information about the layers, which correspond to datasets, and the styles associated with them.

The base url is, to request the image, some parameters are required: layers, srs, width, height, bbox, format, styles. Click the following url, it returns an satellite image from Blue Marble Next Generation, a MODIS-derived 500m true color earth dataset showing seasonal dynamics. styles is used to specify the month. Check OneEarth website for the details.,-44,154,-10&format=image/jpeg&styles=Jan

In Mathematica, image is imported with Import[wms_url]. However, once the image is imported, it loses the geo-reference information, and we need move the image back to its right position (bbox parameter).

The trick is using Raster function to convert image to a graphic object with the right coordinates.

raster = Raster[Reverse[ImageData[img]], {{112, -44}, {154, -10}}];


I borrow the example from this weatherdata blog entry.


The imported image works perfectly with the data from Mathematica.

Mathematica Notebook: wmsimage.nb

Aug 20, 2010

Visualize weather pattern with random walk

When talking about the “good” weather, the daily temperature change is important, slow steady change from day to day is much better than sudden temperature increase or drop.

Here is the daily mean temperature for Bloomington, Indiana.


We define a piecewise function to classify the day to day temperature change. x is (T – Tprevious day), y is the threshold to define the weather change: 1 ~ no change, 2 ~ cold move, 3 ~ mild cold, 4 ~ mild hot, 5 ~ hot move.


Then we can apply the same random walk trick used in the post Visualize irrational number as random walk. The classification results are mapped into the moves: 1 ~ no move {0, 0}, 2 ~ move south {0, –1}, 2 ~ move west {-1, 0}, 3 ~ move east {1, 0}, 4 ~ move north {0, 1}. Then we can get a random walk image (the green dot is starting point {0, 0}, the red one is the end).


This is probably nothing interesting. However, we can use it to compare the patterns among different cities. Here is the results from Washington, Columbus, Indianapolis, Kansas City, Denver. The threshold is 3.5 degree. This shows the certain pattern among cities from ocean to inland.


When we compare the cities across the ocean, a different pattern is shown.


Down the weatherwalk.nb for the detail.