WRF Data in Google Earth

This page shows data from a WRF wind forecast put into a format suitable for display in Google Earth. Google Earth, which is different from Google Maps, is a highly interactive display of map data. Many people do not know that time values can be added to the data so that a time series can be shown. I have developed some software that lets me visualize WRF related data in Google Earth. I can also add data from meteorological towers and a sodar we use. Using Google Earth to display the data does not convey more information than other tools, but it provides more context and zooming features that enhance its use. Also, it just looks great. I am grateful to Google for this tool.

At the bottom of this page, I attached the Google Earth file containing the data use to generate the images on this page. Or you can download it using this link: website.kmz

This is a picture of a Google Earth screen. The time series data is from one of my wind forecasts. If you look closely, two rows of 10 wind turbine can barely be seen just above the middle. These turbines are to scale and when viewing the data in Google Earth, I can zoom in close and see the wind speed contours (isotachs) move around them. Since the contours are for winds at a height of 80 meters and are drawn 80 meters above the surface, I can even zoom in below the lines.

If you have Google Earth on your computer and would like to run this time series of data, you can download it using this link to the file attached at the bottom of the page: website.kmz. The data is in a format called Keyhole Markup Language (KML). It has a lot of similarities to XML and HTML. Usually, the KML text is zipped up and given an extension of kmz. Google Earth can read and write kmz files without using a separate program to unzip or zip them.If you load a file that has time sequence data in it, additional play buttons appear along the top right of the screen to control playing, pausing, and speed. Press the little clock to get a screen for adjusting speed.

The data in my file is in 10 minute increments. I chose 10 minutes because the sodar, which I use to verify forecasts, reports in 10 minute increments. The file has both horizontal and vertical isotachs. the horizontal data is for wind at 80 meters above ground and is drawn at that height. The vertical isotachs show how the wind speed changes with height. This set shows up to 600 meters high (I think). In the picture to the left, you can see that the winds increase up to about 250 meters high and decrease going up from there. Fast winds are shown in red and orange, slow in green and blue. The balloons with wind speeds point to the line for which the speed applies.

My work focuses on wind for wind energy, but similar things could be done with any other meteorological data. I can even draw wind barbs to show wind direction at periodic spacing. I have not tried it yet, but I could draw time series graphs on Google Earth to go along with other displayed data.

This is a picture of some grid cells represented by flat surfaces. I use this for two purposes. First I use it to get a feel for how well the grid cells capture the roughness of the terrain. I have a separate page discussing this and showing examples. For that, go to this page: High Resolution Terrain Data Example. Secondly, by displaying grid domains in Google Earth, I can look at, zoom in, and investigate which terrain features are included in my grids. It is good to not have domain boundaries right at large terrain breaks. I can expand or contract my domains to have their boundaries at good locations and to include terrain features that I would guess to be important in generating the forecasts.

The picture to the left shows a domain with a 200m horizontal resolution. For a high resolution wind forecast, it seems to capture many of the terrain features that would affect the wind. There are, of course, additional domains surrounding this high resolution domain to help reduce the resolution smoothly. The file containing the isotachs (wind contours) also includes these grid cell representations. Currently, I only use fixed location domains, as opposed to moving nests, so there is no time dependent data attached to the grid cells. In the kmz file, this data is currently turned off, but you can use the left sidebar to turn them on or off.

This shows land use data as represented in WRF. The data is for the 1 km resolution domain and shows how a lot of detail is missing at that resolution. The green area that dominates the picture is designated as "shrubland" but it is obvious from the underlying image provided in Google Earth that there is farmland and water bodies in that area. The dark blue area is designated as "dryland cropland and pastureland."  The pinkish color is simply "grassland." Each land use category has its own set of properties such as albedo, thermal inertia, snow cover effects, and roughness. If you downloaded the kmz file mentioned above, this data is included in it, but you have to click on it in the left sidebar for it to be displayed.

So far, the land use information displayed in Google Earth has not been very useful except that it tells me that I want higher resolution data and more recent data than that which comes with WRF. On another page, I described methods to get higher resolution terrain height data from USGS. That same USGS site has newer and higher resolution land use data too, but it uses different categories than what WRF knows how to interpret. A project I want to undertake is to download the data and tell WRF what properties to apply to each category. It should be a manageable project, but requires some "best guesses" in converting from existing WRF categories to the new categories.

I have begun a set of programs to interactively change the land use categories for grid cells based on what the user sees in Google Earth. The current version of that Java program and its documentation can be downloaded from this page: WTOOLS.

Kevin Matthew Nuss,
Jun 26, 2011, 6:55 PM