Useful Information and FAQ for the
Google Earth Interface to CRU TS

1. The purpose of this interface

The Google Earth interface to the CRU TS dataset allows the Mean Temperature and Precipitation variables of that dataset to be investigated spatially. Both gridded data and original observations may be retrieved. To access the dataset in its original form, please follow links from the HRG page: http://www.cru.uea.ac.uk/cru/data/hrg/

2. Getting started

Because of the high spatial resolution of the dataset, the initial view displays 5°x5° 'blocks'. Each of these contains up to 100 0.5° x 0.5° land gridcells, which are revealed by clicking on a block, and following the link then displayed. Individual gridcells may now be clicked on to reveal data series for temperaure and precipitation, along with the option to show 'nearby stations'. These can in turn be clicked on to investigate station observations.

3. How do I get back to the map after clicking on a plot or data link?

If you follow a link to a cell or station data file, or to an enlarged plot, it may not be ovious how to return. However, there is a 'Return to Google Earth' button, top left of the window. Sadly this does not return you to the 'balloon' that was open, just to the position you were in.

4. Why are stations different colours and sizes?

When station placeholders (pins) are displayed, colour and size are used to indicate attributes of the station. The basic colours - red or blue - indicate temperature and precipitation respectively. Brighter, larger pins indicate a 'current' station - one with data for the current year and so likely to be active (note that this last feature isn't active for the 3.24.01 release).

5. Can cells be combined to calculate spatial means?

Yes, but not by Google Earth! If you wish to calculate spatial means, then there are several possibilities: If your area is a country, please look at the CRU CY dataset (http://www.cru.uea.ac.uk/cru/data/hrg/) If your area consists of just a few gridcells, you could download each cell series and then average them (using Microsoft Excel, or similar). Remember that if you are in mid- to high-latitudes then you should take account of different cell areas. For more complicated areas, you could investigate KNMI's Climate Explorer, which includes the CRU TS dataset: http://climexp.knmi.nl/

6. Why are some cell series just flat lines?

Because of the fundamental attribute of the CRU TS dataset - that there are no missing values for any months and any land cells - there must be an approach for cells with no observing stations nearby (this is explained in Harris, et al. (2014). In brief, any cells that have no observations within the CDD (correlation decay distance) of the variable in question, (1200km for mean temperature, 450km for precipitation), will instead be influenced by the underlying 1961-1990 climatology (which is spatially complete). In rare cases (rarer for temperature than for precipitation owing to the much larger CDD), a cell will use the climatology for the whole run. An annual mean from years where every January is the same, every February, etc., will have the same mean value every year. Hence the flat line.

7. What does 'Show nearby stations' actually mean?

The current gridding mechanism for CRU TS - as documented in the paper - is performed in IDL. It uses Delaunay Triangulation to build a 'surface', where three adjacent stations form the vertices of a triangle, with the heights of those vertices proportional to their values. Interpolation to the grid is then effected by measuring the heights of the vertical intercept points between the grid centres and the triangle surfaces. In areas of sparse station coverage, this can result in triangles with one, or even two, vertices outside the CDD (see FAQ 6). So 'Show nearby stations' attempts to show up to 12 of the nearest stations to the cell; if less than three are found inside the CDD circle of influence, the net is widened (to 1.5 x CDD). This only happens if the cell carries 'live' data (ie, is not a flat line - see FAQ 6 again).

8. How to cite?

Harris, I., Jones, P.D., Osborn, T.J. and Lister, D.H. (2014), Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset. International Journal of Climatology 34, 623-642 doi:10.1002/joc.3711 (click doi to access paper)

Last updated: July 2015, Ian Harris

These datasets are made available under the Open Database License. Any rights in individual contents of the datasets are licensed under the Database Contents License under the conditions of Attribution and Share-Alike. Please use the attribution Climatic Research Unit, University of East Anglia