#### Time Series

To study the effect at an individual station what we can do with the HadISD data is to show the time-series from a particular year against the range expected from a climatology period. As HadISD covers the span 1973-2012 (for v1.0.1.2012p), we have used the 30 year period of 1975-2004.

Fig. 1. The daily temperatures from 2010 (green) shown on the 5th - 95th percentile range derived from the 30 year climatology over 1975-2004 (yellow band) for Moscow Botanical Gardens. |

The extreme warm period in late July and early August is clearly visible, and gives some impression as to the intensity and duration of the heat wave at this one station. The magnitude of this event becomes clearer if we show the same plot for Paris-Montsouris (071560-99999, 48.817N, 2.333E) in 2003, Fig. 2.

Fig. 2. The daily temperatures from 2003 (green) shown on the 5th - 95th percentile range derived from the 30 year climatology over 1975-2004 (yellow band) for Paris-Montsouris |

#### Spatial Extent & Voronoi Tiling

However, what about showing the spatial extent of a heat-wave. With station data, we can show the value for each station as a coloured dot. This isn't the clearest way of presenting the data, as is hopefully obvious in Fig. 3.Fig. 3 The 2010 Moscow heat-wave in HadISD for July. Each station has been coloured by the number of degree days over climatology (see text for details) |

To try and improve the presentation of this heat map I played around with something called Voronoi tessellation (also known as Theissen Polygons). This technique divides up an area on which a number of fixed points such that each edge of a polygon bisects the distance between two centres. This is hopefully clear in the example below, which just colours each polygon by random, but also shows the lines which are bisected in red.

Fig. 4 Voronoi tiling. The red lines show the connections between all the points, forming a set of Delaunay Triangles. The Voronoi polygons are formed by joining all the bisectors of the edges of the triangles. |

Fig. 5 The heat map for Moscow in July 2010 using the Voronoi tiling method. The location of each station is shown by a grey dot, usually close to the middle of the polygon, but not always so. |

An alternative way of presenting this kind of data would have been to grid up the individual stations into grid boxes. Although this would have shown a very similar pattern, it would not be immediately clear from the resulting map, how many stations were contributing to a grid box. Some gridding methods do not require any stations within the grid box, but use a weighted average of those stations within a search radius. The gridding process also would act as a smoothing function on the data, reducing the intensity of the maxima and minima.

Personally I think this is a good way of presenting the station data of HadISD in a space filling way without resorting to gridding.