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Keywords:

  • long-term average;
  • UK;
  • climate normal;
  • gridded data;
  • missing data estimation;
  • spatial interpolation;
  • regression

Abstract

Monthly and annual long-term average datasets of 13 climate variables are generated for the periods 1961–90 and 1971–2000 using a consistent analysis method. Values are produced for each station in the Met Office's observing network and for a rectangular grid of points covering the UK at a horizontal spacing of 1 km. The variables covered are mean, maximum, minimum, grass minimum and soil temperature, days of air and ground frost, precipitation, days with rain exceeding 0.2 and 1 mm, sunshine, and days with thunder and snow cover.

Gaps in the monthly station data are filled with estimates obtained via regression relationships with a number of well-correlated neighbours, and long-term averages are then calculated for each site. Gridded datasets are created by inverse-distance-weighted interpolation of regression residuals obtained from the station averages. This method does not work well for days of frost, thunder and snow, so an alternative approach is used. This involves first producing a grid of values for each month from the available station data. The gridded long-term average datasets are then obtained by averaging the monthly grids.

The errors associated with each stage in the process are assessed, including verification of the gridding stage by leaving out a set of stations. The estimation of missing values allows a dense network of stations to be used, and this, along with the range of independent variables used in the regression, allows detailed and accurate climate datasets and maps to be produced. The datasets have a range of applications, and the maps are freely available through the Met Office Website. © Crown Copyright 2005. Reproduced with the permission of Her Majesty's Stationery Office. Published by John Wiley & Sons, Ltd.