The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data |
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Authors: | Nynke Hofstra Mark New Carol McSweeney |
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Affiliation: | (1) School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK;(2) Present address: Environmental Systems Analysis Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, The Netherlands |
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Abstract: | We study the influence of station network density on the distributions and trends in indices of area-average daily precipitation
and temperature in the E-OBS high resolution gridded dataset of daily climate over Europe, which was produced with the primary
purpose of Regional Climate Model evaluation. Area averages can only be determined with reasonable accuracy from a sufficiently
large number of stations within a grid-box. However, the station network on which E-OBS is based comprises only 2,316 stations,
spread unevenly across approximately 18,000 0.22° grid-boxes. Consequently, grid-box data in E-OBS are derived through interpolation
of stations up to 500 km distant, with the distance of stations that contribute significantly to any grid-box value increasing
in areas with lower station density. Since more dispersed stations have less shared variance, the resultant interpolated values
are likely to be over-smoothed, and extreme daily values even more so. We perform an experiment over five E-OBS grid boxes
for precipitation and temperature that have a sufficiently dense local station network to enable a reasonable estimate of
the area-average. We then create a series of randomly selected station sub-networks ranging in size from four to all stations
within the E-OBS interpolation search radii. For each sub-network realisation, we estimate the grid-box average applying the
same interpolation methodology as used for E-OBS, and then evaluate the effect of network density on the distribution of daily
values, as well as trends in extremes indices. The results show that when fewer stations have been used for the interpolation,
both precipitation and temperature are over-smoothed, leading to a strong tendency for interpolated daily values to be reduced
relative to the “true” area-average. The smoothing is greatest for higher percentiles, and therefore has a disproportionate
effect on extremes and any derived extremes indices. For many regions of the E-OBS dataset, the station density is sufficiently
low to expect this smoothing effect to be significant and this should be borne in mind by any users of the E-OBS dataset. |
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