The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data
- 924 Downloads
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.
KeywordsStation Network Regional Climate Model Station Density Gridded Data Extreme Index
We would like to thank all institutes (see Appendix 1 of Klok and Klein Tank (2009)) that made meteorological station data available for the study. This study was funded by the EU project ENSEMBLES (WP 5.1 contract GOCE-CT-2004-50539). NH is also funded by the Dutch Prins Bernhard Cultuurfondsbeurs and the Dutch talentenbeurs.
- Alexander LV, Zhang X, Peterson TC, Caesar J, Gleason B, Klein Tank AMG, Haylock M, Collins D, Trewin B, Rahimzadeh F, Tagipour A, Rupa Kumar K, Revadekar J, Griffiths G, Vincent L, Stephenson DB, Burn J, Aguilar E, Brunet M, Taylor M, New M, Zhai P, Rusticucci M, Vazquez-Aguirre JL (2006) Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res 111:D05109. doi: 10.1029/2005JD006290 CrossRefGoogle Scholar
- Beniston M, Stephenson DB, Christensen OB, Ferro CAT, Frei C, Goyette S, Halsnaes K, Holt T, Jylhä K, Koffi B, Palutikof JP, Schöll R, Semmler T, Woth K (2007) Future extreme events in European climate: an exploration of regional climate model projections. Clim Change 81:71–95. doi: 10.1007/s10584-006-9226-z CrossRefGoogle Scholar
- Hanson CE, Palutikof JP, Livermore MTJ, Barring L, Bindi M, Corte-Real J, Durao R, Giannakopoulos C, Good P, Holt T, Kundzewicz Z, Leckebusch GC, Moriondo M, Radziejewski M, Santos J, Schlyter P, Schwarb MC, Stjernquist I, Ulbrich U (2007) Modelling the impact of climate extremes: an overview of the MICE project. Clim Change 81:163–177CrossRefGoogle Scholar
- Haylock MR, Hofstra N, Klein Tank AMG, Klok EJ, Jones PD, New M (2008) A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J Geophys Res 113, D20119. doi: 10.1029/2008JD010201
- Hofstra N, Haylock M, New M, Jones PD (2009) Testing E-OBS European high-resolution gridded dataset of daily precipitation and surface temperature. J Geophys Res (in press). doi: 10.1029/2009JD011799
- McSweeney CF (2007) Daily rainfall variability at point and areal scales: evaluating simulations of present and future climate. University of East Anglia, Norwich, p 256Google Scholar
- Rubel F, Brugger K, Skomorowski P, Kottek M (2004) Daily and 3-hourly quantitative precipitation estimates for ELDAS, edited. Biometeorology Group, University of Veterinary Medicine, Vienna, 32 pGoogle Scholar
- Simmons AJ, Jones PD, da CostaBechtold V, Beljaars ACM, Kållberg PW, Saarinen S, Uppala SM, Viterbo P, Wedi N (2004) Comparison of trends and low-frequency variability in CRU, ERA-40, and NCEP/NCAR analyses of surface air temperature. J Geophys Res 109:D24115. doi: 10.1029/2004JD005306 CrossRefGoogle Scholar
- Van der Groot E, Orlandi S (2003) Technical description of interpolation and processing of meteorological data in CGMS, pp 16Google Scholar
- Wilks DS (2006) Statistical methods in the atmospheric sciences, 2nd edn. Elsevier, Burlington, p 627Google Scholar