GI Geoscientific Instrumentation, Methods and Data Systems GI Geosci. Instrum. Method. Data Syst. 2193-0864 Copernicus Publications Göttingen, Germany 10.5194/gi-2-199-2013 Optimal design of a climatological network: beyond practical considerations Mauger G. S. 1 Bumbaco K. A. 2 Hakim G. J. 3 Mote P. W. 4 Climate Impacts Group, University of Washington, P.O. Box 355674, Seattle, WA 98195-5672, USA Office of the Washington State Climatologist, University of Washington, P.O. Box 355672, Seattle, WA 98195-5672, USA Department of Atmospheric Sciences, P.O. Box 351640, University of Washington, Seattle, WA 98195-1640, USA College of Earth, Ocean, and Atmospheric Sciences, 104 CEOAS Admin Building, Oregon State University, Corvallis, OR 97331, USA 22 07 2013 2 2 199 212 Copyright: © 2013 G. S. Mauger et al. 2013 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://gi.copernicus.org/articles/2/199/2013/gi-2-199-2013.html The full text article is available as a PDF file from https://gi.copernicus.org/articles/2/199/2013/gi-2-199-2013.pdf

Station locations in existing environmental networks are typically chosen based on practical constraints such as cost and accessibility, while unintentionally overlooking the geographical and statistical properties of the information to be measured. Ideally, such considerations should not take precedence over the intended monitoring goal of the network: the focus of network design should be to adequately sample the quantity to be observed. <br><br> Here we describe an optimal network design technique, based on ensemble sensitivity, that objectively locates the most valuable stations for a given field. The method is computationally inexpensive and can take practical constraints into account. We describe the method, along with the details of our implementation, and present-example results for the US Pacific Northwest, based on the goal of monitoring regional annual-mean climate. The findings indicate that optimal placement of observing stations can often be highly counterintuitive, thus emphasizing the importance of objective approaches. Although at coarse scales the results are generally consistent, sensitivity tests show important differences, especially at smaller spatial scales. These uncertainties could be reduced with improvements in datasets and improved estimates of the measurement error. We conclude that the method is best suited for identifying general areas within which observations should be focused, and suggest that the approach could serve as a valuable complement to land surveys and expert input in designing new environmental observing networks.

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