Optimal design of a climatological network: beyond practical considerations

Abstract

Abstract. 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.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.

Evaluating the Antarctic Observational Network with the Antarctic Mesoscale Prediction System (AMPS)

“…These differences illustrate the need for objective optimal network design (e.g., Huntley and Hakim 2010;Mauger et al 2013), which depends on monitoring goals, region of interest, and the time of year. For instance, to optimize a network to measure West Antarctic weather, our results suggest that Byrd, Antarctica (808S, 1208W), may not be the ideal location for a station due to the relatively short correlation length scales on daily time scales (Fig.…”

“…In closing, a sample of what an objective network analysis (Huntley and Hakim 2010;Mauger et al 2013) can provide is given in Fig. 8, which shows the optimal locations for monitoring the climate of three regions of the continent that were the focus of this study, assuming no existing stations.…”

“…Different monitoring goals, improving technology, and inconsistency in the availability of funding, among other practical issues, have all influenced station placement in the network. Logistical challenges for installing and maintaining an observational network that spans a continent more than half the size of North America are numerous, which motivates research into optimal configurations of the network to maximize performance for a given monitoring goal (e.g., Huntley and Hakim 2010;Mauger et al 2013). Here we assess the spatial coverage of the current Antarctica network as a precursor to a complete network design study.…”

Designing optimal greenhouse gas observing networks that consider performance and cost

“…Network optimization studies typically construct and optimize a single objective function, which is usually related to the performance of the observing network (e.g., Mauger et al, 2013;Ziehn et al, 2014;Nickless et al, 2015). Although single objective optimization problems can consider several aggregated quantities, they still reduce the problem down to a single objective.…”

“…Quantitative methods for designing "optimal" observing networks have been described for inferring carbon dioxide (CO 2 ) emissions, improving weather forecasts, collecting oceanographic data, and monitoring air quality and climate change (e.g., Barth and Wunsch, 1990;Morss et al, 2001;Patra and Maksyutov, 2002;Gloor et al, 2000;Carmichael et al, 2008;Stuart et al, 2007;Mauger et al, 2013). Ziehn et al (2014) and Nickless et al (2015) illustrate recent applications of using optimization methods to design GHG observing networks.…”