Optimal design of a climatological network: beyond practical considerations
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 prece...
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Published in | Geoscientific instrumentation, methods and data systems Vol. 2; no. 2; pp. 199 - 212 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Copernicus GmbH
22.07.2013
Copernicus Publications |
Online Access | Get full text |
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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. |
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AbstractList | 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. |
Audience | Academic |
Author | Mote, P. W Bumbaco, K. A Mauger, G. S Hakim, G. J |
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Cites_doi | 10.1029/2006JD007561 10.1175/1520-0426(1986)003<0564:AMFDAF>2.0.CO;2 10.1029/2011JD016187 10.1175/1520-0477(1996)077<0305:ROFEAO>2.0.CO;2 10.1007/s00382-009-0714-5 10.1175/2009JAMC2062.1 10.1111/j.1600-0870.2006.00200.x 10.1115/1.3662552 10.1175/2007MWR1904.1 10.1175/1520-0469(2001)058<0210:IAOSFI>2.0.CO;2 10.1029/2003EO480001 10.1175/1520-0493(1999)127<0743:AOAFS>2.0.CO;2 10.1017/CBO9780511617652.007 10.3354/cr022099 10.1175/BAMS-87-3-343 |
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