Optimizing the location of weather monitoring stations using estimation uncertainty

Authors

  • Ana M. T. Amorim,

    1. Instituto Superior Técnico, Technical University of Lisbon, Lisbon, Portugal
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  • Alexandre B. Gonçalves,

    Corresponding author
    1. Department of Civil Engineering, Architecture and Georesources, Instituto Superior Técnico, Tech. Univ. of Lisbon, Lisbon, Portugal
    2. ICIST—Institute for Structural Engineering, Territory and Construction, IST, Lisbon, Portugal
    • ICIST, Instituto Superior Técnico, Lisbon, Portugal.
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  • Luís Miguel Nunes,

    1. Faculty of Sciences and Technology, University of Algarve, Algarve, Portugal
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  • António Jorge Sousa

    1. Department of Civil Engineering, Architecture and Georesources, Instituto Superior Técnico, Tech. Univ. of Lisbon, Lisbon, Portugal
    2. CERENA—Centre for Natural Resources and the Environment, IST, Lisbon, Portugal
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Abstract

In this article, we address the problem of planning a network of weather monitoring stations observing average air temperature (AAT). Assuming the network planning scenario as a location problem, an optimization model and an operative methodology are proposed. The model uses the geostatistical uncertainty of estimation and the indicator formalism to consider in the location process a variable demand surface, depending on the spatial arrangement of the stations. This surface is also used to express a spatial representativeness value for each element in the network. It is then possible to locate such a network using optimization techniques, such as the used methods of simulated annealing (SA) and construction heuristics. This new approach was applied in the optimization of the Portuguese network of weather stations monitoring the AAT variable. In this case study, scenarios of reduction in the number of stations were generated and analysed: the uncertainty of estimation was computed, interpreted and applied to model the varying demand surface that is used in the optimization process. Along with the determination of spatial representativeness value of individual stations, SA was used to detect redundancies on the existing network and establish the base for its expansion. Using a greedy algorithm, a new network for monitoring average temperature in the selected study area is proposed and its effectiveness is compared with the current distribution of stations. For this proposed network distribution maps of the uncertainty of estimation and the temperature distribution were created. Copyright © 2011 Royal Meteorological Society

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