Abstract
AbstractIn 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
Highlights
There are several studies mainly focusing on methods of achieving an optimized network for monitoring natural or anthropogenic phenomena, many of which deal with networks for monitoring groundwater quality: in Prakash and Singh (2000), the selection of optimal locations to expand a network for monitoring groundwater levels in a region of India is tested minimizing the kriging variance of estimation and determining where the greatest estimation errors occur
The methods explored in the methodology are usually applied to a series of location problems with unvarying demand surfaces that use concepts as the distance-based coverage
In the case of climatological data collected by weather monitoring stations, which generally is extended to an entire area using interpolation techniques, the quality of a network might be evaluated using other descriptors, such as the uncertainty of estimation that a particular network layout induces
Summary
A more recent study by Yeh et al (2006) shows how the combination of multivariate geostatistical analysis with genetic algorithms is effective in defining an optimal network for monitoring groundwater quality This minimizes the estimation variance of the spatial factor and, in the authors’ opinion, provides enough information to fully understand the spatial phenomenon. Caeiro et al (2002) and Nunes et al (2004a, 2004b) investigated the optimization of sediment sampling sites in the Sado River estuary, Portugal They apply the simulated annealing (SA) optimization algorithm, searching for solutions that minimize both the variance and the average estimation error resulting from kriging interpolators
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