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@Article{CoelhoFerrStepStei:2008:MeExSp,
               author = "Coelho, Caio Augusto dos Santos and Ferro, C. A. T. and 
                         Stephenson, D. B and Steinskog, D. J.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE/CPTEC)} and 
                         School of Engineering, Computing and Mathematics, University of 
                         Exeter, UK and School of Engineering, Compunting, and Mathematics, 
                         University of Exeter, UK and Nansen Environmental and Remote 
                         Center, bjerknes Centre of Climate Research, Bergen, Norway",
                title = "Methods for exploring spatial and temporal variability of extreme 
                         events in climate data",
              journal = "Journal of Climate",
                 year = "2008",
               volume = "21",
               number = "10",
                pages = "2072--2092",
                month = "may",
             keywords = "heat-wave, surface temperature, regional climate, air-temperature, 
                         simulations, model, precipitation, ensemble, rainfall.",
             abstract = "This study presents various statistical methods for exploring and 
                         summarizing spatial extremal properties in large gridpoint 
                         datasts. Extremal properties are inferred from the subset of 
                         gridpoint values that erxceed sufficiently high, time-varying 
                         thresholds. A simple approach is presented for how to choose the 
                         thresholds so as to avoid sampling biases from nonstationary 
                         differential trends within the annual cycle. The excesses are 
                         summarized by estimating parameters of a flexible generalized 
                         Pareto model that can account for spatial and temporal variation 
                         in the excess distributions. The effect of potentially explanatory 
                         factors (e.g., ENSO) on the distribution of extremes can be easily 
                         investigated using this model. Smooth spatially pooled estimates 
                         are obtained by fitting the model over neighboring grid points 
                         while accounting for possible spatial variation across these 
                         points. Extreme value theory methods are also presented for how to 
                         investigate the temporal clustering and spatial dependency 
                         (teleconnections) of extremes. The methods are illustrared using 
                         Northern Hemisphere monthly mean gridde temperatures for 
                         June-August (JJA) summers from 1870 to 2005.",
                  doi = "10.1175/2007JCLI1781.1",
                  url = "http://dx.doi.org/10.1175/2007JCLI1781.1",
                 issn = "0894-8755",
             language = "en",
           targetfile = "coelhoetal2008-jclim.pdf",
        urlaccessdate = "17 jan. 2021"
}


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