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@InProceedings{MaiaMein:2006:DiCoLi,
               author = "Maia, Aline de Holanda Nunes and Meinke, Holger",
          affiliation = "Embrapa Meio Ambiente, PO Box 69, Jaguari{\'u}na, SP, Brazil 
                         (Maia) and Department of Primary Industries and Fisheries, PO Box 
                         102, Toowoomba, Qld 4350, Australia (Meinke) and {}",
                title = "Assessing uncertainty of seasonal probabilistic forecasts: 
                         distribution-free confidence limits",
            booktitle = "Proceedings...",
                 year = "2006",
               editor = "Vera, Carolina and Nobre, Carlos",
                pages = "569--573",
         organization = "International Conference on Southern Hemisphere Meteorology and 
                         Oceanography, 8. (ICSHMO).",
            publisher = "American Meteorological Society (AMS)",
              address = "45 Beacon Hill Road, Boston, MA, USA",
             keywords = "probabilistic forecasts, uncertainty, confidence intervals.",
             abstract = "Probabilistic climate forecasts often rely on information coming 
                         from historical climate series of prognostic variables, 
                         represented by cumulative distribution probabilities functions 
                         (CDFs) or their complement, probability of exceeding functions 
                         (POEs). They are a simple and convenient way to represent 
                         probabilistic information arising from a time series that exhibit 
                         no or only weak auto-correlation patterns. However, if the time 
                         series shows moderate to strong auto-correlation patterns, a 
                         CDF/POE summary will result in some loss of information. Yearly 
                         sequences of rainfall data from a specific month or period 
                         generally exhibit only weak auto-correlation, thus allowing the 
                         CDF/POE representation to convey seasonal climate forecast 
                         information. Useful information required by decision makers is 
                         then derived from such distribution and expressed as the 
                         probability of exceeding a certain threshold (e.g. probability of 
                         exceeding historical median value of rainfall or any other derived 
                         quantity such as agricultural yield or income). Such estimates are 
                         frequently reported without any measure of uncertainty. The degree 
                         of uncertainty depends on the length of the time series and its 
                         internal variability. Lack of uncertainty assessments can lead to 
                         misguided beliefs about the true performance of the forecast 
                         systems (e.g. due to the possible existence of artificial skill, 
                         especially if forecasts are based on short time series) possibly 
                         resulting in inappropriate actions by the decision maker. 
                         Parametric methods to assess uncertainty of percentiles and 
                         probability of exceeding estimates are frequently based on 
                         normality assumptions. However, distributions of some important 
                         climate variables, such as rainfall, are notoriously skewed, 
                         particularly in areas with strong seasonality that can result in 
                         high frequencies of zero rainfall amounts. For such cases there 
                         are often no mathematical transformations available that would 
                         overcome this lack of normality. As an alternative for 
                         Normal-based procedures, we propose the use of distribution free 
                         methods for constructing percentile and POE confidence limits. 
                         Those distribution-free tools are particularly useful for spatial 
                         uncertainty assessments that would require a tedious, 
                         location-by-location checking of assumptions regarding underlying 
                         probability distributions. Normal-based and distribution-free 
                         methods are both available in The Capability Procedure of the 
                         Statistical Analysis System (SAS, version 7 and latter releases). 
                         In this work, we discuss the rationale, advantages and limitations 
                         of both, parametric and non-parametric approaches. We illustrate 
                         these methods by assessing the uncertainty of percentiles and POEs 
                         estimates for 3-monthly rainfall series from locations in 
                         Australia and South America. The SAS codes for computing the 
                         uncertainty measures will also be presented.",
  conference-location = "Foz do Igua{\c{c}}u",
      conference-year = "24-28 Apr. 2006",
             language = "en",
         organisation = "American Meteorological Society (AMS)",
                  ibi = "cptec.inpe.br/adm_conf/2005/10.31.10.20",
                  url = "http://urlib.net/rep/cptec.inpe.br/adm_conf/2005/10.31.10.20",
           targetfile = "569-573.pdf",
                 type = "Climate predictions",
        urlaccessdate = "25 jan. 2021"
}


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