The Object-analogue approach for probabilistic forecasting
Abstract:The object-analogue is a new method to estimate forecast uncertainty and to derive probabilistic predictions of gridded forecast fields over larger regions rather than point locations. The method has been developed for improving the forecast of 10-meter wind speed over the northeast US, and it can be extended to other forecast variables, vertical levels, and other regions.
The object-analogue approach combines the analog post-processing technique (Hopson 2005; Hamill 2006; Delle Monache 2011) with the Method for Object-based Diagnostic Evaluation (MODE) for forecast verification (Davis et al 2006a, b). Originally, MODE is used to verify mainly precipitation forecasts using features of a forecast region represented by an object. The analog technique is used to reduce the NWP systematic and random errors of a gridded forecast field.
In this study we use MODE-derived objects to characterize the wind fields forecasts into attributes such as object area, centroid location, and intensity percentiles, and apply the analogue concept to these objects. The object-analogue method uses a database of objects derived from reforecasts and their respective reanalysis. Given a real-time forecast field, it searches the database and selects the top-ranked objects with the most similar set of attributes using the MODE fuzzy logic algorithm for object matching. The attribute probabilities obtained with the set of selected object-analogues are used to derive a multi-layer probabilistic prediction. The attribute probabilities are combined into three uncertainty layers that address the main concerns of most applications: location, area, and magnitude. The multi-layer uncertainty can be weighted and combined or used independently in such that it provides a more accurate prediction, adjusted according to the application interest.
In this study we present preliminary results of the object-analogue method. Using a database with one hundred storms we perform a leave-one-out cross-validation to verify the model results.