We propose a new method to search for analogs based on forecast objects, i.e., features with distinguishable shapes in a forecast field. Object-based analogs preserve the field spatial covariance, such that the best matches represent similar dynamics. In addition, it reduces spatial dimensionality and allows for advanced algorithms to build the analog set (e.g., artificial intelligence).
Although techniques to select and match objects have been used in object-based forecast verification, they are inadequate to verify continuous variables, such as wind speed. To build the analog set, the predicted and observed quantity must exist, but the current object-based verification techniques cannot always find and match corresponding objects. Thus, a region-based technique for object matching is presented as a step in the Object-Analog methodology, and may be used on its own as a forecast verification technique.
This study validates 10-m AGL wind speed forecasts created with the Object-Analog method. Motivated by frequent storm-induced power outages in the Northeast U.S., the statistical forecasts will be incorporated into the Outage Prediction Model at the University of Connecticut. The training set used for this study comprises 3.3km gridded reforecasts (dynamically downscaled GEFS/R control member) and reanalysis (dynamically initialized with the Four-Dimensional Data Assimilation and Forecasting system, FDDA), both using the WRF model. A leave-one-out cross-validation is used to verify the method results against the corresponding raw deterministic forecasts and traditional grid-based analog ensembles.