Abstract
Analogs are used as a forecast postprocessing technique, in which a statistical forecast is derived from past prognostic states. This study proposes a method to identify analogs through spatial objects, which are then used to create forecast ensembles. The object-analog technique preserves the field’s spatial relationships, reduces spatial dimensionality, and consequently facilitates the use of artificial intelligence algorithms to improve analog selection. Forecast objects are created with a three-step object selection, combining standard image processing algorithms. The resulting objects are used to find similar forecasts in a training set with a similarity measure based on object area intersection and magnitude. Storm-induced power outages in the Northeast United States motivated the method’s validation for 10-m AGL wind speed forecasts. The training set comprises reforecasts and reanalyses of events that caused damages to the utility infrastructure. The corresponding reanalyses of the best reforecast analogs are used to produce the object-analog ensemble forecasts. The forecasts are compared with other analog forecast methods. Analogs representing lower and upper predictability limits provide references to distinguish the method’s ability (to find good analogs) from the training set’s ability (to provide good analogs) to generate skillful ensemble forecasts. The object-analog forecasts are competitively skillful compared to simpler analog techniques with an advantage of lower spatial dimensionality, while generating reliable ensemble forecasts, with reduced systematic and random errors, maintaining correlation, and improving Brier scores.
NCAR Visiting Scientist, Boulder, Colorado.
Current affiliation: Jupiter Technology Systems, Boulder, Colorado.
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