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Journal Title:Atmosphere
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Description:A forecast from a numerical weather prediction (NWP) model can be decomposed into model climate and anomaly. Each part contributes to forecast error. To avoid errors from model climate, an anomaly, rather than a full field, should be used in a model. Model climate is replaced by the observed climate to reconstruct a new forecast for application. Using a Lorenz model, which has similar error characteristics to an NWP model, the following results were obtained. (a) The new anomaly-based method can significantly and steadily increase forecast accuracy throughout the entire forecast period (28 model days). On average, the total forecast error was reduced ~25%, and the correlation was increased by ~100–200%. The correlation improvement increases with the increasing of forecast length. (b) The method has different impacts on different types of error. Bias error was almost eliminated (over 90% in reduction). However, the change in flow-dependent error was mixed: a slight reduction (~5%) for model day 1–14 forecasts and increase (~15%) for model day 15–28 forecasts on average. The larger anomaly forecast error leads to the worsening of flow-dependent error. (c) Bias error stems mainly from model climate prediction, while flow-dependent error is largely associated with anomaly forecast. The method works more effectively for a forecast that has larger bias and smaller flow-dependent error. (d) A more accurate anomaly forecast needs to be constructed relative to model climate rather than observed climate by taking advantage of cancelling model systematic error (i.e., perfect-model assumption). In principle, this approach can be applicable to any model-based prediction.
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Source:Atmosphere, 13(9), 1487
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DOI:
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ISSN:2073-4433
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Rights Information:CC BY
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Compliance:Library
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