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.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.