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Download e-book for kindle: Atmospheric Modeling, Data Assimilation and Predictability by Eugenia Kalnay

By Eugenia Kalnay

ISBN-10: 0511076274

ISBN-13: 9780511076275

ISBN-10: 0521791790

ISBN-13: 9780521791793

ISBN-10: 0521796296

ISBN-13: 9780521796293


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Extra resources for Atmospheric Modeling, Data Assimilation and Predictability

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The score used by Hughes was a standardized anomaly correlation (SAC), which accounted for the larger variability of sea level pressure at higher latitudes compared to lower latitudes. Unfortunately the SAC is not directly comparable to other scores such as the anomaly correlation (discussed in the next section). The fact that until 1976 the 3-day forecast scores from the model were essentially constant is an indication that their rather low skill was more based on synoptic experience than on model guidance.

The second goal of the ensemble forecasting, to provide guidance on the uncertainty of each forecast, is accomplished best by the use of two types of plots. The “spaghetti” plots show a single contour line for all 17 forecasts, and the probabilistic plots show, for example, what percentage of the ensemble predicts 24-h accumulated precipitation of more than 1 inch at each grid point (for probabilistic Quantitative Precipitation Forecasts or pQPF). Both of them provide guidance on the reliability of the forecasts in an easy-to-understand way.

In the 3D-Var approach one defines a cost function proportional to the square of the distance between the analysis and both the background and the observations (Sasaki, 1970). The cost function is minimized directly to obtain the analysis. 2) measures the distance of a field x to the observations (the first term in the cost function) and the distance to the first guess or background x b (the second term in the cost function). The distances are scaled by the observation error covariance R and by the background error covariance B respectively.

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Atmospheric Modeling, Data Assimilation and Predictability by Eugenia Kalnay

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