Download Atmospheric Modeling, Data Assimilation and Predictability by Eugenia Kalnay PDF

By Eugenia Kalnay

This finished textual content and reference paintings on numerical climate prediction covers for the 1st time, not just equipment for numerical modeling, but additionally the real similar parts of knowledge assimilation and predictability. It accommodates all elements of environmental desktop modeling together with an old review of the topic, equations of movement and their approximations, a contemporary and transparent description of numerical equipment, and the selection of preliminary stipulations utilizing climate observations (an vital new technology referred to as facts assimilation).

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Example text

The first goal is illustrated in Fig. 1, prepared at the Climate Prediction Center (CPC, the Climate Analysis Center of the former NMC) for the verification of the NCEP ensemble during the winter of 1997–8. 1: Anomaly correlation of the ensembles during the winter of 1997–8 (controls, T126 and T62 , and ten perturbed ensemble forecasts). ) skill. The control “deterministic” forecast (circles) had an “anomaly correlation” (AC, pattern correlation between predicted and analyzed anomalies) in the 5-day forecast of 80%, which is quite good.

First, there is the difficulty raised by Richardson that the horizontal divergence cannot be measured with sufficient accuracy. Moreover, the horizontal divergence is only one of a class of meteorological unobservables which also includes the horizontal acceleration. And second, if the primitive Eulerian equations are employed, a stringent and seemingly artificial bound is imposed on the size of the time interval for the finite difference equations. The first obstacle is the most formidable, for the second only means that the integration must proceed in steps of the order of fifteen minutes rather than two hours.

The control “deterministic” forecast (circles) had an “anomaly correlation” (AC, pattern correlation between predicted and analyzed anomalies) in the 5-day forecast of 80%, which is quite good. The ten perturbed ensemble members have individually a poorer verification with an average AC of about 73% at 5 days. This is because, in the initial conditions, the control starts from the best estimate of the state of the atmosphere (the analysis), but growing perturbations are added to this analysis for each additional ensemble member.

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