Convective Modeling and Predictabilty Studies

大气数值模式的可预测性存在固有的限制。产生这一极限的主要原因通常被认为是波谱不同分量之间的非线性相互作用；模型可解析的波谱中的不确定性和误差以及由于忽略未解析波而引入的误差随着时间的积分而增加，并在整个波谱中蔓延，最终破坏预报。气象学家现在对用于天气预报的大尺度模型的可预测性有了一些了解；空间分辨率大约为数百公里。近年来，对流和中尺度气象事件的数值模式变得越来越复杂，现在可以研究可预测性的问题。对流尺度模式的空间分辨率约为1 ~ 2 km，中尺度模式的空间分辨率约为20 ~ 100 km。根据该奖项，Droegemeier教授将研究对流尺度模型的可预测性，该模型已被用于研究雷暴流出和微暴的动力学，并在模拟这些对流尺度事件方面取得了一些成功。除了检查模型预测对初始条件不同方面的敏感性外，德罗吉迈尔教授还将寻找用直接多普勒雷达观测初始化模型的方法；他还将继续研究将分段抛物线法应用于气象问题。

There is an inherent limit to the predictability of atmospheric numerical models. The principal reason for this limit is generally considered to be the nonlinear interactions between different components of the wave spectrum; uncertainties and errors in the wave spectrum resolvable by the model and errors introduced by the neglect of unresolved waves grow with integration in time, spreading throughout the spectrum, and eventually destroy the forecast. Meteorologists now have some understanding of predictability of large scale models used for weather forecast; the spatial resolution is roughly hundreds of kilometers. Numerical models for convective- and meso- scale meteorological events have grown in sophistication in recent years, the question of predictability can now be investigated. The spatial resolution of convective-scale models is roughly 1-2 km, and 20-100 km for mesoscale models. Under this award, Professor Droegemeier will investigate the predictability of a convective-scale model, which has been used to study the dynamics of thunderstorm outflow and microbursts and demonstrated some success in simulating these convective-scale events. Besides examining the sensitivity of the model prediction to different aspects of the initial conditions, Professor Droegemeier will look for ways to initialize the model with direct Doppler radar observations; he will also continue his work on adapting the Piecewise Parabolic Method to meteorological problems.