Incorporating the Uncertainty of Atmosphere-Land Surface Interactions into Ensemble Forecasts

集合天气预报的一个常见问题是集合成员的传播不足，因此通常真实状态位于集合预报的范围之外。 以前的研究表明，这种“缺乏传播”在地表附近比在对流层中部更糟，这表明部分问题可能是由于模型中的地表条件。 PI试图通过扰动地表条件和在集合预报中随机处理日射（地表入射太阳辐射）来改善这种情况。 这项研究有两个组成部分：第一，观测/诊断研究，旨在了解地面通量和日射误差的空间和时间特征。 第二部分是设计方法，将地表通量的误差与地表湿度联系起来，并对其进行扰动。 一旦开发出合适的候选方法，将在NOAA预报系统实验室运行的快速更新周期或天气研究和预报模型集合中对其进行测试。 从这项研究的结果有可能找到自己的方式进入业务集合天气预报。集合预报产生的概率预报越来越多地用于各种各样的目的，从洪水预报评估电力需求。 这项研究的目的是提高集合预报的质量和增强其实用性。 研究生接受的培训将有助于他们为人才短缺的集合预报职业做好准备。

A common problem with ensemble weather forecasts is the insufficient spread of the ensemble members so that often the true state lies outside the span of the ensemble forecasts. Previous studies indicate that this "lack of spread" is worse near the surface than it is in mid-troposphere, suggesting that part of the problem may be due to the land surface conditions in the model. The PIs seek to improve this situation by perturbing the land surface condition and treating the insolation (incoming solar radiation at the surface) stochastically in the ensemble forecasts. The research has two components: first, an observational/diagnostic study geared toward the understanding of the spatial and temporal characteristics of errors in surface fluxes and insolation. The second component is to design methodologies to link the errors of the surface fluxes to the surface moisture and perturb it. The isolation will be perturbed with stochastic noise. Once suitable candidate methods have been developed, they will be tested in the Rapid Update Cycle or Weather Research and Forecast model ensemble runs by the Forecast System Laboratory, NOAA. Results from this research have the potential of finding their way into operational ensemble weather forecasts.Probabilistic forecasts generated by ensemble predictions are increasing used for a wide variety of purposes, from flood forecasting to assessing the demand for electricity. This research is aimed at improving the quality of ensemble forecasts and enhancing their usefulness. The training that the graduate students receive will help to prepare them for careers in ensemble forecasting where there is a shortage of talented manpower.