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充分，因此真实状态往往不在集合预报的范围内。此前的研究表明，这种“缺乏扩散”的情况在地表附近比在对流层中层更为严重，这表明部分问题可能是由于模型中的陆地表面条件造成的。PI试图通过扰动地表条件并在集合预报中随机处理太阳辐射(地表的入射太阳辐射)来改善这种情况。这项研究包括两个部分：首先，是一项观察性/诊断性研究，旨在了解地表通量和太阳辐射误差的时空特征。第二个部分是设计方法，将地表通量的误差与地表湿度联系起来，并对其进行扰动。这种隔离会受到随机噪声的干扰。一旦开发出合适的候选方法，它们将在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式集合中进行测试。这项研究的结果有可能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，从洪水预报到评估电力需求。本研究旨在提高集合预报的质量，增强集合预报的有用性。研究生所接受的培训将有助于他们为在人才短缺的群体预测职业生涯做好准备。

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.