Optimal Configurations of Ensemble Prediction Systems for Short-Range QPF

包围预测是许多实验室和大学正在开发的一种技术，试图处理这样一个事实，即不同的预测模型具有不同的优点和缺点，以及不同的初始假设，参数化等，给出不同的结果。 集合预报是一种-本研究旨在探讨混合集合预报系统的最佳配置问题，以提高集合预报的可靠性。中尺度有限区域模式。 本研究计划将研究模式预报对边界层和积云参数化变化以及随机扰动的敏感性。 审查将涉及多个案件（每赛季30 - 60人）中尺度模式在30 km水平网格间距上对冷季和暖季的预报结果进行了分析，以便对误差增长特征和集合预报性能进行严格的统计评估。这项研究很重要，因为它将导致更好地估计降水的可预报性极限，并为如何建立最佳的预报方案提供指导。中尺度集合预报系统，以改进有限计算资源下的降水业务预报。

Ensemble Forecasting is a technique under development at a number of laboratories anduniversities to try to deal with the fact that different forecast models with different strengths and weaknesses and different initial assumptions, pararneterizations, etc., give different results. The ensemble forecast is a sort-of average of all the individual forecasts by different models.Verification studies have shown that ensemble forecasts can be better than any of the individual forecasts within the ensemble.This research will undertake to improve the reliability of ensemble forecasts of precipitation 12 to 36 hours in advance of its occurrence by addressing the issue of optimal configurations of mixed ensemble prediction systems for mesoscale limited-area models. This program of study will examine the sensitivity of model forecasts to variations in boundary layer and cumulus parameterizations as well as stochastic perturbations. The examination will involve multiple cases (between 30 and 60 per season) of mesoscale model forecasts at a horizontal grid spacing near 30 km for the cool and the warm seasons to allow for a rigorous statistical assessment of error growth characteristics and ensemble performance.This research is important because it will lead to better estimates of predictability limits for precipitation and provide guidance on how to construct optimum mesoscale ensemble prediction systems to improve operational forecasts of precipitation with limited computational resources.