Optimal Configurations of Ensemble Prediction Systems for Short-Range QPF

集合预报是一些实验室和大学正在开发的一种技术，它试图处理这样一个事实，即不同的预报模型具有不同的优点和缺点，以及不同的初始假设、参数等，产生不同的结果。集合预报是不同模式的集合预报的平均值。验证研究表明，集合预报可以比集合内的任何单个预报更好。本研究将致力于在降水发生前12-36小时提高集合预报的可靠性，解决中尺度有限区域模式的混合集合预报系统的最佳配置问题。这项研究计划将检验模式预报对边界层和积云参数化以及随机扰动的变化的敏感性。这项研究将涉及冷季和暖季水平网格间距接近30公里的中尺度模式预报的多个案例(每个季节在30到60个之间)，以便对误差增长特征和集合性能进行严格的统计评估。这项研究很重要，因为它将导致对降水可预报界限的更好估计，并为如何建立最佳的中尺度集合预报系统提供指导，以利用有限的计算资源改进降水预报业务。

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.