Optimal Configurations of Ensemble Prediction Systems for Short-Range QPF

短程 QPF 集合预测系统的优化配置

基本信息

批准号：
9908968
负责人：
Steven Mullen
金额：
$ 15.7万
依托单位：
University of Arizona
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2000
资助国家：
美国
起止时间：
2000-09-01 至 2004-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=9908968&HistoricalAwards=false
关键词：
Optimal Configurations Ensemble Prediction Systems

项目摘要

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.

集合预测是许多实验室和大学正在开发的一种技术，旨在解决具有不同优点和缺点以及不同初始假设、参数化等的不同预测模型给出不同结果的问题。集合预报是不同模型的所有单独预报的平均。验证研究表明，集合预报可以优于集合内的任何单独预报。本研究将致力于提高集合预报的可靠性。通过解决中尺度有限区域模型混合集合预报系统的最佳配置问题，在降水发生前 12 至 36 小时进行预测。该研究计划将检查模型预测对边界层和积云参数化以及随机扰动变化的敏感性。检查将涉及冷季和暖季水平网格间距接近 30 公里的中尺度模型预报的多个案例（每个季节 30 到 60 个），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，因为它将更好地估计降水的可预测性极限，并为如何构建最佳中尺度集合预测系统提供指导，以利用有限的计算资源改进降水的业务预报。