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.

合奏预测是许多实验室和潜水公司正在开发的技术，试图处理以下事实，即具有不同优势和劣势以及不同初始假设，寄生虫等不同的预测模型会产生不同的结果。整体预测是通过不同模型对所有个人预测的平均水平。验证研究表明，整体预测可以比整体内的任何个体预测都更好。这项研究将提高整体预测的可靠性，以提高降水预测12至36个小时，以提前对其进行了限制性估计，以提前进行了限制性估计。该研究计划将研究模型预测对边界层和积云参数化的变化以及随机扰动的敏感性。该检查将涉及在30公里和温暖的季节接近30公里的水平网格间距上预测的中尺度模型的多个病例（每季30至60个），以便对误差增长特征和集合的性能进行严格的统计评估，这很重要。该研究将在构建系统上的限制限制，以提高趋势的限制，以限制趋势的限制，以限制趋势的限制。具有有限的计算资源。