论文标题
WRF模拟,模型敏感性和2013年12月新英格兰Ice Storm的分析
WRF Simulation, Model Sensitivity, and Analysis of the December 2013 New England Ice Storm
论文作者
论文摘要
冰暴对电力基础设施构成了巨大的破坏风险。为了改善风暴响应并最大程度地降低成本,能源公司支持了利用数值天气预测(NWP)模型的气象产出的冰量预测技术的开发。该领域的大多数科学文献都集中在NWP模型(例如天气研究和预测模型(WRF)模型)上的应用中,但此类分析往往几乎不提供对使用前的产出保真度的验证。这项研究评估了WRF的性能,描绘了2013年12月21日至23日的新英格兰冰风暴在地面和垂直方面。使用八个行星边界层(PBL)物理学参数化,三个重新分析数据集,两个垂直级别配置以及带有和没有网格的nuding进行一系列灵敏度测试。降水,温度,风速和风向的模拟值在美国东北部和加拿大东南部的几个站位置进行了表面和无线电调查的验证。结果表明,虽然在空间和时间上平均的近表面变量统计量与精选的冰态案例研究相一致,但在站点级别检查时,近表面变量对模型非常敏感。没有单个模型配置为所有变量或车站位置提供最强大的解决方案,尽管一种方案通常会产生最小现实主义的模型输出。总的来说,我们发现仔细的模型灵敏度测试和广泛的验证是最大程度地减少冰暴中基于模型的偏见的必要组成部分。
Ice storms pose significant damage risk to electric utility infrastructure. In an attempt to improve storm response and minimize costs, energy companies have supported the development of ice accretion forecasting techniques utilizing meteorological output from numerical weather prediction (NWP) models. The majority of scientific literature in this area focuses on the application of NWP models, such as the Weather Research and Forecasting (WRF) model, to ice storm case studies, but such analyses tend to provide little verification of output fidelity prior to use. This study evaluates the performance of WRF in depicting the 21-23 December 2013 New England ice storm at the surface and in vertical profile. A series of sensitivity tests are run using eight planetary boundary layer (PBL) physics parameterizations, three reanalysis datasets, two vertical level configurations, and with and without grid nudging. Simulated values of precipitation, temperature, wind speed, and wind direction are validated against surface and radiosonde observations at several station locations across northeastern U.S. and southeastern Canada. The results show that, while the spatially and temporally averaged statistics for near-surface variables are consistent with those of select ice-storm case studies, near-surface variables are highly sensitive to model when examined at the station level. No single model configuration produces the most robust solution for all variables or station locations, although one scheme generally yields model output with the least realism. In all, we find that careful model sensitivity testing and extensive validation are necessary components for minimizing model-based biases in simulations of ice storms.