Mississippi State University, USA.
IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):1421-30. doi: 10.1109/TVCG.2010.181.
Numerical weather prediction ensembles are routinely used for operational weather forecasting. The members of these ensembles are individual simulations with either slightly perturbed initial conditions or different model parameterizations, or occasionally both. Multi-member ensemble output is usually large, multivariate, and challenging to interpret interactively. Forecast meteorologists are interested in understanding the uncertainties associated with numerical weather prediction; specifically variability between the ensemble members. Currently, visualization of ensemble members is mostly accomplished through spaghetti plots of a single mid-troposphere pressure surface height contour. In order to explore new uncertainty visualization methods, the Weather Research and Forecasting (WRF) model was used to create a 48-hour, 18 member parameterization ensemble of the 13 March 1993 "Superstorm". A tool was designed to interactively explore the ensemble uncertainty of three important weather variables: water-vapor mixing ratio, perturbation potential temperature, and perturbation pressure. Uncertainty was quantified using individual ensemble member standard deviation, inter-quartile range, and the width of the 95% confidence interval. Bootstrapping was employed to overcome the dependence on normality in the uncertainty metrics. A coordinated view of ribbon and glyph-based uncertainty visualization, spaghetti plots, iso-pressure colormaps, and data transect plots was provided to two meteorologists for expert evaluation. They found it useful in assessing uncertainty in the data, especially in finding outliers in the ensemble run and therefore avoiding the WRF parameterizations that lead to these outliers. Additionally, the meteorologists could identify spatial regions where the uncertainty was significantly high, allowing for identification of poorly simulated storm environments and physical interpretation of these model issues.
数值天气预报集合通常用于业务天气预报。这些集合的成员是具有略微扰动初始条件或不同模式参数化的单个模拟，或者偶尔两者都有。多成员集合输出通常很大，多变量，并且难以交互解释。天气预报员有兴趣了解与数值天气预报相关的不确定性；特别是集合成员之间的可变性。目前，集合成员的可视化主要通过单个中层压力面高度等高线的 spaghetti 图来完成。为了探索新的不确定性可视化方法，使用天气研究和预报（WRF）模型创建了 1993 年 3 月 13 日“超级风暴”的 48 小时、18 个成员参数化集合。设计了一种工具来交互式探索三个重要天气变量的集合不确定性：水汽混合比、扰动位温、和扰动压力。不确定性使用单个集合成员标准差、四分位距和 95%置信区间的宽度来量化。采用自助法克服不确定性指标对正态性的依赖。为两位气象学家提供了基于丝带和字形的不确定性可视化、 spaghetti 图、等压线色图和数据横切图的协调视图，以便进行专家评估。他们发现这对于评估数据中的不确定性非常有用，特别是在集合运行中找到异常值时，可以避免导致这些异常值的 WRF 参数化。此外，气象学家可以识别不确定性显著较高的空间区域，从而可以识别模拟不良的风暴环境，并对这些模型问题进行物理解释。
