Visualizing Uncertain Tropical Cyclone Predictions using Representative Samples from Ensembles of Forecast Tracks

Abstract

A common approach to sampling the space of a prediction is the generation of an ensemble of potential outcomes, where the ensemble's distribution reveals the statistical structure of the prediction space. For example, the US National Hurricane Center generates multiple day predictions for a storm's path, size, and wind speed, and then uses a Monte Carlo approach to sample this prediction into a large ensemble of potential storm outcomes. Various forms of summary visualizations are generated from such an ensemble, often using spatial spread to indicate its statistical characteristics. However, studies have shown that changes in the size of such summary glyphs, representing changes in the uncertainty of the prediction, are frequently confounded with other attributes of the phenomenon, such as its size or strength. In addition, simulation ensembles typically encode multivariate information, which can be difficult or confusing to include in a summary display. This problem can be overcome by directly displaying the ensemble as a set of annotated trajectories, however this solution will not be effective if ensembles are densely overdrawn or structurally disorganized. We propose to overcome these difficulties by selectively sampling the original ensemble, constructing a smaller representative and spatially well organized ensemble. This can be drawn directly as a set of paths that implicitly reveals the underlying spatial uncertainty distribution of the prediction. Since this approach does not use a visual channel to encode uncertainty, additional information can more easily be encoded in the display without leading to visual confusion. To demonstrate our argument, we describe the development of a visualization for ensembles of tropical cyclone forecast tracks, explaining how their spatial and temporal predictions, as well as other crucial storm characteristics such as size and intensity, can be clearly revealed. We verify the effectiveness of this visualization approach through a cognitive study exploring how storm damage estimates are affected by the density of tracks drawn, and by the presence or absence of annotating information on storm size and intensity.

The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

“…Other task categories such as 'automatic detection of patterns, trends, and anomalies' relates to R6 [LPCRH18], 'prediction/forecasting future trends' corresponds to R8 [WFZ * 15], and 'identifying relationships between different variables' partially corresponds to R7 [WSL * 14]. The remaining categories do not have limited coverage in the visualization research but some of them do not correspond to any task requirements of domain experts.…”

“…This type of work has lots of applications in the oil and gas industry. Liu et al [LPCRH18] presented a visualization of ensemble forecasts by calculating the representative set of the uncertainty data and representing uncertainty by its spatial distribution. Figure 13 shows the results of this technique by selectively sampling the original ensemble and representing in the form of annotated tracks.…”

“…Advances in technology and computational resources have enabled scientists to generate large amounts of data with higher density, complexity, and diversity. Researchers aim to run high-resolution ocean and atmospheric models [SMG * 15], ensembles visualization and analysis [BLLS17, FKRW17,WLSL17,KRRW18], study ensemble uncertainties [SZD * 10, WMK13, ME18, LBR * 17, KTB * 18, AZM * 15], examine the sensitivity of parameters [WLSL17,KBL18,KRRW18], analyze eddies (mesoscale ocean features) and associated uncertainty in high-fidelity simulations [WPS * 16, WHP * 11], interactive querying and filtering in multidimensional data [ME18, KHS * 18b, SSJKF09, LZM14, DPD * 15], and apply predictive analytics [LPCRH18,LMK * 15]. Dealing with data of such volume and complexity for different application requirements presents new challenges for visualization and visual analytics researchers, and has led to the development of many data analysis and visualization techniques.…”

“…Linked views to explore ocean forecast ensembles [HMC * 13] Sampling original sample to produce a smaller representation of ensemble using annotated tracks[LPCRH18] …”

Prediction Uncertainty Estimation for Hate Speech Classification

“…Prediction values were also visualized in geo-spatial research on hurricane tracks [10,29]. Importance of visualization for prediction uncertainty estimation in the context of decision making was discussed in [18,17].…”