COUPLING OF MESOSCALE WEATHER MODELS TO BUSINESS OPERATIONS UTILIZING VISUAL DATA FUSION

摘要

In many industries weather conditions are a critical factor in planning business operations and making effective decisions. Typically, what optimization that is applied to these processes to enable proactive efforts utilize either historical weather data as a predictor of trends or weather forecasts of limited precision. Alternatively, numerical weather models operating at higher resolution in space and time with more detailed physics exist for short-term forecasting (i.e., a few days at the mesoscale) that offer greater precision and accuracy for a more limited region. Although such a model has occasionally been adapted for the specific three-dimensional geographic area and time-scale relevant to the aforementioned decision making (e.g., Carpenter and Bassett, 2001; Snook, 2001), usually it is not. Mesoscale models can be utilized in variety of weather-sensitive decision-making efforts such as emergency planning, energy production, airline operations, risk assessment, agricultural activities, commodity trading, etc. For each of these applications, information is assessed and decisions are made based upon a variety of static and dynamic data sets, a subset of which are weather-related. The utilization of these data and the complexity of the decision-making process changes when high-resolution predictive data are incorporated. These applications imply the coupling of weather simulations with other models, analyses and data. In order for mesoscale models to be utilized in such applications some adaptation is required, including customization of the computational grid and model parameterization focused on the specific weather sensitivity of the business operation process in question. To enable effective assessment and appropriate decisions, focused visualizations must also be designed to integrate business and weather model data, yet still be driven by user goals. These visualizations must employ appropriate mapping of user goals to the design of pictorial content by considering both the underlying data characteristics and the (human) perception of the visualization (Treinish, 1999a). Hence, the resultant visualizations may not show forecasts of weather phenomena directly but the derived properties, which are influenced by weather, and are of direct relevance to the decision maker or industry specialist. In these cases, the information is in terms of the impact of weather, not weather variables produced by a simulation. The problem is illustrated schematically in Figure 1. Two traditional data generators are shown on the top and the bottom (weather and non-weather, respectively). Although visualization is applicable to both, typically this is mutually independent. An approach of visual data fusion to address the visualization design problem in such applications is proposed as one method of coupling mesoscale models to business operations.