Statistical upscaling of numerical weather predictions to enable coupled modelling of local weather impacts

The operation of any city, county or state is dependent to a significant degree upon weather conditions, especially with regard to relative extremes in wind, precipitation or temperature. For example, with precipitation events, local typography and weather conditions influence water runoff and infiltration, which directly affect flooding as well as drinking water quality and availability. The operation of the distribution system of an electric utility, particularly with an overhead infrastructure, can be highly sensitive to local weather conditions, including disruption by strong winds. The impact of such events creates issues of public safety for both citizens and first responders. Therefore, the availability of highly localized weather model predictions focused on municipal public safety operations has the potential to mitigate the impact of severe weather on citizens and local infrastructure. Typically, information at such a scale is simply not available. Hence, what optimization that is applied to these processes to enable proactive efforts utilizes either historical weather data as a predictor of trends or the results of synoptic-scale weather models. Neither source of information is appropriately matched to the temporal or spatial scale of many such operations. While near-real-time assessment of observations of current weather conditions may have the appropriate geographic locality, but by its very nature is only directly suitable for reactive response.

Current state-of-the-art numerical weather prediction (NWP) codes operating a the meso-gamma scalehave been shown to have reasonable skill in being above to predict specific events or combination of weather conditions with sufficient spatial and temporal precision to address this scale mismatch. For example, we are using the WRF-ARW (version 3.1.1) community numerical weather prediction (NWP) model to produce 84-hour forecasts for the York City metropolitan area, which are updated every twelve hours. It operates in a nested configuration, with the highest resolution at two km, utilizing 42 vertical levels. The configuration also includes parameterization and selection of physics options appropriate for the range of geography within the domain from highly urbanized to rural. This includes WSM-6 microphysics (includes explicit ice, snow and graupel), Yonsei University non-local-K scheme with explicit entrainment layer and parabolic K profile in the unstable mixed layer for the planetary boundary layer, Noah land-surface modeling with soil temperature and moisture in four layers, fractional snow cover and frozen soil physics, Grell-Devenyi ensemble cumulus parameterization, and the 3-category urban canopy model with surface effects for roofs, walls, and streets.

However, such a model-based weather forecast is only a prerequisite to the aforementioned optimization of weather-sensitive business operations. Based upon analysis of historical damage events and anecdotal evidence in the region, it is clear, for example, that storm-driven disruptions of the overhead electric distribution network (e.g., poles and wires) are caused by physical interaction of the atmosphere directly with that infrastructure or indirectly via nearby trees. However, reliable modelling with sufficient throughput for operational utilization of such interaction is neither tractable from a computational perspective nor verifiable from observations. Given that and the relative uncertainty in the processes and data, we approached the outage prediction from a stochastic perspective. Historical observations from a local, relatively dense mesonet operated by AWS Convergence Technologies were analyzed along with data from a local electric utility concerning the characteristics of outage-related infrastructure damage from weather events, as well as the information about the distribution network and local environmental conditions. Data from this mesonet are also utilized to validate and improve the results of the WRF configuration. A Quasi-Poisson model was developed to statistically upscale the results of each WRF execution to irregularly shaped substation areas within the utility's service territory. This has enabled the generation of forecasts of the number of jobs to be dispatched to repair storm-induced outages in each area. This approach also incorporates uncertainties in both the weather and outage data. Therefore, the model provides probabilities of outage restoration jobs per substation, which is associated with visualizations that explicitly depict the uncertainty.

Based upon the analysis of the historical data, one of the key linkages between damage and severe storms is the magnitude of wind gusts. However, as is typical for NWP codes, WRF-ARW does not produce a direct representation of gusts. Therefore, a post-processing model was developed using a Bayesian hierarchical approach. It links the physical model outputs with real observations to create a calibrated, statistical representation of gusts. This model post-processes the sustained wind forecast on the topologically regular output generated by WRF based on the gust observations from the mesonet over time. It is used as one of the inputs to the storm impact for each substation area.

This coupled physical-statistical approach has enabled operational prediction of storm impacts on local infrastructure as well as quantification of the uncertainty associated with such forecasts. We will discuss the results to date and its relative effectiveness for operational decision making as well as plans for related applications.