It may be self evident that weather can often have an adverse affect on the delivery of electric power. However, very little attention has been given to understanding the role of weather on power outages in a comprehensive climatological fashion. In this study, the Plymouth State University meteorology program and Public Service of New Hampshire (PSNH) are collaborating to gain a further understanding of the exact types of weather that cause power outages in the PSNH network. The ultimate goal of this research is to support resource management decision making when adverse weather conditions are being forecast. As with any resource management endeavor involving weather forecasting, it is important to know and understand the climatology of past events.

Therefore, eight years of PSNH power outage data (Jan. 1996- Aug. 2004) are examined to develop a climatological composite of weather events that cause major power outages. Only major outage events (> 100 interruptions) are being examined. Over this period a total of 68 major outage events have been attributed to adverse weather. In these eight years, four different types of weather events have been identified to cause power outage: wind storms, winter storms (ice, sleet, snow and wind), thunderstorms, and heat waves. Seasonal and inter-annual variability of these four different storm types have been examined. Initial results show that winter storms that cause power outages occur most often in the transitional months of early winter (Nov, Dec) and late winter/early spring (Feb, Mar). In addition, climatological weather factors such as surface temperature, wind speed and direction, location and central pressure of any cyclone are examined to determine if there are common and forecastable weather characteristics for each type of event. Results of this ongoing research will be presented.

It is hoped that the results of this research will yield important information for the managers at PSNH that will allow them to maximize their resources in the field in order to provide their customers with a more timely and efficient return to service when weather causes outages. It is hoped that the results of this study will be robust enough to develop a criteria based forecasting decision tree to assist the PSNH managers in their day-to-day operations.