Reacting to the Unpredictable

There were quite a few notable weather events in the United States over the course of 2020.  One of particular note was a derecho that developed on the 10th of August in Iowa. After forming and rapidly intensifying, it headed east, sweeping across much of the Midwest causing widespread damage. In total, the storm caused about $7 billion in damage, and featured wind gusts in excess of 100 mph.  It was likely the most damaging thunderstorm event in US history.  For more details see: https://www.weather.gov/dvn/summary_081020

Derechos and thunderstorms inflict a huge amount of damage but have traditionally been very difficult to predict and prepare for.  How, when, and where the convective energy that powers these events is released depends on a lot of different factors that are difficult for weather forecasters to predict with confidence and precision.  In the best case scenario, the National Weather Service’s Storm Prediction Center is able to issue the appropriate watches and warnings several hours before such a storm arrives, but even that is very little time for emergency managers to get prepared.  

Additionally, because they are so sudden, the confusion and uncertainty from before big convective storms lingers well after they are over.  Emergency managers at municipalities and utility companies can be unsure about the exact locations and levels of damage many days after the events have passed.  And because such events can occur up to 15 times a year, they can adversely impact utility reliability metrics like CAIDI, SAIDI and SAIFI. 

Given the difficulty in forecasting these events, the focus must shift to interpreting them as soon as they have occurred. There are many real-time data sources that can be used to reconstruct events and estimate their impacts. Due to the highly non-linear nature of the interaction between weather and infrastructure assets, machine learning is becoming an increasingly powerful tool for modeling such events after the fact. Insights gained from these models can help emergency managers react quickly and decisively. 

Decision support tools based on radar and other real-time weather observations are now a reality. They can eliminate post-storm uncertainty and give emergency responders the situational awareness they need to react to sudden storms like the Derecho on August 10, 2020.