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Reacting to the Unpredictable

image credit: WMT Tower north of Marion, IA (National Weather Service Survey)
Peter Watson's picture
CTO ACW Analytics

Peter is a data scientist who is passionate about using machine learning and other techniques to describe and model the impacts of weather on the infrastructure. He believes data-driven...

  • Member since 2020
  • 9 items added with 2,138 views
  • Jan 6, 2021

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:

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. 

Matt Chester's picture
Matt Chester on Jan 6, 2021

Given the difficulty in forecasting these events, the focus must shift to interpreting them as soon as they have occurred. 

Of course the ounce of prevention is worth the pound in a cure, so hardening systems should be a top priority-- but I agree too often we ignore the speed with which reaction can/should occur and what type of difference that can make. 

Vijay Jayachandran's picture
Vijay Jayachandran on Jan 6, 2021

Agreed. Eliminating the problem should be our ultimate objective. In the interim, however, we must do everything possible to react with speed and precision.

ML-based models can help with both - long-term resilience planning as well as real-time operational response.  

Mark Silverstone's picture
Mark Silverstone on Jan 8, 2021

And because such events can occur up to 15 times a year, they can adversely impact utility reliability metrics like CAIDI, SAIDI and SAIFI.

If these metrics are changing there is a great deal of risk assessment that needs to be done and acted upon in order to manage these types of events. Not a small task.


CAIDI is "Customer Average Interruption Duration Index"

SAIDI is "System Average Interruption Duration Index"

SAIFI is "System Average Interruption Frequency Index"

Who knew? 😊

Peter Watson's picture
Thank Peter for the Post!
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