Comment by mdorazio
10 months ago
Not at all. Look at the growth in human buildings in the most at-risk areas and you’ll see why that number is so big now. It’s only slightly due to an increase in severe weather event frequency / severity.
10 months ago
Not at all. Look at the growth in human buildings in the most at-risk areas and you’ll see why that number is so big now. It’s only slightly due to an increase in severe weather event frequency / severity.
Indeed, and not just building more in more at-risk places but also the cost of building materials, construction labor and code compliance requirements have all generally increased more than baseline inflation. Factors like these tend to greatly increase recent estimates vs historical.
I read a paper a few years back which dove into how the data sources for weather damage assessment have changed a lot over the years. Much of the increase is due to more complete reporting and changes in categorization. Also, nowadays more things are insured and modern IT has made gathering the insurance reporting far more exhaustive. Plus local, state and federal agencies responsible for relief and/or recovery are gathering and reporting increasing amounts of data with each decade since the 70s (in part because their budgets rely on it). Factors like these mean in prior decades the total damage costs may have been more similar to today's than they appear but a lot of the damage data we gather and report now wasn't counted or gathered then.
Although I have no experience related to weather science, I remember the paper because it made me realize how many broad-based, multi-decadal historical data comparisons we see should have sizable error bars (which never make it into the headline and rarely even into the article). Data sources, gathering and reporting methods and motivations are rarely constant on long time scales - especially since the era of modern computing. Of course, good data scientists try to adjust for known variances but in a big ecosystem with so many evolving sources, systems, entities and agencies, it quickly gets wickedly complex.
> Factors like these mean in prior decades the total damage costs may have been more similar to today's than they appear but a lot of the damage data we gather and report now wasn't counted or gathered then
This is definitely part of it. Another part is that people live in more at-risk regions now than in the past (Florida is a great example, population has more than 10x'd since 1950).
Ultimately, the way we think about it is no matter what the underlying cause, weather-related damages could be significantly reduced with better data/forecasts
> weather-related damages could be significantly reduced with better data/forecasts
I agree. My point was only so those surprised by the massive increase in cost estimates can put those numbers in perspective since neither the average quantity nor severity of adverse weather events have changed substantially over the decades.
Providing more granular data to enable more accurate and timely weather forecasts is a sound business thesis even if adverse weather isn't happening 2x more frequently or energetically. It's still a large economic impact where money can be saved. More broadly, better forecasts can improve agricultural yields, reduce business disruption and increase throughput of transportation networks.