this post was submitted on 06 Jul 2025
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Interested in people's opinions on this. Sounds like they were notified.
They massively missed the amount of rainfall. They expected a fairly typical flood event, not 20 feet of water in an hour.
As someone who knows nothing about the NWS, would they have gotten it right or at least closer if they didn't have funding cuts? Also to be clear, I don't support cutting their funding, just wondering if this was unavoidable.
This is a matter of storms being able to supercharge themselves due to global warming. Don’t let people reframe this against the NWS. It is like Hurricane Erick the other week that jumped to category 3 out of nowhere, storms are being rapidly supercharged. This flood broke all sorts of records, so to phrase it as the previous commenter did is unfair.. In the near future, their ability to even predict as well as they did will degrade though, at which point you will hear more of ‘they never knew what they were talking about anyways.’ This lays the groundwork for that.
People aren't reframing it directly against the NWS, they're blaming the fascist regime for cutting funds to important services. NWS has already stated the cuts are effecting the amount of data they can collect which directly effects the accuracy of their models, making the NWS less effective.
Our local weather person said on air that the cuts had affected the model accuracy. Something having to do with weather balloons not being sent up.
That's a very difficult question to answer. I don't work there but I studied meteorology and I'm a volunteer for a forecast office. They take in so many data sources that serve as input into forecast models and humans. The qualify of the data has dropped, but which data exactly could have made a difference would be difficult or impossible to pinpoint. It's possible better quality data would have helped, but we don't know for sure.
Not sure if you can answer this. But do you have any idea how these forecasts normally work? Like how do they normally predict flooding
For flooding it is a typical estimation from modelling from things like topography maps and known precipitation events. It is all rough estimations without collecting monitoring data, which is what funding for these agencies tend to be used for.
It requires a lot of information to accurately model and predict if any given river valley can handle 1.75 inches in a 3 mile radius in 4 hours, versus 3.25 inches in a 1 mile radius in 2 hours. How much does a 0.2 inch rainfall event in the previous 24 hours impact? What about 1 inch in the same area in 24 hours? (And so on.)
Ok that makes sense. Thank you. It's a really fascinating subject to me
I can't speak for the modeling regarding the amount of rain, but I'm was published in college for watershed analysis and now work in municipal development and review drainage plans.
When use LIDAR to update watershed maps and can estimate the amount of water rise that will occur at a specific location with a specific amount of rainfall. One thing that makes predictions in central Texas is the heavy development and vast difference in development standards.
Some municipalities are very strict, and only allow 15%-25% impervious cover, while others don't care. And with tree coverage it's hard to tell from serials if the ground is sod, mud, gravel, or concrete.
More importantly, many local jurisdictions don't require rainwater detention, and counties typically don't have the resources to inspect stuff installed without their knowledge outdlside of cities, so as development increases runoff into the watershed increases, as there's less pervious cover and what cover remains is more quickly saturated. Also, the grading of land and removal of vegetation leads to less friction, speeding up runoff. If upstream water is flowing faster, then the water rises higher and faster.
Thank you so very much. I really appreciate that explanation
There are models that predict precipitation amounts and river/lake stage (level), yes, but usually it's up to the lead meteorologist to make the call after reviewing those models combined with their experience with the situation. Precip rate is also an output of some forecast models that is considered when issuing an advisory. These models consume huge amounts of data that influence the output prediction in complex, chaotic ways. Better quality data can often produce better forecasts and lead to better decisions but whether or not the specific event would have had a better outcome is harder to answer.