Comment by hyperrail
13 hours ago
The drought map used here is partly subjective opinion.
https://droughtmonitor.unl.edu/About/WhatistheUSDM.aspx
> Who draws the map?
> Meteorologists and climatologists from the NDMC, NOAA and USDA take turns as the lead author of the map, usually two weeks a time. The author’s job is to do something that a computer can’t. When the data is pointing in different directions, they make sense out of it.
> How do we know when we're in a drought?
> No single piece of evidence tells the full story, and neither do strictly physical indicators. That’s why the USDM isn’t a statistical model
Doesn't seem like all climate scientists are fans of it either. From a 2022 critique of a news story also based on this map:
https://cliffmass.blogspot.com/2022/04/is-large-portion-of-w...
> The essential message is that weather and climate data do not support the claims of extreme or severe drought in eastern Washington this year.
> There is no expectation of water problems over or near the Columbia Basin. The Drought Monitor graphics, which are created subjectively, are sufficiently problematic and deficient that they should not be considered or applied to any serious decision making.
cliff is an expert but also famously sort of a "climate contrarian" and his takes are regularly cited by climate skeptics and conservative irritants here in the PNW. just noting his takes don't exist in a vacuum.
Contrarian experts are really important imo, and I don't think their efforts should be devalued just because nuts might be attracted to them. As long as they're properly engaging in the scientific method I reckon that they're perfectly fine to quote.
So? You’re trying to engage in tu quoque without saying it explicitly. If you think the argument is wrong, make a counter-argument. Don’t just say that the arguer hangs out with people you don’t like.
Cliff in an expert, he worked in the Obama administration on climate, and unsurprisingly, he is being cited for having opinions the support the thesis of the article.
FWIW you can go back and look at historical data rather than rely on a snapshot of 2022 written in April.
Basically it’s complicated. Some areas did experience extreme droughts that year and others faired well.
BPA was able to lever up their reserves early due to those same forecasts which allowed them excess supply to sell when other utilities experienced extreme heat (drought) and couldn’t produce enough.
> Notably, Bonneville was able to offer much needed support to other Pacific Northwest and California utilities during late-summer heatwaves and scarcity events. Our hydropower operations planners and traders positioned the power system to maximize supply, enabling us to deliver significant amounts of power across the West to help keep the lights on during a string of energy emergencies.
https://www.bpa.gov/-/media/Aep/finance/annual-reports/ar202...
I like the map. It's usually on track but sometimes it's quite a bit off. I've seen it say drought when it's been wet --maybe just not as wet as usual. It also doesn't indicate when above average and I do not think it averages precip out when a wet week was extremely wet and the next one dry. It'll say it was dry last week. In other words you could have cumulative average precip but it's only counting last week's precipitation.
And a lot of hard work, sounds like: https://droughtmonitor.unl.edu/About/AbouttheData/DroughtCla...
> [Authors] bring together the physical climate, weather and hydrology data and reconcile that with local expert feedback, impact reports and conditions observations. The author is also responsible for weighing different indicators based on what’s most appropriate for a particular place and time of year. In the West, for example, winter snowpack has a stronger bearing on water supplies than in the East
It also sounds like that old adage of - All models are wrong but some are useful. Alas, we probably only know how useful they where afterwards.
It is, and the subjective assessment component is a black box. That said, the USDM has many other components that are objective, so it's far from being a subjective measure -- I would argue that the Fed Funds rate, for instance, is determined far more subjectively.
Also, there just isn't a more objective measure of drought out there, let alone a fully objective measure.
Also also, it's unclear to me that this black box is being gamed any harder than most other black boxes in our system. If you want to game agriculture, you game the farm bill.
i think calling it "subjective opinion" is kind of disingenuous. it is a subject matter expert interpreting the data. there is a vast gulf between that and someone else simply offering their opinion on the matter.
"Subjective" and "objective" are well-defined terms. Perhaps this misleads the reader about expertise, but it is not objective.
I worked in weather for TV as a technician and I was lucky enough to work with meteorologists. I thought they were high priests in the church of science, however, I detected a gambling mentality going on.
I was just surprised at how subjective their work was, with differing opinions regarding the big picture depending on whom you asked and what their background was, as in university, whether they had worked for the navy or whether they had worked for the government.
The big surprise of the gambling mentality reminded me of people that dedicate their lives to losing as much money as possible betting on horses. These people know the form, the weather and so much, yet they do their own bets.
It was kind of the same when working out what the weather would be in Springfield tomorrow. Would it just be cloudy or actual rain? That would be a 'bet'.
The next day the observations would come in and the meteorologists would either win or lose their 'bet'. The guy who has been to Springfield and knows the local geography well would have his own reasons for his 'bet', whereas the guy who was more interested in long term storm development would have another rationale for his 'bet'.
Then there would be 'wrong all the time me', able to look at the low level cloud from contrails (which are really huge in some wavelengths on the satellite pictures) to assume rain every day.
Hence climate and weather is highly subjective even if it is highly educated and vastly experienced professionals that are interpreting the data.
There is also the additional issue of computer models constantly chasing global changes. About 10-15 years back I used to talk with folks that worked on weather modeling and they were in a state of frustration in that as soon as they could make models that could work on older data sets to do reasonable predictions, the global weather patterns had change just subtly enough that it made them just kind of average on forward predictions.
This was right before GPU compute started to become a big thing, I do wonder if they now use machine learning models on these to speed up model iteration? I would hope so, but even then there is the human factor as you said. Eventually someone has to make the call on what the data shows and how to present it to the world.
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