2/ For Austria and NL where 90% of the at risk group is vaccinated we expect 30%-50% of the ICU patients to be vaccinated if assuming a VE-ICU of ~90%.
3/ Let's show the results of the formula in 2D as function of
x) vaccination level
y) VE agains ICU
The resulting rate (calculation on right) of vaccinated in ICU is shown in the cell.
It can be used as look-up table to estimate (roughly) the VE.
4/ Example The Netherlands: With around 90% vaccinated we expect something between 15%-50% vaccinated in ICU depending on the VE-ICU.
RIVM reported around 15% (right). In order for this to work with 90% vaccinated, VE-ICU needs to be above 95%.
10/ Assuming March as the date where most elderly were fully vaccinated in NL, I compared the derived NL VE versus (time) with the Swedish study on waning efficiency.
1/ April resists warming.
Remember: warming causes cooling.
If you’re freezing, you're actually warming.
Colder weather confirms it’s warmer.
We must prevent cooling to stop warming.
Yes, it still was the warmest April in SW models.
Now pay your CO2 tax please and eat vegan.
2/ We check ourselves. The ClimDiv curve is even cooling 1.37C compared with the stable USCRN sites.
3/ Expand the range to 115 years.
Stable USCRN sites show nothing.
ClimDiv now shows warming—entirely from adjustments.
Wrong ones: cooling rural, not towns.
Signal upside down.
That's not science—it’s appalling.
1/ I was told non US GHCN “raw” is adjusted already.
-----TRUE-----
Now I see it. Gosh.
Composite. 2x adjusted. NOAA doesn’t even know where non-US stations are—or what they’re measuring. Their own US data (USCRN) is light-years better. But for “global”? It’s clown-tier level.
2/ And here it is—the DOUBLE-adjusted COMPOSITE.
Not raw. I doubted @connolly_s at first—like someone denying their 2nd-hand car is stolen, crash-salvaged, and repainted twice. Turns out he was right.
NOAA’s “global” QCU (non-US): not raw.
3/ Credit where due.
Normally I block on first bad-faith signal.
But intuition said: bait him back.
Let’s see what he hands over.
And he did:
✔ Clown location
✔ 120% urbanized
✔ Composite
✔ Adjusted twice
Thanks for the assist.
1/ The WMO’s temperature station classification study isn’t a glamorous reading —but it’s the bare minimum anyone aggregating climate data should know about every single station. They don’t.
2/ Class 1 is “bare minimum” for climate-grade weather station suitability. One means maybe ok. met.no/publikasjoner/…
I’ll be counting impressions. I’ll know if you didn’t read.
(you’re allowed to LLM TlDR it.)
Next up: NOAA climate site requirements (HLR). 👇 x.com/orwell2022/sta…
3/ The NOAA HLR system makes WMO classes look gentle.
Most stations? Fail spectacularly.
1/ Digging deeper, we find 3 USCRN sites with 2 IDs — a legacy historical one and a USCRN. That’s big. It means we can stitch together long-term time series for 3 “golden” stations. Why haven’t @NOAA or @hausfath done this? Not the “right” narrative result? 🙃 Let’s take a look
2/ Here is an example of such a pair. STILLWATER. Note that you can see the wind fence around the precipitation gauge on satellite picture — that round structure. ncei.noaa.gov/access/crn/pdf…
3/ Well, let’s do it. We try. And...
...no hockey stick.
Despite STILLWATER being a growing urban area.
So... where’s the hockey stick? Anyone?
We're told it should be there. But the best data says no.
1/ Mr. @hausfath packed multiple fallacies into one graph. We replicate: he used homogenized data. We get the same.
Bottom right shows the raw. His fallacy: claiming that USCRN-ClimDiv agreement in the modern era (where adjustments are ~zero) validates strong past adjustments.
3/ His fallacy is blatant bad faith. Measurement validation isn't done by induction. He claims adjustments are valid because USCRN-ClimDiv align from 2008-2024—yet no adjustments were made in that period. Then he asserts past adjustments are proven. Exceptional level of malice.
4/ Another fallacy: He cherry-picked 1970—the coldest point in 100 years. He highlights only post-1970 warming in green, hiding earlier trends. But the real scandal? Extreme (false) pre-1970 adjustments, erasing the 1930s warmth with absurd corrections.
1/ New tool - let's test with VALENTIA (hourly) overlay: solid agreement. A model (ERA5) is only as good as its ground truth measurements constraints it. We saw good US results before, but obvious heat bias in polar regions—nothing measured to compare with there anyway.
2/ Now we match the 1940-2024 range. Note temp vs. anomaly scale—same curve, just shifted. A trick to amplify range. Few notice. Climate stripes? Perfect for manipulation—e.g. add offset (ECMWF) to make it red “=warm"= behavior science (manipulative).
3/ With the 1940–2024 range matched, comparison improves. For a clearer view, monthly temps are shown on top left, yearly in the middle—overlaying ERA5. Not perfect overlay, but ERA5 is A) a cell average (of a weather model) and B) fed by adjusted data.