Orwell2024🏒 Profile picture
Nov 1, 2021 11 tweets 8 min read Read on X
1/ Expected rate of vaccinated in ICU.

(Correcting here the spotted mistake in the explanation part.)

Here is the formula explained. It depends on vaccination rate and vaccine efficiency.

The special case for 90% vaccinated is shown on the right as function of VE-ICU.
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%.
5/ VE look up example using UK PHE report week 43

assets.publishing.service.gov.uk/government/upl…

using the data from Table 5:

We look up the expected VE-emergency-care for 4 different age groups with different vaccination levels and resulting emergency care rates.

Result: VE ~ 96% in each case.
6/ Discussion: The relative number of vaxxed-patients in ICU is irrelevant (increasing with vax level).

Only the absolute total reduction of ICU patiens matters (shown below).

1-(𝑟_𝑣𝑎𝑥∙(1−𝑉𝐸_𝐼𝐶𝑈)+𝑟_𝑢𝑛𝑣𝑎𝑥)

Here we have >90% reduction! Nothing more to gain.
7/ Another way to look to the same data:

VE-ICU as function of % vaccinated in ICU. Each line for a different vaccination level.

public.tableau.com/app/profile/or…
8/ Use example: estimate VE-ICU based on latest numbers "percent vaccinated in ICU" from NL (from @BertMulderCWZ ).
9/ Here the link to the article by Prof. Suess in @KURIERat today
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.

Result assuming AUG ~ day 150.

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More from @orwell2022

Sep 10
They're trolling / insulting. The request was clear: compare ERA5 2km / @meteoblue with @AEMET_CValencia sensor at an hourly level. If they match at night, cloudy days, winter, but the sensor shows higher T in summer clear skies / no wind / day 👉 sensor is heat-biased. So? Go.
Thanks, @meteoblue. Normal conversation can be so easy. If the Spanish gentlemen would now provide access to their hourly station dataset, we can overlay it with the fine-grid ERA5 2km hourly product and see what's going on. Does that sound like a way forward @AEMET_CValencia ?

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@meteoblue @AEMET_CValencia He clearly doesn’t understand their response nor my request. At this stage, I just want him to provide THE HOURLY DATA. What the answer actually means is that the 30 km cell is more representative of the region’s climate—yes, it’s better than the station. Well done @ChGefaell 👍.

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Read 10 tweets
Sep 3
1/ Such places have no credibility for accurate bias free measurements. It's the opposite of a stable environment and per default a diesel powered urban expedition place. We see how the melting starts around the airport and the town.

How to measure? 👉 open.substack.com/pub/orwell2024…


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2/ Here we see another example (Alaska). Russian high-lat regions are among the worst. It's a deception to take measurements from such places and claim that you've done 'science' while actually just picking up dirt. Why not Everest dirt basecamp next?
3/ It escalated quickly. Similar to @BMcNoldy from Miami, master's student @Daaanvdb also used airport data instead of professional equipment, like what's available at @UNISvalbard.

Let's do better and use proper data from a better looking station.


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Read 19 tweets
Jun 27
1/ As mentioned, Europe is too urbanized for climate measurements. Shown below is just the UHI effect. As mentioned, ANY type of urban landscape altering increases surface temperatures as well. The Netherlands and Benelux regions are all fully biased and unfit for climate science
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2/ As mentioned previously, North Sweden is the most credible place for climate measurements due to its development, peace, and ability to capture high-quality data. Besides Sweden, only the US provides reliable historic data. All other regions are not credible and biased today. Image
3/ Source: YCEO Surface Urban Heat Islands: Spatially-Averaged Daytime and Nighttime Intensity for Annual, Summer, and Winter.

It's from 2003. Now it's even more urbanized = worse.

developers.google.com/earth-engine/d…
Read 12 tweets
Jun 18
1/ Remember the scenic document from @NOAA's USCRN. All rural places without man-made objects?



It's 145 pages long, each page one station. They should ALL be there, right?

Nope. How naive to believe that it's done in good faith.

Ready? 🧵 ncei.noaa.gov/pub/data/uscrn…
2/ This finding didn't emerge out of nowhere. Result of me telling @connolly_s that his detailed check of USCRN is a waste of time.

I repeated for weeks...then I swore...

They present the best only and hide urban stations. Bad faith. Consciously.


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3/ So, what do they look like, the ones they don't like to show? Urban areas. Airports...

Remember their requirements: It should be like the Everglades. No man-made environmental alterations. Stable. Representative.


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Read 12 tweets
May 22
1/ Let's do some checks: Compare the SST data model to water (ground) truth, thermometers in the water.

The green dots are the available @CDIPBuoys, a well maintained network. Probably the best buoy network (by @USACEHQ). Haven't seen any better one.

cdip.ucsd.edu/m/deployment/s…

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2/ Florida: the gulf area showing up red at the anomaly chart. The buoy shows nominal at average values. 25C versus +26-27C in the SST model. That's a +1C heat bias. Image
3/ Next - Hawaii. Buoys are below average. SST product is showing heat anomalies there.

14th May: buoy 24.5C vs. 25.5C SST.
+1C heat bias

Interesting. It's apparently too warm, as long as you don't stick a real thermometer into the water to measure and realize: it's cold. Image
Read 7 tweets
May 13
1/ Let's revisit this result from AIRS satellite measurements over 17 years, showing a +0.36W increase in forcing alongside a 40 ppm rise in CO2 concentration.

Does this align with the "observed" (questionable) increase in global temperature anomaly (+0.6C)?

Let’s do a check.
Image
2/The IPCC reports a calculated CO2 forcing of +0.5W, as detailed on the NOAA AGGI page, which you can find here:



The SW calculation overestimates by 40% compared to the +0.36W derived by the AIRS satellite, marking the first significant discrepancy. gml.noaa.gov/aggi/aggi.html
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3/ Now we return to Happer's paper, showing that doubling CO2 from 400 --> 800 ppm results in +3W of forcing.


This is consistent with +3.5W reported by the NOAA AGGI (+3.5W).

arxiv.org/pdf/2006.03098

gml.noaa.gov/aggi/aggi.html

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Read 7 tweets

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