1) The CDC report is very well done and properly normalized. No further calculations are needed. All is done and ready.
2) The German report is poor. No normalization by doses, nor by sex. In General the report is of a very low quality.
4/ So for the German PEI report, some calculations and estimations were needed. The administered dose per age group are also not published and need to be estimated.
Approach: use German 2019 population pyramid and multiply by vaccination rate (estimated). Here the result.
5/ Remarks. Germany hasn't given a general recommendation for vaccinating children below 18.
Thanks to the #STIKO, they didn’t follow the unethical example of CDC.
That’s why the German curve begins at 18 only. The data below 18 is fortunately not available.
6/ The general quality of the German PEI reports are of low quality (lacking dose/age normalization etc.).
The comparison with the high quality report by CDC shows this very clearly. But judge for yourself.
9/ Bonus round: why vaccinating children is unethical:
Using the same type of normalization as decribed above, I estimated the age dependant vaccine CFR from the German PEI report and compared it with the C19 IFR for healthy. IFR source below here:
10/ All safety features have fallen. This here is unprecedented. Politics make medical decisions now and ignore #STIKO. The kids must be vaccinated at all costs it looks like. And rumors say that STIKO will change it’s recommendation. Based on new data? No.
Clearly CDC US and PEI DE have an underreporting issue while Canada and Israel show similar (higher) numbers.
What about @Lareb_NL ? Nothing to report? Silence?
14/ The potential root cause for the age curve has been discussed by @bringsmileback here: a combination of testosteron and stronger Th1 inflammatory response in young boys compared to older men:
@Dodders75 @MetEireann Next: UHI effect in Uppsala (big city 🟩) vs. Linköping (small town 🟥). Uppsala's fewer frost days reflect its urban growth. While UHI—locally beneficial as it reduces frost—isn't climate change, it distorts data at 99% of global stations.
1/ Let's check real measurements. GHCNv4 stations that continuously operated 1900JAN-2025JAN.
ERA5 (weather model) shows warming were least stations can constrain it. Warming doesn't like places with real stations? Avoiding measurements? We have noticed this previously.
2/ The ERA5 model's +1.75°C anomaly aligns with megacity 'BU' levels—the top curve. Absurd and exposing the ERA5 reanalysis (weather model and not data) as non credible. Real, unbiased historical stations in non-urban areas show a very cold January.
3/Next - the most reliable station region only: US
-1.5°C 🥶
Even cities (high BU) could escape despite UHI. We saw snow in TX/Florida/AL and deep frozen alligators.
Data and visuals aligns. ✅
(ERA5 SW is hallucinating heat where unconstrained.)
1/ Deutschland, die Energienarren der Welt: Thread.
Hier ist die Preiskurve (31 Tage, stündlich). Kaufe teuer, verkaufe billig. Bottom Nailers (oder auch Narren). Angeblich importieren sie, weil es billiger ist? Nein. Die Sonne scheint eben nicht nachts.
2/ Quelle: Agora Energiewende – de facto der Familienbetrieb der Grünen. Man sieht sofort, was los ist: Deutschland, auf einem Irrweg, in bestem Stil echter Narren. Verkaufen billig, kaufen teuer, alles im Namen der „Rettung“. Klar, wer nachts Sonne braucht, zahlt eben drauf.
3/ Jetzt wierholen wir zusammen, wie echte Hofnarren:
„Importieren ist billiger“
„Schweden hat versagt“
„Lauterbach rettet Leben“
@roberthabeck for Chancellor. Ab hier anders.
1/ The use of the BI (bigness index) to classify rural/urban areas is flawed. Landsat-derived GHSL BU (Global Human Settlement Layer Built-Up) data shows the rural curve (in green 🟩) consistently trailing the urbanized GHSL BU data (10% BU = typically for small towns 🟧).
3/ Analyzing further: nearly half of the stations are classified as rural (BI=0). This is complete nonsense, as the GHSL built-up percentages 2020 for these stations clearly indicate. Nearly all are, in fact, urban—which explains why they see no difference to officially urban.
3/ Now let's try GOTHENBURG. Hold on a second... what's happening? It looks like we've accidently landed in the US Midwest—in the middle of nowhere, where hockey sticks don't flourish. Nice flatliner we have here, just like CHAMA.
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 ?
@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 👍.