aduesterhus.bsky.social
Scientist with a personal view on Statistics in Geo- & Earth Science. Working on data science, sea-level and long-term prediction.
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But still there is the predicate missing in the sentence, or? I guess in the old day language courses they would have gone towards "name the parts of the sentence" and without the predicate it would be very tricky to argue that it is a full sentence as it claims to be (oc there are exceptions).
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It is a shocking development. Many cannot imagine how many elderly just start their computer and open skype to talk with their loved ones. It is not the best, but it is around for a long long time. Now it will mean to retrain a lot of people who are not really happy about technological change.
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Just came to it because I remembered one of Chris articles: rmets.onlinelibrary.wiley.com/doi/full/10....
In the end the question is what influences what and in which hierarchy. Know not enough about the pacific, but would be interesting to have a full system from Kuroshio to gulf stream.
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How much is it connected to the northward shift expected in the Atlantic region (Hasn't Chris also written papers on that)? So is there a connection between the two (because there we are quite sure about the northern shift, or?). And when we are add it: Is there a Golf stream connection to pacific?
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Man könnte auch das Wahlrecht anpassen: entweder Wahlkreise für Auslandsdeutsche schaffen oder die Möglichkeit geben ausschl. die Zweitstimme abzugeben. Da mit dem neuen Wahlrecht quasi alle Mandate via Zweitstimme vergeben werden, wäre damit zumindest die Wahlteilnahme ausreichend gewürdigt.
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One option might be how generally the sources would present the topic climate change in the future... looking at the media it is often doom and gloom rather a realistic description (which would be bad enough).
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Yes, but unfortuneately the coining of such a simple word for a complex situation is used as an excuse not to invest in more renewables (some even want to reduce them).
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Somehow an indication of a land-sea mask would have helped.
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Everything, which is out of training leads usually to underperformance by statistical towards dynamical predictions. Statistical model extrapolate, dynamics should usually follow physics (not necessary is better, but in the last 50 years or so this is the reason why we do dynamical predictions).
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Interesting would then be: How do both methods perform by events with return times of >40 years. That is the criticism many have with statistical (like NN) predictions. The other is that directly or indirectly the calibration metric is applied as verification metric. Not really sure this is solved.
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Somehow poetic...
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Somehow poetic...
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Brings me to the question: what to do with those discarded ideas? Have multiple of those stranded somewhere in the process and in the end they will never see the light of day as they are not perfect or do not fit into the timeframe.
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On rainfall by hurricanes and its sensitivity to influence factors, we had a paper out last year: iopscience.iop.org/article/10.1...
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Tp be honest, most are other peoples papers, as due to my research I usually make other peoples work possible rather doing the fancy highly citable stuff for myself. 😉
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On the one side the US had a lot of recent international influx in their team (partly due to their own league, MLC) and on the other side is Pakistan currently in a deep crisis. Still it was a good performance pushing them over the line.
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The research was done together with Sebastian at Uni Hamburg supported by the project Coming decade funded by BMBF. Myself funded for this by #nckf at DMI and A4 project funded by Marine Institute 7/7
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This study demonstrate that predictions of distributions are possible, but require creative approaches for the verification. It opens a new dimension and increases the temporal resolution to look at these predictions, 6/7
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Main physical result: different seasons show different skills. Especially with involved ice processes the skill between hindcasts and historicals vary considerately. We use a quite normal distributed variable, so the results are in most cases close to a correlation analysis. 5/7
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But as we cannot evaluate them by a single value like correlation, we do it by counting how often over a given time span one simulation wins against another. It is a different form of looking at verification and a hopefully much more accessible for communication purposes. 4/7
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In this study we propose a strategy to evaluate full distributions relatively against each other. For this we employ the IQD to compare hindcasts, historicals and climatology vs. a reference (assimilation simulation). 3/7
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Up to know, almost everybody predicts mean values over a given time span. While it is convenient to do, it is only part of the full story. Because distributions of a variable are not necessarily reflected by an average value. 2/7
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Thanks! Will find out tomorrow.
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Nevertheless, due to structural problems in the East (less industry and people once left stay away), the countryside areas do not really recover. The major towns there get some bost, but outside of it it stays sometimes around 115:100 and more men vs. woman.
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To demonstrate that with Germany: The 18 to 29 age bracket is effectively a uni map (red more men, green to blue more woman): commons.wikimedia.org/wiki/File:Ge... The 30 to 29 bracket is then much more balanced again due to industrial distribution commons.wikimedia.org/wiki/File:Ge...
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Internationally you expect the following trends: in countryside more men than women (sometimes quite extreme). In uni-towns especially in the early 20s more women then man, but for both spikes (depend on uni). In industrial centres w/o uni another increase (mainly male) in the early 30s.
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Many worlds theory -> ensembles ✅
Multiple dimensions -> decadal predictions works regularly in a 6-dim-space (plus a few rolled up) ✅
Observer’s paradox -> signal-to-noise paradox ✅ 😉
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Well, luck seems to be to have a 4.5h delay on a 4.5h train ride.