Never heard of HawkEye 360 or the blackshark.ai, but both look neat. My group has very very strong opinions against digital twins.
Feedback: not that it’s needed, but CTVC often writes data-driven article with neat graphics. It’d take a lot of work, and would be a different kind of content, but could be neat to see some analysis.
Long and short of it is that, from their perspective, there is no one-size-fits-all solution to climate/weather predictions - and that increasing resolution and computational power can't solve all prediction problems. Instead arguing for a range of models of varying complexity and scale to gain understanding rather than a high-power-blackbox approach. Anti-digital-twin crowd says that to funnel investment/effort towards a few top-end-models over-weights their reliability and accuracy and acts as a climate deus ex machina.
Pro digital-twin crowd doesn't disagree with this, per say, but instead think:
"The top-end models are the central conduit through which society benefits from our research. However, we do not stress enough to funders and society the challenges in developing reliable top-end models (it’s up there with finding evidence for supersymmetry). We are not ambiNous enough (compared with the parNcle physicists) in proposing the means to address this issue." (see slides below)
Never heard of HawkEye 360 or the blackshark.ai, but both look neat. My group has very very strong opinions against digital twins.
Feedback: not that it’s needed, but CTVC often writes data-driven article with neat graphics. It’d take a lot of work, and would be a different kind of content, but could be neat to see some analysis.
Against? I’d love to hear your thoughts or see an article w that perspective.
And thanks for the general feedback too 🙏
Long and short of it is that, from their perspective, there is no one-size-fits-all solution to climate/weather predictions - and that increasing resolution and computational power can't solve all prediction problems. Instead arguing for a range of models of varying complexity and scale to gain understanding rather than a high-power-blackbox approach. Anti-digital-twin crowd says that to funnel investment/effort towards a few top-end-models over-weights their reliability and accuracy and acts as a climate deus ex machina.
Pro digital-twin crowd doesn't disagree with this, per say, but instead think:
"The top-end models are the central conduit through which society benefits from our research. However, we do not stress enough to funders and society the challenges in developing reliable top-end models (it’s up there with finding evidence for supersymmetry). We are not ambiNous enough (compared with the parNcle physicists) in proposing the means to address this issue." (see slides below)
Here's the basis of the hierarchy group:
Original: https://journals.ametsoc.org/view/journals/bams/86/11/bams-86-11-1609.xml
Newer: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018RG000607
pro hierarchies slides: https://www.wcrp-climate.org/images/grand_challenges/clouds/hierarchie_models_workshop/documents/01_01_Held.pdf
pro digital twins slides: https://www.wcrp-climate.org/images/grand_challenges/clouds/hierarchie_models_workshop/documents/01_2_Palmer.pdf