Having worked in data science you just described all of science. It's extremely difficult to get good data so you can build decent models. Weather forecasting abd climate science will always be wrong.
Fuck, this reminded me of a PHD student I tried to help with their thesis project. It was apparently too hard to get good data for their proposed model (lazy & would take time). So, find any old data and jam it into their model and voila, thesis done. I stopped helping before that point.
It’s honestly not his fault either. Science is publish or die these days. Taking your time to craft an excellent paper is rewarded less than the shotgun approach of firing off like 15 that barely meet the most basic requirements
Dr. Mototaka Nakamura (nasa jpl, mit, international pacific research center, etc) wrote a book a year or two ago called “confessions of a climate scientist” that basically explains how climate modeling is complete junk science and that there are very many important aspects to the climate that we barely understand at all and don’t even include in modeling. Most of the book is in Japanese, but he goes over the important stuff in English. Think it costs $1 on kindle, well worth the read.
It seems like it ought to be possible, but Navier-Stokes shows that you simply cannot.
If you don't understand differential equations and how to solve them, and you don't know what the Navier-Stokes equation is, then you won't be able to fully grasp the futility of weather forecasting and climate science.
I can't give you a full education in physics and such, but let me break it down for you.
We have discovered "laws" of nature that describe, perfectly, the behavior of matter and energy in all circumstances.
These laws are entirely consistent and not in any way contradictory.
The laws give rise to mathematical formula that aren't just difficult to solve, but provably IMPOSSIBLE to solve.
Except in some extraordinary circumstances, we cannot make any predictions at all about the behavior of matter and energy. These extraordinary circumstances are so rare that in order to achieve them we must build purpose-built machines designed to create the conditions. (IE, laminar flow.)
Whether or not you can exactly measure the initial conditions -- the state of matter before you make your predictions -- is largely irrelevant. You can't measure anything exactly (error is everywhere) and that in and of itself means you can never make any meaningful predictions.
That said, there are certain broad and general observations we can make. IE, heat flows from hot to cold, etc... We can definitely measure temperature and heat flow and indeed, it will always flow hot to cold, even though we cannot predict everything about the flow.
Rather than using your imagination, use the measured properties of fluids. Use physics. See how the Navier-Stokes equations are? They describe ALL motion of all fluids (gasses and liquids) for all time. Nothing can move or stay motionless without conforming to those equations.
Yet, we cannot predict, in the slightest, what will happen in the next moment, let alone the next day or year.
Having worked in data science you just described all of science. It's extremely difficult to get good data so you can build decent models. Weather forecasting abd climate science will always be wrong.
Fuck, this reminded me of a PHD student I tried to help with their thesis project. It was apparently too hard to get good data for their proposed model (lazy & would take time). So, find any old data and jam it into their model and voila, thesis done. I stopped helping before that point.
It’s honestly not his fault either. Science is publish or die these days. Taking your time to craft an excellent paper is rewarded less than the shotgun approach of firing off like 15 that barely meet the most basic requirements
Ha yes this is true. The field was computer vision and AI, which is evolving so rapidly you'd see many papers published every week.
Dr. Mototaka Nakamura (nasa jpl, mit, international pacific research center, etc) wrote a book a year or two ago called “confessions of a climate scientist” that basically explains how climate modeling is complete junk science and that there are very many important aspects to the climate that we barely understand at all and don’t even include in modeling. Most of the book is in Japanese, but he goes over the important stuff in English. Think it costs $1 on kindle, well worth the read.
No they won’t, we already know that all things interact with some convergent plane. We know that all changes cause a propagation of interactions.
It ought to be possible to analyze the entire atmosphere as well as the external forces acting on it.
Gravoscope WIP.
It seems like it ought to be possible, but Navier-Stokes shows that you simply cannot.
If you don't understand differential equations and how to solve them, and you don't know what the Navier-Stokes equation is, then you won't be able to fully grasp the futility of weather forecasting and climate science.
So basically energy can’t be predictable enough in any space because from any point it will never be consistent?
I can't give you a full education in physics and such, but let me break it down for you.
Whether or not you can exactly measure the initial conditions -- the state of matter before you make your predictions -- is largely irrelevant. You can't measure anything exactly (error is everywhere) and that in and of itself means you can never make any meaningful predictions.
That said, there are certain broad and general observations we can make. IE, heat flows from hot to cold, etc... We can definitely measure temperature and heat flow and indeed, it will always flow hot to cold, even though we cannot predict everything about the flow.
My imagination figures that just because you’re inside the fluid doesn’t mean you can’t view the whole container.
Rather than using your imagination, use the measured properties of fluids. Use physics. See how the Navier-Stokes equations are? They describe ALL motion of all fluids (gasses and liquids) for all time. Nothing can move or stay motionless without conforming to those equations.
Yet, we cannot predict, in the slightest, what will happen in the next moment, let alone the next day or year.