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Monday and Wednesday will be summarizing what we've seen (and hopefully learned) over the course of the last month and a half.
So I asked you to do three things by Monday, 4/13:
This is because minima are increasing faster than maxima, so the two are getting closer together.
In some cases, it appears that maxima are not increasing much at all, perhaps because of cloudier conditions.
Minima may be increasing faster in part because of night-time warming (when it is typically cooler).
I let you know about some new data -- modern temperature data -- from a small town (Custar) with a good weather station not far from Bowling Green (county seat of Wood County, and the station from which most of our modern Fletcher data derived).
I asked you to find the DTR for the Custar data (you have columns for the MaxAirTemp and MinAirTemp -- the DTR would be the difference of those two variables -- DTR=max-min).
Regress the DTR, modeling it as a linear function: i.e. \[ DTR(t)=\beta t + \alpha \]
The question is whether the parameter $\beta$ is significantly different from 0, and whether in fact it's negative -- so that the DTR in Custar has been decreasing over the 38 years or so of data.
Here are some Mathematica commands to get started with the normals (creating the important variables).
If there were no climate change, then we would expect the climate normals to be periodic functions -- December wrapping right back into January. However, if, as we expect, there's global warming going on, then we'll see that normals at the end of the year are actually a little higher than the normals at the beginning of the year.
And, if our hypothesis is right that the minima are changing more rapidly than the maxima, then the coefficient of the minimum will be larger than the coefficient of the maximum.
See what you discover!
This is an interesting one. When I started looking for interesting Covid-19 models, I found this one InsightMaker, and it incorporated real data.
As applied math modelers, we need to make sense of real data. We're going to look at how some modelers tried to make sense of real data; how we need to change their model to reflect the real world, which intruded upon their theories....
Some good links that I might recommend (a few of which we'll focus on):
In particular, we will implement The SIR Model for Spread of Disease - The Differential Equation Model in InsightMaker.
(An excellent introduction to SIR models, from both the infectious disease and mathematical sides)
Questions:
This on-line estimator (i.e., a model!) allows one to estimate deaths, as well as death by age-category.