Think Differently. Solve the Impossible.

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About the Host

Daniel Stih is an aerospace engineer, software engineer, indoor environmental consultant, and author of 12 books. Through engineering, environmental investigations, and interdisciplinary research, he explores how people define problems, interpret evidence, and make decisions under uncertainty. Learn more in Why I Think This Way.

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Show Notes

ANACONDA
You may find installing ANACONDA helpful.
Website: https://www.anaconda.com/products/ai-navigator

Spyder
After installing and launching Anaconda, you may see this application. I used Spyder to write and execute the Python script. It’s FREE! You might also be able to run Spyder stand-alone You can download it here;
Website: https://www.spyder-ide.org/

Metostat
Become a patron and support Meteostat. Go hereto get more details on the script below.
https://meteostat.net/en/

Python Script:
#modified script using the sample one found on meteostat.net

pip install meteostat

#Import Meteostat library and dependencies

from datetime import datetime
import matplotlib.pyplot as plt
from meteostat import Point, Daily
from meteostat import Stations

#Get nearby weather stations
stations = Stations()

# the older weather station in Phoenix, AZ is Phoenix Sky Harbor It's been keeping weather records since 1895. It's one of the oldest sources of weather in the region the coordinates for Sky Harbor are: Latitide 33.4342 N. Longitude -112.0119 W

stations = stations.nearby(33.4342, -112.0119)
station = stations.fetch(1)

# Check the data structures. It’s not required. For more on the tables and what fields are available see metostat.net
# Print DataFrame
print(station)

# Set time period
start = datetime(1895, 1, 1)
end = datetime(2024, 11, 1)

# Create Point for Phoenix, AZ
location = Point(33.4347,  -112.011)

# Get daily data
data = Daily(location, start, end)
data = data.fetch()

# Plot line chart including average, min and max temperature
data.plot(y=['tavg', 'tmin', 'tmax'])
plt.show()

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Transcript

Welcome to The Daniel Stih Podcast.

The theme of today is technology and curiosity.

Have you ever wondered about the data behind the headlines on global warming?

In this episode, we're going to explore how to access and visualize temperature data yourself.

This isn't about convincing you whether global warming is real or not.

My mother used to tell me, do you believe everything someone tells you?

This is about empowering you on how to explore and see for yourself if something is exactly as you're being told and just to see for yourself.

Question everything.

So for the climate change issue, when we read stories in the news and hear about it, how it's getting hotter by X number of degrees and breaking records and sure it's hot, we're being told something based on someone else's observation.

We're being told something based on somebody else looking at the data.

But what if you want to see the data yourself?

In 2013, 19 members of the Granite Mountain Hotshots, a firefighting team battling the Yarnell fire in Arizona, died.

At that time, I chose Yarnell to get temperature data, because I myself was considering exploring.

Okay, let me actually see the data besides seeing on the front page of the newspaper, because news channels around the country were capitalizing on the poor souls that died in that fire, saying global warming had caused it, and we should expect more fires in the years to come.

So how bad?

How much?

You know, how bad is this?

I had to buy the data at the time.

There was no free data.

And by data, I mean 50 or 100 years of data, because as it turns out, most weather stations hardly have 20 years of data.

Fortunately, now there's an open source, allows us to do this for free and much easier using Python.

For those of you familiar with Python or computer programming, this is going to be amazingly easy.

For those of you who have never used Python, no worries.

It does help if you have some computer hacker skills.

First, you have to install Python on your computer.

Details are in the show notes.

I started with the Python script available on mediostat.net.

This is the open source project created and maintained by Christian Lampretzsch.

He's the founder and maintainer.

It's made possible by generous backers and contributors who keep the project going.

You can make a contribution as well as become a backer.

For those of you not familiar with Python, basically a backend super Excel fast way of analyzing data and plotting.

Most importantly, plotting it.

It can plot 100 years of data in a heartbeat.

So I used to live in Phoenix, so I'm going to choose Sky Harbor Airport as the location for the weather station because it turns out it's the oldest and has the most, the longest history of data.

What we want is temperature going back is on every day for as many years as possible.

20 years really isn't that far.

Even 50 or 100 in the history of man and automobiles and urban development.

It'd be nice to go back to when the Industrial Revolution started.

So the notes on GitHub make it easy to understand how to change the location of the weather station for where the weather station you're looking at, and to see the data structures like what else besides the minimum and maximum temperature is there, and what days are there data missing and so forth.

So you can plot stuff any way you want for anything.

Perhaps you want to see how many days of the year there's missing data, or how far back the actual data goes, what weather stations are available in your area.

Now for the fun part, how do you tell if the temperature is actually rising or not?

For this, use what we call the fat pencil test.

What is the fat pencil test?

You take a Sharpie, a magic marker, a highlighter, a fat writing tool, or something, something fat.

You put it on the plot, and you try to cover all of the data points.

Temperature goes up.

In the summer, you're going to get some data points rising, goes down in the winter, lowering.

You try to cover all the points of your data.

That's called the fat pencil test.

If all those data points are within your fat pencil, then you look at the pencil.

Is the pencil sloped up or down?

Is your fat magic marker, is it angled up or down or flat?

That's the fast and easy way, besides having to analyze it with some other kind of statistical method, which then you kind of throw the baby out with the bath water in terms of is it generally rising or not?

I mean, if it's generally rising, you're going to have some days that go down, some days that go up, and then it's kind of like two steps forward, one back.

There's some super cold winters as well as some super hot summers.

In the end, you led to believe the temperature is rising.

So in the end, if you put this fat marker or Sharpie or highlighter on your data plot, covering all the ups and downs, you should then be able to look at the Sharpie or the highlighter, the magic marker, and see is it pointed up or down or flat.

For the city of Phoenix, it appears there was a slight cooling actually in the early 60s.

There's also some missing days there.

Maybe they changed the weather station, the technology changed.

So those are factors too, right?

How's it calibrated over a hundred years?

But then it definitely seems like, or it's not actually definite, to me it seems like there's a slight warming trend for the low temperature, but not for the maximum temperature.

And my theory on this is, as Phoenix developed, and there's more concrete, more blacktop, more cars, more people, the nighttime temperature is, it couldn't cool off as fast.

The low temperature is trending upwards.

The high maximum temperature in the daytime, to me, doesn't seem to be obviously rising.

If you'd like to comment on this, fantastic.

That's why I'm doing this.

I want you to show me what your plot looks like.

And I want you to let me know how you check the data and what you did, if you took any points out, if you added any, if you found they were missing points on certain days, and you did something to account for that.

Links are in the show notes to the example and data plot I did for the city of Phoenix, as well as how to install Python on the package for the Medial Stat.

If you have questions or comments, please put them in the show notes.

I'd love to hear from you.

Let me know if you used more than one weather station and average data for multiple weather stations.

That's what has to be done when you read a headline in the news, such as the temperature in Arizona is rising due to global warming.

Either they're misspeaking and they're really meant to say the city of Phoenix temperature is rising.

If they in fact did mean to say the state of Arizona, then they must have mixed weather with Flagstaff, a cold climate with Phoenix, a warm climate and weather stations that are new versus old, and have data points missing and not.

That's why it's good to see the data for yourself and ask those people, show me your data.

I guarantee you, if you ask, find those reporters, they'll stare you towards, oh, I got that from somebody else.

And they'll stare you from, oh, you can get that yourself here.

I'm like, well, don't you have it?

You can do anything you like with this data.

That's what exploration and discovery is all about.

Keep track of what you do, document it so others can get the same answer, verify it for themselves.

Explore, discover, have fun, think differently, question everything, live fully.

Until next time, I'm Daniel Stih.

Thanks for listening to my podcast, and I hope you find this inspiring and fun and enlightening.

Have a great day.

What did you think of today's topic?

I'd love to hear your thoughts and questions.

Put them in the comments.

If it inspired you, be sure to like it and share it with someone you know who might benefit.

Thanks for tuning in, and I'll catch you in the next one.

Until then, take care and keep moving forward.