The Best Way To Measure Winter As A Whole

April is winding down.  I was sitting on post this until the end of the month.  After all, we've had light snow in late April even early May in recent years so I wanted to be sure we could put a bow winter and store it away.  

How do we accurately measure winter as a whole?  More specifically how do we define a harsh winter? No one gold measurement standard exists.

Over the years, I've been toying with different ways to appropriately answer this question. I've posted this question on social media several times.  What I found out was that everyone remembers winter in their own way, much of which is strongly influenced by personal events that occurred coincident with specific winter weather events.  All of which seems highly subjective and personal.

We all see the weather through our own personal preset lens. Highly personal memories can alter our baseline perspective on weather (strong recency effect) especially when comparing current weather to past weather.  I remember posting follow up questions in response to many comments about winter. Some took the responses personally.  Their experiences were strong. These helped reinforce an already established weather vantage point. Their perspective was steadfast. Good luck changing it. I quickly found that the entire project slowly became an exercise in recognizing cognitive bias and recency effect.

Knowing all of this, I compiled several standard metrics (average temperatures and snowfall) used by the National Weather Service and NOAA with a few that I like to call "non-traditional metrics" that attempt to take into account public perception, recency effect and overall bias as outlines above. 

The result was a 21 different metrics that measure different components of the winter as a whole.  There is no 100% completely objective way to determine the harshness of winter but I think this is a great move forward. 

Here is the list in no particular order:

1. Snowfall vs normal
2. Average temperatures vs normal
3. Days with snow on the ground between December and February
4. Days with 1" snowfalls November through April
5. Days with 4" snowfalls November through April
6. Days below normal - DJF
7. Days below normal - January/February
8. Longest stretch highs under 30
9. Days with Highs under 30
10. Days with highs above 40
11. Days with highs under 20
12. Nights below zero
13. Days Wind Chill below zero
14. Days Wind Chill below -15
15. Longest stretch below normal temps DJF
16. Longest stretch snow on ground 1"+
17. Days under 30 degrees & wind gusts 20+ mph
18. Longest stretch between 40 degree days
19. March snowfall
20. April snowfall
21. Days between first and last snowfall

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I calculated all 21 metrics for each winter going back to the late 1940s.  Some of the wind chill data had to be reconstructed using temperatures and wind speed prior to the early 1970s. Once the numbers were found, I calculated a 15 year average for each one working back in time starting with this winter.  So the first 15 year period was 2011-2025, the second was 1996 to 2010 and so on.  My goal was to compare winters to the average of the winters close to it.  Each winter metric that was "harsher" than the 15 year average, I colored the block blue.

First chart is colored blue with the actual numbers.  The second is without the actual numbers and looks cleaner.

You'll notice that most of the extreme winters we remember are represented.  While this past winter (2024-25) was by most measures more "winter-like" than recent years, historically it was in the middle of the pack with 10 of the 21 metrics checked.

Here is the entire chart for reference: DOWNLOAD it since its a large image.

Chart without numbers.  DOWNLOAD it since its a large image.

The winter with a high number of blue boxes represents a harsher winter.

Here are some notable harsh winters:

Now notable not-so-harsh winters:

Cleveland Opening Day Baseball History Updated

Since the middle of March, we've been watching the potential for some pretty serious cold for early April around Opening Day at Progressive Field.  There's been a lot of warming in the stratosphere over the north pole (warm colors below) which usually takes a couple of weeks under ideal circumstances to make its way down into the troposphere where weather directly impacts us. (Often times the stratosphere warming doesn't propagate down into the troposphere so the upper atmospheric warming remains, what we call, decoupled from the lower atmosphere. In those cases, little impact in the form of cold occurs.)

Warming at the top of the atmosphere creating cold at the surface seems counterintuitive but that warming and the stratosphere creates high pressure at the surface in the high latitudes.  Here is a primer from NOAA. There are many ingredients that have to be present for this to occur. Some originate in the tropics which make their way to the pole.

Where that change propagates south is typically where the cold sets up. In many instances, the long-range computer models have a difficult time ascertaining this.

Here is European Model each day from March 26 through April 1 showing temperatures for Tuesday afternoon April 8th (Opening Day in Cleveland). Notice it didn't pick up on the cold over the Great Lakes/Ohio Valley until March 29. Each day the cold became stronger and more pervasive across the northern half of the country.

Today (April 4), notice how extensive the cold is for April 8. The normal high is in the mid-50s for the second week of April. This translates to mid 30s!

850 mB temperatures (5000 ft level) drop to -12 to -14C. Good bet for some lake effect snow with accumulations late Monday into Tuesday.  It is late in the season so the lake water temperature is around 34 degrees Fahrenheit. You need a lake water temperature 850 MB temperature difference of at least 13°C  to start generating lake effect snow. So that difference is going to be pretty close to the cutoff.

We all remember Opening Day 2007. The game was cancelled then moved to Milwaukee. If that game were to be played in Cleveland, it would have been 28°

Technical weather aside, I did some research looking at all of the Opening Days for not only Cleveland Guardians history but ALL Cleveland professional baseball history going back to 1871 when they were called the Cleveland Forest Citys. This is when Cleveland professional baseball was in the National League. They were also known as the Cleveland Spiders and the Cleveland Blues.

Here is the complete list. It shows the year, date, either the game time temperature or the high temperature for the day depending on what was available (color coded), the result of the game and whether or not we had precipitation that day.

A few things to note: 

• There have been 12 home openers where we had game time temperatures in the 30s:

     

• The 1979 home opener was the coldest at 32°.

•  1899 opening day at League Park was the warmest. Game time temperature 84°.

• The most precipitation ever on opening day was 1981, April 11th at the old stadium. We had 1.44" of rain.

 

Most recently the 2006 opening day had 0.61" of rain. 

Opening day April 13th 1962 we had some scattered snow. 

History of Snowfall After March 20

We usually see snow after the first day of spring. Rarely does this NOT happen. How often?  Here is a nice calendar of ALL of the snowfalls in Cleveland since the late 1800s.

A few things to note as you read the chart.  For March, only snows greater than one inch. Between April 1 and 15, only snows at/greater than 1/2".  Between April 16-30, snow amounts greater than 0.1".  After May 1st, all snows including trace amounts.  I color coded select ranges for easy reference.

The latest snow (trace) was May 27, 1927

Note there has been no snow after May 27.  Any frozen precipitation after May 27 was hail.

PI day: Why is PI important?

It's one of my favorite days of the year!  A day when geeks of all ages can show off their PI stuff and skills at recitation.  I used to know PI to 75 digits. Your kids might have PI day activities planned at school today. I'm sure back in 1737 when Leonard Euler first used the symbol π, he never envisioned the fascination with π that developed since.  There are t-shirts, π plates (yes, I have two), π mugs...and on and on. Novelty websites have just about every π trinket you can think of!

What is PI " π"?

PI is the number that represents the ration of a circle's circumference to its diameter.

The interesting part is that PI is non-repeating and never ending so its very nature is an approximation. Mathematicians have tried to find patterns within π since ancient times. Archaeologists believe that the ancient Egyptians constructed the Great Pyramid of Giza with knowledge of π. Greek mathematician Archimedes was the first to calculate a range for π  using polygons.

Throughout it's history, π has become a fascination among mathematicians and more recently computer programmers. Welsh mathematician William Jones was the first to use the symbol π to represent the ratio of a circle's circumference to it's diameter in the early 1700s. In the 1940s, a little over 1000 digits of π were known.

As of July 2024, the record for PI calculation is 202 trillion Digits.

Read about it HERE.

The last 50 digits are:  5683914937 4658179610 2004394122 9823988073 3622511852

In 2014, scientist Ed Karrel calculated more than 10 QUADRILLION hexadecimal digit of PI.

  Using hexadecimals make it faster to calculate. Converting hex to base 10 numbers which we all use each day is very difficult when you have these many digits according to Ed.  Here is his blog.

To put the number of digits in Ed Karrel's calculation into perspective, you only need 39 digits of PI to calculate the circumference of the observable universe (assuming it's a sphere) to the accuracy of a width of the inside of an atom!

To put it another way, if you were to recite EVERY digit, it would take you 317,000,000 years to complete. You'd need to start before the dinosaurs were alive in order to finish today!

All of this great if you are a math or computer geek. But why should the rest of us care?

PI is present in every aspect of our lives. PI is used in most calculation in the development of all the world's infrastructure. All communications, CAT scans, MRI machines, genetic research, propulsion systems (space and military aircraft), quantum physics....the list goes on. Famous scientific discoveries and the math that describes them incorporate PI:

* The calculation for determining the horsepower of your car has PI in it.

* Einstein's famous equation that describes relativity which is now directly applied in
   satellite calibration has PI in it. Here it is in very simple form:

* The math that determines electric force (electricity) includes PI.

*  How about the speed and volume of blood flow inside the first artificial heart? You bet.
    PI is included in that calculation too.

* Want to figure out the position of two planets nearest to the earth? You need PI.

* Radio communications, cellphones, GPS satellites (see Einstein's equation above) computer hard drive/processor technology were both developed using mathematics that incorporates the number "PI".

* Airlines use PI to calculate flying distance around the earth

* Manufacturing uses PI to figure out how much of a substance will fit into a volume
   of circular or cylindrical space

You might not like math. You might not get PI.  Just remember that PI (3.14159...) is integrated into our everyday life unlike any other number. Without it, your daily life would be totally different.

How Common Are Spring Temperature Swings In Northern Ohio?

Our human nature is an exercise in self-deception. In the case of weather, our biases can undermine how we look at weather forecasts and past weather events. I like to call it "Behavioral Meteorology". Here's is an example that I guarantee has been said thousands of times over the last several days: "This Ohio weather is crazy! I don't remember changes like this. Wait five minutes and it'll change."

Just look at the high temperatures over the last 2 weeks. 46° to 28°.   40° to 63°.  33° to 52°. Plenty of ups and downs. It's easy to draw the conclusion that these big changes are unusual. 

Look at the temperature forecast through St. Patrick's Day across the eastern US.

But when we look at the entire middle of the country, the locations that see the greatest frequency of day-to-day temperature change might surprise you. The central US -- Eastern Nebraska, northeastern Kansas, northwestern Missouri, portions of Iowa and southern South Dakota -- experience the largest day-to-day temperature swings especially in March and April. Ohio is on the list but no where near the top.

How did I figure this out? I downloaded the high temperatures for each day for 20 cities over the last 50 years. I found the day-to-day high temperature differences and counted up the instances when it was more than 20 degrees. I plotted the results on the map above. For northern Ohio, I tabulated the totals for each month and graphed them below.

This year was the coldest in northern Ohio in a decade (winter of 2014-15). The number of days with high temperatures under 30 was the highest in 7 years. We had triple the number of days with snow on the ground compared to last winter. Large high temperature changes this winter only happened 4 times of at least 15 degrees.

Snow and cold in December through February has been replaced with rain and milder air. These warmer changes are welcomed. But subconsciously, they make us feel uneasy as we try to make sense of rapid temperature changes historically.  Psychologists call this "Cognitive Dissonance". None of us like to feel uncertain or conflicted.  We all have a built in motivation to reduce conflicting ideas by altering the existing conditions in our mind to create consistency. How many times recently have you had a conversation with someone and they said, “What is the deal with this crazy weather…what is going on here?”

Our weather perceptions are powerful.

Here a chart showing the March-April-May instances since 1950 where the high temperatures change more than 20 degrees day-to-day. The average is between 4 and 6 each spring. No big changes over the years in northern Ohio.

We view the weather through our own senses. We interpret all of this information through our own individual frame work. This frame work is built through our own experiences coupled with a hard-wired lens that shapes what/how we view what occurs around us.  We are inclined to favor information that reinforces our comfort level and preconceived notions. This is called a "Confirmation Bias". The problem is that by creating "consistency" through favoring information, we can create a new false interpretation of the weather which we believe to be true. In a nutshell, our own human nature deceives us. Our biases "cloud"--no pun intended--our judgment of the weather changes.

As much as we perceive these fluctuations to be a new thing here in northern Ohio, it is quiet common in early spring. Will these big changes continue in the weeks ahead?  Just look at the temperatures over the next 2 weeks!

US and Great Lakes Snowfall Recap Thus Far

Snowfall across northern Ohio started out impressive in late November. More than 60"+ inches of snow fell in parts of the snowbelt. The most in parts of eastern Lake and the northern 2/3rds of Ashtabula county since the record setting lake effect event in November of 1996.

Photo: NWS Cleveland

Snow across northern Ohio and the Great Lakes started out with a bang in November and December. Here are some great images from Meteorologist Ben Noll showing snowfall vs normal by November 22 around the Great Lakes and nationally.

Snowfall vs Normal through November 22

After the big snow at the end of the month, snowfall recovered fast. Blue and Purple areas indicated WELL ABOVE seasonal snow.

Nationally through December 10:

Here is the snowfall vs normal map through December 18th:

Now snowfall vs normal through January 1st.  Notice how the departures began to drop around the Great Lakes snowbelt areas. Below normal snowfall the latter half of December.  The November snow kept the overall seasonal totals above normal.

National snowfall numbers also started to drop.

The snowfall decline accelerated in January. The snowbelts still had spots with seasonal snow above normal driven by the late November snows. Snowfall was well below normal since January. Much of the interior regions were running well below normal snowfall by early February.

The middle of the US, upper mid-west and most of the Ohio Valley was well below normal by early February.

By March 1st, the seasonal snow across the Great Lakes was almost back to normal. Snowbelt of northern Ohio, PA and western New York only slightly above due to the late November snowfall.

Seasonal snowfall across the US east of the Rocky Mountains through March 1 is characterized like this:

• Well below normal snowfall in the central US, upper mid-west, Ohio Valley including Pennsylvania, New Jersey and parts of New England.

• Slightly above normal snowfall in the Great Lakes snowbelts due to early season snow. Below normal snowfall since January 1st.

• Above normal snowfall in the south from southern Missouri, Arkansas, Kentucky and Tennessee.

• Above normal snowfall along the gulf coast due to the late January record snowfall:

Ohio and Pennsylvania snow depth animation from November 20 through March 3:

My next post will dive into northern Ohio snowfall further comparing this year to years similar