If the rain this winter was snow how much would we have?

It's a great question.  

After all actual snowfall this winter versus the 20 or 30 year averages was extremely low.  

Snowfall as of this writing in Cleveland is barely above 20 inches.  Yet the liquid precipitation (rain and snow combined) is north of 12 inches!

Before we attempt to answer this let's recap the winter storm track. After one of the warmest winters on record here in northern Ohio and across a large portion of the Great Lakes and Ohio valley, storm systems coming out of Texas ended up producing more rain on the southern end of the track with heavy snow staying across the northern states. Total precipitation (rain and snow) was well above normal in areas that saw above normal snowfall vs areas that saw more rain than snow.

Total precipitation from November through December was well below normal. January through mid March total precipitation was well above normal.

So how much snow would all of this precipitation give us if we converted ALL of it to snow?  

Converting liquid precipitation to snow using a (15" of snow to 1" liquid ratio) would give us these numbers (orange) vs actual snowfall (blue).  15 to 1 ratio is arbitrary as wetter snow would closer to 10 to 1 and super-dry snow could be as high as 25 to 1.  I took the middle ground.  The higher the orange line, the more total precipitation thus more potential snow (if converted).

Next we need to find the difference between the actual snowfall (blue above) each winter from the total converted liquid to snowfall (orange) to give us the remaining potential snow.  The larger the difference, the more liquid precipitation (converted to snow) that winter. I plotted each year's difference in the bar graph below.  

The top 10 highlighted below with 2022-23 highlighted for reference.

Bottom line is that the winter liquid precipitation (after subtracting the actual snowfall) this winter when converted to potential snow yielded a number just outside the top 10 highest since 1950 (graphic above). This winter had the highest liquid to potential snow since the winter of 2017-18 and a 36% increase comparing this winter to last winter and 65% increase compared to two winters ago. Yet this winter didn't crack the top of the list but its up there. 

1949-50:  227"

1951-52:  202"

2011-12:  198"

2007-08:  197"

1990-91:  193"

2006-07:  192"

1950-51:  191"  

1958-59:  180"

2016-17:  169"

1974-75:  167"

2010-11:  163"

2022-23:  162"

How Does The Christmas 2022 Blizzard Compared to the January 1978 Blizzard?

Just last week we remembered the Blizzard of 1978 or the White Hurricane as it would be known as after the fact. Ohio turnpike was shutdown gate to gate.  Twenty foot high snowdrifts. Wind gusts above 80 mph in spots across Ohio and beyond.  If you lived during the storm, the memories will live forever.

A blizzard is defined as three hours of sustained winds of 35+ mph, 1/4 mi or lower visibility due to blowing and/or falling snow.  The last condition is key as you don't need falling snow for a blizzard to occur.  Recently the Blizzard of Christmas 2022 created conditions that in some respects rival the '78 blizzard.  Below is a comparison between the initial 12 hour period for each blizzard. The data includes precipitation type, temperature, wind chill, wind direction, speed and gusts followed by air pressure from Cleveland Hopkins Airport:

A few elements stand out here.  You'll notice the wind chill values of the 1978 storm are greatly reduced compared to what many publications sited years ago. The wind chill calculation changed in 2001 to reflect new science on the subject of heat loss on the body.  Earlier estimates put the wind chill at under -60° but in reality, the chills probably bottomed out at -30°,  maybe -40° with the higher gusts with the new formula.  Other notable findings from the observations above:

* The temperature drop during the December 2022 storm was greater (42° to 0° in 8 hours).  The 1978 storm had a sharper drop. 43° to 9° in 5 hours.

* 2022 storm created wind gusts nearing 50 mph at Hopkins with some reports above 60 mph once the temperatures dropped into the single digits.  These wind gusts (50+ mph) took roughly 5-7 hours to reach after peak temperatures. The 1978 storm had gusts reaching 70+ within 3 hours of peak temperatures. 

* Official wind chill values were LOWER during the 2002 storm.

* 1978 storm was far deeper with lowest air pressure at 958 mB vs 993.0 mB with the 2022 storm.

* Snow depth was much deeper in 1978.  16" before the storm versus no snow on the ground at the start of the 2022 storm.

What about the conditions after the initial 12 hours?  Here are the hourly observations for each storm over the next 48 hours. 2022 storm on left.  1978 storm on right. Color coding is identical to the image above.

Here are some notable findings in these 48 hours

2022 storm:

* Temperatures remained below zero for another 12 hours and under 10 degrees for another 19 hours.

* Sustained wind speeds stayed at 30 mph for 12 hours with gusts near 40 mph for 24 hours.

* Wind chills remained at under -20° for 24 another hours.

The progression of the arctic cold was fast and widespread. After 7 days the extreme cold had faded.

7 day temperatures vs normal during and following blizzard

1978 storm:

* Temperatures never dropped below zero; remained below 10 degrees for only 9 hours

* Sustained winds were similar to the 2022 storm (30 mph for 12 hours) with 40+ gusts 

*  Wind chills stayed between -5° and -15° as temps stayed between 5° and 15° degrees

The cold following the '78 blizzard was just as harsh and quick but it lingered much longer.

7 day temperatures vs normal during and following Blizzard

The surface features with both storms look very similar.

January 1978 Blizzard

December 2022 Blizzard

Psychology tells us that our experiences especially during extreme events tend to leave a stronger mark in our mind more than more benign events.  The January 2022 blizzard was one of those events.  The blizzard around Christmas 2022 was also a memorable one--certainly not the worst in northern Ohio history but it's on the list (maybe top 5)--and should be remembered in the same breath as the '78 storm.

Lake Erie Ice Cover Update

High resolution image of Lake Erie, January 16,2023

I finally updated the daily Lake Erie ice coverage charts for each season since 1972. I separated this into month periods starting with November and ending with May.  This is all color coded. 

• Light blue blocks indicates ice coverage under 1%
• Bright blue is 2% to 10%
• Dark Blue is 11% to 25%
• Purple is 26% to 50%
• Pink is 51% to 74%
• White indicates at least 75% coverage

You'll notice several things here:

1. Significant ice typically doesn't develop until late December.
2. Mid/late 1970s AND the early 2000s stand out as the most significant ice coverage period in  January and early Februar
3. First half of February is typically the peak for ice coverage
4. November 23, 2014 was the earliest ice cover
5. May 17, 1982 was the latest ice cover 

Back in 2021 and 2022, I did some research on the conditions needed for rapid ice development.  Links are  HERE  and  HERE

NOVEMBER

DECEMBER

JANUARY

FEBRUARY

MARCH

APRIL

MAY

Number of days with ice coverage 90% or higher:

 

Number of days with ice coverage 10% or higher (since 2010-11)

All Lake Erie ice data from THIS SITE BACK TO 1972

Graph comparing this year (2022-23) to years past

Canadian ice service for more historical perspective

Max ice coverage each winter

Above/Normal Winter Temperatures in Cleveland: Is there a Trend?

How often have we heard the saying "Perception is Reality"?  

No other phrase describes the weather better than this one. We look at past winters through our own personal lens molded by our own experiences in that weather. Maybe we were on our way to a Christmas party in a snow storm similar to this past Christmas blizzard. Perhaps it was a really warm Christmas like 2019 or 1982.  Maybe it was a severe thunderstorm that caused damage to your house.  Events like this leave an indelible mark in our minds placing more weight on these weather events versus others. Psychology tells us that we typically remember these extremes more than the averages.  Perception is indeed reality. 

Below are the total number of days where the average temperature was above normal and below normal for Cleveland, Ohio (NWS station at Hopkins Airport).  I looked at every winter since 1960-61 (December through February) using the SC ACIS site.  All normals are calculated using the period 1991-2020.

 

DJF Days Above Normal

DJF Days Below Normal

If we just plot the higher end above normal days (at least +10 degrees above normal) this is what we find:

DJF Days at least 10 degrees ABOVE NORMAL

Now the lower end below normal days (at least -10 degrees below normal):

Below Zero Wind Chill History: What Years Experienced More?

It's been two years since I updated this chart.  It shows total hours at each below zero level from last winter (2021-2022) back to the winter of 1976-1977.

Here are some take home points: 

*  No -20 wind chill since early 2019

*  Coldest winter (wind chill) in recent memory was 2013-14.  Also the last time we had a long duration period with wind chills under -30

* Winter of 1992-1993 was the only one without a wind chill below zero. 1997-98 was close.

* Some notable winters (total hours below zero):  

2018-19:  669

2017-18:  842

2008-09:  772

1996-97:  1022

1993-94:  1605

1989-90:  1089

1983-84:  1958

1976-77:  2125

How Do Meteorologists Define Precipitation Chances? Part 2

Part 1 of the Precipitation Percentage topic talked about the technical definition and my definition and usage.  What about on-air meteorologists?  How do they define precipitation percentage?  How do they come up their number for their forecast? Do they even use percentages?  Here are some answers from on-air meteorologists across the country?

• "I actually don't include percentages in my forecasts as I feel they can be confusing to the audience. My understanding of them as issued by the NWS is that percentage od the forecast area would receive any precip."

• "On our 12 hour planners the pops are specific to the city of choice. On the 7 day its the pops for our viewing area."

• "There's an art to this, it seems, but like NOAA, we use coverage and confidence as our main factors in determining POPs for our zones. I tell our customers, "You should expect to cross with rain at least six of every ten times you hear us forecast a '60% chance of rain'."

• "My working definition for precipitation percentages is the percent of my viewing that will see rain. So, if the rain chance is 70%, then about 70% of the viewing area would see rain and about 30% of the DMA would not. However, if that 70% sees rain, then there is a 100% chance of rain for them.  I typically used MOS guidance to help me out a bit on rainfall percentage. I also just use model guidance and common sense, especially if I’ve seen a similar weather pattern on the models or maps. Sometimes the models will say it’s a big chance, but other variables keep the chance down....confidence plays a huge role in choosing POPs. I want was much information as I can to make the best forecast I can with precip. I do also believe that rainfall intensity sometimes does play a factor. If I see potential for heavy rain, that will affect my decision making. Also, the major counties or cities in our DMA. If there is a better bet for rain in those location, I have, at times, adjusted the forecast to reflect the population center of our viewing area."

• "I fill out a spreadsheet with all the model data to get an analytical view of the numbers. That has high/low/PoPs to compare and see trends from day to day. Also helps to compare anomalies with models that are trending drier/wetter/colder/hotter than the rest."

• "We don't use POPs on-air, or convey rain as a percentage on our forecasts. However, we do use "isolated" "few", "scattered", and "widespread" to describe rain coverage."

• "We use POPs as defined by confidence in the forecast times and the coverage of the rainfall. While we use this traditional method in calculating the POP we do tailor them more to the audience as strictly the chance of seeing rain for the forecast area."

• "We don't use pops on air because of the confusion you're likely looking to highlight.  We use words to qualify precipitation placement and likelihood.  There is a bit of POPs in the making of that, though.  Main variables being coverage and confidence of at least .01" rain in our area... Oh... also, we use coverage * confidence for a given time period."

• "PoP to me is the percent of the populated area that will get rained on at some point during the forecast period. I know that is not technically right. But it works."

• "...Percent chance of any one location seeing precipitation during the stated time period. Not areal coverage or duration. We show precipitation icons for any PoP 30% and above and do not use 10% in daily forecasts (we do in hourly forecasts). We do not use 50% for either daily or hourly forecasts as it invites “forecast skeptics” to make “50/50 guess”-type comments."

• "I use it as a general number for chance of rain in our averaged area as a whole. It's tough to do broadly for a whole forecast area right? Since it's really more useful for a specific point."

• "For instance I may put 50% chance of rain on a graphic, even knowing the chance is 10% on a specific city and 85% in Central parts of the state. You have to eyeball it a little...then explain further in your forecast who's actually most likely?"

What Does Percentage of Precip Actually Mean? Part 1

What does the Percentage of Precipitation actually mean on a weather forecast?  

Does it mean we'll receive rain 40% of the time, 40% of the area?  If there’s a 30% chance that it will rain, then is there a 70% chance that it won’t rain?

Or is it something else entirely?

It's one of the most commonly asked questions of meteorologists.  Everyone seems to have their own definition. Is there a standard?  

The technical definition below is taken from the NWS:  (Link here)

"the probability of precipitation is simply a statistical probability of 0.01" inch of more of precipitation at a given area in the given forecast area in the time period specified"

This is calculated by multiplying two numbers:  Forecaster confidence (Percentage) and Areal Coverage.  So if the forecaster is very confident (90%) that rain will develop but only 30% of the area in question will receive the rain, then the final precipitation probability would be: 

Percentage of Precip. = 0.90 x  0.30

Percentage of Precip =  0.27  Rounded up to 0.30 or 30%

Got it?  😮

Here's what it doesn't mean:   "If there’s a 30% chance that it will rain, then there a 70% chance that it won’t rain."

It also doesn't factor in:  How long it will rain,  How much precipitation,  Intensity of the rain/precipitation  

Summary from the NWS

This can get confusing. Plus it's hard to visualize for the lay person nor is it practical.  

The other more non-technical way is defining precipitation percentages as the amount of the region--overall coverage--that will see precipitation over a certain time frame.  This is a great benefit to an on-air meteorologist who needs to cover dozens of counties over several minutes. A generalized map can work IF the weather event is more large scale like widespread showers with no breaks.

January 2019 winter storm

The image below gives the higher likelihood (brighter green colors), time of precipitation and other useful information about duration and intensity.  All useable and relatable information for the viewer.

The problem with this generalization arises when precipitation is heavy in one location and/or spotty in another. The forecast details become highly localized. Here is an example from our first snow on November 15, 2021. The shoreline was pounded with heavy lake effect snow.  Inland hardly any precipitation with sunshine. One number doesn't work in this case.

A generalized map in this case becomes unrelatable because most viewers don't necessarily care what's happening 50 miles away unless it impacts their lives in some way.  Attempting to give a percentage for each location taking into account the numerous dry periods and pockets is just not practical for the on-air meteorologist. The conditions in this example change too fast. 

Here's another example of a line of storms moving across northern Ohio at 7:21PM on October 23, 2020. Viewers impacted by the storms in Lorain county or near Mansfield would interpret the forecast as 100% chance of rain.  Yet people in Akron or Canton would guess 0% given the lack of rain at this point. Yet another example of how ONE PERCENTAGE NUMBER doesn't even begin to tell the complete weather forecast story.

How about the extended forecast? The 5,7 or 8-day forecast that you see on television or a weather app utilizes a percentage. It's been a staple and weather forecasts for a very long time. But often times they lack context and qualifying information similar to the graphic above to make the number useful. We at WJW FOX 8 add some basic text but that too has limitations. 

 

On air meteorologists have time against them. They have a small amount of time (usually under 2 minutes) to deliver quality, usable information to the viewer. They need to do this in such a way that addresses as much of the viewing area as possible. For the Cleveland market it encompasses 25 counties and roughly 8000 square miles. 

Unfortunately, one percentage number doesn't do the forecast justice.  It lacks context. It lacks specificity.  So how do we get around this? What's the solution?

Unfortunately there is no cut and dry answer.  For me, I believe the beginning of any workable answer lies first in basic psychology and perception. That is we must always remember that most people visualize the weather through their own spatial filter.  They visualize the weather conditions or forecast through what we can see literally in our own backyard, where we work or live. Their weather universe is what we can see. If that percentage is describing something outside of their event horizon, it's irrelevant. How many times have you looked out the window, saw the weather, made an assessment and determined your daily activities only based upon that?  We all do it.  

For on-air meteorologists in my view, we need to take this viewer centered, subjective view of the weather into account.  How do we do this?  

I have my own subjective definition of what precipitation percentage defines. For me it's a combination of three elements:  I call it the "THE THREE C's".

CONFIDENCE

COVERAGE

CRITICAL

CONFIDENCE, as mentioned earlier is subjective with each forecaster. This is based upon the forecasters expert analysis of the situation. On the extended forecast I rarely put a 50% or higher further out than 5 days because the confidence is so low in most instances (see graphic below)

COVERAGE is what portion and how much of the viewing area will receive precipitation. Pretty self explanatory.  See earlier image.

CRITICAL is accessing what elements are the most important and how they impact the viewer. For me this is the meat and potatoes of the entire forecast!  

How intense is the precipitation?  How long and how hard will it rain or snow?  When is this occurring?  How will this impact the viewer? How many will be impacted?  I factor these into the overall weather setup and weight them.  Is this occurring for the first time in the season or has this been a reoccurring event? 

For example if the rain will be light and last only a brief time at midnight where it impacts only a small amount of people then I weight the precipitation event less than if it was going to downpour over an hour at rush hour.  If we anticipate a foot of heavy wet snow to fall as kids head to school in November, this will be weighted more significantly than if it was an inch of snow in February (public perception).  Some of what's Critical as defined here is quantifiable.  Some are not. 

I take into account as much information from the "THREE Cs" as possible and come up with a number in the form of a percentage that best fits the weather scenario.  It's taken me years to learn how to do this.  I've trained myself to complete this exercise each day almost subconsciously as I assess the weather forecast specifics.  

So on my graphics the percentage you see is my best interpretation of the "THREE Cs"

Example from December 19, 2022

MLB 2022 Recap - Were There Any Significant Changes?

Here are numerous charts showing where the 2022 MLB season compares to recent seasons. 

All data is from Fangraphs and Baseball-Reference