ATMO 1010 - IL-8 - Seth Zacharias

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Salt Lake Community College *

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1010

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Apr 3, 2024

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Seth Zacharias Jeana Mascio ATMO 1010 24 March 2024 Forecasting Lake Effect Snow 1. What two times of year is GSL effect snow most frequent? GSL effect snow is most frequent during late fall and early winter, usually November to December, and again during late winter and early spring, typically February to March. During these periods, a combination of cold air masses moving over the relatively warmer waters of the Great Salt Lake can lead to considerable snowfall in surrounding areas, particularly along the Wasatch Front. 2. What time of day is GSL effect snow most frequent? GSL effect snow is most frequent during nighttime and early morning. This is because during these times, the temperature contrast between the relatively warmer waters of the Great Salt Lake and the cooler air masses moving over them is more prominent, leading to an increase in atmospheric instability and snowfall. Additionally, during nighttime and early morning, the land cools more rapidly than the lake, further increasing temperature difference and likelihood of snowfall. 3. The authors identified two key factors in forecasting GSL effect snow: lake- 700mb temperature difference and 850-700mb relative humidity. How do these two factors combine to determine the probability of GSL effect

snow? (Hint: describe the graph that plots both of the factors.) ( http://wasatchweatherweenies.blogspot.com/2012/02/new-perspectives- on-lake-effect.html ) During winter, lower divergences between temperatures is sufficient in creating a lake effect, primarily due to changes in humidity between the Winter and Spring seasons. 4. Using the maps above, find the value of ALL of the factors below at 6pm (the current time) AND 6am (the next morning). Describe how the value or character of each changes throughout the evening. a. Wind Direction and Speed 6 am: NW, 5 knots 6 pm: NW, 10 knots b. Wind Direction change from surface to 700mb 6 am: NW 6 pm: NW c. Lake surface temperature 6 am: 11°C 6 pm: 11°C

d. 700mb temperature 6 am: -10°C 6 pm: -7°C e. Lake surface-700mb temperature difference 6 am: Difference: 21°C 6 pm: Difference: 18°C f. 850-700mb relative humidity (hint: look at Discussion "IL-8 Calculating relative humidity from stuve diagram") 6 am: Saturated 6 pm: Near Saturated g. Location of surface low 6 am: SE Utah 6 pm: Central Colorado h. Location of 700mb trough 6 am: One centered in Salt Lake, one in St. George 6 pm: Center of Colorado and below Utah/Colorado border

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5. Based on the data you collected in question 4, what do you think the likelihood is for Great Salt Lake Effect snow in the Salt Lake Valley during the night? Considering the temperature difference between the lake surface and the 700mb map, the possibility for GSL effect snow is high. 6. How many snow plows do you think UDOT should keep out overnight for the Salt Lake Valley (between 0 and 25)? Why did you choose this number? As my dad always says, it’s better to be over prepared than not prepared at all, so my thoughts would be to use 10 snow plows because 25 would be too much and I believe a lower number like 5 would not be able to get the job done. 7. Did your lake effect forecast verify? What do you think went wrong or right with your forecast? I think my lake effect forecast did verify because it did snow and the forecast did match up with the list. 8. Do you think you chose the right number of snowplows for the Salt Lake Valley on this night? Why or why not? I think I chose a little too many snow plows, given the minimal snow accumulation in the Salt Lake Valley and the absence of significant ongoing precipitation during the night. However, I still think choosing a low number like 5 plows would have not provided sufficient response in case of unexpected

accumulation and a high number like 25 would have resulted in unnecessary expenditure of resources. 9. Describe at least three reasons why weather forecasting is challenging. - Atmospheric Complexity: The atmosphere is a highly dynamic and complex system with countless interacting variables such as temperature, pressure, humidity, wind speed and direction, and more. - Limited Observations: While modern technology has vastly improved the ability to observe and monitor the atmosphere, there are still gaps in coverage, especially in remote or oceanic regions. - Chaos Theory and Butterfly Effect: Weather is a chaotic system, meaning it is highly sensitive to initial conditions. Small errors in the initial data or modeling assumptions can amplify over time, leading to significant differences between forecasted and observed weather.

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