1. Extratropical Cyclones and Their
Uncertain Nature: A Case Study of
the January 2016 Mid-Atlantic
Winter Storm
Mussie Kebede
Howard University
4/14/2016
A Qualitative Research Study
2. Purpose
• To better understand the challenges that are
faced when predicting extratropical winter
storms.
• To foster communication with researchers
about my interests.
• To ultimately improve long range weather
forecasting
3. What is an “Extratropical Cyclone?”
• Defined as “A cyclone of
intensity for which the
primary energy source is
baroclinic, that is, results
from the temperature
contrasts between warm
and cold air masses”
(National Hurricane
Center).
• Occurs outside of the
Tropics
earthobservatory.nasa.gov
4. Research Question
• How did uncertainty affect predictions for the
January 2016 Mid-Atlantic Winter Storm?
5. January 22-24, 2016 Winter Storm
• Impacted Mid-Atlantic and Northeastern
United States
• Mainly a snow event
• More uncertainty in predicting snowfall totals
for the Northeast compared to Mid-Atlantic
• Cost the United States economy between
$500 million to $1 billion.
6. How do we determine uncertainty?
• A comparison of predicted and observed
conditions (model verification).
• Numerical Weather models generate a
number of storm related time dependent
indices including:
1. Projected storm track (direction of low)
2. Intensity (central surface low pressure)
3. Amount of precipitation (snowfall)
7. Day 1-2 Prediction
Total Precip over 2.5 days (NCDC,
NOAA)
Potential Vorticity (NCDC, NOAA)
Units: 2x10^-6 Km^2 / kgs
11. Results
• GFS Model predicted storm to track further inland.
• Observations had it track up the coast which lead to
enhanced accumulations.
• Also predicted faster moving storm which is out to sea
by the 24th of January
• Observations show the storm not too far from
northeast coast.
• Model predicted 5-10 inches of total snowfall
throughout the Mid-Atlantic
• Observation show totals ranging between 15 and 25
inches
12. Conclusions
• GFS Model performed very well in the long
range (7 days) given the current difficulty of
such predictions.
13. Implications
• Improved predictions
– More informed decision making by citizens
– Mitigate dangers associated with power outages,
coastal flooding & wind damage.
14. Citations
"NOMADS Web Interface - Web Plotter V.2 -
Control File Selection." NOMADS Web
Interface - Web Plotter V.2 - Control File
Selection. National Atmospheric and Oceanic
Administration, n.d. Web. 14 Mar. 2016.
"Daily Weather Map." Daily Weather Map.
National Oceanic & Atmospheric
Administration, n.d. Web. 14 Mar. 2016.
Hello, my name is Mussie Kebede and I am a first year graduate student studying Atmospheric Sciences here at Howard University
Important to note that it did produce winter weather for large parts of the south as well
Important to note that the process by which verification is done is more quantitative therefore it is beyond the scope of my presentation
Using GFS model for purpose of this presentation
Will be focusing on #1 & #3 for the purposes of this presentation
GFS Model
12Z refers to GMT of day (12Z = 7am EST)
Potential Vorticity shows where circulation is.
Total Precip is in liquid equivalent
Predicted between 5-10 inches (liquid equivalent assuming 10:1 ratio) of snow
Predicted between 5-10 inches (liquid equivalent assuming 10:1 ratio) of snow
