1. Clouds, Fog, and Precipitation
Current Weather
Clouds and Fog
Precipitation Formation and Types
Rime Ice
For Next Class: Read Chapter 7: pp

2. Atmospheric Temperatures and Stability
Figure 7.18

3. Stable vs. Unstable Air

4. Three Examples of Stability
Figure 7.19

5. Cloud Types and Identification
Figure 7.22

6. Cumulonimbus Development
Figure 7.23

7. Chapter 6, Figure 6.28 Labeled

8. Fog  
Advection fog
Evaporation fog
Upslope fog
Valley fog
Radiation fog

9. Advection Fog
Figure 7.24

10. Chapter 6, Unnumbered Figure 1d, Page 178 Labeled

11. Evaporation Fog
Figure 7.25

12. Valley Fog
Figure 7.25
Figure 7.26

13. Evaporation and Radiation Fog
Figure 7.28

14. Radiation Fog
Figure 7.27

15. Chapter 6, Figure 6.24 Labeled

16. Collision-Coalescence vs. Bergeron
Chapter 6, Unnumbered Figure 1, Page 175 Labeled

17. Chapter 6, Figure 6.26 Labeled

20. Snow Particle Photomicroscopy
Graupel

21. Snow Particle Photomicroscopy

22. Photo by Marie Freeman, Appalachian State University
Observations of Snow Particle Characteristics during Snow Events in the Southern Appalachian Mountains
Boone, NC
Heather Guy1, L. Baker Perry1, Montana A. Eck1, Spencer Rhodes2 and Sandra E. Yuter2
1 Appalachian State University, Boone, North Carolina, U.S.A. Contact:
2 North Carolina State University, Raleigh, NC, U.S.A.
Introduction
Case Study 1: Deep moist layer (Miller A)
Case Study 2: Shallow Upslope flow
In the southern Appalachian Mountains snowfall totals are poorly predicted at times and unexpected snowfall can have widespread consequences. Large uncertainties surrounding cloud microphysical properties and their relationship with cloud structure and snowfall totals remains one of the key limitations for snowfall prediction. Observations of cloud properties and how they relate to synoptic conditions and snowfall totals are necessary to understand and reduce this uncertainty.
Start: 11:55 UTC 8 Dec 2017
End: 16:35 UTC 9 Dec 2017
Synoptic Classification: MA
Total Precipitation (LWE): mm
Snowfall: cm
Start: 21:35 UTC 13 Mar 2018
End: 17:15 UTC 14 Mar 2018 Synoptic Classification: U
Total Precipitation (LWE): mm
Snowfall: cm
MASC (a) and MRR (b) instruments installed at Appalachian State University, Boone, NC (1,008 masl)
(a)
(b)
This study uses digital photographs of snow particles from a Multi-Angle Snowflake Camera (MASC) and observations from a vertically pointing Micro Rain Radar (MRR) to explore how cloud structure impacts crystal formation and snowfall totals.
# Particles
Fall speed (m/s)
Complexity
Max diameter (mm)
# Particles
Fall speed (m/s)
Complexity
Max diameter (mm)
Radar Reflectivity
Radar Reflectivity
The Snow Season in Boone, NC
Spectral Width
For the winter of the annual snowfall total in Boone was 5 cm higher than average ( ). We classified 16 individual storms, the MASC was in operation during 14 of these.
Synoptic Classification Scheme
Miller A cyclone
Miller B cyclone (to the North or South)
SE tracking clipper
NE tracking low
Stationary front
NW upslope flow not tied to one of the above.
Unclassified
Case Study 3: Snow/ Rain transition, SE
Start: 10:35 UTC 24 Mar 2018
End: 03:35 UTC 25 Mar 2018
Synoptic Classification: SE
Total Precipitation (LWE): mm
Snowfall: cm
Wind data not available
Snowfall is not proportional to total liquid water equivalent precipitation.
Understanding snowflake properties is key to accurately predicting snowfall.
Snow Properties & Isotopic Fractionation
Snowflake properties from the MASC:
# Particles
Fall speed (m/s)
Complexity
Max diameter (mm)
Radar Reflectivity
The isotopic (δ18O, δD) composition of precipitation is an important tracer used in hydrological and paleoclimatic studies. Cloud microphysical processes effect isotopic fractionation but the physical processes involved and their relative effect on observed δ18O and deuterium excess (d = δD ‐ 8δ18O) remain an area of active research. Simultaneous MRR observations, MASC photographs and isotope measurements can help solve this problem. Thirteen isotope samples collected during storms in Boone this year demonstrate how δ18O and d may relate to snow particle properties (adjacent).
MASC photos are used to estimate particle fall speed, maximum diameter and complexity (see adjacent).
Estimating degree of snowflake riming
The complexity parameter, χ (Garret, 2014) is used to estimate the degree
of snowflake riming.
χ = 1 = perfect circle
χ < 1.35 = Heavy riming, lumps/ graupel
χ > 1.75 = Aggregates
χ = 1.81
χ = 1.16
χ = 1.33
χ = 2.15
All snowflake properties measured ( ):
Spectral Width
Conclusions
Average storm properties by synoptic class:
Storms with a higher % of graupel have smaller snowfall totals relative to the liquid water equivalent precipitation.
Improving cloud microphysical schemes in forecast models to include accurate snow particle size and riming distributions may improve snowfall total predictions.
A longer term record is necessary to verify some of the relationships identified here. Particularly between cloud characteristics and synoptic class, and isotopic fractionation.
This work was funded by the College of Arts & Sciences at Appalachian State University

23. What is Rime Ice? 23

24. Rime Ice
A coating of tiny, white, ice particles caused by the rapid freezing of supercooled water droplets on impact with an object.

25. Rime Ice on Mt. Mitchell 25

26. Rime Ice on Beech Mountain

27. Heavy Rime Icing on Mt. Washington27

28. Rime Icing on
Grandfather
Mountain