Clouds, Fog, and Precipitation Current Weather Clouds and Fog Precipitation Formation and Types Rime Ice For Next Class: Read Chapter 7: pp. 182-192

Atmospheric Temperatures and Stability Figure 7.18

Stable vs. Unstable Air https://scied.ucar.edu/webweather/weather-ingredients/stable-unstable-air

Three Examples of Stability Figure 7.19

Cloud Types and Identification Figure 7.22

Cumulonimbus Development Figure 7.23

Chapter 6, Figure 6.28 Labeled

Fog   Advection fog Evaporation fog Upslope fog Valley fog Radiation fog

Advection Fog Figure 7.24

Chapter 6, Unnumbered Figure 1d, Page 178 Labeled

Evaporation Fog Figure 7.25

Valley Fog Figure 7.25 Figure 7.26

Evaporation and Radiation Fog Figure 7.28

Radiation Fog Figure 7.27

Chapter 6, Figure 6.24 Labeled

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

Chapter 6, Figure 6.26 Labeled

Snow Particle Photomicroscopy Graupel

Snow Particle Photomicroscopy

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: guyh@appstate.edu 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): 18.2 mm Snowfall: 15.0 cm Start: 21:35 UTC 13 Mar 2018 End: 17:15 UTC 14 Mar 2018 Synoptic Classification: U Total Precipitation (LWE): 10.3 mm Snowfall: 20.3 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 0100 - 0300 0300 - 0500 0500 - 0700 0700 - 0900 1100 - 1300 1300 - 1500 The 2017-18 Snow Season in Boone, NC Spectral Width For the winter of 2017-18 the annual snowfall total in Boone was 5 cm higher than average (1989-2018). 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 2200 - 0000 0000 - 0200 0200 - 0400 0900 - 1100 1100 - 1300 1300 - 1500 1500 - 1700 0400 - 0600 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): 25.4 mm Snowfall: 11.4 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 (2017-18): Spectral Width Conclusions Average storm properties by synoptic class: 1000 - 1200 1200 - 2000 2200 - 0500 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

What is Rime Ice? 23

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

Rime Ice on Mt. Mitchell 25

Rime Ice on Beech Mountain

Heavy Rime Icing on Mt. Washington 27

Rime Icing on Grandfather Mountain