1. 2 3 4 Source: Weather Prediction Center 5 6

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Using The Froude Number to Improve Orographic Snow

Forecasts in the Green Mountains of Vermont

Michael J MuccilliNOAA/NWS Burlington, VT

NROW XV 2014

NROW XV 2014 – Michael J Muccilli

Top 3 Take-Aways

The Froude Number is a useful tool for determining the characteristics of Orographic Snow Events

How and Why the Froude Number works for orographic snow forecasts

How to use in Operations

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Outline

Motivation

Synoptic Overview

Define the Froude Number

Green Mountain Study

Operational Use at WFO BTV

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Motivation

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January 2nd - 3rd, 2012

Source: Weather Prediction Center

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December 28th - 29th, 2011 6


Source: Weather Prediction Center

December 28th - 29th, 2011

January 2nd – 3rd, 2012

Froude = 4.4

Froude = 0.91

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Synoptic Overview

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Terrain of WFO Burlington County Warning Area

4395 feet100 feet

15 Miles

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Synoptic Overview

Upper Level Trough or Closed Low Progressing through the Region

Source: NOAA ESRLComposite of 25 Upslope Cases

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Synoptic Overview

Surface & Low Level Pressure System exiting the Region

Source: NOAA ESRLComposite of 25 Upslope Cases

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Synoptic Overview

Increasing west to northerly flow in low levels along with lingering low level moistureSource: NOAA ESRL

Composite of 25 Upslope Cases

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Synoptic Overview

St. Jean et al. (2004) found specific important factors to the development of significant upslope snow events:

Near-Saturated Conditions from surface to ridge-top level

Strong low level winds (>10 m/s) with significant cross-barrier component

Equivalent potential temperature decreasing with height in the low levels

Event duration of at least 12 hours

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Objectives

Make it easier to identify significant orographic (upslope) snow events and placement/orientation of heavy snow using:

I. The Froude NumberII. Low/Mid Level Humidity ProfilesIII. Vertical Profile of Wind Speed & DirectionIV. Low level Stability

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The Froude Number

Potential Temperature (Surface & Mountain Top) Mountain Height Speed of Wind Perpendicular to the Barrier

Froude Number Equation Brunt-Vaisala Equation

***RESULT: A Unit-less expression that represents the flow of air when it comes in contact with a barrier (Green Mountains)

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The Froude Number

Subcritical (Blocked), Froude < 1 Precipitation likely to fall

upwind of barrier

Critical, Froude =~1 Precipitation likely to fall

along barrier

Supercritical (Unblocked), Froude > 1 Precipitation likely to fall on

lee side of barrier

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Data & Methods - Green Mountain Study 25 cases (2007 – 2012)

12 “Blocked” (Froude < 1) 13 “Unblocked” (Froude >1)

11 Stations Used 2 Champ Valley 5 West Slopes 3 East Slopes 1 East Valley

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Data & Methods - Green Mountain Study

Froude Number calculated for each event, in the mid-point of each event

Used archived NAM/RUC Soundings at KBTV After calculation, grouped by calculated Froude

Number into 8 bins 0.25, 0.25 - 0.49, 0.50 - 0.84, 0.84 - 0.99 1.0 - 1.33, 1.34 - 1.75, 1.76 - 2.0, >2.0

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Froude > 2Fr 1.76-2Fr 1.34-1.75Fr 1.0-1.33Fr 0.85-0.99Fr 0.50-0.84Fr 0.25-0.49Fr <0.25

Results 19


Results – 3 Types of Events

Consistent with theory behind the Froude number Can then be separated into 3 Types of Events

Froude > 1: Spine and eastward (Unblocked) Froude 0.5 – 1: Heaviest Western Slopes (Classic

Blocked) Froude < 0.5: Champlain Valley (Very Blocked)

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Fr >1Fr 0.5-1Fr <0.5

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Unblocked Froude #: 4.4 Low level RH 80-90% WNW winds

throughout low levels Well Mixed to

Mountain Height

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UnblockedFroude #: 4.4


Classic Western Slopes Blocked Froude #: 0.98 Low level RH ~90% West winds veering to

Northwest throughout low levels

Isothermal layer below mountain height

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BlockedFroude #: 0.98

Results – 3 Types of Events “Champlain Valley Powder” Very Blocked Froude #: 0.07 Low level RH 95-100% Northwest winds veering to

North throughout low levels (WAA)/(Less Perpendicular Flow)

Strong Inversion below mountain height (Very Stable)

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Very BlockedFroude #: 0.07

Results – Blocked vs. Unblocked

Blocked

Unblocked

Source: NOAA ESRLComposite of 12 Blocked/13 Unblocked Upslope Cases

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Results – Snow Ratios 29


Snow Ratios were found to be much greater than average synoptic snowfall cases in CWA Long Term Average (Baxter et al 2005) – 13:1 Upslope cases averaged 28:1 Ranged from 7:1 to 71:1



Snow Ratios were found to have some association with 850mb Temperatures Average 850mb temperature during events was -13C Highest ratios occurred when 850mb temperatures were

between -11C and -15C, 25-35:1



-8 -9 -10 -11 -12 -13 -14 -15 -16 -17 -18 -190

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10

15

20

25

30

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40

45

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Average Snow-Liquid Ratios vs. Average 850 mb Temps

850 mb Temperatures (C)

Snow

-Liq

uid

Ratio

s

R2O- Use at WFO BTV

Worked with SOO to develop into GFE Smart Tool

Calculates Froude Number, wind speed/direction, relative humidity, and QPF

Run off local WRF model, NAM, & GFS

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R2O- Use at WFO BTV

Also have a real-time calculation based off vertical temperature profile along mountain chain

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R2O- Use at WFO BTV

These two products have led to: An increased situational awareness of upslope

events Improved forecasts of location of heavy snow

band, snow intensity, and snow amounts Improved lead time and verification scores of

winter weather products

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In Summary

The Froude Number is a useful tool in determining characteristics of orographic snow bands

Events can be unblocked (Fr > 1), Blocked (Fr < 1), or Very Blocked (Fr << 1)

Can be transitioned into operations and used to improve orographic snowfall forecasts

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ReferencesBaxter, M.A., C.E. Graves, and J.T. Moore, 2005: A Climatology of Snow-to-Liquid Ratio for the Contiguous United States. Wea. Forecasting, 20, 729-744.

Bell, G.D. and L.F. Bosart, 1988: Appalachian Cold Air Damming. Mon. Wea. Rev., 116, 137-161.

Chen, S-H., Y.L Lin, and Z. Zhao, 2008: Effects of Unsaturated Moist Froude Number and Orographic Aspect Ratio on a Conditionally Unstable Flow over a Mesoscale Mountain. J. of the Meteor. Soc. Japan, 86, 353-367.

Chu, C-M. and Y.L. Lin, 2000: Effects of Orography on the Generation and Propagation of Mesoscale Convective Systems in a two-dimensional conditionally unstable flow. J. of Atmos. Sci., 57, 3817-3837.

Lee, L.G. and H. Gerapetritis, 2012: The Northwest Flow Snow Event of 11 February 2012.

NOAA Earth System Research Laboratory (ESRL), Physical Sciences Division (PSD), 2012: Interactive Plotting and Analysis Pages. [Available online at http://www.esrl.noaa.gov/psd/]

NOAA Air Resources Laboratory (ARL), 2012: Gridded Meteorological Data Archives. [Available online at http://ready.arl.noaa.gov/archives.php]

National Weather Service Burlington, VT, 2012: Daily Climate Maps. [Available online at http://www.weather.gov/btv/climatemaps]

Keighton, S., L. Lee, B. Holloway, D. Hotz, S. Zubrick, J. Hovis, G. Votaw, L.B. Perry, G. Lackmann, S.E. Yuter, C. Konrad, D. Miller, and B. Etherton, 2009: A Collaborative Approach to Study Northwest Flow Snow in the Southern Appalachians. Bull. Amer. Meteor. Soc., 90, 980-999.

Rutledge, S.A. and P. Hobbs, 1983: The Mesoscale and Microscale Structure and Organization of Clouds and Precipitation in Midlatitude Cyclones. VIII: A Model for the “Seeder-Feeder” Process in Warm-Frontal Rainbands. J. Atmos. Sci., 40, 1185-1206.

Sisson, P.A., D. St. Jean, E. Evenson, W.E. Murray, S.F. Hogan, L. Bosart, D. Keyser, and B. Smith, 2004: Applying local research to National Weather Service operations: Forecasting heavy mountain snowfalls in Vermont and Northern New York. Preprints, 11th Conference on Mountain Meteorology and the Annual Mesoscale Alpine Program, Bartlett, NH, Amer. Meteor Soc., 17.5. St. Jean, D.P., and P.A. Sisson , 2004: Characteristics of upslope snowfall events in northern new York state and northern Vermont: Diagnostics and model simulations of several northwest flow cases. Preprints, 20th Conference on Weather Analysis and Forecasting. Seattle, WA, Amer. Meteor. Soc., 18.4.

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http://www.esrl.noaa.gov/psd/

http://www.esrl.noaa.gov/psd/

http://ready.arl.noaa.gov/archives.php

http://www.weather.gov/btv/climatemaps

Questions?

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Documents

1. 2 3 4 Source: Weather Prediction Center 5 6