An interactive algorithm to nowcast snowfall rates from lake‐effect snow using

both satellite and model data

2013 Great Lakes Operational Meteorology Workshop

Chung K K1 & Guilong. Li2

1National Lab for Nowcasting and Remote Sensing Meteorology2Atmospheric Science and Application Unit

Meteorological Service of CanadaEnvironment Canada

Objective:

To present a computer algorithm to nowcast snowfall rates from lake-effect snow using both satellite and model data

Outline:1. Background

2. The idea

3. Methodology

4. Results

5. Conclusion and Future Works

Background

Impact of lake-effect snow:• Heavy lake-effect snow bands can pose a

significant weather hazard to the public causing airport shutdown and dangerous driving conditions

Forecast of lake-effect snow:• NWP model not able to resolve this

small scale phenomenon

Nowcast of lake-effect snow:• Satellite• Radar• Surface observations

The need:A real-time estimation of the actualsnowfall rates from snow bands to helpalert the public of what is happening

From theweathernetwork.com

Dec 07, 2010 at 4:05 pmLondon, Ontario

An interactive algorithm to nowcast snowfall rates from lake-effect snow

- The algorithm first takes the forecaster’s input on snow bands locations, then the algorithm will use both the model and satellite data to calculate snowfall rates along a snow band.

- The result is a better nowcast of real-time snowfall rates to help improve warnings and alert the public of heavy snow

The Idea

• A line of snow squall viewed from a satellite is a snapshot of the time evolution of cumulus from the initial to mature stage.

• The dynamic and thermodynamic forcings that generate a lake snow squall are reflected by cloud top cooling rates ( ascent rates) during the developing stage.

• How much snow falling out of the snow squall is determined by: air mass ascent rates, available moisture, snow-liquid ratio, the thickness of the clouds, dry air entrainment, and others.

2 °C

-5 °C

-12 °C

-18 °C

-21 °C

50 km/hr

60 km/hr

A side view of a snow band

Cold air mass

Unstable boundarylayer

MethodologySteps 1 & 2

Step 1:Forecasters to identify snow squalls

Cloud top IR Temp vs Distance

-30

-25

-20

-15

-10

-5

0

0 50 100 150 200 250 300 350 400

Distance (km)

IR T

em

pe

ratu

re (

C)

3x3 Pixels

Slope is determinedusing a 5-points runningaverage

dT/dx = -0.26 °C/km

Cloud top

56 km/hr

Satellite data retrieved:cloud top temperature as a function of distance

Step 2: Calculate dT/dx along the developing section of the snow squall

Cumulus developmentstage

MethodologyStep 3: Retrieve model sounding data at different points along the line of snow squall

- Lake modified air temperature

- Lift the parcel to EL (i.e. to sat-derived cloud top temperature)

-Saturated lapse rate

-Cloud thickness

-Precipitation liquid

-Boundary winds

MethodologyStep 4: Calculations of snow rates

• We know temperature gradient dT/dx along the “development section” of the snow squall and so we can calculate the cloud top cooling rates [dT/dt = U * dT/dx].

• We can calculate the mean saturated adiabatic lapse rate within the boundary layer γw from the sounding data.

• We can calculate the parcel vertical velocity (ω) by (dT/dt)/γw.

• We can calculate cloud condensed water (q in gm-3) from sounding data as well as the vertical moisture fluxes at different points along the snow squall (flux = q * ω).

• We can then calculate the snowfall rates at different points along the snow squall up to the shoreline using : snowfall intensity = vertical moisture flux * snow-liquid ratio.

• Note: snow to liquid ratio used is 1:15

MethodologyStep 5: Inland snowfall rates modification

The snowfall rates at any point (x) inland

along the snow band is parameterised by:

Point: o

Point: x

At point o:Snowfall rate = So

Cloud top temp = To

LCL temp = TLCL

At any point x inland:Snowfall rate = Sx

Cloud top temp = Tx

LCL temp = TLCL

ox

Overview

INPUT:

(Lat, Long) for squall lines.

program to generatecloud top IR temperatures

along the snow band.

Program to calculate various parameters

+Snowfall rates along

the snow band

Satellite Data

+

Model sounding data(retrieved from CMC)

Output:Tabular/Graphic

Forecasters toidentify

snow squalls

The algorithm is applied to different lake-effect snow cases

Case 1: December 07, 2010 at 1815Z

YXU: 1703-1804Z ~ 1 cm1804-1905Z = 6 cm

WGD:1706-1807Z ~ 3 cm 1807-1904Z ~ light

U850 = 56 km/h

6 hoursnowfall(18 – 00Z)

WGDYXU

Case 2: December 08, 2010 at 1215Z

YXU: 1103-1203Z ~ no snow obs1203-1303Z ~ 1 cm

WGD:1110-1206Z = ??/SOG2 1206-1308Z ~ 9 cm

U850 = 46 km/h

6 hoursnowfall(12 – 18Z)

WGD

YXU

HigherLayer clouds?

Case 3: January 03, 2012 at 0245Z

YXU: 0200-0300Z = 4 cm0300-0400Z = 2 cm

WGD:No Obs

U850 = 56 km/hInstantaneous snow rate from radar

Note: heaviest echoes not right over YXU

Case 4: January 03, 2012 at 1145Z

YXU: 1100-1200Z = 4 cm1200-1300Z = 4 cm

WGD:No snow obs

U850 = 56 km/h

Instantaneous snow rate from radar

Note: heavies echoes right over YXU

Case 5: February 21, 2013 at 0915Z

vv ~ 0.3 m/s

U850 ~ 46 km/h

Conclusion

1. An interactive algorithm is developed to combine forecaster’s input on snow bands locations with model and satellite data to produce a better nowcast of snowfall rates from lake-effect snow.

2. The algorithm is applied to several snow squall events and produce some “satisfactory” results.

3. This algorithm helps improve warnings and alert the public of heavy snow

Forecaster inputs snow band locations

Algorithm does the

calculations

OutputSnowfall rates

along snow band

Future works

• To incorporate a more feasible snow-liquid ratio scheme into the algorithm

• To use higher resolution model data

• To use more observations for evaluations

• Other suggestions?

thestar.blogs.com

Questions?ReferencesByrd, G.P., and D. Schleede, 1998: Mesocale Model Simulation of the 4-5 January 1995 Lake-Effect Snowstorm.Weather and Forecasting, 13, 893-920.

Ellenton, G.E., and M.B. Danard, 1978: Inclusion of Sensible Heating in Convective Parameterization Applied toLake-Effect Snow. Monthly Weather Review, 107, 551-565.

Hjelmfelt, M.R., 1989: Numerical Study of the Influence of Environmental Conditions on Lake-Effect Snowstormsover Lake Michigan. Monthly Weather Review,118, 138-150.

Hsu, H.M., 1987: Mesoscale Lake-effect Snowstorms in the Vicinity of Lake Michigan: Linear Theory and NumericalSimulatios. Journal of the Atmospheric Science, 40, 1019-1040.

Kidder, Q. S., and T. H. Vander Haar, 1995: Satellite Meteorology. Academic Press Inc.

Lavoie, R.L., 1972: A Meteoscale Numerical Model of Lake-Effect Storm. Journal of the Atmospheric Science,1025-1040.

Iribarne, J.V., and W.L. Godson, 1981: Atmospheric Thermodynamics. 2nd Edition. D. Reidel Publishing Compay.

Liu, A.Q., and W.K. Moore, 2004: Lake-Effect Snowstorms over Southern Ontario, Canada, and Their AssociatedSynoptic-Scale Environment. Monthly Weather Review, 132, 2595-2609.

Rogers, R.R., 1979: A Short Course in Cloud hysics. 2nd Edition, Pergamon Press Ltd.

Outline --- this slide will not be shown

Objective- To present a computer algorithm to

nowcast snowfall rates using both satellite and model data

IntroductionOpener

- Lake-effect snow is a weather hazard to the public- It is very difficult to determine the snowfall rates

from these heavy snow bands because they are narrow

- Near real-time estimation of the actual snowfall rates from these snow bands help alert the public

- Topic- To outline the formulation of this interactive

algorithm to nowcast snowfall rates from lake-effect snow

- To show a few examples to demonstrate how this algorithm works and how it perform

- Thesis (idea convey)Forecasters’ expertise analysis on snow bands locations,

combined with model and satellite data, can make a better nowcast of snowfall rates from lake-effect snow.

A better nowcast of real-time snowfall rates help improve warnings and alert the public of heavy snow

The Body- The idea behind this algorithm- Methodology: step 1 – locate the snow band- Methodology: step 2 – Retrieve cloud top temperature- Methodology: step 3 – Retrieve model sounding data- Methodology: step 4 – Snowfall rates over the lake- Methodology: Step 5 – Snowfall rates modification

overland- Overview of the algorithm- Examples

Conclusion- Restate the thesis- Same as above + events show how this algorithm works

- Action for future works- Need a better snow-liquid ratio scheme- Use higher resolution model data