1. 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 K 1 & Guilong. Li 2 1 National Lab for Nowcasting and Remote Sensing Meteorology 2 Atmospheric Science and Application Unit Meteorological Service of Canada Environment Canada

2. 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

3. 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 actual snowfall rates from snow bands to help alert the public of what is happening From theweathernetwork.com Dec 07, 2010 at 4:05 pm London, Ontario

4. 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

5. 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 boundary layer

6. Methodology Steps 1 & 2 Step 1: Forecasters to identify snow squalls Slope is determined using a 5-points running average 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 development stage

7. Methodology Step 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

8. Methodology Step 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 γ w from the sounding data. We can calculate the parcel vertical velocity (ω) by (dT/dt)/γ w. 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

9. Methodology Step 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 = S o Cloud top temp = T o LCL temp = T LCL At any point x inland: Snowfall rate = S x Cloud top temp = T x LCL temp = T LCL o x

10. Overview INPUT: (Lat, Long) for squall lines. program to generate cloud 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 to identify snow squalls

11. The algorithm is applied to different lake- effect snow cases

12. Case 1: December 07, 2010 at 1815Z YXU: 1703-1804Z ~ 1 cm 1804-1905Z = 6 cm WGD: 1706-1807Z ~ 3 cm 1807-1904Z ~ light U 850 = 56 km/h 6 hour snowfall (18 – 00Z) WGD YXU

13. Case 2: December 08, 2010 at 1215Z YXU: 1103-1203Z ~ no snow obs 1203-1303Z ~ 1 cm WGD: 1110-1206Z = ??/SOG2 1206-1308Z ~ 9 cm U 850 = 46 km/h 6 hour snowfall (12 – 18Z) WGD YXU Higher Layer clouds?

14. Case 3: January 03, 2012 at 0245Z YXU: 0200-0300Z = 4 cm 0300-0400Z = 2 cm WGD: No Obs U 850 = 56 km/h Instantaneous snow rate from radar Note: heaviest echoes not right over YXU

15. Case 4: January 03, 2012 at 1145Z YXU: 1100-1200Z = 4 cm 1200-1300Z = 4 cm WGD: No snow obs U 850 = 56 km/h Instantaneous snow rate from radar Note: heavies echoes right over YXU

16. Case 5: February 21, 2013 at 0915Z vv ~ 0.3 m/s U 850 ~ 46 km/h

17. 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 Output Snowfall rates along snow band

18. 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

19. Questions? References Byrd, 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 to Lake-Effect Snow. Monthly Weather Review, 107, 551-565. Hjelmfelt, M.R., 1989: Numerical Study of the Influence of Environmental Conditions on Lake-Effect Snowstorms over 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 Numerical Simulatios. 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 Associated Synoptic-Scale Environment. Monthly Weather Review, 132, 2595-2609. Rogers, R.R., 1979: A Short Course in Cloud hysics. 2 nd Edition, Pergamon Press Ltd.

21. Outline --- this slide will not be shown Objective -To present a computer algorithm to nowcast snowfall rates using both satellite and model data Introduction Opener -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