1. Snow to Liquid Ratio Variation with Temperature: Whose Assumptions are These, Anyway? Robert A. Weisman and Jacob Yurek* EAS Dept., Saint Cloud State University *Current affiliation: DTN/Meteorologix, Burnsville, MN Email: raweisman@stcloudstate.eduraweisman@stcloudstate.edu Web: http://web.stcloudstate.edu/raweisman/http://web.stcloudstate.edu/raweisman/

2. 10/26/2006Weisman & Jurek - NPWSC 062 Snow-to-Liquid Ratios  Huge operational problem –if we could get QPF right…(Hah!) –Regional knowledge not well known 10 years ago  Depends on Cloud Physics within Each Cloud –Efficiency of Ice Crystal Growth Rate  Having saturated layer with temperature colder than -15°C  Having a feeder layer with high liquid water content  Good Review by Baumgardt, NWS LaCrosse Baumgardt, NWS LaCrosseBaumgardt, NWS LaCrosse  Effect of moist-melting layers  Effect of ground temperatures, especially in recent mild years  Compaction Issues

3. 10/26/2006Weisman & Jurek - NPWSC 063 Forecast Tools – Proxy Methods  Knowledge of individual cloud physics lacking  10:1 or bust!...except maybe lake effect? (1995) –Baxter et al. 2005 SLU Research on mean snow:liquid by area SLU Research on mean snow:liquid by areaSLU Research on mean snow:liquid by area  Mean ratio based on NWS Forecast Areas Mean ratio based on NWS Forecast Areas Mean ratio based on NWS Forecast Areas –Roebber et al. 2003 Extrapolation of Snow-to-liquid ratio based on model data Extrapolation of Snow-to-liquid ratio based on model dataExtrapolation of Snow-to-liquid ratio based on model data  Best: Know what sounding will look like –Forecast soundings often stink!

4. 10/26/2006Weisman & Jurek - NPWSC 064 Forecast Tools – Proxy Methods(2)  Sounding Snapshots  Critical Thicknesses –1000-500 mb 5400 m  Mostly underground away from ocean –1000-850 mb 1200 m  Critical Temperature Structure –Being Saturated at Critical Temperatures for Ice Crystal growth –Wetzel Winter Weather Forecasting Ingredients looks at 600 mb temperatures Wetzel Winter Weather Forecasting Ingredients Wetzel Winter Weather Forecasting Ingredients

5. 10/26/2006Weisman & Jurek - NPWSC 065 Snow-to-liquid vs Surface Temp.  Frequently used simplification  Old “Study”? –Gives a single value conversion for surface temperature ranges –Advanced from “everything is 10:1” –Version previously posted on NWS Quad Cities, IA/IL website (not found now)

6. 10/26/2006Weisman & Jurek - NPWSC 066 Still found at ToolKit - Envirocast - the weather & watershed newsletterToolKit - Envirocast - the weather & watershed newsletter

7. 10/26/2006Weisman & Jurek - NPWSC 067 Jacob Yurek Senior Research Project  Frequent error in bad snow forecasts.  Single ratio doesn’t make sense from operational experience –Empirical Rules Based on Henn, Jurek, and Weisman work to come later  Determine snow:liquid for long term staffed reporting station: –Minneapolis-St. Paul International Airport, MN (KMSP)

8. 10/26/2006Weisman & Jurek - NPWSC 068 Methods  Time period: 1961-1995 –Source: Local Climatological Data for KMSP  822 Cases –No sleet, freezing rain, nor mixed precipitation allowed in any case –Must have produced measurable liquid –Before period when ASOS used entirely for liquid equivalent

9. 10/26/2006Weisman & Jurek - NPWSC 069 Methods cont.  Stratified by surface temperature categories as noted in old “study” –34-28°F (261 cases) –27-20°F (240 cases) –19-15°F (117 cases) –14-10°F (87 cases) –9-0°F (90 cases) –-1 to -20°F (27 cases)

10. 10/26/2006Weisman & Jurek - NPWSC 0610 Mean Values Other Statistics? Look at Distributions

11. 10/26/2006Weisman & Jurek - NPWSC 0611 28 – 34°F 10:1

12. 10/26/2006Weisman & Jurek - NPWSC 0612 27 – 20°F 10:1 15:1 20:1

13. 10/26/2006Weisman & Jurek - NPWSC 0613 19 – 15°F 10:1 15:1 20:1 30:1

14. 10/26/2006Weisman & Jurek - NPWSC 0614 14 – 10°F 10:1 15:1 20:1 30:1 25:1 40:1

15. 10/26/2006Weisman & Jurek - NPWSC 0615 9 – 0°F 15:1 20:1 30:1 25:1

16. 10/26/2006Weisman & Jurek - NPWSC 0616 -1 – -20°F 20:1 30:1 40:1

17. 10/26/2006Weisman & Jurek - NPWSC 0617 Results  Study contaminated by observational bias  Anecdotal confirmation  In fact, look at Mean ratio based on NWS Forecast Areas Mean ratio based on NWS Forecast AreasMean ratio based on NWS Forecast Areas  Next step: –Is Cooperative Data contaminated? –New, independent data taken without assumptions

18. 10/26/2006Weisman & Jurek - NPWSC 0618 Empirical SCSU Snow Forecasting Rules  Wet snow (850 temp. of -2 to 0°C) usually 5-8:1 –Surface temperature: 32-34°F –Can be 10:1 for surface temperature of 28-32°F  Dry “dendritic snowfall” –850 mb temp of -8 to -4°C  Saturated at -10 to -15°C in sounding  Surface temperatures generally in the 20’s°F  Saturated at -10 to -15°C in sounding –Wet snow: around 10:1 –Dry snow: 12-15:1  Colder “dendritic snowfall” –850 mb temp of -12 to -5°C  Saturated at -10 to -15°C in sounding –Surface temperature: 18-24°F –15-20:1  Even colder or lake effect: 20+:1

19. 10/26/2006Weisman & Jurek - NPWSC 0619 References Baumgardt, D., 1998: Wintertime cloud microphysics review. NWS LaCrosse, WI. http://www.crh.noaa.gov/arx/micro/micrope.php http://www.crh.noaa.gov/arx/micro/micrope.php Baxter, M. A., C. E. Graves, and J.T. Moore, 2005: A climatology of snow- to-liquid ratio for the contiguous United States. Wea. and Forecasting, 20, 729-744. http://www.eas.slu.edu/CIPS/Research/snowliquidrat.html http://www.eas.slu.edu/CIPS/Research/snowliquidrat.html COMET module: http://www.comet.ucar.edu/class/aes_canada/06- 1/html/descriptions/snowRatio.htm http://www.comet.ucar.edu/class/aes_canada/06- 1/html/descriptions/snowRatio.htmhttp://www.comet.ucar.edu/class/aes_canada/06- 1/html/descriptions/snowRatio.htm National Environmental Education and Training Foundation, 2003: ToolKit. EnviroCast: The Weather and Watershed Newsletter, 1(3), http://www.stormcenter.com/envirocast/2003-01-01/envirocast- article2.php http://www.stormcenter.com/envirocast/2003-01-01/envirocast- article2.php http://www.stormcenter.com/envirocast/2003-01-01/envirocast- article2.php Roebber, P. J., S. L. Bruening, D. M. Schultz, and J. V. Cortinas, Jr., 2003: Improving snowfall forecasting by diagnosing snow density. Wea. and Forecasting, 18, 264-287. http://sanders.math.uwm.edu/cgi- bin-snowratio/sr_intro.pl http://sanders.math.uwm.edu/cgi- bin-snowratio/sr_intro.plhttp://sanders.math.uwm.edu/cgi- bin-snowratio/sr_intro.pl Wetzel-Seemann, S. W., and J. E. Martin, 2001: An operational ingredients-based methodology for forecasting mid-latitude winter season precipitation. Wea. and Forecasting, 16, 156-167. http://speedy.meteor.wisc.edu/~swetzel/winter/winter.html http://speedy.meteor.wisc.edu/~swetzel/winter/winter.html