The Challenges of Accurate Snowfall Forecasts: Implications for Observing Strategies and Future Research Efforts

1. The Challenges of Accurate Snowfall Forecasts: Implications for Observing Strategies and Future Research Efforts Dr. David Schultz CIMMS and NOAA/NSSL Norman, Oklahoma

2. “Forecasting snowfall is a mesoscale challenge cloaked in a synoptic-scale culture.” Dr. Louis Uccellini, Director, NOAA/NCEP 3 October 2002, Midatlantic Winter Storms Conference

3. OBJECTIVES • Discuss the theory and some supporting observations for the importance of snow microphysics in determining snowfall. • Discuss the density of new snowfall and attempts to predict it. • Present research advances required to improve snowfall forecasting.

4. Climatology Heavy snow is favored 2.5° to the left of the track of the 500-mb vorticity maximum (Goree and Younkin 1966). Personal experience, pattern recognition: “This looks like a 6-inch snowstorm.” Rules of Thumb Average 24-h snowfall in inches is 1/2 of the maximum indicated 200-mb warm advection in °C (Cook 1980). Maximum “potential” snowfall is twice the average mixing ratio at 700 mb (Garcia 1994). For the problems with rules of thumb, see Schultz et al. (2002), Comments on “An operational ingredients-based methodology for forecasting midlatitude winter season precipitation”. Methodologies for Forecasting Snowfall

5. Mesoscale effects Conditional symmetric instability (e.g., Schultz and Schumacher 1999) Mesoscale banding (Novak et al. 2002) Cloud microphysics Is this the last frontier? Methodologies for Forecasting Snowfall

6. TOP-DOWN APPROACH • Dan Baumgardt (NWS WFO La Crosse, WI) has been advocating the “top-down” approach to forecasting. • Starts at the top of the environmental sounding and traces a hydrometeor trajectory down to the surface • Considers three levels in the sounding: • ice-producing level • warm layer • cold surface layer

7. Steps in Producing Snow 1. Is it cold enough to activate ice nuclei? Function of temperature and type of substrate 2. Is the ice crystal growing by deposition? Function of temperature and supersaturation 3. Is the snow collecting supercooled liquid water as it falls through the cloud (riming)? Function of temperature, supersaturation, and vertical motion 4. Are the snow crystals aggregating? Function of temperature, crystal shape, and amount of turbulence 5. Is the phase changing?

8. 1. THEORY: Will Ice Be Produced in the Cloud? • Is it cold enough to activate ice nuclei? • Ice nuclei are a subset of cloud condensation nuclei (CCN) that act as a surface for ice growth to initiate. • Some ice nuclei have crystal structures similar to ice. • Only 1 in 108 airborne particles nucleates ice at –20°C. • Every 4°C drop in temperature increases the number of ice nuclei by tenfold. • Ice nuclei activate at different temperatures. • Ice 0°C • Silver iodide –4°C • Kaolinite –9°C • Vermiculite –15°C • Pseudomonas syringae (bacteria from decaying leaves) –2°C

9. 1. OBSERVATIONS: Will Ice Be Produced in the Cloud? Oklahoma City soundings for snow/rain/freezing rain/ice pellet cases (Michael Schichtel 1988,OU M.S. thesis)

10. arbitrary cut-off temperatures are not appropriate--- think probabilistically! 1. OBSERVATIONS: Will Ice Be Produced in the Cloud? snow–no-snow cut-off temperature advocated by Wetzel and Martin (2001) cloud-top temperature (°C) vs cloud-top pressure (hPa) from 64 soundings during snowfall events at Albany, Minneapolis, and Denver (Schultz et al. 2002).

11. OBSERVATIONS: SEEDER–FEEDER PROCESS • Ice crystals from a mid to high layer of clouds fall into a lower layer of supercooled liquid water clouds, sparking ice nucleation • Distance between clouds is less than about 5000 feet (1.5 km)

12. OBSERVATIONS: SEEDER–FEEDER PROCESS (Hentz)

13. 2. THEORY: How does ice grow in cloud? • Growth by deposition (vapor condenses directly onto ice crystal as ice) Bergeron–Findeisen process • Function of supersaturation with respect to ice (temperature) and pressure

14. 2. THEORY: How Does Ice Grow in Cloud? maximum depositional growth rate (dendrites) (Dennis Lamb, Penn State)

15. 2. OBSERVATIONS: How Does Ice Grow in Cloud? After 30 mins., dendrites grow to 10 times the mass of the next largest ice crystal. Fukuta and Takahashi (1999)

16. 2. OBSERVATIONS: How does ice grow in cloud? (Mahoney)

17. 2. OBSERVATIONS: How does ice grow in cloud? (Mahoney)

18. 2. OBSERVATIONS: How Does Ice Grow in Cloud? • Waldstreicher (2001) • http://www.erh.noaa.gov/er/hq/ssd/snowmicro/ • Following Auer and White (1982) • Intersection of temps of –12 to –18°C and omega at least 10 microbars s-1 in RH>75% • 4 winters in northeast PA and central NY, 55 synoptic-scale snow events that met warning criteria, 75 synoptic-scale snow events that met advisory level, examined Eta/Mesoeta output. • 76% of warning-level events showed this intersection, whereas only 9% of advisory-level events met this criteria

19. OBSERVATIONS: How does ice grow in cloud? 2 ft of snow during rush hour

20. NSHARP utility at the Storm Prediction Center

21. AWIPS utility to estimate the residence time of ice crystals in the dendritic-growth region (minutes) (Dan Baumgardt, NWS)

22. rimed dendrite graupel 3. THEORY: How does ice grow in cloud? • Growth by accretion: ice crystal collects supercooled liquid water drops (riming to produce graupel) • Solid evidence of saturation at –1 to –5°C (David Babb)

23. Growth by accretion will eventually dominate ice-crystal growth Fukuta and Takahashi (1999)

24. 3. THEORY: How does ice grow in cloud? • Hallett–Mossop (1974) secondary ice production mechanism • Rime will splinter at –5 to –10°C as it freezes, thus producing more ice nuclei • These rime splinters can get lifted in the updraft again, thus acting to sweep out more of the supercooled liquid water. • Increased precipitation efficiency

25. Convective snow environments • Deeper circulation (likely to reach toward colder temps and produce ice nuclei, acts as a seeder to supercooled liquid water regime) • Strong vertical motions, heavy precipitation • Greater possibility of riming • Look for elevated CAPE (Trapp et al. 2001) • Thundersnow

26. 4. THEORY: How does ice grow in cloud? • Growth by aggregation: joining of multiple ice crystals to form a snowflake • Most important at 0 to –5°C as surface of ice becomes sticky, with a secondary maximum around –15°C due to interlocking dendrites

27. 4. OBSERVATIONS: How does ice grow in cloud? Enhancing growth by turbulence IMPROVE II NOAA/ETL S-band Radar 13–14 December 2001 Reflectivity (Houze et al.)

28. IMPROVE II NOAA/ETL S-band Radar 13–14 December 2001 • aggregation &/or riming enhanced by the turbulent overturning bright band • turbulence likely overwhelmed by fall speeds of rain Upward Radial Velocity (Houze et al.)

29. 5. Hydrometeor-altering environments • Warm layers: snow->rain, sleet, freezing rain • Wet-bulb temperature and dry layers: rain-> snow (e.g., Kain et al. 2000)

30. Summary of Top-Down Microphysics Approach for Snow • Need ice nuclei (cold temps to activate or seeder–feeder) • Need growth mechanism • Deposition (vertical motion at –15°C) • Riming (supercooled liquid water at –1 to –5°C) • Aggregation (near 0°C and/or turbulent) • Embedded convection (CAPE) • Diabatic effects (advection small)

31. Even if you were able to predict the liquid equivalent perfectly • . . . you’d still have to know the snow density. • Usually this is assumed to be 10 inches of snow to 1 inch of liquid water (snow ratio). • This will vary, however, depending on ice-crystal habit (function of RH and T), degree of riming, surface compaction due to weight and wind. • Need to consider crystal shape when formed and the compaction of crystals on the ground.

32. isometric crystals isometric crystals Apparent crystal density for a single ice crystal 45–50 s after seeding (Fukuta 1969) columns dendrites Apparent ice crystal density at a growth time of 10 minutes (Takahashi et al. 1991) Density can vary by a factor of 2–9, depending on crystal shape

33. Apparent density will decrease, then stabilize as crystal grows (Fukuta and Takahashi 1999)

34. Density will decrease as snowflakes increase in size, but it is not a simple relationship. (Rogers 1974)

35. Factors Affecting Snow Ratio Snow ratio versus liquid equivalent for snowfall from five stations in western Canada (Courtesy of Gabor Fricska and Alex Cannon)

36. (Courtesy of Melanie Wetzel) Factors Affecting Snow Ratio • Simple measures like lower-tropospheric temperature rarely work, except in very special cases.

37. NWS snow-density vs temp. table * Function of surface temperature only! * Developed as a guide for QC of observations * Not intended as substitute for obs or as a forecast method

38. Roebber et al. (2003):“Improving Snowfall Forecasting by Diagnosing Snow Density,” Wea. Forecasting. • GOAL: To do better than the 10:1 ratio. • PROBLEM: Science on what controls the snow ratio is unknown. • Dataset constructed of 1650 snowfall events at 28 radiosonde stations in the U.S. > 2 inches snow (0.11 inch liquid) with wind <= 9 m/s • Snow densities binned: • heavy 1:1 – 9:1 • average 9:1 – 15:1 • light > 15:1

39. 10 to 1 ratio (13%) (Roebber, Bruening, Schultz and Cortinas)

41. Properties of Snow Ratio • A principal component analysis isolates factors influencing snow ratio: • Month (solar radiation) • Temperature profile (low–mid, mid–upper) • RH profile (low–mid, mid, upper) • External compaction (wind speed, liquid equivalent) • Compaction of snowfall once on the ground was the most crucial parameter to predict snow ratio (wind speed and liquid equivalent).

42. How are we doing now? • For diagnosing snow ratio class (heavy, average, light) in a test sample: 10:1 rule 45.0% correct climo 41.7% correct NWS table 51.7% correct

43. How are we doing now? • For diagnosing snow ratio class (heavy, average, light) in a test sample: 10:1 rule 45.0% correct climo 41.7% correct NWS table 51.7% correct • Ensemble of neural networks that are fed sounding parameters, surface windspeed, and liquid-equivalent amount: 60.4% correct

44. How are we doing now? • For diagnosing snow ratio class (heavy, average, light) in a test sample: 10:1 rule 45.0% correct climo 41.7% correct NWS table 51.7% correct • Ensemble of neural networks that are fed sounding parameters, surface windspeed, and liquid-equivalent amount: 60.4% correct • Heidke skill score improves 184% between NWS table (0.120) and neural network (0.341)

45. The Fall Velocity of Snow and Why It Matters • These sensitivities to snow fall speed will impact where snows will fall in numerical models with small horizontal grid spacing. Fukuta and Takahashi (1999)

46. 850-hPa wind dir. Overprediction: bias > 140% (solid lines) Underprediction: bias < 90% (dashed lines) Colle et al. (1999)

47. Idealized MM5 2-D Simulation IPEX IOP 3 (Courtesy of Brian Colle)

48. What do we need to do to forecast snow better?:Observations • Larger quantity and in real time (daily to every 1-minute) – Cooperative Weather Observers upgrade – Weather Support to Deicing Decision Making (WSDDM) • Better quality – Take measurements! Don’t rely on simplistic tables or constant snow ratios. – Nolan Doesken’s snow measurement video • Observations of crystal types • Can dual-polarimetric radars be of use? • Can satellite IR data be used to estimate cloud-top temperature for identifying activation of ice nuclei?

49. The Promise of Polarimetric Radar • Hydrometeor discrimination – real-time algorithm exists for discriminating rain, nonaggregated ice crystals, aggregated dry snow, and aggregated wet snow – discrimination among the habits of nonaggregated ice crystals is also possible • Quantitative analysis – If the snow is heavily aggregated, then reliable quantitative measurements of liquid equivalent, snow density, or snowfall rate are difficult at this time. – If snow is nonaggregated or moderately aggregated, then robust estimates of ice water content can be made. • Multiparameter (dual-pol, dual-wavelength) radar measurements provide the best promise for snow quantification. (Courtesy of Alexander Ryzhkov)

50. (Jay Hanna, NESDIS) http://www.ssd.noaa.gov/PS/PCPN/ice-images.html