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The Challenges of Accurate Snowfall Forecasts: Implications for Observing Strategies and Future Research Efforts. Dr. David Schultz CIMMS and NOAA/NSSL Norman, Oklahoma . “Forecasting snowfall is a mesoscale challenge cloaked in a synoptic-scale culture.”

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slide1
The Challenges of Accurate Snowfall Forecasts: Implications for Observing Strategies and Future Research Efforts

Dr. David Schultz

CIMMS and NOAA/NSSL

Norman, Oklahoma

slide2

“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

objectives
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.
methodologies for forecasting snowfall
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
methodologies for forecasting snowfall5
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
top down approach
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
steps in producing snow

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?

1 theory will ice be produced in the cloud
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
1 observations will ice be produced in the cloud
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)

1 observations will ice be produced in the cloud10

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).

observations seeder feeder process
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)
2 theory how does ice grow in cloud
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
2 theory how does ice grow in cloud14
2. THEORY: How Does Ice Grow in Cloud?

maximum

depositional

growth rate

(dendrites)

(Dennis Lamb, Penn State)

slide15

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)

2 observations how does ice grow in cloud18
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
slide21

AWIPS utility to estimate the residence time of ice crystals in the dendritic-growth region (minutes)

(Dan Baumgardt, NWS)

3 theory how does ice grow in cloud

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)

slide23

Growth by accretion

will eventually dominate

ice-crystal growth

Fukuta and Takahashi (1999)

3 theory how does ice grow in cloud24
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
convective snow environments
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
4 theory how does ice grow in cloud
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
slide27

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.)

slide28

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.)

5 hydrometeor altering environments
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)
summary of top down microphysics approach for snow
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)
even if you were able to predict the liquid equivalent perfectly
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.
slide32

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

slide34

Density will decrease as snowflakes increase in size, but it is not a simple relationship.

(Rogers 1974)

factors affecting snow ratio
Factors Affecting Snow Ratio

Snow ratio versus liquid equivalent for snowfall from five stations in western Canada

(Courtesy of Gabor Fricska and Alex Cannon)

factors affecting snow ratio36

(Courtesy of Melanie Wetzel)

Factors Affecting Snow Ratio
  • Simple measures like lower-tropospheric temperature rarely work, except in very special cases.
nws snow density vs temp table
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

roebber et al 2003 improving snowfall forecasting by diagnosing snow density wea forecasting
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
slide39

10 to 1 ratio (13%)

(Roebber, Bruening,

Schultz and Cortinas)

properties of snow ratio
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).
how are we doing now
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

how are we doing now43
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

how are we doing now44
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)

the fall velocity of snow and why it matters
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)

slide46

850-hPa

wind dir.

Overprediction: bias > 140% (solid lines)

Underprediction: bias < 90% (dashed lines)

Colle et al. (1999)

idealized mm5 2 d simulation
Idealized MM5 2-D Simulation

IPEX IOP 3

(Courtesy of Brian Colle)

what do we need to do to forecast snow better observations
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?

the promise of polarimetric radar
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)

slide50

(Jay Hanna, NESDIS)

http://www.ssd.noaa.gov/PS/PCPN/ice-images.html

what do we need to do to forecast snow better research
What do we need to do to forecast snow better?:Research

• Better understanding of precipitation processes, esp. in orography

• Climatologies of snowstorm soundings (Eric Ware, OU)

• Relationship between sounding structure and crystal type

• Relationship between crystal type and density

  • Lack of understanding of cloud microphysical and aerosol processes
  • Lack of understanding of electrical effects on microphysics
  • Idealized microphysical simulations?
what do we need to do to forecast snow better numerical weather prediction
What do we need to do to forecast snow better?:Numerical Weather Prediction

• Improved microphysical parameterizations

– Models are very sensitive to cloud microphysical

parameterizations, especially at high resolution (<10

km) (Brian Colle and collaborators).

• Parameterization to predict snow depth explicitly

• Recognition that “one parameterization does not fit all.”

• Statistical prediction techniques

– Roebber et al. (2003) neural net will be tested by 11 groups this winter (NWS offices, HPC, TV station, Canadian Weather Centres)

cloud microphysics the ultimate limitation
Cloud Microphysics . . . The Ultimate Limitation?
  • Steady progress on the synoptic and mesoscale dynamics of snowfall forecasting
  • Microphysics, by contrast, has not been advancing as quickly, but there is an increasing recognition of its importance.
  • Unobserved in-cloud quantities will ultimately limit our ability to forecast snowfall (e.g., microphysics, electrical charges, vertical motion).
  • Forecasters, researchers, and the public need to recognize these limitations, otherwise disappointment in snowfall forecasts will continue.
acknowledgments
Acknowledgments

Dan Baumgardt (NWS, La Crosse, Wisconsin)

Harold Brooks (NSSL)

John Cortinas (NSSL/CIMMS)

Norihiko Fukuta (University of Utah)

Jay Hanna (NOAA/NESDIS)

Robert Houze (University of Washington)

Jack Kain (NSSL/CIMMS)

David Kingsmill (Desert Research Institute)

David Novak (NWS, Eastern Region SSD)

Paul Roebber and Sara Bruening (University of

Wisconsin–Milwaukee)

Alexander Ryzhkov (NSSL/CIMMS)

Jeff Waldstreicher (NWS, Eastern Region HQ)

Eric Ware (University of Oklahoma)

Melanie Wetzel (Desert Research Institute)