Transcript The Intermountain Precipitation Experiment (IPEX)

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.”
Dr. Louis Uccellini, Director, NOAA/NCEP
3 October 2002, Midatlantic Winter Storms Conference
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
• Mesoscale effects
– Conditional symmetric instability
Schultz and Schumacher 1999)
– Mesoscale banding (Novak et al. 2002)
• Cloud microphysics
– Is this the last frontier?
(e.g.,
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
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?
• 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?
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 Cloud?
snow–no-snow cut-off
temperature advocated by
Wetzel and Martin (2001)
arbitrary
cut-off
temperatures
are not
appropriate--think
probabilistically!
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
• 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)
OBSERVATIONS: SEEDER–FEEDER PROCESS
(Hentz)
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 Cloud?
maximum
depositional
growth rate
(dendrites)
(Dennis Lamb, Penn State)
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 cloud?
(Mahoney)
2. OBSERVATIONS:
How does ice grow in cloud?
(Mahoney)
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
OBSERVATIONS: How does ice grow in
cloud?
2 ft of snow
during rush
hour
NSHARP utility at the Storm Prediction Center
AWIPS utility
to estimate
the residence
time of ice
crystals in the
dendriticgrowth region
(minutes)
(Dan Baumgardt,
NWS)
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
rimed dendrite
graupel
(David Babb)
Growth by accretion
will eventually dominate
ice-crystal growth
Fukuta and Takahashi (1999)
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
• 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?
• 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
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.)
IMPROVE II NOAA/ETL S-band Radar
13–14 December 2001
aggregation &/or riming enhanced by the turbulent overturning
turbulence likely overwhelmed by fall speeds of rain
bright
band
Upward Radial Velocity
(Houze et al.)
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
• 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
• . . . 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 icecrystal 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.
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
Apparent
density will
decrease,
then stabilize
as crystal
grows
(Fukuta and
Takahashi 1999)
Density will decrease as snowflakes increase in
size, but it is not a simple relationship.
(Rogers 1974)
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 Ratio
• Simple measures like lower-tropospheric temperature rarely
work, except in very special cases.
(Courtesy of Melanie Wetzel)
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.
• 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
– average
– light
1:1 – 9:1
9:1 – 15:1
> 15:1
10 to 1 ratio (13%)
(Roebber, Bruening,
Schultz and Cortinas)
20
20
BUFFALO, NEW YORK (BUF)
106 Snowfall Events
15
15
ALBANY, NEW YORK (ALB)
104 Snowfall Events
10
10
5
5
0
2
6
10
14
18
22
26
30
34
38
42
46
0
50
2
30
PITTSBURGH, PENNSYLVANIA (PIT)
48 Snowfall Events
25
6
10
14
18
22
26
30
34
38
42
46
20
WASHINGTON/DULLES, VIRGINIA (IAD)
22 Snowfall Events
15
20
15
10
10
5
5
0
0
2
6
10
14
18
22
26
30
34
38
42
46
50
50
2
6
10
14
18
22
26
30
34
38
42
46
50
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?
• 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 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 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
• These sensitivities to
snow fall speed will
impact where snows
will fall in numerical
models with small
horizontal grid
spacing.
Fukuta and Takahashi (1999)
850-hPa
wind dir.
Overprediction: bias > 140% (solid lines)
Underprediction: bias < 90% (dashed lines)
Colle et al. (1999)
Idealized MM5 2-D Simulation
IPEX IOP 3
(Courtesy of Brian Colle)
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
• 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)
http://www.ssd.noaa.gov/PS/PCPN/ice-images.html
(Jay Hanna, NESDIS)
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
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,
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
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)