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Severe Weather ApplicationsSevere Weather Applications
David BrightNOAA/NWS/Storm Prediction Center
AMS Short Course on Methods and Problems of Downscaling
Weather and Climate VariablesJanuary 29, 2006
Atlanta, GA
Where Americas Climate and Weather Services Begin
OutlineOutline
• Overview of the Storm Prediction Center (SPC)
• Implicit downscaling and hazardous mesoscale phenomena– Parameter evaluation
• SPC ensemble diagnostics
OutlineOutline
• Overview of the Storm Prediction Center (SPC)
• Implicit downscaling and hazardous mesoscale phenomena– Parameter evaluation
• SPC ensemble diagnostics
Overview of the SPC: MissionOverview of the SPC: Mission
The Storm Prediction Center (SPC) exists
solely to protect life and property of the American people
through the issuance of timely and accurate watch and forecast products dealing with hazardous mesoscale
weather phenomena.
• Hail, Wind, Tornadoes
• Excessive rainfall
• Fire weather
• Winter weather
Overview of the SPCOverview of the SPC
HAZARDOUS PHENOMENA
• TORNADO & SEVERE THUNDERSTORM WATCHES
• WATCH STATUS MESSAGE
• CONVECTIVE OUTLOOK
• MESOSCALE DISCUSSION
• FIRE WEATHER OUTLOOK
• OPERATIONAL FORECASTS ARE BOTH DETERMINISTIC AND PROBABILISTIC
Overview of the SPC ProductsOverview of the SPC Products
75% of all SPC products are valid for < 24h period
OutlineOutline
• Overview of the Storm Prediction Center (SPC)
• Implicit downscaling and hazardous mesoscale phenomena– Parameter evaluation
• SPC ensemble guidance
Implicit DownscalingImplicit Downscaling
• We don’t explicitly downscale at the SPC• However, SPC forecasters implicitly incorporate
spatial and temporal downscaling– Models are run at O(10 km) grid spacing– Model output available at O(hours)– Minimum grid spacing to resolve explicitly modeled
convection ~3 km – Even if thunderstorms (and mesocyclones) are explicitly
modeled, severe phenomena (hail, wind, tornadoes) occur at finer scales
• Idealized example…
Trough and associated cold front within the domain of a mesoscale model
ΔX ~ 10 km
ΔX ~ 10 km
Convergence region minimally resolved bymesoscale model at about 4 ΔX
Narrow region of pre-frontal convergence
Thunderstorms are not resolved by mesoscale modelat only 1 to 2 ΔX
ΔX ~ 10 km
Thunderstorms then develop within pre-frontal convergence zone
A grid point model:• does not resolve wavelengths of ~1-3ΔX • minimally resolves wavelengths of ~4ΔX • fully resolves wavelengths of ~10ΔX
ΔX ~ 10 km
The ability to predict phenomena in an NWP model is scale dependent
• Today’s NWP models do not explicitly predict most hazardous mesoscale phenomena of interest to the SPC
• The human needs to understand interactions between the large-scale (well resolved) environment and storm-scale (poorly resolved) phenomena
• Parameter evaluation (e.g., Johns and Doswell 1992)
SPC Downscaling and SPC Downscaling and Parameter EvaluationParameter Evaluation
Parameter Evaluation: Parameter Evaluation: CAPE vs. Deep Layer ShearCAPE vs. Deep Layer Shear
Shear
CAPE
Adapted from AMS Monograph Vol. 28 Num. 50 Pg. 449
Refined Parameter InvestigationsRefined Parameter Investigations A simple product of CAPE and shear
Gradual increase between classes, with discrimination between thunder, severe, and significant severe
90%
10%
50%
75%
25%
A complex parameter space is evaluated for modern severe stormforecasting
OutlineOutline
• Overview of the Storm Prediction Center (SPC)
• Implicit downscaling and hazardous mesoscale phenomena– Parameter evaluation
• SPC ensemble diagnostics
Example 1Example 1
• Basic Ensemble CAPE and Shear Basic Ensemble CAPE and Shear AnalysisAnalysis
SREF Parameter EvaluationSREF Parameter Evaluation
• Probability surface CAPE >= 1000 J/kg– Generally
low in this case
– Ensemble mean < 1000 J/kg (no gold dashed line)
CAPE (J/kg)Green solid= Percent Members >= 1000 J/kg; Shading >= 50%
Gold dashed = Ensemble mean (1000 J/kg)F036: Valid 21 UTC 28 May 2003
• Probability deep layer shear >= 30 kts– Strong mid
level jet through Iowa
10 m – 6 km Shear (kts)Green solid= Percent Members >= 30 kts; Shading >= 50%
Gold dashed = Ensemble mean (30 kts)F036: Valid 21 UTC 28 May 2003
SREF Parameter EvaluationSREF Parameter Evaluation
• Convection likely WI/IL/IN– Will the
convection become severe?
3 Hour Convective Precipitation >= 0.01 (in)Green solid= Percent Members >= 0.01 in; Shading >= 50%
Gold dashed = Ensemble mean (0.01 in)F036: Valid 21 UTC 28 May 2003
SREF Parameter EvaluationSREF Parameter Evaluation
• Combined probabilities very useful
• Quick way to determine juxtaposition of key parameters
• Not a true probability– Not
independent– Different
members contribute
Prob Cape >= 1000 X Prob Shear >= 30 kts X Prob Conv Pcpn >= .01” F036: Valid 21 UTC 28 May 2003
SREF Parameter EvaluationSREF Parameter Evaluation
Severe ReportsRed=Tor; Blue=Wind; Green=Hail
Prob Cape >= 1000 X Prob Shear >= 30 kts X Prob Conv Pcpn >= .01” F036: Valid 21 UTC 28 May 2003
• Combined probabilities a quick way to determine juxtaposition of key parameters
• Not a true probability
– Not independent– Different
members contribute
• Fosters an ingredients-based approach on-the-fly
SREF Parameter EvaluationSREF Parameter Evaluation
Example 2Example 2
• Calibrated, Probabilistic Severe Calibrated, Probabilistic Severe Thunderstorm GuidanceThunderstorm Guidance
Bright and Wandishin (Paper 5.5, 18th Conf. on Prob. and Statistics, 2006)
SREF 24h calibrated probability of a severe thunderstormF027 Valid 12 UTC 11 May 2005 to 12 UTC 12 May 2005
SVR WX ACTIVITY12Z 11 May 2005 to 12Z 12 May, 2005
a= Hail; w=Wind; t=Tornado
Example 3Example 3
• Calibrated, Probabilistic Cloud-to-Calibrated, Probabilistic Cloud-to-Ground Lightning GuidanceGround Lightning Guidance
Bright et al. (2005), AMS Conf. on Meteor. Appl. of Lightning Data
Essential Ingredients to Cloud Essential Ingredients to Cloud ElectrificationElectrification
• Identify what is most important and readily available from NWP models
• From: Houze (1993); Zipser and Lutz (1994); MacGorman and Rust (1998); Van Den Broeke et al. (2004)– Super-cooled liquid water and ice must be present– Cloud top exceeds charge-reversal temperature zone– Sufficient vertical motion in cloud from mixed-phase region
through the charge-reversal temperature zone
Combine Ingredients into Single Combine Ingredients into Single ParameterParameter
• Three first-order ingredients (readily available from NWP models):– Lifting condensation level > -10o C– Sufficient CAPE in the 0o to -20o C layer – Equilibrium level temperature < -20o C
• Cloud Physics Thunder Parameter (CPTP) CPTP = (-19oC – Tel)(CAPE-20 – K) K
where K = 100 Jkg-1 and CAPE-20 is MUCAPE in the 0o C to -20o C layer
Consider this Denver sounding from 00 UTC 4 June 2003
CPTP=(-19oC – Tel)(CAPE-20 – K) K
CAPE-20 ~ 450 Jkg-1Tel ~ -50o CK = 100 Jkg-1
=> CPTP = 108
Operational applications really only interested in CPTP > 1
LCL Temp
EL Temp
CAPE-20
-20o C
0o C
Now consider this Vandenberg sounding on 00 UTC 3 Jan 2004
CPTP=(-19oC – Tel)(CAPE-20 – K) K
CAPE-20 ~ 160 Jkg-1Tel ~ -17o CK = 100 Jkg-1
=> CPTP = -1.2
Although instability exists andmodels forecast convective pcpn, warm equilibrium level (-17 C) implies lightning is unlikely (CPTP < 0)
LCL Temp
EL Temp
CAPE-20
-20o C
0o C
SREF Probability CPTP SREF Probability CPTP >> 1 1
15h Forecast Ending: 00 UTC 01 Sept 2004Uncalibrated probability: Solid/Filled; Mean CPTP = 1 (Thick dashed)
3 hr valid period: 21 UTC 31 Aug to 00 UTC 01 Sept 2004
SREF Probability Precip SREF Probability Precip >> .01” .01”
15h Forecast Ending: 00 UTC 01 Sept 2004Uncalibrated probability: Solid/Filled; Mean precip = 0.01” (Thick dashed)
3 hr valid period: 21 UTC 31 Aug to 00 UTC 01 Sept 2004
Joint Probability (Assume Independent)Joint Probability (Assume Independent)
15h Forecast Ending: 00 UTC 01 Sept 2004Uncalibrated probability: Solid/Filled
P(CPTP > 1) x P(Precip > .01”)3 hr valid period: 21 UTC 31 Aug to 00 UTC 01 Sept 2004
Perfect Forecast
No Skill
Climatology
P(CPTP > 1) x P(P03I > .01”)
Uncalibrated ReliabilityUncalibrated Reliability (5 Aug to 5 Nov 2004)(5 Aug to 5 Nov 2004)
Frequency[0%, 5%, …, 100%]
Calibrated Ensemble Thunder Probability Calibrated Ensemble Thunder Probability
15h Forecast Ending: 00 UTC 01 Sept 2004Calibrated probability: Solid/Filled
3 hr valid period: 21 UTC 31 Aug to 00 UTC 01 Sept 2004
Calibrated Ensemble Thunder ProbabilityCalibrated Ensemble Thunder Probability
15h Forecast Ending: 00 UTC 01 Sept 2004Calibrated probability: Solid/Filled; NLDN CG Strikes (Yellow +)
3 hr valid period: 21 UTC 31 Aug to 00 UTC 01 Sept 2004
Perfect Forecast
No Skill
Perfect Forecast
No Skill
Calibrated Reliability Calibrated Reliability (5 Aug to 5 Nov 2004)(5 Aug to 5 Nov 2004)
Calibrated Thunder Probability
Climatology
Frequency[0%, 5%, …, 100%]
Example 4Example 4
• Calibrated, Probabilistic Snowfall Calibrated, Probabilistic Snowfall Accumulation on Roads GuidanceAccumulation on Roads Guidance
• SREF probability predictors(1) Two precipitation-type algorithms
• Baldwin algorithm in NCEP post. • Czys algorithm applied in SPC SREF post-processing.
(2) Two parameters sensitive to lower tropospheric and ground temperature
• Snowmelt parameterization: Evaluates fluxes to determine if 3” of snow melts over a 3h period.
• Simple algorithm: Function of surface conditions, F (Tpbl, TG, Qsfc net rad. flux,)
Goal: Examine the parameter space around the lower PBL T, ground T, and precip type and calibrate using road sensor data.
SREF 32F Isotherm(2 meter air temp)
Mean (dash)
Union (At leastone SREF member ator below 32 F - dots)
Intersection (All members at or below 32F- solid)
3h probability of freezing or frozen pcpn (NCEP algorithm; uncalibrated)
Example: New England Blizzard (F42: 23 January 2005 03Z)
SREF 32F Isotherm(Ground Temp)
Mean (dash)
Union (At leastone SREF member ator below 32 F - dots)
Intersection (All members at or below 32F- solid)
3h calibrated probability of snow accumulating on roads
SREF 32F Isotherm(2 meter air temp)
Mean (dash)
Union (dots)
Intersection (solid)
3h probability of freezing or frozen pcpn (Baldwin algorithm; uncalibrated)
Example: Washington, DC Area (F21: 28 February 2005 18Z)
SREF 32F Isotherm(Ground Temp)
Mean (dash)
Union (dots)
Intersection (solid)
3h calibrated probability of snow accumulating on roads
VerificationVerification
Reliability Diagram: All 3 h forecasts (F00 – F63); 35 days (Oct 1 – Apr 30)
Economic Potential ValueReliability
SummarySummary• Downscaling of severe weather forecasts are
largely implicit
• Human forecasters downscale by identifying associations between large-scale environment and storm-scale hazards
• Objective downscaling plays an increasingly important role in providing initial forecast guidance