Severe Weather ApplicationsSevere Weather Applications

David BrightNOAA/NWS/Storm Prediction Center

david.bright@noaa.gov

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