1 development and calibration of ensemble based hazardous weather products at the storm prediction...

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Development and Calibration of Development and Calibration of Ensemble Based Hazardous Ensemble Based Hazardous

Weather Products at theWeather Products at the Storm Prediction Center Storm Prediction Center

David Bright

Gregg Grosshans, Jack Kain, Jason Levit, Russ Schneider, Dave Stensrud, Matt Wandishin, Steve Weiss

October 11, 2005NCEP Predictability Discussion Group

Where Americas Climate and Weather Services Begin

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STORM PREDICTION CENTERSTORM PREDICTION CENTER

MISSION STATEMENT

The Storm Prediction Center (SPC) exists

solely to protect life and property of the American people

through the issuance of timely, accurate watch and forecast products

dealing with tornadoes, wildfires and other hazardous mesoscale weather

phenomena.

MISSION STATEMENT

The Storm Prediction Center (SPC) exists

solely to protect life and property of the American people

through the issuance of timely, accurate watch and forecast products dealing with hazardous mesoscale weather

phenomena.

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• Hail, Wind, Tornadoes

• Excessive rainfall

• Fire weather

• Winter weather

STORM PREDICTION CENTERSTORM PREDICTION CENTER HAZARDOUS PHENOMENA

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• TORNADO & SEVERE THUNDERSTORM WATCHES

• WATCH STATUS MESSAGE• CONVECTIVE OUTLOOK

– Day 1; Day 2; Day 3; Days 4-8• MESOSCALE DISCUSSION

– Severe Thunderstorm Potential/Outlook Upgrade

– Thunderstorms not expected to become severe– Hazardous Winter Weather– Heavy Rainfall

• FIRE WEATHER OUTLOOK– Day 1; Day 2; Days 3-8

• OPERATIONAL FORECASTS ARE BOTH DETERMINISTIC AND PROBABILISTIC

SPC Forecast ProductsSPC Forecast Products

75% of all SPC products are valid for < 24h period

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Tornadoes

Probability of 2 or more tornadoes Low (10%)

Probability of 1 or more strong (F2-F4) tornadoes Low (<5%)

Wind Probability of 10 or more severe wind events Mod (60%)

Probability of 1 or more wind event > 65 knots Low (10%)

Hail

Probability of 10 or more severe hail events Low (10%)

Probability of 1 or more hailstones >2 inches Low (<5%)

Combined Severe Hail/Wind

Probability of 6 or more combined severe wind/hail events Mod (60%)

Severe Thunderstorm Watch 688 Probability Table

EXPERIMENTAL WATCH PROBABILITIES

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CONVECTIVE OUTLOOKSOperational through Day 3

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Thunderstorm Outlooks:Thunderstorm Outlooks:

24h General Thunderstorm

12h Enhanced Thunderstorm (Tonight)

12h Enhanced Thunderstorm (Today)

24h Period (> 10%)

12h Periods (> 10%; 40%; 70%)

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• Operational emphasis on…– Observational data– Short-term, high-resolution NWP guidance– Specific information predicting hazardous mesoscale

phenomena• NWP needs range from the very-short range to medium range

– Very short-range: Hourly RUC; 4.5 km WRF-NMM– Short-range: NAM, GFS, SREF– Medium-range: GFS, ECMWF, MREF

• Today’s focus: SREF– Overview of the ensemble product suite– Specific ensemble calibrated guidance

Product Guidance at the SPCProduct Guidance at the SPC

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Overview of Ensemble GuidanceOverview of Ensemble Guidance

Objective: Provide a wide range of ensemble guidance covering all of

the SPC program areas

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Sample of Ensemble Products Available…

http://www.spc.noaa.gov/exper/sref/

MEAN & SD: 500 mb HGHT

MEAN: PMSL, DZ, 10M WIND MEAN: MUCAPE, 0-6 SHR, 0-3 HLCY

SPAGHETTI: SFC LOW

11http://www.spc.noaa.gov/exper/sref/

PROB: DENDRITIC GROWTH

Omega < -3-11 < T < -17

RH > 80%PROB: SIG TOR PARAM > 3

MEDIAN, UNION, INTERSECTION:SIG TOR PARAM

MAX OR MIN: MAX FOSBERG INDEX

STP = F (mlCAPE, mlLCL, SRH, Shear)Thompson et al. (2003)

STP = F (mlCAPE, mlLCL, SRH, Shear)Thompson et al. (2003)

Sample of Ensemble Products Available…

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F63 SREF POSTAGE STAMP VIEW: PMSL, HURRICANE FRANCES F63 SREF POSTAGE STAMP VIEW: PMSL, HURRICANE FRANCES

Red = EtaBMJ

Yellow= Yellow= EtaKFEtaKF

Blue = RSM

White = White = OpEtaOpEta

SREF Member

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Combined Probability Combined Probability

• Probability surface CAPE >= 1000 J/kg– Relatively

low – Ensemble

mean is < 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

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• 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

Combined Probability Combined Probability

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• Convection is 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

Combined Probability Combined Probability

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Prob Cape >= 1000 X Prob Shear >= 30 kts X Prob Conv Pcpn >= .01” F036: Valid 21 UTC 28 May 2003

Combined Probability Combined Probability

• A quick way to determine juxtaposition of key parameters

• Fosters an ingredients-based approach

• Not a “true” probability– Dependence– Different

members contribute

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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 Probability Combined Probability

• A quick way to determine juxtaposition of key parameters

• Fosters an ingredients-based approach

• Not a “true” probability– Dependence– Different

members contribute

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F15 SREF PROBABILITYTPCP x RH x WIND x TMPF(< .01” x < 10% x > 30 mph x > 60 F)

Ingredients for extreme fire weatherconditions over the Great Basin

Combined ProbabilityCombined Probability

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Objective: Develop calibrated probabilistic guidance for CG

lightning

Calibrated Thunderstorm GuidanceCalibrated Thunderstorm Guidance

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Combine Lightning Ingredients Combine Lightning Ingredients into a Single Parameterinto a Single Parameter

• 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

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Example CPTP: One MemberExample CPTP: One Member

18h Eta Forecast Valid 03 UTC 4 June 2003

Plan view chart showing where grid point soundings support lightning (given a convective updraft)

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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

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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

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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

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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%]

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Adjusting ProbabilitiesAdjusting Probabilities

• Calibrate ensemble thunderstorm guidance based on the observed frequency of occurrence

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Ensemble Thunder CalibrationEnsemble Thunder Calibration1) Bin separately P(CPTP > 1) and P(P03M > 0.01”) into

11 bins (0-5%; 5-15%; …; 85-95%; 95-100%)2) Combine the two binned probabilistic forecasts into one

of 121 possible combinations (0%,0%); (0%,10%); … (100%,100%)

3) Use NLDN CG data over the previous 366 days to calculate the frequency of occurrence of CG strikes for each of the 121 binned combinations

• Construct for each grid point using 1/r weighting

4) Bin ensemble forecasts as described in steps 1 and 2 and assign the observed CG frequency (step 3) as the calibrated probability of a CG strike

5) Calibration is performed for each forecast cycle (09 and 21 UTC) and each forecast hour; domain is entire U.S. on 40 km grid (CG strike within ~12 miles)

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Before Calibration

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Joint Probability (Assumed Independence)Joint Probability (Assumed Independence)

P(CPTP > 1) x P(Precip > .01”)3 hr valid period: 21 UTC 31 Aug to 00 UTC 01 Sept 2004

15h Forecast Ending: 00 UTC 01 Sept 2004Uncorrected probability: Solid/Filled

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After Calibration

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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

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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

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Perfect Forecast

No Skill

Perfect Forecast

No Skill

Calibrated ReliabilityCalibrated Reliability (5 Aug to 5 Nov 2004)(5 Aug to 5 Nov 2004)

Calibrated Thunder Probability

Climatology

Frequency[0%, 5%, …, 100%]

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3h probability of > 1 CG lightning strike within ~12 mi09Z and 21Z SREF valid at F003 through F063 May 15 – Sept 15 2005

Economic Potential ValueReliability

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12h probability of > 1 CG lightning strike within ~12 mi09Z SREF valid at F012 through F063 May 15 – Sept 15 2005

Economic Potential ValueReliability

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Objective: Develop calibrated probabilistic guidance of the

occurrence of severe convective weather

(Available for 3h, 12h, and 24h periods; calibration not described today)

Calibrated Severe Thunderstorm Calibrated Severe Thunderstorm GuidanceGuidance

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24h probability of > 1 severe thunderstorm within ~25 miSREF: 2005051109Valid 12 UTC May 11, 2005 to 12 UTC May 12, 2005

SVR WX ACTIVITY12Z 11 May to 12Z 12 May, 2005

a= Hailw=Wind

t=Tornado

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Hail > .75” Wind > 50 kts Tornado

24h probability of > 1 severe thunderstorm within ~25 mi21Z SREF valid at F039 through F039 (i.e., Day 1 Outlook)May 15 – Sept 15 2005

Economic Potential ValueReliability

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Objective: Develop calibrated probabilistic guidance of snow accumulation on road surfaces

Experimental Calibrated Snow Experimental Calibrated Snow Accumulation GuidanceAccumulation Guidance

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• Use frequency of occurrence technique -- similar to the calibrated probability of CG lightning

• Produce 8 calibrated joint probability tables • Take power mean (RMS average) of all 8

tables for the 3h probability of snow accumulating on roads in the grid cell

• Calibration period is Oct. 1, 2004 through Apr. 30, 2005

• MADIS “road-state” sensor information is truth (SREF is interpolated to MADIS road sensor)

Ensemble Snow CalibrationEnsemble Snow Calibration

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• SREF probability predictors(1) Two precipitation-type algorithms

• Baldwin algorithm in NCEP post. (Pr[Sn, ZR, IP])• Czys algorithm applied in SPC SREF post-processing.

(Pr[Sn, ZR, IP])

(2) Two parameters sensitive to lower tropospheric and ground temperature

• Snowmelt parameterization (RSAE)– Evaluates fluxes to determine if 3” of snow melts over a 3h period. If yes, then parameter is assigned: 273.15 – TG. (Pr[>1; >2; >4])

• Simple algorithm (RSAP) F (Tpbl, TG, Qsfc net rad. flux,) where values > 1 indicate surface cold enough for snow to accumulate. (Pr[>1])

Goal: Examine the parameter space around the lower PBL T, ground T, and precip type and calibrate using road sensor data.

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Frequency Calibration Tables

LAYER SREF INGREDIENT 1 SREF INGREDIENT 2

1 Prob(RSAE > 1) Prob(Baldwin Snow, ZR, or IP) 2 Prob(RSAE > 2) Prob(Baldwin Snow, ZR, or IP)

3 Prob(RSAE > 4) Prob(Baldwin Snow, ZR, or IP)

4 Prob(RSAE > 1) Prob(Czys Snow, ZR, or IP) 5 Prob(RSAE > 2) Prob(Czys Snow, ZR, or IP)

6 Prob(RSAE > 4) Prob(Czys Snow, ZR, or IP)

7 Prob(RSAP > 1) Prob(Baldwin Snow, ZR, or IP)

8 Prob(RSAP > 1) Prob(Czys Snow, ZR, or IP)

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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 (Baldwin 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

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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

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6h Prob Snow Accum on RoadsOct 15, 2005 (F006 v15 UTC)

3h Prob Snow Accum on RoadsOct 15, 2005 (F006 v15 UTC)

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Blind Test

• Calibration period: Oct 1, 2004 through April 30, 2005• 5 days randomly selected for each month in the sample =>

35 days in test• Test days withheld from the monthly calibration tables (i.e.,

cross validation used) • The SREF forecasts were reprocessed for the 35 days and

verified against the MADIS surface state observations (F03 – F63)

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Verification

Reliability Diagram: All 3 h forecasts (F00 – F63); 35 days (Oct 1 – Apr 30)

Economic Potential ValueReliability

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Test Results• 3 h forecast results (F00 – F63)

– Forecast are reliable– Brier score is a 21% improvement over sample

climatology– ROC area = .919– Ave probability where new snow detected: 23%– Ave probability where new snow not detected: 4%– Economic value for a wide range of users peaking

over 0.7

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Road-Snow: Summary • Method appears reliable – although 3h

probabilities rarely exceed 50%• Highlights importance of ground temp

predictions from SREF and deterministic models• Possible improvements:

– Bias correction to 2m and ground temps from SREF*– Statistical post-processing of 2m and ground temps*

prior to road-state calibration– Addition of asphalt tile to LSM of SREF members

* See the next slide for temp correction information

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Under dispersiveSREF 2m tempforecast (F15) and cold bias

F15 cold bias in2m temp removedbut remains under dispersive

Uniform VOR after statistical adjustment to SREF

Raw 2m Temp

Bias adjusted 2m Temp

Recalibrated 2m Temp

Bias adjustmentand recalibration with the additionof asphalt-typeground temptile in LSM might be very useful for snowaccumulationfrom SREF

F15 SREF 2m TempVerf Period: ~August, 2005