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Severe Weather Applications

Severe Weather Applications. David Bright NOAA/NWS/Storm Prediction Center david.bright@noaa.gov AMS Short Course on M ethods and P roblems of D ownscaling W eather and C limate V ariables January 29, 2006 Atlanta, GA. Where Americas Climate and Weather Services Begin. Outline.

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Severe Weather Applications

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  1. Severe Weather Applications David Bright NOAA/NWS/Storm Prediction Center david.bright@noaa.gov AMS Short Course on Methods and Problems of Downscaling Weather and Climate Variables January 29, 2006 Atlanta, GA Where Americas Climate and Weather Services Begin

  2. Outline • Overview of the Storm Prediction Center (SPC) • Implicit downscaling and hazardous mesoscale phenomena • Parameter evaluation • SPC ensemble diagnostics

  3. Outline • Overview of the Storm Prediction Center (SPC) • Implicit downscaling and hazardous mesoscale phenomena • Parameter evaluation • SPC ensemble diagnostics

  4. Overview 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.

  5. Overview of the SPC HAZARDOUS PHENOMENA • Hail, Wind, Tornadoes • Excessive rainfall • Fire weather • Winter weather

  6. Overview of the SPC Products • TORNADO & SEVERE THUNDERSTORM WATCHES • WATCH STATUS MESSAGE • CONVECTIVE OUTLOOK • MESOSCALE DISCUSSION • FIRE WEATHER OUTLOOK • OPERATIONAL FORECASTS ARE BOTH DETERMINISTIC AND PROBABILISTIC 75% of all SPC products are valid for < 24h period

  7. Outline • Overview of the Storm Prediction Center (SPC) • Implicit downscaling and hazardous mesoscale phenomena • Parameter evaluation • SPC ensemble guidance

  8. Implicit 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…

  9. Trough and associated cold front within the domain of a mesoscale model ΔX ~ 10 km

  10. Narrow region of pre-frontal convergence Convergence region minimally resolved by mesoscale model at about 4 ΔX ΔX ~ 10 km

  11. Thunderstorms then develop within pre-frontal convergence zone Thunderstorms are not resolved by mesoscale model at only 1 to 2 ΔX ΔX ~ 10 km

  12. The ability to predict phenomena in an NWP model is scale dependent • A grid point model: • does not resolve wavelengths of ~1-3ΔX • minimally resolves wavelengths of ~4ΔX • fully resolves wavelengths of ~10ΔX ΔX ~ 10 km

  13. SPC Downscaling and Parameter Evaluation • 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)

  14. Parameter Evaluation: CAPE vs. Deep Layer Shear Shear   CAPE Adapted from AMS Monograph Vol. 28 Num. 50 Pg. 449

  15. Refined Parameter Investigations A simple product of CAPE and shear 90% 75% 50% 25% 10% Gradual increase between classes, with discrimination between thunder, severe, and significant severe

  16. A complex parameter space is evaluated for modern severe storm forecasting

  17. Outline • Overview of the Storm Prediction Center (SPC) • Implicit downscaling and hazardous mesoscale phenomena • Parameter evaluation • SPC ensemble diagnostics

  18. Example 1 • Basic Ensemble CAPE and Shear Analysis

  19. SREF Parameter Evaluation 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 surface CAPE >= 1000 J/kg • Generally low in this case • Ensemble mean < 1000 J/kg (no gold dashed line)

  20. SREF Parameter Evaluation 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 • Probability deep layer shear >= 30 kts • Strong mid level jet through Iowa

  21. SREF Parameter Evaluation 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 • Convection likely WI/IL/IN • Will the convection become severe?

  22. SREF Parameter Evaluation Prob Cape >= 1000 X Prob Shear >= 30 kts X Prob Conv Pcpn >= .01” F036: Valid 21 UTC 28 May 2003 • Combined probabilities very useful • Quick way to determine juxtaposition of key parameters • Not a true probability • Not independent • Different members contribute

  23. SREF Parameter Evaluation 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 Severe Reports Red=Tor; Blue=Wind; Green=Hail

  24. Example 2 • Calibrated, Probabilistic Severe Thunderstorm Guidance • Bright and Wandishin (Paper 5.5, 18th Conf. on Prob. and Statistics, 2006)

  25. SVR WX ACTIVITY 12Z 11 May 2005 to 12Z 12 May, 2005 a= Hail; w=Wind; t=Tornado SREF 24h calibrated probability of a severe thunderstorm F027 Valid 12 UTC 11 May 2005 to 12 UTC 12 May 2005

  26. Example 3 • Calibrated, Probabilistic Cloud-to-Ground Lightning Guidance Bright et al. (2005), AMS Conf. on Meteor. Appl. of Lightning Data

  27. Essential Ingredients to Cloud Electrification • 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

  28. Combine Ingredients into Single Parameter • Three first-order ingredients (readily available from NWP models): • Lifting condensation level > -10o C • Sufficient CAPE in the 0o to -20oC 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

  29. Consider this Denver sounding from 00 UTC 4 June 2003 CPTP=(-19oC – Tel)(CAPE-20 – K) K CAPE-20 ~ 450 Jkg-1 Tel ~ -50o C K = 100 Jkg-1 => CPTP = 108 Operational applications really only interested in CPTP > 1 EL Temp -20o C CAPE-20 0o C LCL Temp

  30. Now consider this Vandenberg sounding on 00 UTC 3 Jan 2004 CPTP=(-19oC – Tel)(CAPE-20 – K) K CAPE-20 ~ 160 Jkg-1 Tel ~ -17o C K = 100 Jkg-1 => CPTP = -1.2 Although instability exists and models forecast convective pcpn, warm equilibrium level (-17 C) implies lightning is unlikely (CPTP < 0) EL Temp -20o C CAPE-20 0o C LCL Temp

  31. SREF Probability CPTP > 1 3 hr valid period: 21 UTC 31 Aug to 00 UTC 01 Sept 2004 15h Forecast Ending: 00 UTC 01 Sept 2004 Uncalibrated probability: Solid/Filled; Mean CPTP = 1 (Thick dashed)

  32. SREF Probability Precip > .01” 3 hr valid period: 21 UTC 31 Aug to 00 UTC 01 Sept 2004 15h Forecast Ending: 00 UTC 01 Sept 2004 Uncalibrated probability: Solid/Filled; Mean precip = 0.01” (Thick dashed)

  33. Joint Probability (Assume Independent) 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 2004 Uncalibrated probability: Solid/Filled

  34. Uncalibrated Reliability (5 Aug to 5 Nov 2004) Frequency [0%, 5%, …, 100%] Perfect Forecast No Skill Climatology P(CPTP > 1) x P(P03I > .01”)

  35. Calibrated Ensemble Thunder Probability 3 hr valid period: 21 UTC 31 Aug to 00 UTC 01 Sept 2004 15h Forecast Ending: 00 UTC 01 Sept 2004 Calibrated probability: Solid/Filled

  36. Calibrated Ensemble Thunder Probability 3 hr valid period: 21 UTC 31 Aug to 00 UTC 01 Sept 2004 15h Forecast Ending: 00 UTC 01 Sept 2004 Calibrated probability: Solid/Filled; NLDN CG Strikes (Yellow +)

  37. Calibrated Reliability (5 Aug to 5 Nov 2004) Frequency [0%, 5%, …, 100%] Perfect Forecast Perfect Forecast No Skill Climatology No Skill Calibrated Thunder Probability

  38. Example 4 • Calibrated, Probabilistic Snowfall Accumulation on Roads Guidance

  39. Goal: Examine the parameter space around the lower PBL T, ground T, and precip type and calibrate using road sensor data. • SREF probability predictors • 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, Qsfcnet rad. flux,)

  40. Example: New England Blizzard (F42: 23 January 2005 03Z) SREF 32F Isotherm (2 meter air temp) Mean (dash) Union (At least one SREF member at or below 32 F - dots) Intersection (All members at or below 32F- solid) SREF 32F Isotherm (Ground Temp) Mean (dash) Union (At least one SREF member at or below 32 F - dots) Intersection (All members at or below 32F- solid) 3h probability of freezing or frozen pcpn (NCEP algorithm; uncalibrated) 3h calibrated probability of snow accumulating on roads

  41. Example: Washington, DC Area (F21: 28 February 2005 18Z) SREF 32F Isotherm (2 meter air temp) Mean (dash) Union (dots) Intersection (solid) SREF 32F Isotherm (Ground Temp) Mean (dash) Union (dots) Intersection (solid) 3h probability of freezing or frozen pcpn (Baldwin algorithm; uncalibrated) 3h calibrated probability of snow accumulating on roads

  42. Verification Reliability Economic Potential Value Reliability Diagram: All 3 h forecasts (F00 – F63); 35 days (Oct 1 – Apr 30)

  43. Summary • 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

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