Severe Weather Applications David Bright NOAA/NWS/Storm Prediction Center 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

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

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

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.

Hail, Wind, Tornadoes Excessive rainfall Fire weather Winter weather Overview 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 Products 75% of all SPC products are valid for < 24h period

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

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…

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

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

Thunderstorms are not resolved by mesoscale model at 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 Parameter Evaluation

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

Refined 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 storm forecasting

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

Example 1 Basic Ensemble CAPE and Shear AnalysisBasic Ensemble CAPE and Shear Analysis

SREF 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 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 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 Evaluation

Severe Reports Red=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 Evaluation

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

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

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

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

Combine Ingredients into Single Parameter Three first-order ingredients (readily available from NWP models): –Lifting condensation level > -10 o C –Sufficient CAPE in the 0 o to -20 o C layer –Equilibrium level temperature < -20 o C Cloud Physics Thunder Parameter (CPTP) CPTP = (-19 o C – T el )(CAPE -20 – K) K where K = 100 Jkg -1 and CAPE -20 is MUCAPE in the 0 o C to -20 o C layer

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

Now consider this Vandenberg sounding on 00 UTC 3 Jan 2004 CPTP=(-19 o C – T el )(CAPE -20 – K) K CAPE -20 ~ 160 Jkg-1 Tel ~ -17 o 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) LCL Temp EL Temp CAPE o C 0 o C

SREF Probability CPTP > 1 15h Forecast Ending: 00 UTC 01 Sept 2004 Uncalibrated 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 >.01” 15h Forecast Ending: 00 UTC 01 Sept 2004 Uncalibrated 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) 15h Forecast Ending: 00 UTC 01 Sept 2004 Uncalibrated 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 Reliability (5 Aug to 5 Nov 2004) Frequency [0%, 5%, …, 100%]

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

Calibrated Ensemble Thunder Probability 15h Forecast Ending: 00 UTC 01 Sept 2004 Calibrated 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 (5 Aug to 5 Nov 2004) Calibrated Thunder Probability Climatology Frequency [0%, 5%, …, 100%]

Example 4 Calibrated, Probabilistic Snowfall Accumulation on Roads GuidanceCalibrated, Probabilistic Snowfall Accumulation 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 (T pbl, T G, Q sfc 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 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) Example: New England Blizzard (F42: 23 January Z) 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 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 Z) SREF 32F Isotherm (Ground Temp) Mean (dash) Union (dots) Intersection (solid) 3h calibrated probability of snow accumulating on roads

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

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