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Application of Short Range Ensemble Forecasts to Convective Aviation Forecasting

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Application of Short Range Ensemble Forecasts to Convective Aviation Forecasting

David Bright

NOAA/NWS/Storm Prediction Center

Norman, OK

Southwest Aviation Weather Safety Workshop

October 23-24, 2008

Where Americas Climate and Weather Services Begin

- Introduction
- Short Range Ensemble Forecasts (SREF)
- Future Aviation Ensemble Applications

- Introduction
- The SPC, predictability, and ensembles

- Short Range Ensemble Forecasts (SREF)
- Future Aviation Ensemble Applications

STORM PREDICTION CENTER

LOCALIZED HIGH IMPACT

WEATHER

- Hail, Wind, Tornadoes
- Fire Weather
- Winter Weather
- Excessive Rainfall

The Butterfly Effect

- Ensemble forecasting can be formally traced to the discovery of the "Butterfly Effect" (Lorenz 1963, 1965)…
- The atmosphere is a non-linear, non-periodic, dynamical system such that tiny errors grow ... resulting in forecast uncertainty that increases with time resulting in limited predictability
- “Does the flap of a butterfly’s wings in Brazil set off a tornado in Texas?” (Lorenz 1972)
- Or, does the formation of an isolated storm over the Mogollon Rim set off a flash flood near Phoenix?
- Predictability is a function of temporal and spatial scales: e.g., thunderstorms are inherently less predictable than synoptic scale cyclones

Observations, Assimilation, and Models

- Observations
- Data gaps
- Error
- Representative
- QC

- Analysis
- Models
- LBCs, etc.

Satellite

Aircraft and Land

Reality

One

model

forecast

- Numerical weather models...
- All forecasts contain errors that increase with time
- Doubling time of small initial errors
~1 to 2 days

- Maximum large-scale (synoptic to planetary) predictability ~10 to 14 days

- Ensembles…
- A collection of models that provide information on the range of plausible forecasts, statistical measures of confidence, and extend predictability
- Scales to the problem of interest
- Increasing in popularity
- Requires “tools” to view the large number of models using a slightly different approach (statistical)

Weather forecasting: It’s impossible to

be perfectly correct all of the time!

- Introduction
- Short Range Ensemble Forecasts (SREF)
- Currently available through the SPC website

- Future Aviation Ensemble Applications

- Develop specialized guidance for the specific application (convection, severe storms, fire weather, winter weather)
- Design guidance that…
- Help blend deterministic and ensemble approaches
- Provide guidance for uncertainty/probabilistic forecasts
- Provide guidance that aids confidence (i.e., better deterministic forecasts)
- Illustrates plausible scenarios
- Allows for diagnostic analysis –
not just a statistical black-box

Ensembles Available at the SPC: SREF

- EMC SREF system (21 members)
- 87 hr forecasts four times daily (03, 09, 15, 21 UTC)
- North American domain
- Model grid lengths 32-45 km
- Multi-model: Eta, RSM, WRF-NMM, WRF-ARW
- Multi-analysis: NAM, GFS initial and boundary conds.
- IC perturbations and physics diversity
- Recently added bias-correction to some fields (not covered and not available on the SPC webpage)

All example products available on the SPC webpage

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

http://w1.spc.woc.noaa.gov/exper/sref/frames.php?run=case_2007060803

Basic Overview

[MN]: = Mean

[SP]: = Spaghetti

[MNSD]: = Mean/SD

[MAX]: Max value at each grid point

SREF 500 mb Mean Height, Wind, Temp

Instability

[MN]: = Mean

[PR]: Probability

[MDXN]: Median/union & intersect

[SP]: = Spaghetti

[MNSD]: = Mean/SD

SREF Pr[MUCAPE > 1000 J/kg] & Mean MUCAPE=1000 (dash)

Winds and Vertical Shear

[MN]: = Mean

[SP]: = Spaghetti

[MNSD]: = Mean/SD

[MDXN]: Median/union & intersect

[MAX]: Max value at each grid point

SREF Pr[ESHR > 30 kts] & Mean ESHR=30 kts (dash)

“Effective Shear” (ESHR; Thompson et al. 2007, WAF) is the bulk shear in the lower half of the convective cloud

Precipitation

[MN]: = Mean

[SP]: = Spaghetti

[MNSD]: = Mean/SD

[MDXN]: Median/union & intersect

[MAX]: Max value at each grid point

SREF Pr[C03I > .01”] and Mean C03I = .01” (dash)

C03I = 3hr Convective Precipitation

Severe Weather

[MN]: = Mean

[SP]: = Spaghetti

[MNSD]: = Mean/SD

[MDXN]: Median/union & intersect

[MAX]: Max value at each grid point

SREF Combined or Joint Probability

Probability of convection in moderate CAPE, moderate shear environment

Pr [MUCAPE > 1000 J/kg] X

Pr [ESHR > 30 kts] X

Pr [C03I > 0.01”]

Post-Processed Guidance

[PR]: = Probability (Calibrated)

SREF 3h Calibrated Probability of a Thunderstorm

Thunderstorm =

> 1 CG Lightning Strike in 40 km grid box

(Bright et al., 2005) http://ams.confex.com/ams/pdfpapers/84173.pdf

Calibrated SREF Thunder Reliability

Frequency

[0%, 5%, …]

Perfect Forecast

No Skill

Climatology

Calibrated Thunder Probability

SREF 3h Calibrated Probability of a Severe Thunderstorm

Severe Thunderstorm =

> 1 CG Lightning Strike in 40 km grid box

and

Wind > 50 kts or Hail > 0.75” or Tornado

(Bright and Wandishin, 2006) http://ams.confex.com/ams/pdfpapers/98458.pdf

Severe

Verification

Hail > .75”

Wind > 50 kts

Tornado

21 UTC F039 24h forecasts from 15 April to 15 October 2005

ROC Area= .86

Ave Hit = 15%

Ave Miss= 3%

(24h Fcst: F39, 21Z only)

21 UTC SREF only

Aviation Guidance

[MN]: = Mean

[MD]: Median/union & intersect

[MAX]: Max value at each grid point

[CPR]: Conditional probability

[PR]: Probability

SREF Probability Convective Cloud Top > 37 KFt

Probability Echo Tops >= 35KFT

- 107 total severe reports in CONUS
- 3 tornadoes over nrn New Mexico (~22 UTC)

- Introduction
- Short Range Ensemble Forecasts (SREF)
- Future Aviation Ensemble Applications
- Applications and calibration under development
- One hourly SREF thunderstorm guidance (through F036) *
- Calibration of potential impacts of convection in SREF ^
- Rapid Refresh Ensemble Forecast (RREF) – 1hr updates, RUC based *
- Storm scale (e.g., supercells, squall lines) applications being evaluated

- Applications and calibration under development

*Not discussed today

^Collaborating with John Huhn, Mitre Corp.

Probability (%) aircraft is inside grid box

20 km Grid (AWIPS 215)

Data from John Huhn, Mitre

21 UTC Composite < 10 KFT

21 UTC Composite > 25 KFT

Probability (%) aircraft is inside grid box at exactly 21 UTC

SREF F036: 3h Calibrated

Probability of a T-storm

Valid: 21 UTC 8 June 2007

SREF F036: Impact

Potential Below 10 KFT

Valid: 21 UTC 8 June 2007

SREF F036: Impact

Potential Above 25 KFT

Valid: 21 UTC 8 June 2007

- NOAA Hazardous Weather Testbed (HWT)
- HWT Spring Experiment
- Focused on experimental high-res WRF forecasts since 2004 (dx ~2-4 km)

- Convection allowing ensemble forecasts (2007-2009) to address uncertainty
- 10 WRF members
- 4 km grid length over 3/4 CONUS
- Major contributions from: SPC, NSSL, OU/CAPS, EMC, NCAR
- Resolving convection explicitly in the model

- Evaluate the ability of convection allowing ensembles to predict:
- Convective mode (i.e., type of severe wx)
- Magnitude of severe type (e.g., peak wind)
- Aviation impacts (e.g., convective lines/tops)
- QPF/Excessive precipitation
- Year 1 Objective (2007): Assess the role
of physics vs. initial condition uncertainty at

high resolution

2003 Spring Experiment

Probability of Supercell Thunderstorms

F026: Valid 02 UTC 22 Apr 2008

UH > 50 + 25 mi

Radar BREF 0142 UTC 22 Apr 2008

Jack Hales

View of the left split looking south from Norman, OK (0145 UTC 22 Apr 2008)

(Numerous large hail reports up to 2.25”)

Convective Mode: Linear Detection

- Determine contiguous areas exceeding 35 dbZ
- Estimate mean length-to-width ratio of the contiguous area; search for ratios > 5:1
- Flag grid point if the length exceeds:
- 200 miles

Probability Linear Mode Exceeding 200 miles

Squall Line Detection

F024: Valid 00 UTC 18 Apr 2008

Linear mode + 25 miles

Probability Linear Mode Exceeding 200 miles

Squall Line Detection

F026: Valid 02 UTC 18 Apr 2008

Linear mode + 25 miles

Probability Linear Mode Exceeding 200 miles

Squall Line Detection

F028: Valid 04 UTC 18 Apr 2008

Linear mode + 25 miles

Linear Convective Mode: Impacts

Aviation impacts ~ 01 UTC

18 April 2008

Image provided by Jon Racy

- Ensemble approach to forecasting has many similarities to the deterministic approach
- Ingredients based inputs
- Diagnostic and parameter evaluation

- Ensembles contribute appropriate levels of confidence to the forecast process
- Ability to view diagnostics and impacts in probability space

- Calibration of ensemble output can remove systematic biases and improve the spread
- Ensemble techniques scale to the problem of interest (weeks, days, or hours)
- Ensemble systems are here to stay…and will evolve downscale toward high-impact forecasting