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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. Outline. Introduction

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Application of short range ensemble forecasts to convective aviation forecasting

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


Outline
Outline Aviation Forecasting

  • Introduction

  • Short Range Ensemble Forecasts (SREF)

  • Future Aviation Ensemble Applications


Outline1
Outline Aviation Forecasting

  • Introduction

    • The SPC, predictability, and ensembles

  • Short Range Ensemble Forecasts (SREF)

  • Future Aviation Ensemble Applications


STORM PREDICTION CENTER Aviation Forecasting

LOCALIZED HIGH IMPACT

WEATHER

  • Hail, Wind, Tornadoes

  • Fire Weather

  • Winter Weather

  • Excessive Rainfall


The Butterfly Effect Aviation Forecasting

  • 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


What are the sources of these tiny errors
What are the Sources of These Tiny Errors? Aviation Forecasting

Observations, Assimilation, and Models

  • Observations

    • Data gaps

    • Error

    • Representative

    • QC

  • Analysis

  • Models

  • LBCs, etc.

Satellite

Aircraft and Land


Reality Aviation Forecasting

One

model

forecast


  • Numerical weather models... Aviation Forecasting

    • 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!


Outline2
Outline Aviation Forecasting

  • Introduction

  • Short Range Ensemble Forecasts (SREF)

    • Currently available through the SPC website

  • Future Aviation Ensemble Applications


Ensemble guidance at the spc
Ensemble Guidance at the SPC Aviation Forecasting

  • 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


Nws ncep short range ensemble forecast sref

Ensembles Available at the SPC: SREF Aviation Forecasting

NWS/NCEP Short Range Ensemble Forecast (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 Aviation Forecastingon 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 Aviation Forecasting

[MN]: = Mean

[SP]: = Spaghetti

[MNSD]: = Mean/SD

[MAX]: Max value at each grid point



Instability Aviation Forecasting

[MN]: = Mean

[PR]: Probability

[MDXN]: Median/union & intersect

[SP]: = Spaghetti

[MNSD]: = Mean/SD


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


Winds and Vertical Shear Aviation Forecasting

[MN]: = Mean

[SP]: = Spaghetti

[MNSD]: = Mean/SD

[MDXN]: Median/union & intersect

[MAX]: Max value at each grid point


SREF Pr[ESHR Aviation Forecasting> 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 Aviation Forecasting

[MN]: = Mean

[SP]: = Spaghetti

[MNSD]: = Mean/SD

[MDXN]: Median/union & intersect

[MAX]: Max value at each grid point


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

C03I = 3hr Convective Precipitation


Severe Weather Aviation Forecasting

[MN]: = Mean

[SP]: = Spaghetti

[MNSD]: = Mean/SD

[MDXN]: Median/union & intersect

[MAX]: Max value at each grid point


SREF Combined or Joint Probability Aviation Forecasting

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 Aviation Forecasting

[PR]: = Probability (Calibrated)


SREF 3h Calibrated Probability of a Thunderstorm Aviation Forecasting

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 Aviation Forecasting

Frequency

[0%, 5%, …]

Perfect Forecast

No Skill

Climatology

Calibrated Thunder Probability


SREF 3h Calibrated Probability of a Severe Thunderstorm Aviation Forecasting

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 Aviation Forecasting

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 Aviation Forecasting

[MN]: = Mean

[MD]: Median/union & intersect

[MAX]: Max value at each grid point

[CPR]: Conditional probability

[PR]: Probability


SREF Probability Convective Cloud Top Aviation Forecasting> 37 KFt


Test version 03z sref day 1 enh guidance summer 2008
Test Version: 03Z SREF Day 1 ENH Guidance (Summer 2008) Aviation Forecasting

Probability Echo Tops >= 35KFT


Severe weather on june 9 2007
Severe Weather on June 9, 2007 Aviation Forecasting

  • 107 total severe reports in CONUS

    • 3 tornadoes over nrn New Mexico (~22 UTC)


Outline3
Outline Aviation Forecasting

  • 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

*Not discussed today

^Collaborating with John Huhn, Mitre Corp.


Gridded flight composite 20 km december 2007 to august 2008 above 250 kft
Gridded Flight Composite (20 km) Aviation ForecastingDecember 2007 to August 2008 – Above 250 KFT

Probability (%) aircraft is inside grid box

20 km Grid (AWIPS 215)

Data from John Huhn, Mitre


Gridded flight composite 40 km december 2007 to august 2008
Gridded Flight Composite (40 km) Aviation ForecastingDecember 2007 to August 2008

21 UTC Composite < 10 KFT

21 UTC Composite > 25 KFT

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


Example of potential impacts probability thunderstorm x gridded composites
Example of Potential Impacts Aviation ForecastingProbability Thunderstorm X Gridded Composites

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


Future applications storm scale ensemble ssef
Future Applications: Aviation ForecastingStorm Scale Ensemble (SSEF)

  • 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 updraft helicity 50 m 2 s 2
Probability Updraft Helicity Aviation Forecasting> 50 m2/s2

Probability of Supercell Thunderstorms

F026: Valid 02 UTC 22 Apr 2008

UH > 50 + 25 mi


Observed radar
Observed Radar Aviation Forecasting

Radar BREF 0142 UTC 22 Apr 2008


Probability updraft helicity 50 m 2 s 21
Probability Updraft Helicity Aviation Forecasting> 50 m2/s2

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 Aviation Forecasting

  • 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 Aviation Forecasting

Squall Line Detection

F024: Valid 00 UTC 18 Apr 2008

Linear mode + 25 miles


Probability Linear Mode Exceeding 200 miles Aviation Forecasting

Squall Line Detection

F026: Valid 02 UTC 18 Apr 2008

Linear mode + 25 miles


Probability Linear Mode Exceeding 200 miles Aviation Forecasting

Squall Line Detection

F028: Valid 04 UTC 18 Apr 2008

Linear mode + 25 miles


Linear Convective Mode: Impacts Aviation Forecasting

Aviation impacts ~ 01 UTC

18 April 2008

Image provided by Jon Racy


Summary ensemble applications in convective aviation forecasting
Summary: Ensemble Applications Aviation Forecastingin Convective/Aviation Forecasting

  • 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


Spc sref products on web
SPC SREF Products on WEB Aviation Forecasting

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

Questions/Comments…

[email protected]


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