Weather Hazard Modeling Research and development:Recent Advances and Plans

Friends and Partners of Aviation WeatherNBAA Convention, Nov 2-3, 2016

Stephen S. Weygandt Curtis Alexander, Stan Benjamin,

Geoff Manikin*, Tanya Smirnova, Ming HuNOAA Earth System Research Laboratory

*NOAA National Centers for Environmental Prediction

Valid

Loop of radar reflectivity Observations

High-resolution models of hazardous weathernow making very realistic forecasts

Missing radar observations

Given forecasts are not always exactly right

How to improve decision making with inexactforecasts? How can we continue to make forecastsbetter?

Veryrealistic forecasts

15 hour HRRR forecast

Storm details can be wrong

NGM model guidance…around 1990

V2

V1

Continued forecast improvement for Rapidly updated, High-resolution models

Critical Success Index

HRRRReflectivityVerification

Improvement each year…V1

HRRR HRRRV2

NCEP version

22z + 5hr forecasts

RadarObserved

03z

Continued forecast improvement for Rapidly updated, High-resolution models

RAP - upper-airverification

Ongoing study to evaluate how forecast improvements map to aviation decision-making improvements (GSD, AvMet)

Improvement to moisture / microphysics / precipitation,boundary layer / surface energy balance, ensemble assimilation

Higher resolution

models with better physics

Rawins SatelliteAircraft RadarSurface Profiler

Accurate prediction of hazardous weatherWhat are the key requirements?

Rapid updating

Best use of observations (Ensemble data

assimilation)

13-kmReality

ORD

LAX

ORD

3-hr forecast

LAX

6-hr forecastWind

Errors

3-km

-- Fresher forecasts-- Time-lagged ensembles

The benefits of rapid updating:June 16, 2014 NE twin tornadoes

21z 16 June radar obs17z + 4hr

15z + 6hr 14z + 7hr

16z + 5hr

~21z 16 June

Successive HRRR runs converging to solution

HRRR example of consistent solutions:May 11, 2014 NE squall-line

15z+7h 16z+6h 17z+5h

HRRR Updraft HelicityTime-Lagged Ensemble

Successive HRRR runs have consistent solution22z 11 May

Storm Reports

(obs)

Arkansas tornadoes – Sunday 27 April 2014

✖

✖

3 fatalities

HRRR (and RAP) Future MilestonesHRRR MilestonesArkansas Tornadoes April 27 2014

8 fatalities

Tornado Track

15z+10h

19z+6h18z+7h

17z+8h16z+9h

Arkansas tornadoes: very challenging case

Time-lagged ensemble of specific hazard indicator

HRRR Updraft Helicity

00 UTC

Deterministic

Model ensembles: Key to assessing likelihood

Member 3

Observations

Member 2

Member 1

Supercellprobabilitypast hour

19 UTC

00 UTC

How do ensembles forecasts help?

Inches of snow

| | | | | | | | |0 1 2 3 4 5 6 7 8

100%

80%

60%

40%

20%

0%

Singlesnowforecast

Actualsnow-fall

Old way – one“deterministic”forecast

How do ensembles forecasts help?

Inches of snow

| | | | | | | | |0 1 2 3 4 5 6 7 8

100%

80%

60%

40%

20%

0% 0 50

2520 15 20

10 5

Forecastsnow

accumulationchances

Old way – one“deterministic”forecast

New way – runan “ensemble”of forecasts –get informationabout range ofpossible outcomes

Example: Run model 100 times with slightly different “settings”

How do ensembles forecasts help?

Inches of snow

| | | | | | | | |0 1 2 3 4 5 6 7 8

100%

80%

60%

40%

20%

0% 0 50

2520 15 20

10 5

Almost NO chanceOf less than 2”

Forecastsnow

accumulationchances

Old way – one“deterministic”forecast

New way – runan “ensemble”of forecasts –get informationabout range ofpossible outcomes

Example: Run model 100 times with slightly different “settings”

How do ensembles forecasts help?

Inches of snow

| | | | | | | | |0 1 2 3 4 5 6 7 8

100%

80%

60%

40%

20%

0% 0 50

2520 15 20

10 5

50 / 50 chance

of 5” or more

Forecastsnow

accumulationchances

Old way – one“deterministic”forecast

New way – runan “ensemble”of forecasts –get informationabout range ofpossible outcomes

Example: Run model 100 times with slightly different “settings”

How do ensembles forecasts help?

Inches of snow

| | | | | | | | |0 1 2 3 4 5 6 7 8

100%

80%

60%

40%

20%

0% 0 50

2520 15 20

10 5

Forecast snowaccumulation

chances

Singlesnow

forecast

Old way – one“deterministic”forecast

New way – runan “ensemble”of forecasts –get informationabout range ofpossible outcomes

you still get the deterministic forecast

Even withoutan ensemble

forecast

21z 16 June radar obs

How do ensembles forecasts help?

Inches of snow

| | | | | | | | |0 1 2 3 4 5 6 7 8

100%

80%

60%

40%

20%

0% 0 50

2520 15 20

10 5

Forecast snowaccumulation

chances

Singlesnow

forecast

Old way – one“deterministic”forecast

New way – runan “ensemble”of forecasts –get informationabout range ofpossible outcomes

Forecast users will make theirown assumptions about therange of possible outcomes

Usersassume arange ofpossible outcomes

How do ensembles forecasts help?

Inches of snow

| | | | | | | | |0 1 2 3 4 5 6 7 8

100%

80%

60%

40%

20%

0% 0 50

2520 15 20

10 5

Oldsinglesnow

forecast

Actualsnow-fall

Forecast snow

accum-ulation

chances

Old way – one“deterministic”forecast

New way – runan “ensemble”of forecasts –get informationabout range ofpossible outcomes

One more key benefitfrom ensembles:

How do ensembles forecasts help?

Inches of snow

| | | | | | | | |0 1 2 3 4 5 6 7 8

100%

80%

60%

40%

20%

0% 0 50

252015 20

10 5

Actualsnow-fall

Bettersinglesnow

forecast

Forecast snow

accum-ulation

chances

Old way – one“deterministic”forecast

New way – runan “ensemble”of forecasts –get informationabout range ofpossible outcomes

One more key benefitfrom ensembles:“ensemble data assimilation” improves the deterministic forecast

Need ensembles the mostfor the most challenging

forecast situations

Types of ensembles:

1) Ensembles of opportunity-- Time-lagged ensemble (HRRR-TLE)-- Multi-model ensembles (SSEO)ADS: computationally inexpensiveDISADS: model clustering, members not independent

2) “Designed” ensembles-- Perturbed members of single ensemble system(Global Ensemble Forecast System, HRRR-Ensemble)ADS: More useful spread, ensemble assimilation DISADS: computationally expensive

More information on ensembles

Model BModel A

Example ofmodel clustering

Actual

HRRR-TLE example: 18 April 2016

HRRR-TLE forecastprobability of 6hr QPF exceeding 100 year average return interval (ARI)

23z+13h

accum. precip.

00z+12h 01z+11h

Obs.12-hrprecip.

HRRR Time-Lagged Ensemble (HRRR-TLE):GSD real-time product generation

Real-Time Web Graphics (and grids via LDM/FTP)

Echo-Top> 25 kft

Echo-Top> 30 kft

Probability of condition)

Echo-Top> 35 kft

MVFR IFR

http://rapidrefresh.noaa.gov/hrrrtle

HRRR Ensemble (HRRRE)

Proof-of-conceptReal-time

demonstration

• Single core (ARW)• Ensemble DA (GSI-EnKF) • RAP mean + perturbations

Assimilation Forecast20 members 00z - Three mem to 30 hr1 hr cycling 03z - Three mem to 27 hr21 fcsts / day 12z - Six mem to 18 hrStart 21z day zero 15z - Eighteen mem to 15 hrEnd 18z day one 18z - Eighteen mem to 12 hr

Web Graphics (real-time HRRRE to resume Nov. 2016)

http://rapidrefresh.noaa.gov/HRRRE

Planned Fall 2016 Refinements • Add radar reflectivity data assimilation• Stochastic physics • Apply HRRR-TLE statistical processing• Include lagged members?

Comparison: HRRR-TLE and HRRRE

HRRR-Ensemble15z + 7 hr forecast

Time-Lagged Ensemble15z-17z initializations

Updraft Helicity

> 25 m2/s2

fromDifferent

members

A challenge of ensembles: Displaying output / conveying information

Postage stamp plots

Who is the user?

Feed ensemble informationinto decision support systems?

Spaghetti plots

Need more creativedisplay methodsExtract hazard specific informationSimplify display,only most critical information

Snow accumulationwith time

Plumediagrams

Probabilityregions

Paintball plots

Mean andspread

Rapid Refresh and HRRRNOAA hourly updated models

13km Rapid Refresh (RAP)

3km High Resolution Rapid Refresh (HRRR)

Version 3 -- NCEP implement 23 Aug 2016Version 4 – GSDPlanned NCEP – Early 2018

RAP(13-km)

HRRR (3-km)Version 2 – NCEP implement 23 Aug 2016Version 3 – GSD Planned NCEP – Early 2018

HRRR v3 plan HRRR-E (Ensemble)