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ItriCorp-NRLMRY STTR N06-T037

Ensemble Forecast Application System [EFAS]for

Conveying METOC Forecast Certainty in Decision Making

Summary

Jim EtroItri Corporation, CEO

831-324 -0499 (office)

703-489-8507 (mobile)

jim.etro@itricorp.com

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ItriCorp - NRL Monterey Collaboration

Itri CorporationJim Etro, PI

NRL Monterey John Cook

Science Team Lead Scott Sandgathe

Sponsor/TPOCONR/Ron Ferek

Contract # NOOOI4-08-C-OI73

Lead Engineer Arthur Etro

Science TeamNRL - J Hansen

NRL- C Bishop

NPS - T Eckel

FNMOC - M Sestak

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Significant Findings

Ensembles help quantify forecast uncertainty, and

NOGAPS & WW3 & COAMPS Ensembles can be represented in a deterministic

forecast to convey:

A value, a range of the value, and a measure of confidence in the value

At All FCST TAUs Ensembles offer the most skillful deterministic forecast

WRT Post Processing

On global scale and in data sparse areas such as oceans and upper atmosphere the model

analysis can be used for ensemble bias corrections and calibrations

In most cases Bias Corrections gives best results. Spread Adjustments (by least squares)

have not shown improved skill

An object oriented architecture can be implemented to

• Accommodate any ensemble or sets of ensembles

• be upgraded and improved as science and operational experiences grow

• Provide new products and valuable comparison and validations

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Ensemble Handling Process

From Operational NOGAPS & WW3 and COAMPS Ensembles from FNMOC

Determine METOC parameters to be input into Products

Post Process at every Grid Point in the domain of interest (maybe global or can be 1 grid point)

Extract Results and Apply into Application/TDA

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Ensemble Post Processing1. Bias Correction to Parameters by capture of average TAU error across all

members to their verifying analysis then averaging of all the errors for 30

previous days applied to current TAU.

- Use Kalman Filter (Jim Hanson) in Next Stage

2. Spread Adjustment to Parameters by Linear Regression using Method of

Ordinary Least Squares over 60 previous days to establish coefficients.

- No Value

3. Simple Density (Kernel) Smoothing of Histogram of Parameters from

Members at each point to Form PDF

4. Determine

1. Ensemble Averages from Raw and Bias Corrected Averages

2. Most Likely Value From PDF

3. Range from PDF

4. Confidence from PDF or Parameter Value Counting of each Member

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Comparisons & Validations - NOGAPS

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Forecasted Parameter

TauBest Ensemble Average

Conditioning Method

Ensemble Improvement Over Single Model FCST

Ave ErrorEnsemble's Ave

Global Improvement

Ensemble’s Improvement in a Mid-Lat

Location

Analysis –Single Model

Analysis –Best Ensemble

06 Bias Correction 132.5 40.0 .9 mb 1 – 3 mb

SFC Pressure 24 Bias Correction 182.5 98.75. .8 mb

48 Bias Correction 275.0 178.75 .9 mb 2 – 6 mb

06 Bias Correction .67 .48 .20: K

SFC Temperature 24 Bias Correction .98 .74 .24: K

48 Bias Correction 1.23 .94 .29: K

06 Bias Correction .222 .164 .057 M .1 - .26 M

Sig Wave Hgt 24 Bias Correction .243 .174 .069 M

48 Bias Correction .313 .230 .082 M .1 – 1.5 M

06 Bias Correction 1.04 .64 .4 m/s 1 – 3 m/s

SFC Wind Speed 24 Bias Correction 1.38 1.0 .38 m/s

48 Bias Correction 1.68 1.25 .43 m/s 2 – 3.5 m/s

06 Raw Ensemble 30 23 7:

SFC Wind Direction 24 Raw Ensemble 38 30 8:

48 Raw Ensemble 47 38 9:

06 Raw Ensemble 24 19 5 %

Total Clouds 24 Raw Ensemble 29 23 6 %

48 Raw Ensemble 22 17 5 %

http://services.itriware.com/Workflow/lab/ensemble/index.html

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0

5000

10000

15000

20000

winddir 48hr 30s-

30n

winddir 48hr north

30-90

winddir 48hr

global

Raw Ensemble Average

Bias Corrected Average

0

5000

10000

15000

20000

windspd 48hr 30s-

30n

windspd 48hr north

30-90

windspd 48hr global

Raw Ensemble Average

Bias Corrected Average

0

0.5

1

1.5

2

windspd 48hr 30s-30n

windspd 48hr north 30-90

windspd 48hr global

Raw Ensemble Average

Bias Corrected Average

010203040506070

winddir 48hr 30s-30n

winddir 48hr north 30-90

winddir 48hr global

Raw Ensemble Average

Bias Corrected Average

Cou

nts

Co

un

ts

48hr Wind Direction

48hr Wind Speed

Deg

rees

Met

ers/

Sec

error

error

Error

Analysis - Fcst

NOGAPS

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0

50

100

150

200

250

300

350

400

SFC Pressure 30s-30n

SFC Pressure 30n-90n

SFC Pressure global

Raw Ensemble Average

Bias Corrected Ensemble Average

Analysis - One Member

0

5000

10000

15000

20000

25000

SFC Pressure 30s-30n

SFC Pressure 30n-90n

SFC Pressure

global

Raw Ensemble Average

Bias Corrected Ensemble Average

NOGAPS48hr SFC Pressure

Cou

nts

Met

ers

error

0

0.5

1

1.5

2

2.5

3

sig wave 48hr 30s-

30n

sig wave 48hr north 30-90 lat

sig wave 48hr south

30-90lat

sig wave 48hr global

Raw Ensemble Average - Analysis

Bias Corrected Average - Analysis

Analysis - One Member

02000400060008000

10000120001400016000

sig wave 48hr 30s-

30n

sig wave 48hr

north 30-90 lat

sig wave 48hr

south 30-90lat

sig wave 48hr

global

Raw Ensemble Average

Bias Corrected Average

WW348hr Sig_Wave_Height

Co

un

ts

Met

ers error

Error

Analysis - Fcst

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Comparisons & Validations - COAMPS

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Forecasted Parameter

Tau LvLBest Ensemble

Average Conditioning Method

Ensemble Improvement Over Single Model FCST

Ave Error Ensemble's Ave Global

ImprovementAnalysis –

Single ModelAnalysis –Ensemble

Virtual Temperature (derived)

12All

Bias Correction ≈.7:K ≈.5:K .2:K

24 Bias Correction ≈1.4:K ≈1:K .3:-.4:K

Sea Lvl Pressure12

MSLBias Correction ≈1.2mb ≈.6mb ≈.6mb

24 Bias Correction ≈1.6mb ≈1mb ≈.6mb

http://services.itriware.com/Workflow/lab/ensemble/index.html

Impact on vertical stability???

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0

0.2

0.4

0.6

0.8

1

1.2

1.4

1

Raw Ensemble Average

Bias Corrected Average

Analysis - One Member

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1

Raw Ensemble Average

Bias Corrected Average

Analysis - One Member

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1

Raw Ensemble Average

Bias Corrected Average

Analysis - One Member

24hr COAMPS Virtual Temp

all Lvls

8450 meters

10 meters

Error

Analysis - Fcst

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Run Times (on NRL Work Station)

NOGAPS and WW-3 EnsemblesOperate on 16 Members, One Level, 6 parameters, 00Z Tau 0-54 every 6hrs

– 3.3 hrs/day (1.8 hrs/day for spread adjustment)

COAMPS EnsembleOperate on 32 Members, 40 Levels, SLP and full parameter (TV), 00Z &12Z Tau 12,24

– 8.5 hrs/day (6.2 hrs/day for spread adjustment)

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Forecast ConfidenceUser Focused

Tolerance of Forecast Erroris the amount of a parameter’s variability that can experienced during the

operations before the impact of that parameter on events is noticeable

and important, i.e., the event and/or its outcome isn’t sensitive to the

experienced variability.

Confidencein the forecasted value of a parameter at a point (x,y,z,t) means that the

forecasted value is with-in the Tolerance of Forecast Error.

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Tolerance for Forecast Error (at all TAU) of a Parameter’s

Value at a Forecast Location (x,y,x,t):

Significant Wave Height: 2 feet / .61 metersHIGH CONFIDENCE ≥ 80% [configurable] of the ensemble members are all within 2 feet or .61 meters [configurable] of the

value.

MEDIUM CONFIDENCE ≥ 50% and < 80% [configurable] of the ensemble members are all within 2 feet or .61 meters

[configurable] of the value.

LOW CONFIDENCE < 50% [configurable] of the ensemble members are all within 2 feet or .61 meters [configurable] of the

value.

Wind Speed: 5 knots / 2.57 m/sHIGH CONFIDENCE ≥ 80% [configurable] of the ensemble members are all within 5 knots or 2.57 m/s [configurable] of the

value.

MEDIUM CONFIDENCE ≥ 50% and < 80% [configurable] of the ensemble members are all within 5 knots or 2.57 m/s

[configurable] of the value.

LOW CONFIDENCE < 50% [configurable] of the ensemble members are all within 5 knots or 2.57 m/s [configurable] of the

value.

Wind Direction: 10⁰

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14Confidence Chart

TC 27W - DOLPHIN

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Ensemble-Deterministic

Forecast Products

Confidence ChartWave Heights

Confidence Chart

Wave Heights

OTSR

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FY10-11Objective of Continued Work:

Prepare EFAS operating on NOGAPS and WW3 Ensembles for Transition

into FNMOC.

Tasks:

– Establish EFAS into SVC environment (simulates SVC at NRL)

– Interface ISIS to extract ensemble/parameters from and put post processed

ensemble parameter field back into it

– Make three products from post processed fields for use by OTSR watch standers at

Norfolk and Pearl Harbor (Calibrated Ensemble Parameter Average, Confidence, @

Sea FCST, OTSR Route Recommendation)

• Interface WXMap with Calibrated Ensemble Parameter Average Chart and Confidence

Chart

• Interface METCAST with @ Sea FCST and OTSR Route Recommendation (??

METCAST or what does Norfolk and Pearl use for OTSR??)

– Establish Beta Test Plan for Transition and submit to AMOP