itricorp-nrlmry sttr n06-t037itricorp.com/docs/ideas_papers/ensemble_forecast... · 2016. 11....
TRANSCRIPT
There are no boundaries when enthusiasm and excitement are applied to the task at hand.
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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)
There are no boundaries when enthusiasm and excitement are applied to the task at hand.
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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
There are no boundaries when enthusiasm and excitement are applied to the task at hand.
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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There are no boundaries when enthusiasm and excitement are applied to the task at hand.
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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
There are no boundaries when enthusiasm and excitement are applied to the task at hand.
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
There are no boundaries when enthusiasm and excitement are applied to the task at hand.
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
There are no boundaries when enthusiasm and excitement are applied to the task at hand.
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
There are no boundaries when enthusiasm and excitement are applied to the task at hand.
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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
There are no boundaries when enthusiasm and excitement are applied to the task at hand.
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???
There are no boundaries when enthusiasm and excitement are applied to the task at hand.
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
There are no boundaries when enthusiasm and excitement are applied to the task at hand.
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)
There are no boundaries when enthusiasm and excitement are applied to the task at hand.
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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There are no boundaries when enthusiasm and excitement are applied to the task at hand.
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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There are no boundaries when enthusiasm and excitement are applied to the task at hand.
14Confidence Chart
TC 27W - DOLPHIN
There are no boundaries when enthusiasm and excitement are applied to the task at hand.
Ensemble-Deterministic
Forecast Products
Confidence ChartWave Heights
Confidence Chart
Wave Heights
OTSR
There are no boundaries when enthusiasm and excitement are applied to the task at hand.
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