topic 1.5: operational guidance and skill in forecasting structure change presented by john knaff...
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Topic 1.5: Operational Topic 1.5: Operational guidance and skill in guidance and skill in forecasting structure changeforecasting structure change
Presented by Presented by John KnaffJohn KnaffCIRA/CSUCIRA/CSU
Working Group:Working Group: C. Guard, J. Kossin, T. Marchok, B. C. Guard, J. Kossin, T. Marchok, B. Sampson, Sampson,
T. Smith, N. SurgiT. Smith, N. Surgi
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Presentation OutlinePresentation Outline
Review Typical Measures of TC Review Typical Measures of TC structurestructure
Operational TC Structure GuidanceOperational TC Structure Guidance Intensity verificationIntensity verification
– Long-term operationalLong-term operational– Long-term guidanceLong-term guidance
Wind Radii VerificationWind Radii Verification– 2005 Atlantic2005 Atlantic
Summary and RecommendationsSummary and Recommendations
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Measures of TC StructureMeasures of TC Structure
IntensityIntensity– Minimum Sea Level Pressure (MSLP)Minimum Sea Level Pressure (MSLP)– Maximum Surface Winds (MSW)Maximum Surface Winds (MSW)**
Wind StructureWind Structure– Radii of significant winds/wind speed Radii of significant winds/wind speed
thresholds (e.g., Radii of 34-, 50-, 64-kt thresholds (e.g., Radii of 34-, 50-, 64-kt winds) winds) **
Pressure DistributionPressure Distribution– Outer closed or outer closed & circular isobarOuter closed or outer closed & circular isobar
* Are used for the verification presented here* Are used for the verification presented here
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Current TC Intensity Current TC Intensity Guidance MethodsGuidance MethodsFew MethodsFew Methods 24-h Dvorak extrapolation – 24-h Dvorak extrapolation –
subjective/statisticalsubjective/statistical Purely statistical models based Purely statistical models based
on historical best tracks on historical best tracks (CLIPER, climatology, analogs)(CLIPER, climatology, analogs)
Statistical-dynamical models Statistical-dynamical models that use NWP forecasts of that use NWP forecasts of environmental conditions to environmental conditions to make statistical forecastsmake statistical forecasts
Numerical Weather Prediction Numerical Weather Prediction (NWP) methods that make (NWP) methods that make deterministic forecasts.deterministic forecasts.
Consensus Methods, Consensus Methods, combination of skillful forecast combination of skillful forecast methods methods
WeaknessesWeaknesses Short-term, subjectiveShort-term, subjective Do not use current synoptic Do not use current synoptic
information, conservative best information, conservative best used for verificationused for verification
Conservative - cannot predict Conservative - cannot predict rapid intensity changes, timing rapid intensity changes, timing at long leads, over intensify at long leads, over intensify weak systems, poorly handle weak systems, poorly handle high latitude decayhigh latitude decay
Spin-up issues, over intensify Spin-up issues, over intensify some systems, poor physical some systems, poor physical initialization, parameterized initialization, parameterized physicsphysics
Only as good as the Only as good as the independent guidance independent guidance
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Current Operational TC Current Operational TC Wind Structure GuidanceWind Structure Guidance
Fewer methodsFewer methods Climatology (tabular, Climatology (tabular,
equation form as a equation form as a function of intensity)function of intensity)– Vary among forecast Vary among forecast
centerscenters Simple Statistical Simple Statistical
(CLIPER models)(CLIPER models)– Basin dependentBasin dependent
Numerical Weather Numerical Weather Prediction.Prediction.
WeaknessesWeaknesses Only as good as the Only as good as the
climatology, not climatology, not documenteddocumented
Based on past Based on past operational estimates operational estimates (are they good?)(are they good?)– Scatterometry, aircraft?Scatterometry, aircraft?
Effected by resolution, Effected by resolution, and vortex initializationand vortex initialization
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Operational Intensity Operational Intensity VerificationVerification
Caveats:Caveats: Datasets come from the RSMC, Miami Datasets come from the RSMC, Miami
(NOAA/TPC) and USA/Joint Typhoon (NOAA/TPC) and USA/Joint Typhoon Warning Center.Warning Center.
– 1-minute sustained 10-m wind1-minute sustained 10-m wind– Post season re-analyzed intensities making use of Post season re-analyzed intensities making use of
all available intensity estimates (i.e., Dvorak, all available intensity estimates (i.e., Dvorak, AMSU, Quickscat, Aircraft (flight-level, MSLP) etc.)AMSU, Quickscat, Aircraft (flight-level, MSLP) etc.)
Measurements for VerificationMeasurements for Verification– All intensitiesAll intensities1.1. Mean Absolute Errors (MAE)Mean Absolute Errors (MAE)2.2. Percent Reduction in Variance (PRV)Percent Reduction in Variance (PRV)
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Long-Term Operational Long-Term Operational Intensity Verification (MAE)Intensity Verification (MAE)
N. Atlantic
5
10
15
20
25
30
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Year
Mea
n A
bso
lute
Err
or
(kt)
24
48
72
E. Pacific
5
10
15
20
25
30
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Year
Mea
n A
bso
lute
Err
or
(kt)
24
48
72
All intensities
Forecasts fromRSMC, Miami(NOAA/TPC)
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Long-Term Operational Long-Term Operational Intensity Verification (MAE)Intensity Verification (MAE)
Southern Hemisphere
5
10
15
20
25
30
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Year
Mea
n A
bso
lute
Err
or
(kt)
24
48
W. Pacific
5
10
15
20
25
30
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Year
Mea
n A
bso
lute
Err
or
(kt)
24
48
72
All intensities
Forecasts fromUSA/JTWC
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Intensity Verification other Intensity Verification other RSMCsRSMCs
Range 0h 12h 24h 48h 72h
Average error (kt)
3 6 9 14 16
RMSE (kt) 4 6 9 12 13
Bias (kt) -1 -1 -2 -4 -2
Skill against persistence
6% 31% 43% 50%
Sample (number of forecasts verified)
310
303
291
255
213
24-h 48-h 72-h
2004 5.1 (9.9) 7.1 (13.8) 8.1 (15.8)
2003 4.9 (9.5) 6.5 (12.7) 7.6 (14.8)
2002 5.0 (9.7) 7.0 (13.6) N/A
2001 5.2 (10.1) 6.9 (13.4) N/A
2000 5.9 (11.5) N/A N/A
Tropical Storm Intensity
2004-2005RSMC, La Reunion
2000-2004RSMC, Tokyom/s & (kt)
Taken from annual reports
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Trends in MAETrends in MAE
24-h 48-h 72-h
ATL (1986-2005)
-0.8 -1.0 -1.4
EPAC (1990-2005)
0.0 -0.8 -1.9
SHEM (1991-2005)
2.0 2.8 N/A
WPAC (1986-2005)
-0.2 -0.6 -1.1
kt per decade
Significance is marginal (70%) using annual number of degrees of freedom
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Long-Term Operational Long-Term Operational Intensity Verification (PRV)Intensity Verification (PRV)
N
n
N
nn
oo
poPRV
1
2
1
2
)(
)(0.1100
Where o is the observed intensity and p is the predicted intensity and the overbar represents a mean value.
• Can be negative if the numerator (forecast error variance) is larger than the denominator (climatological variance)•This methodology penalizes forecasts methodologies for having non-random errors (e.g., bias)
Variance of the forecast errors
Variance climatological errors
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Long-Term Operational Long-Term Operational Intensity Verification (PRV)Intensity Verification (PRV)
24-h Variance Explained
-60%
-40%
-20%
0%
20%
40%
60%
80%
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Year
Per
cen
t R
edu
ctio
n i
n V
aria
nce
WPAC
ATL
EPAC
SHEM
48-h Variance Explained
-60%
-40%
-20%
0%
20%
40%
60%
80%
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Year
Per
cen
t R
edu
ctio
n i
n V
aria
nce
WPAC
ATL
EPAC
SHEM
All intensities
Forecasts fromRSMC, Miami(NOAA/TPC) in EP and ATL
All intensities
Forecasts fromUSA/JTWC in WP and SH
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Long-Term Operational Long-Term Operational Intensity Verification (PRV)Intensity Verification (PRV)
72-h Variance Explained
-60%
-40%
-20%
0%
20%
40%
60%
80%
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Year
Per
cen
t R
edu
ctio
n i
n V
aria
nce
WPAC
ATL
EPAC
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Trends in PRVTrends in PRV
24-h 48-h 72-h
ATL (1986-2005) 14.7 22.3 41.0
EPAC (1990-2005) 1.5 1.4 3.3
SHEM (1991-2005) 2.0 2.8 N/A
WPAC (1986-2005) 3.2 3.0 3.6
Percent per decade
Significant (95%) in the Atlantic, marginally significant (70%) in the other basins when based on annual number of degrees of freedom
Intensity Guidance MethodsIntensity Guidance Methods Atlantic and East Pacific
SHIFOR (1988-present) Statistical Hurricane Intensity FORecast, which uses simple climatology and persistence parameters
SHIPS (1991-present, ATLC) Statistical Hurricane Intensity Prediction Scheme, (1996-present, EPAC) which uses climatology, persistence and real-
time atmospheric and oceanic parametersGFDL (1995-present) Operational version of the Geophysical Fluid Dynamics
Laboratory hurricane modelGFDN (2001-present) GFDL model initialized from Navy global model fieldsSHIFOR5 (2001-present) Updated version of SHIFOR with 5 day forecasts
West PacificCLIM (1985-present) Climatological analog modelSTIFOR (1991-present) Statistical Typhoon FORecast Model, similar to SHIFORGFDN (1995-present) GFDL model initialized from Navy global model fieldsAFW (2000-present) MM5 mesoscale model adapted to typhoon forecasts JTYM (2001-present) Japanese Meteorological Agency limited area typhoon modelST5D (2002-present) Updated STIFOR model and extended to 5 daysSTIPS (2003-present) Statistical Typhoon Intensity Prediction Scheme, similar to
SHIPSST10 (2005-present) Ensemble version of STIPS
Southern HemisphereCLIM(2000- present) Climatological analog.GFDN (2000- present) GFDL model initialized from Navy global model fieldsTC-LAPS (2001-present) BOM limited area prediction systemST5D (2004 –present) 5-day Climatology and persistenceSTIPS(2005 – present) STIPS SH (experimental run at NRLM)ST10 (2005 – present) STIPS SH ensemble (experimental, run at NRLM)
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Comparison with Best 48-hr Comparison with Best 48-hr Intensity Guidance Intensity Guidance
Atlantic
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
60%
70%
80%
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Year
% R
edu
ctio
n i
n V
aria
nce
RMTC, Miami
Guidance
East Pacific
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
60%
70%
80%
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Year
% R
edu
ctio
n i
n V
aria
nce
RMTC, Miami
Guidance
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Comparison with Best 48-hr Comparison with Best 48-hr Intensity GuidanceIntensity Guidance
Southern Hemisphere
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
60%
70%
80%
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Year
% R
edu
ctio
n i
n V
aria
nce
JTWC
Guidance
West Pacific
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
60%
70%
80%
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Year
% R
edu
ctio
n i
n V
aria
nce
JTWC
Guidance
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Best Guidance (Atlantic, E. Best Guidance (Atlantic, E. Pacific and W. Pacific)Pacific and W. Pacific)
Atlantic East Pacific West Pacific1991 SHIPS SHIFOR CLIM1992 SHIFOR SHIFOR CLIM1993 SHIFOR SHIFOR CLIM1994 SHIFOR SHIFOR CLIM1995 SHIFOR SHIFOR CLIM1996 SHIPS SHIFOR GFDN1997 GFDL SHIPS STIFOR1998 SHIPS SHIPS STIFOR1999 SHIPS SHIPS STIFOR2000 SHIPS SHIFOR STIFOR2001 SHIPS SHIPS ST5D2002 SHIPS GFDL ST5D2003 SHIPS SHIFOR STIPS2004 SHIPS GFDL STIPS2005 SHIPS SHIPS ST102006-preliminary2006-preliminary GFDLGFDL SHIPS/ICONSHIPS/ICON ST10ST10
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Summary: Intensity Summary: Intensity ForecastingForecasting There is evidence that forecasts, in general, There is evidence that forecasts, in general,
are improving very slowlyare improving very slowly Operational forecast improvements are being Operational forecast improvements are being
driven by improvements in guidance driven by improvements in guidance methodsmethods
Statistical-dynamical and regional/specialized Statistical-dynamical and regional/specialized NWP guidance are most skillfulNWP guidance are most skillful
Global models do not have skill and have Global models do not have skill and have larger errors than climatology and larger errors than climatology and persistence based forecastspersistence based forecasts
Consensus methods created from skillful Consensus methods created from skillful guidance have been demonstrated to guidance have been demonstrated to produce better forecasts than single methods produce better forecasts than single methods
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Wind Radii verificationWind Radii verification
Caveats:Caveats: Post-season reanalyzed estimates of R34 Post-season reanalyzed estimates of R34
used for verificationused for verification– These make use of the best available data These make use of the best available data
(variable)(variable) QuickScat, AMSU, SSMIQuickScat, AMSU, SSMI Flight-level windsFlight-level winds SFMRSFMR Ships/buoysShips/buoys
One year, Atlantic onlyOne year, Atlantic onlyMethods:Methods: MAE in n. miMAE in n. mi Hit & False alarm rates.Hit & False alarm rates.
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Wind Radii Wind Radii Guidance/ForecastsGuidance/Forecasts
OFCL – Official RSMC, Miami forecastOFCL – Official RSMC, Miami forecast AVNI – NOAA Global Forecast SystemAVNI – NOAA Global Forecast System GFTI – NOAA/GFDL model forecastsGFTI – NOAA/GFDL model forecasts MRCL – Multiple linear regression MRCL – Multiple linear regression
CLIPER Model, 3-daysCLIPER Model, 3-days DRCL – Statistical-Parametric CLIPER DRCL – Statistical-Parametric CLIPER
model, 5-daysmodel, 5-days
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Wind Radii Verification (R-34 Wind Radii Verification (R-34 kt)kt)
10
25
40
55
70
85
100
12-h 24-h 36-h 48-h 72-h
Forecast Hour
MA
E (
n m
i)
OFCL
AVNI
GFDT
MRCL
DRCL
1596 1456 1316 1156 872
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Wind Structure VerificationWind Structure Verification
0.2
0.4
0.6
0.8
1
12-h 24-h 36-h 48-h 72-h
Forecast Hour
Hit
Rat
e OFCL
AVNI
GFDT
MRCL
DRCL
0
0.2
0.4
0.6
0.8
1
12-h 24-h 36-h 48-h 72-h
Forecast Hour
Fal
se A
larm
Rat
e
OFCL
AVNI
GFDT
MRCL
DRCL
Probability of DetectionRadii of 34-kt winds
Probability of False DetectionRadii of 34-kt winds
MAE vs. False Alarm Trade off
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Influence of Intensity Influence of Intensity Forecast Errors on Wind Forecast Errors on Wind RadiiRadii
0
10
20
30
40
50
60
12-h 24-h 36-h 48-h 72-h 96-h 120-h
Forecast Hour
MA
E (
n m
i)
OFCL
DRCL
DRCC
Forecasts using Best Track Intensities
Forecasts usingForecast intensities
1832 1700 1544 1400 1104 860 668
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Influence of Intensity Influence of Intensity Forecast Errors on Wind Forecast Errors on Wind RadiiRadii
0
0.2
0.4
0.6
0.8
1
12-h 24-h 36-h 48-h 72-h 96-h 120-h
Forecast Hour
Fal
se A
larm
Rat
e
OFCLDRCLDRCC
0.5
0.6
0.7
0.8
0.9
1
12-h 24-h 36-h 48-h 72-h 96-h 120-h
Forecast Hour
Hit
Rat
e
OFCL
DRCL
DRCC
False Alarm Decrease
Hit RateIncrease
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Summary: Wind Radii Summary: Wind Radii ForecastingForecasting There are very few models There are very few models
– poor understanding of processespoor understanding of processes– poor developmental datasetspoor developmental datasets
There are no skillful modelsThere are no skillful models– No higher level statistical modelsNo higher level statistical models– NWP does not initialize the vortex properly, have NWP does not initialize the vortex properly, have
sufficient resolution (global)sufficient resolution (global) More accurate intensity forecasts will More accurate intensity forecasts will
improve wind radii forecastsimprove wind radii forecasts There is a trade off between false alarm rates There is a trade off between false alarm rates
and MAE in current forecasts schemes (i.e. and MAE in current forecasts schemes (i.e. symmetric forecasts produce smaller MAE symmetric forecasts produce smaller MAE statistics)statistics)
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Summary TC Structure Summary TC Structure ForecastingForecastingOverall Forecasting of TC structure change is rather poor.Overall Forecasting of TC structure change is rather poor. The forecasting process is still subjectiveThe forecasting process is still subjective
– Best intensity forecasts are used as a baseline that Best intensity forecasts are used as a baseline that is modified by the forecasteris modified by the forecaster
– Skillful wind radii guidance is unavailableSkillful wind radii guidance is unavailable Intensity forecasts are improving very slowly and are Intensity forecasts are improving very slowly and are
being driven by improved guidancebeing driven by improved guidance Wind radii forecasting is in its infancy and is hindered Wind radii forecasting is in its infancy and is hindered
byby– Poor developmental datasetsPoor developmental datasets– Poor physical understandingPoor physical understanding– Has not been an operational priorityHas not been an operational priority– Only recently have such forecasts been verified. Only recently have such forecasts been verified.
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Future/Current Research Future/Current Research and Developmentand Development 1.1. Making better use of existing technology and Making better use of existing technology and
datasets. Examples include :datasets. Examples include :– Diagnostic studies to understand tropical cyclone wind Diagnostic studies to understand tropical cyclone wind
field growthfield growth– Development of models to predict structure change Development of models to predict structure change
using environmental and storm conditionsusing environmental and storm conditions– Model output statistics to predict wind radiiModel output statistics to predict wind radii– Indices to predict rapid intensification, annular Indices to predict rapid intensification, annular
hurricanes, secondary eyewall formation, etc.hurricanes, secondary eyewall formation, etc.– Forecast techniques to improve short-term intensity Forecast techniques to improve short-term intensity
forecasts that leverage existing and longstanding forecasts that leverage existing and longstanding satellite technologiessatellite technologies
– Probabilistic models that account for track, intensity Probabilistic models that account for track, intensity and wind radii error distributionsand wind radii error distributions
– … … Items for discussionItems for discussion
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Future/Current Research Future/Current Research and Developmentand Development2.2. Development of new technology.Development of new technology. Next Generation Hurricane Forecast Model, the Next Generation Hurricane Forecast Model, the
Hurricane – Weather Research and Forecasting (H-Hurricane – Weather Research and Forecasting (H-WRF) Model is being tested/developedWRF) Model is being tested/developed
– Physical initialization using a 3D-var framework that Physical initialization using a 3D-var framework that can make use of Dopplar radar information (WR-88D, can make use of Dopplar radar information (WR-88D, airborne) and new instrument packages on the G-IV, airborne) and new instrument packages on the G-IV, and satellite data. and satellite data.
– Coupling with waves via the Wavewatch III wave modelCoupling with waves via the Wavewatch III wave model– Parameterizations developed using the recent results Parameterizations developed using the recent results
of the CBLAST experimentof the CBLAST experiment New instrumentation on operational aircraft New instrumentation on operational aircraft
– like the SFMR.like the SFMR.– Others etc. Discussion?Others etc. Discussion?
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RecommendationsRecommendations
Continued development of consensus methods to Continued development of consensus methods to improve intensity forecasts.improve intensity forecasts.
Use of model output statistics, particularly in wind radii Use of model output statistics, particularly in wind radii prediction.prediction.
Greater effort toward high resolution NWP that has Greater effort toward high resolution NWP that has physical initialization and advanced data assimilation physical initialization and advanced data assimilation capabilitiescapabilities
Effort to make the newest technology/instrumentation Effort to make the newest technology/instrumentation and resulting observations available to real-time and resulting observations available to real-time operational centers and tropical cyclone researchers.operational centers and tropical cyclone researchers.
Additional recommendation based on knowledge gained Additional recommendation based on knowledge gained during the process of writing the topic summaryduring the process of writing the topic summary
There is a need for more operational scatterometry There is a need for more operational scatterometry and similar active remote sensing instrumentation to and similar active remote sensing instrumentation to detect tropical cyclone wind fields. None is currently detect tropical cyclone wind fields. None is currently planned.planned.
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