An Automated Synoptic An Automated Synoptic Typing System Using Typing System Using Archived And Real-time Archived And Real-time NWP Model OutputNWP Model Output

Robert DahniRobert Dahni

Meteorological SystemsMeteorological Systems

Central Operations and Systems BranchCentral Operations and Systems Branch

Bureau of MeteorologyBureau of Meteorology

1919thth International Conference on Interactive Information and Processing International Conference on Interactive Information and Processing Systems for Meteorology, Oceanography, and Hydrology, Amer. Meteor. Soc., Systems for Meteorology, Oceanography, and Hydrology, Amer. Meteor. Soc., Long Beach, California, February, 2003.Long Beach, California, February, 2003.

11 February 200311 February 2003

OverviewOverview

• BackgroundBackgroundMENTORMENTOR

• Synoptic ClassificationSynoptic Classificationmanualmanualcorrelation-based map-patterncorrelation-based map-patterneigenvector-basedeigenvector-based

• ToolsToolsSynoptic TyperSynoptic TyperMap BrowserMap Browser

• ExamplesExamplesweather variables associated with synoptic typesweather variables associated with synoptic types

• Future DevelopmentsFuture Developments

BackgroundBackground

• MENTOR (Ryan et al, 2003)MENTOR (Ryan et al, 2003)

“Mentor is a web-based system which allows “Mentor is a web-based system which allows forecasters to record in real-time their assessments forecasters to record in real-time their assessments of likely meteorological “problems of the day”, of likely meteorological “problems of the day”, forecast difficulty and their estimates of the value of forecast difficulty and their estimates of the value of objective guidance … entries accumulate in the objective guidance … entries accumulate in the Mentor database, and can be quickly analysed and Mentor database, and can be quickly analysed and searched by forecasters to assist in subsequent searched by forecasters to assist in subsequent forecasting decisions. Automatic synoptic type forecasting decisions. Automatic synoptic type classification is an important element of the system.”classification is an important element of the system.”

Manual classificationManual classification

• Treloar and Stern (1993)Treloar and Stern (1993)• Direction, strength and curvature of the surface flowDirection, strength and curvature of the surface flow• 50 synoptic types50 synoptic types• 0900 hours EST MSLP station data (1957-2002)0900 hours EST MSLP station data (1957-2002)• SE AustraliaSE Australia• Spreadsheet (Excel) computationSpreadsheet (Excel) computation• Updated using NCEP grids (1948-2001)Updated using NCEP grids (1948-2001)• Interpolated to station locationsInterpolated to station locations• Updated synoptic types (Stern, 2003)Updated synoptic types (Stern, 2003)

Correlation-basedCorrelation-basedmap-pattern classificationmap-pattern classification

• Jasper and Stern (1983); seasonal; Jasper and Stern (1983); seasonal; sampling; 22 years;38 synoptic typessampling; 22 years;38 synoptic types

• Updated using NCEP grids (1948-2001)Updated using NCEP grids (1948-2001)• 2.52.5oo resolution (SE Australia) resolution (SE Australia)• 00UTC MSLP analyses00UTC MSLP analyses• Correlation thresholdsCorrelation thresholds

(0.7, 0.75, 0.8, 0.85, 0.9, 0.95)(0.7, 0.75, 0.8, 0.85, 0.9, 0.95)• Number of keydays (<100)Number of keydays (<100)• Number of synoptic typesNumber of synoptic types

(10, 15, 20, …, 90, 95)(10, 15, 20, …, 90, 95)• Minimum group size (1%)Minimum group size (1%)• Resources IDL 5.5 and UNIX serverResources IDL 5.5 and UNIX server

NCEPNCEPgridsgrids

correlatecorrelate

statisticsstatistics

correlationcorrelationmatrixmatrix

keydayskeydays

synopticsynoptictypes (csv)types (csv)

synopticsynoptictypestypes

(binary)(binary)

catalogcatalog

derivederive

analyseanalyse

Daily data Daily data (years)(years)

Disk orDisk or

RAM (Mb)RAM (Mb)

CPU CPU (hours)(hours)

5454 780780 1515

Eigenvector-based classificationEigenvector-based classification

• Dahni and Ebert (1998) – automated objective synoptic typingDahni and Ebert (1998) – automated objective synoptic typing• Simple pattern recognition scheme with fields of MSLP as inputSimple pattern recognition scheme with fields of MSLP as input• METANAL; 00UTC MSLP analyses; 1.5METANAL; 00UTC MSLP analyses; 1.5oo resolution; 1970-1993 resolution; 1970-1993• Principal components and cluster analysis techniquesPrincipal components and cluster analysis techniques• First 5 principal components; 20 clusters; MelbourneFirst 5 principal components; 20 clusters; Melbourne

G rid P o int A na lys e s

G ra phic a l P lo ts

C lus te rs

C lu s te r A n alys is

D e rive d V a ria ble s

F e a ture sP rin c ip al C o m p o n e n t A n alys is

A no m a lie s

S e a s o n

S ub-G rid

PCs

D e pe nde nt D a ta

G rid P o int A na lys e s /P ro g no s e s

C la s s ific a tio n

C lus te rs

D e rive d V a ria ble s

F e a ture s

A no m a lie s

S e a s o n

S ub-G rid

Inde pe nde nt D a ta

P C s

Eigenvector-based classificationEigenvector-based classification

0

10

20

30

40

50

60

70

80

1 2 3 4 5 6 7 8 9 10

Principal Component

Exp

lain

ed V

aria

nce

(%

)

NCEP grids (NCEP grids (MSLP, 850 hPa MSLP, 850 hPa temperature, 1000 and 500 hPa temperature, 1000 and 500 hPa geopotential height and wind, geopotential height and wind, precipitable water, OLRprecipitable water, OLR););00, 06, 12 and 18UTC analyses; 00, 06, 12 and 18UTC analyses; 2.52.5oo resolution; 1948-2001 resolution; 1948-2001

Synoptic TyperSynoptic Typer

• Interactive Interactive (GUI-based) (GUI-based) mode for mode for developmentdevelopment

• Developed Developed on PC on PC (Windows) (Windows) using IDL 5.5using IDL 5.5

• Cross-Cross-platform platform (Windows, (Windows, Linux, UNIX) Linux, UNIX) applicationapplication

Graphical User InterfaceGraphical User Interface

Synoptic TyperSynoptic Typer

• Non-interactive (batch) mode for Non-interactive (batch) mode for operational implementation (UNIX)operational implementation (UNIX)

• Existing CExisting C++++ module used to extract module used to extract NWP grids from real-time the NWP grids from real-time the NEONS/ORACLE databaseNEONS/ORACLE database

• Automatic synoptic classification of Automatic synoptic classification of real-time NWP model output (e.g. real-time NWP model output (e.g. GASP, EC and LAPS) GASP, EC and LAPS)

• Real-time synoptic type guidance Real-time synoptic type guidance stored in the Forecast Databasestored in the Forecast Database

• Automatic synoptic type for the Automatic synoptic type for the MENTOR systemMENTOR system

STNNUM, FCST_TIME, SYNT 086071, 2002092600, 7086071, 2002092700, 7086071, 2002092800, 18066062, 2002092600, 2066062, 2002092700, 13066062, 2002092800, 12040842, 2002092600, 1040842, 2002092700, 3040842, 2002092800, 13014015, 2002092600, 3014015, 2002092700, 11014015, 2002092800, 14009225, 2002092600, 8009225, 2002092700, 8009225, 2002092800, 12023090, 2002092600, 7023090, 2002092700, 7023090, 2002092800, 5094010, 2002092600, 11094010, 2002092700, 5094010, 2002092800, 16

Map BrowserMap Browser

Graphical User InterfaceGraphical User Interface • InteractiveInteractive

• NCEP gridsNCEP grids

• Vector, Barb or Vector, Barb or StreamlineStreamline

• Derived fieldsDerived fields

• Tropical CyclonesTropical Cyclones

• Synoptic TypesSynoptic Types

• Mean fieldsMean fields

• Interpolate dataInterpolate data

• Weather variablesWeather variables

• Batch modeBatch mode

Example Example (manual classification and Melbourne (manual classification and Melbourne rainfall)rainfall)

Treloar and Stern (1993)Treloar and Stern (1993)Synoptic Types=50Synoptic Types=50

NCEP grids, Years=1948-2001NCEP grids, Years=1948-2001Days=19724Days=19724

Rain Days > 30 mm = 127Rain Days > 30 mm = 127

Synoptic Synoptic TypeType

FreqFreq

(%)(%)

Rain DaysRain Days> 30 mm (%)> 30 mm (%)

4141 4.04.0 18.118.1

4343 1.51.5 17.317.3

2727 3.43.4 15.015.0

Example Example (correlation-based map-pattern (correlation-based map-pattern classification and Melbourne rainfall)classification and Melbourne rainfall)

Synoptic Synoptic TypeType

FreqFreq(%)(%)

Rain DaysRain Days> 30 mm (%)> 30 mm (%)

4949 1.11.1 18.118.1

2121 2.12.1 11.811.8

2828 1.51.5 8.78.7

99 2.02.0 7.97.9

4646 1.31.3 7.97.9

NCEP gridsNCEP gridsYears=1948-2001Years=1948-2001

Days=19724Days=19724Threshold=0.90Threshold=0.90

Synoptic Types=50Synoptic Types=50Rain Days > 30 mm = 127Rain Days > 30 mm = 127

Example Example (correlation-based map-pattern (correlation-based map-pattern classification and Melbourne heat waves)classification and Melbourne heat waves)

Synoptic Synoptic TypeType

FreqFreq(%)(%)

Heat Heat Wave Wave

Days (%)Days (%)

1717 2.12.1 22.822.8

3434 1.61.6 12.512.5

88 2.02.0 10.310.3

44 3.43.4 9.69.6

NCEP gridsNCEP gridsYears=1948-2001Years=1948-2001

Days=19724Days=19724Threshold=0.90Threshold=0.90

Synoptic Types=50Synoptic Types=50Heat Wave Days = 136Heat Wave Days = 136

Future DevelopmentsFuture Developments

• Synoptic Types: operational implementation, multiple input fields, Synoptic Types: operational implementation, multiple input fields, correlate sequence of days, extension to other regions …correlate sequence of days, extension to other regions …

• Associate Weather Variables with Synoptic Types: significant Associate Weather Variables with Synoptic Types: significant rainfall, heat waves, fog events, forecast errors (verification) …rainfall, heat waves, fog events, forecast errors (verification) …

For further information go to the following web site:For further information go to the following web site:

http://www.bom.gov.au/inside/cosb/mss/projects/synoptictyper/http://www.bom.gov.au/inside/cosb/mss/projects/synoptictyper/