1. An Automated Synoptic Typing System Using Archived And Real-time NWP Model Output Robert Dahni Meteorological Systems Central Operations and Systems Branch Bureau of Meteorology Abstract and paper prepared for 19 th International Conference on Interactive Information and Processing Systems for Meteorology, Oceanography, and Hydrology, Amer. Meteor. Soc., Long Beach, California, February, 2003. BMRC Seminar 23 October 2002

2. Overview Background (MENTOR, GASM)Background (MENTOR, GASM) Synoptic Classification (manual, correlation- based map-pattern and eigenvector-based classifications)Synoptic Classification (manual, correlation- based map-pattern and eigenvector-based classifications) Synoptic TyperSynoptic Typer Tools (Map Browser, IDL)Tools (Map Browser, IDL) Examples (weather variables associated with synoptic types)Examples (weather variables associated with synoptic types) Future DevelopmentsFuture Developments ReferencesReferences

3. Background MENTOR (Ryan et al, 2003) “Mentor is a web-based system which allows forecasters to record in real-time their assessments of likely meteorological “problems of the day”, forecast difficulty and their estimates of the value of objective guidance … entries accumulate in the Mentor database, and can be quickly analysed and searched by forecasters to assist in subsequent forecasting decisions. Automatic synoptic type classification is an important element of the system.”MENTOR (Ryan et al, 2003) “Mentor is a web-based system which allows forecasters to record in real-time their assessments of likely meteorological “problems of the day”, forecast difficulty and their estimates of the value of objective guidance … entries accumulate in the Mentor database, and can be quickly analysed and searched by forecasters to assist in subsequent forecasting decisions. Automatic synoptic type classification is an important element of the system.”

4. Background GASM (Dahni and Stern, 1995) “The analogue technique achieved … operational status through the Australia-wide implementation of the Generalised Analogue Statistics Model … and the Forecast Guidance System component of the Australian Integrated Forecast System (Kelly and Gigliotti, 1997).” Requires major re-development: - replace analogue retrieval with synoptic classification; - associate weather variables with synoptic types; and - generate OBJECTIVE weather forecast guidance.GASM (Dahni and Stern, 1995) “The analogue technique achieved … operational status through the Australia-wide implementation of the Generalised Analogue Statistics Model … and the Forecast Guidance System component of the Australian Integrated Forecast System (Kelly and Gigliotti, 1997).” Requires major re-development: - replace analogue retrieval with synoptic classification; - associate weather variables with synoptic types; and - generate OBJECTIVE weather forecast guidance.

5. Background Select circulation data and environmental data Classificationtype? Circulation-to-EnvironmentClassification Environment-to-CirculationClassification Compile classification first; relate to environment later Classify circulation data by environment-based criteria The two fundamental approaches to synoptic classification (Yarnal, 1993)

6. Synoptic Classification Jasper and Stern (1983) correlation-based; seasonal; sampling; 22 years; June 1957 - May 1979; 38 synoptic types; SE AustraliaJasper and Stern (1983) correlation-based; seasonal; sampling; 22 years; June 1957 - May 1979; 38 synoptic types; SE Australia Treloar and Stern (1993) manual; direction, strength and curvature of the surface flow; 1957-2002; 50 synoptic types; SE AustraliaTreloar and Stern (1993) manual; direction, strength and curvature of the surface flow; 1957-2002; 50 synoptic types; SE Australia Dahni and Ebert (1998) eigenvector-based; principal components and cluster analysis; 1970-1993; first 5 principal components; 20 clusters; MelbourneDahni and Ebert (1998) eigenvector-based; principal components and cluster analysis; 1970-1993; first 5 principal components; 20 clusters; Melbourne

7. Manual 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)

8. Correlation-based map-pattern classification Jasper and Stern (1983) Updated using NCEP grids (1948-2001) 2.5 o resolution 00UTC MSLP analyses (SE Australia) Correlation thresholds (0.7, 0.75, 0.8, 0.85, 0.9, 0.95) Number of keydays (<100) Number of synoptic types (10, 15, 20, …, 90, 95) Minimum group size (1%) Resources IDL 5.5 (gale) NCEPgrids correlate statistics correlationmatrix keydays synoptic types (csv) synoptictypes(binary) catalog derive analyse Daily data (years) Disk or RAM (Mb) CPU (hours) 221305 5067028

9. Eigenvector-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 Principal components and cluster analysis techniquesPrincipal components and cluster analysis techniques METANAL gridsMETANAL grids 00UTC MSLP analyses00UTC MSLP analyses 1.5 o resolution1.5 o resolution 1970-19931970-1993 Specific locations (regions) around AustraliaSpecific locations (regions) around Australia

10. Eigenvector-based classification

12. Synoptic Typer Interactive (GUI- based) mode for development Developed on PC (Windows) using IDL 5.5 Cross-platform (Windows, Linux, UNIX) application Non-interactive (batch) mode for operational implementation (UNIX) Graphical User Interface

13. Synoptic Typer Existing C ++ module used to extract NWP grids from real- time NEONS/ORACLE databaseExisting C ++ module used to extract NWP grids from real- time NEONS/ORACLE database Automatic classification of real-time NWP model output (e.g. GASP, EC and LAPS)Automatic classification of real-time NWP model output (e.g. GASP, EC and LAPS) Real-time synoptic type guidance stored in Forecast DatabaseReal-time synoptic type guidance stored in Forecast Database Automatic synoptic type for MENTOR systemAutomatic synoptic type for MENTOR system

14. Synoptic Typer STNNUM, FCST_TIME, SYNT 086071, 2002092600, 7 086071, 2002092700, 7 086071, 2002092800, 18 066062, 2002092600, 2 066062, 2002092700, 13 066062, 2002092800, 12 040842, 2002092600, 1 040842, 2002092700, 3 040842, 2002092800, 13 014015, 2002092600, 3 014015, 2002092700, 11 014015, 2002092800, 14 009225, 2002092600, 8 009225, 2002092700, 8 009225, 2002092800, 12 023090, 2002092600, 7 023090, 2002092700, 7 023090, 2002092800, 5 094010, 2002092600, 11 094010, 2002092700, 5 094010, 2002092800, 16

15. Synoptic Typer NCEP grids (MSLP, 850 hPa temperature, 1000 and 500 hPa geopotential height and wind, precipitable water, OLR); 00, 06, 12 and 18UTC analyses; 2.5 o resolution; 1948-2001 METANAL grids (MSLP, 850 hPa temperature, 1000, 850, 700, 500, 300 and 250 hPa geopotential height, 500 hPa wind speed); 00 and 12UTC analyses; 1.5 o resolution; 1970-1993

16. Tools Synoptic TyperSynoptic Typer Map BrowserMap Browser MapGridMapGrid Map AnnotatorMap Annotator Correlation-based map-pattern classificationCorrelation-based map-pattern classification Associating weather variables to synoptic typesAssociating weather variables to synoptic types IDL 5.5IDL 5.5 Managed desktopManaged desktop UNIX serverUNIX server

17. Map Browser Graphical User Interface InteractiveInteractive METANAL gridsMETANAL grids NCEP gridsNCEP grids Vector, Barb or StreamlineVector, Barb or Streamline Derived fieldsDerived fields Tropical CyclonesTropical Cyclones Synoptic TypesSynoptic Types Mean fieldsMean fields Interpolate dataInterpolate data Batch modeBatch mode

18. MapGrid InteractiveInteractive McIDAS gridsMcIDAS grids NetCDF filesNetCDF files Sigma levelSigma level Pressure levelPressure level Plot, Contour or FillPlot, Contour or Fill Vector, Barb or StreamlineVector, Barb or Streamline Overlay fieldsOverlay fields Difference fieldsDifference fields Derived fieldsDerived fields Batch modeBatch mode Graphical User Interface

19. Map Annotator Graphical User Interface Meteorological Annotation ToolMeteorological Annotation Tool InteractiveInteractive Open/Save ImagesOpen/Save Images AnnotateAnnotate FrontsFronts TroughsTroughs RidgesRidges Weather SymbolsWeather Symbols CloudsClouds Significant WeatherSignificant Weather etcetc

20. Example (manual classification and Launceston Airport rainfall) Treloar and Stern (1993) Synoptic Types=50 NCEP grids, Years=1948-2001 Days=19724 Rain Days > 20 mm = 280 Synoptic Type Freq(%) Rain Days > 20 mm (%)398.929.6 375.016.1 414.012.5

21. Example (correlation-based map-pattern classification and Melbourne rainfall) Synoptic Type Freq (%) Rain Days > 30 mm (%)142.013.9 191.710.4 541.110.4 551.013.0 NCEP grids Years=1952-2001Days=18623Threshold=0.90 Synoptic Types=55 Rain Days > 30 mm = 115

22. Future Developments MOF Model Archive LAPS grids 0.75 o resolution 00 and 12UTC analyses and prognoses (+12, +24, +36 and +48) July 1996-2002 Synoptic Types: operational implementation, multiple input fields, correlate sequence of days, extension to other regions …Synoptic Types: operational implementation, multiple input fields, correlate sequence of days, extension to other regions … Associate Weather Variables with Synoptic Types: fog events, significant rainfall, forecast errors (verification) …Associate Weather Variables with Synoptic Types: fog events, significant rainfall, forecast errors (verification) … Replacement of GASM …Replacement of GASM …

23. References Dahni, R. R. and Ebert, E. E. 1998. Automated objective synoptic typing to characterize errors in NWP model QPFs. 12 th Conference on Numerical Weather Prediction, Amer. Meteor. Soc., Phoenix, Arizona. Dahni, R. R. and Stern, H. 1995. The development of a generalised UNIX version of the Victorian Regional Office’s operational analogue statistics model. BMRC Research Report 47, Bureau of Meteorology, Melbourne, Australia. Jasper, J. D. and Stern, H. 1983. Objective classification of synoptic pressure patterns over southeast Australia. Proceedings 2 nd International Meeting on Statistical Climatology, Lisboa, Portugal. Kelly, J. and Gigliotti, P. 1997. The Australian Integrated Forecast System (AIFS): Overview and current status. 13 th International Conference on Interactive Information and Processing Systems for Meteorology, Oceanography, and Hydrology, Amer. Meteor. Soc., Long Beach, California. Ryan, C. J., Jha, A. and Joshi, S. 2003. MENTOR – A performance support system for forecasters. 19 th International Conference on Interactive Information and Processing Systems for Meteorology, Oceanography, and Hydrology, Amer. Meteor. Soc., Long Beach, California.

24. References Stern, H. 2003. Progress on a knowledge-based internet weather forecasting system. 19 th International Conference on Interactive Information and Processing Systems for Meteorology, Oceanography, and Hydrology, Amer. Meteor. Soc., Long Beach, California. Treloar, A. B. A. and Stern, H. 1993. A climatology and synoptic classification of Victorian severe thunderstorms. 4 th International Conference on Southern Hemisphere Meteorology and Oceanography, Amer. Meteor. Soc., Hobart, Australia. Yarnal, B. 1993. Synoptic Climatology in Environmental Analysis, Belhaven Press, London. Abstract and Extended Abstract (PDF) for 19 th International Conference on Interactive Information and Processing Systems for Meteorology, Oceanography, and Hydrology, Amer. Meteor. Soc., Long Beach, California, February, 2003. http://ams.confex.com/ams/annual2003/19IIPS/abstracts/55276.htmhttp://ams.confex.com/ams/pdfview.cgi?username=55276