1 / 28

Multi-Year Examination of Dense Fog at Burlington International Airport

This research study aims to understand the long-term occurrence of dense fog at Burlington International Airport (BTV) and improve short-term forecasts. The study analyzes hourly weather data from January 1979 to December 2003 and identifies six types of fog events. Preliminary findings show that radiation fog, fog produced by precipitation, and fog resulting from the lowering of cloud base comprise the majority of fog events. Wind rose data reveals distinct directional trends for different fog types. The study also analyzes synoptic patterns and their influence on fog formation.

octavious
Download Presentation

Multi-Year Examination of Dense Fog at Burlington International Airport

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Multi-Year Examination of Dense Fog at Burlington International Airport John M. Goff NOAA/NWS Burlington, VT

  2. Emphasis of Research • To examine the long-term occurrence of dense fog at Burlington International Airport (BTV) in an effort to understand synoptic and mesoscale signals that favor its formation. • To improve short term low instrument flight rule (LIFR) forecasts at BTV.

  3. Data Specifics • Hourly weather data at BTV from January 1979 to December 2003 (24 yrs.) • Data coincidental with NCEP North American Regional Reanalysis (NARR) data • Criteria for dense fog occurrence: visibility  0.5 km

  4. Data Specifics Contd. • Fog classification similar to Tardy 2000. • Six types used, including: - radiation fog (type RF) (wind speed  5 knots under mainly clear skies at fog onset) - advection fog (type AF) (wind speed must be > 5 knots with sudden drop in vis.) - fog produced by precipitation (type PF) (precipitation must fall within 3 hours of fog onset) - fog resulting from the lowering of cloud base (type LCB) - fog resulting from the evaporation of surface moisture at sunrise (type EF) - indeterminate (type IF)

  5. Preliminary Findings • Fog types RF, PF, and LCB comprise 94% of all events • About 5 RF and 9 PF or LCB events per year

  6. Preliminary Findings Contd. • Frequency distribution plots of dominant fog types: - type RF maximum in late summer/early fall - combined types PF/LCB maximum in cold season (Nov – Mar)

  7. Wind Rose Data • Wind rose plots were compiled for all type RF, and combined type PF/LCB events • Distinct directional trends in the data are evident in the plots - Type RF events – light northeast to east flow - Combined type PF/LCB events – variable wind speeds predominately from the north or northwest

  8. Wind Rose Data Type RF Events • Type RF (34% of all events ) – drainage wind from northeast to east - Strong mesoscale signal that radiation fog drifts across runway from Winooski River valley to immediate northeast and east - Few events with onset wind directions outside of the 045 to 135 sector

  9. Wind Rose Plot for all RF Events

  10. BTV ASOS Site Location and Surrounding Topography

  11. Wind Rose Data for Combined Type PF/LCB Events • Combined types PF/LCB (60% of all events) – variable wind speeds predominantly from the north and northwest • Strong north/northwest signal supports prior evidence that this flow regime enhances low level mesoscale convergence in the northern Champlain Valley

  12. Wind Rose Plot for All Type PF and LCB Events

  13. Mesoscale Convergent Signature in Northern Champlain Valley

  14. NARR Data Analysis • Mean sea level pressure plots compiled across the eastern U.S. at time of onset of each type RF, PF and LCB event • Several synoptic patterns identified favoring each dominant fog type

  15. NARR Analysis of Type RF Events • Anticyclone building into northern Vermont from the north or northwest • Anticyclone building into northern Vermont from the west or southwest • Anomalous/indeterminate events • Many events appear to be preceded by a weak frontal passage some 6 to 18 hours in advance

  16. Frequency Distribution of Identified Synoptic Patterns

  17. NARR Analysis of Type RF Events Contd. • Anticyclone building into northern Vermont from north or northwest

  18. NARR Analysis of Type RF Events Contd. • Anticyclone building into northern Vermont from west or southwest

  19. NARR Analysis of Combined Type PF/LCB Events • Cold or occluded frontal passage • Approach of warm front • Convergent northerly flow north or west of surface cyclone

  20. Frequency Distribution of Identified Synoptic Patterns

  21. NARR Analysis of Combined Type PF/LCB Events Contd. • Cold or occluded frontal passage

  22. NARR Analysis of Combined Type PF/LCB Events Contd. • Approach or passage of warm front

  23. NARR Analysis of Combined Type PF/LCB Events Contd. • Convergent northwest flow on west to northwest side of surface cyclone

  24. Other Findings • Did antecedent precipitation affect the likelihood of RF events? - most likely no

  25. Future Initiatives • Focus on long-duration RF, PF and LCB events per importance to aviation • Composite analysis of long-duration events using NARR data (McGill U.)

  26. Limitations • Differences in hourly data (pre-ASOS vs. human observer) • Study addresses low visibility/dense fog events only. Do signatures identified pertain to all IFR events?

  27. Conclusions • 24 years of dense fog climatology examined • Majority of events were either radiation fog, or fog resulting from precipitation or lowering of cloud base • Clear directional trends in wind data • Several synoptic mean sea level pressure patterns favor the dominant events

  28. Acknowledgements • The author would like to thank Paul Sisson (SOO WFO BTV) for overall guidance and assistance with this project • Thanks is also given to Eyad Atallah ofMcGill University for work on the composite analysis, and to Conor Lahiff of WFO BTV for help with the wind rose plotting software

More Related