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Strategies to Improve Radiation Fog Forecasting at Elmira, NY (KELM)

Strategies to Improve Radiation Fog Forecasting at Elmira, NY (KELM) . Robert Mundschenk, Michael Evans, Michael L. Jurewicz, Sr., and Ron Murphy – WFO Binghamton, NY Aviation Sub-Regional Workshop September 16, 2008. Outline. Motivation Methodology Study Results A New Forecaster Tool

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Strategies to Improve Radiation Fog Forecasting at Elmira, NY (KELM)

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  1. Strategies to Improve Radiation Fog Forecasting at Elmira, NY (KELM) Robert Mundschenk, Michael Evans, Michael L. Jurewicz, Sr., and Ron Murphy – WFO Binghamton, NY Aviation Sub-Regional Workshop September 16, 2008

  2. Outline • Motivation • Methodology • Study Results • A New Forecaster Tool • Summary

  3. Motivation for Research • KELM experiences radiation fog frequently with resultant IFR / LIFR / VLIFR conditions • Historically, this has been a challenging site for aviation forecasters • Unexpected dense fog development • Overly pessimistic forecasts (lighter fog than expected)

  4. Favorable Location for Fog KELM Chemung River Valley

  5. Methodology • Fog Season - April 1st through November 1st • Compile a list of dates with clear skies and light winds • 3 Full Seasons worth of data (2001-2003) • Minimum observed visibilities and time durations of IFR conditions were tabulated • Many parameters thought to be pertinent to fog formation were tabulated • NAM Boundary layer wind, lapse rate and RH forecasts • Observed minimum temperatures • Observed Cross-over temperature • Observed Chemung River temperatures • Amount of recent rainfall, if any • Time of year (month)

  6. Results • Most reliable indicators of low visibilities (dense fog): • Light wind speeds (< 13 kts) around 950 mb (700 feet AGL) from NAM BUFKIT soundings • Overnight low temperature colder than the cross-over temperature • Large differences between the observed Chemung River temperatures and the minimum air temperature (air temperature at least 20 degree F colder than the river temperature). • Heavy rain during the period 4 to 10 days prior to the event in question • Model low-level RH and lapse rate correlated weakly with fog occurrence

  7. Composite MSLP for Dense Fog Cases at 0600 UTC

  8. Composite MSLP for Lighter Fog Cases (BR) at 0600 UTC

  9. Composite 850 mb for Dense Fog Cases at 0600 UTC

  10. Composite 850 mb for Lighter Fog Cases (BR) at 0600 UTC

  11. A New Forecaster Tool • Based on study results, a “pattern recognition” tool was developed • Data inputs from BUFKIT soundings, observed data and forecaster input • Help forecasters better differentiate between favorable and unfavorable nights for fog formation • Provides links to past events that most closely match the set of expected conditions

  12. A New Forecaster Tool

  13. A New Forecaster Tool

  14. Final Thoughts • This tool allows forecasters to use pattern recognition to forecast fog • The tool promotes a probabilistic approach to fog forecasting • Similar tools can be developed for other TAF sites • Verification will be done next summer • We still need to know more about what parameters can help us to forecast fog

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