1.1 Applying Probabilistic Aviation Forecast Grids from the LAMP/HRRR Meld

Monday, 23 January 2017: 11:00 AM
Conference Center: Skagit 2 (Washington State Convention Center )
Adam D. Schnapp, NOAA/NWS/Meteorological Development Laboratory & KBRwyle, Silver Spring, MD; and B. Glahn and J. E. Ghirardelli

Handout (6.4 MB)

The Meteorological Development Laboratory (MDL) has developed enhanced gridded ceiling height and visibility forecast guidance by statistically post-processing the forecasts from the Localized Aviation MOS Program (LAMP) and High Resolution Rapid Refresh (HRRR) Model. The statistical blend of the forecasts is known as the LAMP/HRRR Meld and has shown improvement over existing guidance.  Probabilistic forecasts of multiple discrete ceiling height and visibility event thresholds were developed with the Regression Estimation of Event Probabilities technique, and deterministic forecasts were derived by applying thresholds to the probabilistic forecasts so that forecasts are unbiased and the skill is maximized.  The LAMP component incorporates large-scale, Global Forecast System (GFS) model-based MOS predictors and the most recent observations which are critical to short-term forecasts.  The convection-allowing HRRR model contributes rapidly-updated numerical weather prediction forecasts of ceiling height and visibility with high detail. The HRRR component is incorporated through a MOS technique developed from a time-lagged ensemble of HRRR forecasts.  The LAMP/HRRR Meld probabilistic forecast grids complement the deterministic forecast grids, enhance aviation weather forecasting situational awareness, and can facilitate improved risk management in the aviation community.

The LAMP/HRRR Meld products provide baseline gridded probabilistic and deterministic guidance for aviation weather forecasters.  This presentation will discuss the interpretation and application of the probabilistic guidance grids with regard to the strengths and weaknesses of the components and the forecast development technique.  Example probabilistic guidance grids will be reviewed alongside deterministic forecast grids and strategies for decision making based on probabilistic forecasts will be discussed.

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