Statistical Forecasting of Ceiling for New York City Airspace Based on Routine Surface Observations

Periods of sufficiently low cloud ceiling can significantly affect Air Traffic Control (ATC) operations by necessitating Instrument Flight Rules (IFR) and thereby diminishing airport capacity. It is particularly important for ATC planning purposes to anticipate transitions into and out of IFR conditions. The Federal Aviation Administration's (FAA) Aviation Weather Research Program (AWRP), through their Terminal Ceiling and Visibility Product Development Team, is currently developing products to address wintertime ceiling and visibility issues in the Northeastern United States. This project aims to provide automatic forecast guidance at the 0-12 hour horizons via a number of forecast technologies. The present study will highlight preliminary results from the application of statistical forecasting techniques based on routine surface meteorological observations. These techniques are an extension of those employed in the recently completed San Francisco Marine Stratus Initiative, also sponsored by AWRP.

A brief overview of the statistical methodology of model development will be presented. It is based on non-linear regression and includes unique components for optimized predictor scaling, predictor subset selection, and the identification of synergy between predictor pairs. Initial development is focused on modeling ceilings at LaGuardia (LGA) based on a 1977-2004 archive of regional observations. A general evaluation of model performance on independent cases will be presented, including a breakdown by forecast horizon. Forecast performance will be compared to those of persistence, the operational Terminal Aerodrome Forecasts (TAF), and NWP (via the RUC and MM5). The development space will also be classified into several phenomenological types. Separate statistical models will be developed for each type and subsequently evaluated for improvements in forecast accuracy. The study will conclude with near-term plans for real-time implementation and future development.