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Using Reforecasts to Improve Forecasting of Fog and Visibility for Aviation

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  • 1 Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado
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Abstract

Fifteen years of forecasts from the National Oceanic and Atmospheric Administration’s Second-Generation Global Medium-Range Ensemble Reforecast (GEFS/R) dataset were used to develop a statistical model that generates probabilistic predictions of cloud ceiling and visibility. Four major airports—Seattle–Tacoma International Airport (KSEA), San Francisco International Airport (KSFO), Denver International Airport (KDEN), and George Bush Intercontinental Airport (KIAH) in Houston, Texas—were selected for model training and analysis. Numerous statistical model configurations, including the use of several different machine learning algorithms, input predictors, and internal parameters, were explored and verified through cross validation to develop skillful forecasts at each station. The final model was then compared with both probabilistic climatology-based forecasts and deterministic operational guidance. Results indicated significantly enhanced skill within both deterministic and probabilistic frameworks from the model trained in this study relative to both operational guidance and climatology at all stations. Probabilistic forecasts also showed substantially higher skill within the framework used than any deterministic forecast. Dewpoint depression and cloud cover forecast fields from the GEFS/R model were typically found to have the highest correspondence with observed flight rule conditions of the atmospheric fields examined. Often forecast values nearest the prediction station were not found to be the most important flight rule condition predictors, with forecast values along coastlines and immediately offshore, where applicable, often serving as superior predictors. The effect of training data length on model performance was also examined; it was determined that approximately 3 yr of training data from a dynamical model were required for the statistical model to robustly capture the relationships between model variables and observed flight rule conditions (FRCs).

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/WAF-D-15-0108.s1.

Corresponding author address: Gregory R. Herman, Dept. of Atmospheric Science, Colorado State University, 200 West Lake Street, 1371 Campus Delivery, Fort Collins, CO 80523. E-mail: gherman@atmos.colostate.edu

Abstract

Fifteen years of forecasts from the National Oceanic and Atmospheric Administration’s Second-Generation Global Medium-Range Ensemble Reforecast (GEFS/R) dataset were used to develop a statistical model that generates probabilistic predictions of cloud ceiling and visibility. Four major airports—Seattle–Tacoma International Airport (KSEA), San Francisco International Airport (KSFO), Denver International Airport (KDEN), and George Bush Intercontinental Airport (KIAH) in Houston, Texas—were selected for model training and analysis. Numerous statistical model configurations, including the use of several different machine learning algorithms, input predictors, and internal parameters, were explored and verified through cross validation to develop skillful forecasts at each station. The final model was then compared with both probabilistic climatology-based forecasts and deterministic operational guidance. Results indicated significantly enhanced skill within both deterministic and probabilistic frameworks from the model trained in this study relative to both operational guidance and climatology at all stations. Probabilistic forecasts also showed substantially higher skill within the framework used than any deterministic forecast. Dewpoint depression and cloud cover forecast fields from the GEFS/R model were typically found to have the highest correspondence with observed flight rule conditions of the atmospheric fields examined. Often forecast values nearest the prediction station were not found to be the most important flight rule condition predictors, with forecast values along coastlines and immediately offshore, where applicable, often serving as superior predictors. The effect of training data length on model performance was also examined; it was determined that approximately 3 yr of training data from a dynamical model were required for the statistical model to robustly capture the relationships between model variables and observed flight rule conditions (FRCs).

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/WAF-D-15-0108.s1.

Corresponding author address: Gregory R. Herman, Dept. of Atmospheric Science, Colorado State University, 200 West Lake Street, 1371 Campus Delivery, Fort Collins, CO 80523. E-mail: gherman@atmos.colostate.edu

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