Forecasting Warm-Season Burnoff of Low Clouds at the San Francisco International Airport Using Linear Regression and a Neural Network

Andrew R. Dean School of Meteorology, University of Oklahoma, Norman, Oklahoma

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Brian H. Fiedler School of Meteorology, University of Oklahoma, Norman, Oklahoma

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Abstract

In this study, both linear regression and a nonlinear neural network are used to forecast burnoff of low clouds in the warm season at San Francisco International Airport (SFO). Both forecast systems show skill scores between 0.2 and 0.25 in comparison with use of climatological values. The neural network is slightly more skillful. The forecast systems are derived from 45 yr of NCEP–NCAR reanalysis data and SFO surface observations. A forecast is attempted for both the time of burnoff and the probability of being burned off by 1000 Pacific standard time. The lack of significant superiority of the neural network over linear regression is not due to a failing of the neural network as a method. When both methods are applied to a statistical prediction of the afternoon temperature at SFO, based on early morning conditions, the neural network has a skill score of 0.446 and the linear regression has a skill score of 0.290.

Corresponding author address: Dr. Brian Fiedler, School of Meteorology, University of Oklahoma, 100 E. Boyd St., Rm. 1310, Norman, OK 73019. bfiedler@ou.edu

Abstract

In this study, both linear regression and a nonlinear neural network are used to forecast burnoff of low clouds in the warm season at San Francisco International Airport (SFO). Both forecast systems show skill scores between 0.2 and 0.25 in comparison with use of climatological values. The neural network is slightly more skillful. The forecast systems are derived from 45 yr of NCEP–NCAR reanalysis data and SFO surface observations. A forecast is attempted for both the time of burnoff and the probability of being burned off by 1000 Pacific standard time. The lack of significant superiority of the neural network over linear regression is not due to a failing of the neural network as a method. When both methods are applied to a statistical prediction of the afternoon temperature at SFO, based on early morning conditions, the neural network has a skill score of 0.446 and the linear regression has a skill score of 0.290.

Corresponding author address: Dr. Brian Fiedler, School of Meteorology, University of Oklahoma, 100 E. Boyd St., Rm. 1310, Norman, OK 73019. bfiedler@ou.edu

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