| Title | Identification of key parameters for aviation forecasts of ceiling and visibility |
| Author | Braaten, D.; Tucker, D.; Pan, C.; Jirak, I.; Browning, P. |
| Author Affil | Braaten, D., University of Kansas, Department of Physics and Astronomy, Lawrence, KS. Other: NOAA, National Weather Service |
| Source | Miscellaneous Publication of the Byrd Polar Research Center, No.M-419, p.61-63, ; Antarctic weather forecasting workshop, Columbus, OH, May 17-19, 2000, edited by E.N. Cassano and L.R. Everett; U. S., National Science Foundation. Publisher: Ohio State University, Byrd Polar Research Center, Columbus, OH, United States |
| Publication Date | 2000 |
| Notes | In English. 6 refs. Ant. Acc. No: 84193. GeoRef Acc. No: 284833 |
| Index Terms | aircraft; climate; clouds (meteorology); organizations; ice; meteorology; forecasting; statistical analysis; visibility; atmosphere; clouds; government agencies; identification; NOAA; numerical models; prediction |
| Abstract | The purpose of this paper is to describe a cooperative research project between the University of Kansas and NOAA/NWS forecast office. The main purposes of this work is to objectively identify important parameters in predicting ceiling and visibility, and to improve the accuracy of ceiling and visibility predictions. While this project uses a single airport (Kansas City), the methodology and analysis techniques can be applied anywhere and can be readily modified to adapt to the observational and model data available. This includes Antarctic flight operations. Unfortunately, cloud cover is the most difficult of meteorological variables for numerical models to predict. The Model Output Statistics (MOS) output for prediction of ceiling and visibility is heavily dependent on the most recent station observations rather than the output of the numerical model. Because of this factor there has not been the increase in the quality of ceiling and visibility forecasts which have been realized for other forecast variables. Development of a TAF (Terminal Area Forecasts) forecast system is currently underway. The present focus is to develop the TAF forecast system using least squares multiple linear regression techniques. A list of predictor variables for ceiling forecasts and visibility forecasts have been developed which come from observations, satellite, and model data. The complete predictor variable list for ceiling forecasts is given in a table. Then next step is to calculate the coefficients in the multiple linear regression models and eliminate those parameters with small coefficients. The result will be the optimized TAF forecast system, and will require that each of the critical predictor variables be inputted at the time the forecast is made. |
| Publication Type | conference paper or compendium article |
| Record ID | 62005125 |