1. 1) Verification of individual predictors Development of improved turbulence forecasts for aviation Debi Turp © Crown copyright Met Office and the Met Office logo are registered trademarks Met Office FitzRoy Road, Exeter, Devon, EX1 3PB United Kingdom Tel: 01392 885680 Fax: 01392 885681 Email: debi.turp@metoffice.gov.uk Turbulence is one of the main meteorological hazards for en-route air traffic. The UK Met Office is one of two World Area Forecast Centres responsible for providing numerical global forecasts of turbulence to aviation. At present these forecasts are produced using a predictor for wind shear turbulence and for mountain wave turbulence. Research has shown that combining a number of turbulence predictors into a single forecast improves forecasting skill. 5) Summary All predictor combinations performed better than any individual predictor, and had nearly the same performance scores as each other The predictor combinations were similar and contained 1/Ri and the convective turbulence predictors, highlighting the importance of including a predictor for convective turbulence The skill of the combined predictors was not sensitive to the method of normalisation of the component predictors The combination predictor C3 (which had the highest AUC score and also contained the fewest predictors) is recommended for operational implementation. 2) Predictors and data used Predictors were used to generate forecasts of moderate or greater turbulence for the 3 month period December 2010-February 2011. Automated aircraft observations of derived equivalent vertical gust Forecasts from set of predictors Observations matched with forecasts Contingency tables and Relative Operator Characteristic (ROC) curves (see example) produced for each predictor, using different thresholds for forecasting turbulence 4) Results Performance of the individual and combination predictors for moderate or greater turbulence: 3) Combining predictors to make a single forecast Method used by Pepe and Thompson (2006): Calculate predictor p from individual predictors p1 and p2 using: p = p1 + p2 p = p2 + p1 where varies between -1 to 1 and 1 to -1 respectively. Calculate area underneath ROC curve (AUC) for each predictor pair, for a set of values Optimum combination = where AUC is greatest Repeat to find the optimum combination of several predictors 4 combination predictors produced, using different starting pairs and normalisation values: C1 = (1.5*ConR)-(0.3*ConP)+(0.2*XCON)+(0.1*1/Ri)+(0.1*KLFB) C2 = (2.0*ConR)-(1.9*ConP)+(0.1*XCON+(0.1*1/Ri)+(0.1*CP)+(0.1*KLFB) C3 = (0.2*ConR)-(0.3*ConP)+(0.2*XCON)+(1.9*1/Ri) C4 = (0.4*ConR)-(0.2*ConP)+(0.1*XCON)+(2.6*1/Ri)+(0.1*CP) component predictors normalised using median value in combinations C1 and C2 component predictors normalised using maximum value in combinations C3 and C4 References: Colson, D. and Panofsky, H. A., 1965. Q. J. R. Meteorol. Soc., 91, p507-513. Ellrod, G. P. and Knapp, D. I., 1992. Weather and Forecasting, 7, p150-165. Knox, J. A., McCann, D. W. and Williams, P. D., 2008. J. Atmos. Sci., 65, p3292-3304. Pepe, M. S. and Thompson, M. L., 2006. Biostatistics, 1, p123-140. Area under ROC curve is also measure of forecast skill; the closer the value is to 1 the greater the skill The closer the curve is to the top left hand corner, the greater the skill of the forecasts Colson-Panofsky Index (CP; Colson and Panofsky 1965) Ellrods TI1 Index (TI1; Ellrod and Knapp 1992) Inverse Richardson Number (1/Ri) Buoyancy component of a turbulence predictor based on Lighthill Ford theory (KLFB; Knox et al. 2008) Wind shear component of a turbulence predictor based on Lighthill Ford theory (KLFS; Knox et al. 2008) Convective rainfall rate (ConR) Convective precipitation rate (ConP) Convective precipitation rate extended by 3 gridpoints (XCON)