Adding value to numerical weather predictions for the aviation industry in South Africa.

The main objective of this study is to determine whether human forecasters at the main airports of South Africa add value to the raw numerical weather prediction model output when they provide forecast services to the aviation industry. Dataset pairs of weather observations made at these airports and terminal aerodrome forecasts are constructed for three forecast systems: the human forecasters, persistence forecast and the raw output from the 12 km resolution Unified Model administered by the South African Weather Service. These three dataset pairs are independently evaluated by a forecast verification system developed at the South African Weather Service. A Monte Carlo method is used to determine the significance of the verification results obtained from calculating the proportion correct, hit rate, false alarm ratio, critical success index, Heidke skill score and the Pierce skill score of the various forecasts. In general, it is found that the forecaster-adapted forecasts are superior to the raw model output, thus providing evidence that the aviation industry may benefit most from forecasts routinely issued by South African Weather Service forecasters