Abstract:
A Tropical Temperate Trough (TTT) is a type of weather system that links the tropics and the extra-tropics across southern Africa. TTT events have been studied statistically in detail, but very little research has been done to study this phenomenon dynamically and especially on a seasonal scale. This study therefore focuses on the predictability of the characteristics of TTTs across southern Africa on a seasonal scale, by using a state-of-the-art seasonal forecasting model, namely the GloSea5 developed by the UK Met Office. Gridded hindcast data for the months of November, December, January and February from 1996/1997 to 2009/2010 are compared to observed data. The different ensemble members of the GloSea5 model (with lead-times of 1 week up to 2 months) are first compared separately to the observed data, after which the model average, with a 0-month, a 1-month and a 2-month lead-time, is calculated and also compared to the observed dataset.
TTT events have distinctive characteristics during the formation and the development phases. Most prominent of these characteristics are the cloud bands associated with these weather systems, which have a north-west to south-east orientation and move from west to east across southern Africa. To identify the TTTs, daily outgoing long-wave radiation values are processed by a Meteorological Robot (MetBot), with a strict criterion to identify the cloud bands that characterise these systems. The MetBot’s algorithm produces the information needed to further investigate the different characteristics of TTTs, such as the frequency, the location and the intensity of these systems. Analysis of the MetBot output includes calculating the Root Mean Square Error, the percentage error and in some cases the percentage deviation of the number of cloud bands, as well as the anchor point, the centroid position, the area, the tilt and the minimum and maximum OLR values of the cloud bands.
This investigation revealed that the characteristics of TTT events can to some extent be predicted on a seasonal scale for the summer rainfall season of southern Africa. The model used in this study fared particularly well with a 1-month lead-time forecast (compared to a 0-month and a 2-month lead-time forecast). The intensity and the location of the cloud bands associated with TTT events are forecast with a smaller percentage error than the frequency of these systems, as the frequency of TTTs tend to be significantly under-predicted by the model. For some predicted quantities, such as the area of the cloud bands, a bias-adjustment is necessary which produces significantly better results with smaller percentage errors. In the conclusions, suggestions are made on possible future studies, and how to develop this study further to create seasonal forecasts with higher skill with special regards to TTT events.
