Abstract:
Warnings of severe weather with a lead time longer that two hours require the use of
skillful numerical weather prediction (NWP) models. In this study, we test the performance of
the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Conformal Cubic Atmospheric
Model (CCAM) in simulating six high-impact weather events, with a focus on rainfall
predictions in South Africa. The selected events are tropical cyclone Dineo (16 February 2017), the
Cape storm (7 June 2017), the 2017 Kwa-Zulu Natal (KZN) floods (10 October 2017), the 2019 KZN
floods (22 April 2019), the 2019 KZN tornadoes (12 November 2019) and the 2020 Johannesburg floods
(5 October 2020). Three configurations of CCAM were compared: a 9 km grid length (MN9km) over
southern Africa nudged within the Global Forecast System (GFS) simulations, and a 3 km grid length
over South Africa (MN3km) nudged within the 9 km CCAM simulations. The last configuration
is CCAM running with a grid length of 3 km over South Africa, which is nudged within the GFS
(SN3km). The GFS is available with a grid length of 0.25 , and therefore, the configurations allow
us to test if there is benefit in the intermediate nudging at 9 km as well as the effects of resolution
on rainfall simulations. The South AfricanWeather Service (SAWS) station rainfall dataset is used
for verification purposes. All three configurations of CCAM are generally able to capture the spatial
pattern of rainfall associated with each of the events. However, the maximum rainfall associated
with two of the heaviest rainfall events is underestimated by CCAM with more than 100 mm. CCAM
simulations also have some shortcomings with capturing the location of heavy rainfall inland and
along the northeast coast of the country. Similar shortcomings were found with other NWP models
used in southern Africa for operational forecasting purposes by previous studies. CCAM generally
simulates a larger rainfall area than observed, resulting in more stations reporting rainfall. Regarding
the different configurations, they are more similar to one another than observations, however, with some suggestion that MN3km outperforms other configurations, in particular with capturing the
most extreme events. The performance of CCAM in the convective scales is encouraging, and further
studies will be conducted to identify areas of possible improvement.
