A snow forecasting decision tree for significant snowfall over the interior of South Africa

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dc.contributor.author Stander, Jan Hendrik
dc.contributor.author Dyson, Liesl L.
dc.contributor.author Engelbrecht, Christina Johanna
dc.date.accessioned 2016-10-18T10:08:51Z
dc.date.available 2016-10-18T10:08:51Z
dc.date.issued 2016-09
dc.description This paper emanates from the work that J.H.S. conducted to obtain his MSc at the University of Pretoria under the supervision of L.D. en_ZA
dc.description.abstract Snowfall occurs every winter over the mountains of South Africa but is rare over the highly populated metropolises over the interior of South Africa. When snowfall does occur over highly populated areas, it causes widespread disruption to infrastructure and even loss of life. Because of the rarity of snow over the interior of South Africa, inexperienced weather forecasters often miss these events. We propose a five-step snow forecasting decision tree in which all five criteria must be met to forecast snowfall. The decision tree comprises physical attributes that are necessary for snowfall to occur. The first step recognises the synoptic circulation patterns associated with snow and the second step detects whether precipitation is likely in an area. The remaining steps all deal with identifying the presence of a snowflake in a cloud and determining that the snowflake will not melt on the way to the ground. The decision tree is especially useful to forecast the very rare snow events that develop from relatively dry and warmer surface conditions. We propose operational implementation of the decision tree in the weather forecasting offices of South Africa, as it is foreseen that this approach could significantly contribute to accurately forecasting snow over the interior of South Africa. SIGNIFICANCE : • A method for forecasting disruptive snowfall is provided. It is envisaged that this method will contribute to the improved forecasting of these severe weather events over South Africa. • Weather systems responsible for snowfall are documented and the cloud microphysical aspects important for the growth and melting of a snowflake are discussed. • Forecasting methods are proposed for the very rare events when snow occurs over the interior of South Africa when the air is relatively dry and somewhat warmer. en_ZA
dc.description.department Geography, Geoinformatics and Meteorology en_ZA
dc.description.librarian am2016 en_ZA
dc.description.sponsorship South African Weather Service; University of Pretoria en_ZA
dc.description.uri http://www.sajs.co.za/ en_ZA
dc.identifier.citation Stander JH, Dyson L, Engelbrecht CJ. A snow forecasting decision tree for significant snowfall over the interior of South Africa. S Afr J Sci. 2016;112(9/10), Art. #2015-0221, 10 pages. http://dx.DOI.org/ 10.17159/sajs.2016/20150221. en_ZA
dc.identifier.issn 0038-2353 (print)
dc.identifier.issn 1996-7489 (online)
dc.identifier.other 10.17159/sajs.2016/20150221
dc.identifier.uri http://hdl.handle.net/2263/57346
dc.language.iso en en_ZA
dc.publisher AOSIS Open Journals en_ZA
dc.rights © 2016. The Author(s). Published under a Creative Commons Attribution Licence. en_ZA
dc.subject Ridging surface high en_ZA
dc.subject Geopotential thickness en_ZA
dc.subject Cut-off low en_ZA
dc.subject Deposition en_ZA
dc.subject Relative humidity en_ZA
dc.title A snow forecasting decision tree for significant snowfall over the interior of South Africa en_ZA
dc.type Article en_ZA


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