Development of a Weather-Type Classification Scheme for Karachi by using Multivariate Techniques

Naeem Sadiq

Abstract


Weather typing or categorization continues to be popular and numerous methods have been developed over the past century for this investigation. This paper attempts to classify the types of weather for Karachi on the basis of diurnal data according to seasonal classification. The idea of using weather classification in climate change research is inspired by both uncertainties accompanying climate simulation on a regional scale, and the conflicting results of examining long-term instrumental series by traditional statistical methods. Multivariate techniques of Principal component analysis and Cluster analysis are used to obtained different types of weather for each season separately. Calculations show that we have 14, 5, 8, 7 and 8 different weather types for Monsoon, winter, summer, spring and autumn seasons respectively. Noticeable greater variations came into view for monsoon season and winter appears as least varied season. The aim of this research is to investigate the properties of the adopted clustering procedure, the consequences of the modifications introduced, and the physical interpretation of the weather types in terms of meteorological variables.

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References


Aldenderfer M. S. and R. K. Blashfield (1984): Cluster Analysis, Sage Publications, Newbury Park. Cal.

Barry, R.G. and A. H. Perry, 1973.Synoptic Climatology: Methods and Applications, Methuen, London.

Christensen, W.1. and R.A. Bryson, 1966: ‘An investigation of the potential of component analysis for weather classification’, Mon. Wea. Rev. 94, 697—709.

Davies T.D., Kelly P.M., P. Brimblecombe, G. Farmer and R.J. Barthelmie, 1986: Acidity of Scottish rainfall influenced by climatic change. Nature 322: 359-361.

Hair, Anderson, Tatham and Black (1998): Multivariate Data Analysis. 5th edition, Prentice Hall, US.

Harper (1999) Numerical Palaeobiology. John Wiley & Sons.

Houghton, J.T., G.J. Jenkins and J.J. Ephramus, 1990: Climate Change: the IPCC scientific Assessment, Cambridge University Press, Cambridge.

Huth R, I. Nemesova, N. Klimeperova (1993): “Weather categorization based on the average linkage clustering technique: An application to European mid-latitudes”, Journal of Climatology, 13, 817-835 (1993)

Johnson, R., and D. Wichern. 1998. Applied Multivariate Statistical Analysis. Prentice Hall, Upper Saddle City, NJ.

Kalkstein, L. S., P.C. Dunne and R. S. Vose, R. S. 1990: ‘ Detection of climatic change in the western North American Arctic using a synoptic climatiological approach’, J. Climate, 3, 1163-1167.

Kalkstein, L.S. and P. Corrigan, 1986: ‘A synoptic climatological approach for geographical analysis: assessment of sulfur dioxide concentrations’, Ann. Assoc. Am. Geogr., 76, 381-395.

Sadiq N, (2007): “Classification of weather at Karachi region”, M.Phil. Thesis, University of Karachi.

Sharma S (1996): Applied Multivariate Techniques, John Wiley, USA.

Stone, R.C.1989: ‘Weather types at Brisbane, Queensland: an example of the use of principal components and cluster analysis’, Int .J. Climatol., 9, 3-32.

Yarnal, B. and D. A. White 1987: ‘Subjectivity in a computer-assisted synoptic climatiology I: classification results’, J. Climatolol., 7, 119-128.


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