Applied Ecology and Environmental Sciences
ISSN (Print): 2328-3912 ISSN (Online): 2328-3920 Website: https://www.sciepub.com/journal/aees Editor-in-chief: Alejandro González Medina
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Applied Ecology and Environmental Sciences. 2014, 2(6), 141-145
DOI: 10.12691/aees-2-6-3
Open AccessArticle

The Influence of Air Temperature Controls in Estimation of Air Temperature over Homogeneous Terrain

Fariza Yunus1, Jasmee Jaafar2, , Zamalia Mahmod2 and Nursalleh K Chang1

1Malaysian Meteorological Department, Jalan Sultan 46667 Petaling Jaya, Selangor, Malaysia

2Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, Malaysia

Pub. Date: December 22, 2014

Cite this paper:
Fariza Yunus, Jasmee Jaafar, Zamalia Mahmod and Nursalleh K Chang. The Influence of Air Temperature Controls in Estimation of Air Temperature over Homogeneous Terrain. Applied Ecology and Environmental Sciences. 2014; 2(6):141-145. doi: 10.12691/aees-2-6-3

Abstract

Variation of air temperature from one place to another is caused by air temperature controls. In general, the most important control of air temperature is elevation. Another significant independent variable in estimating air temperature is the location of meteorological stations. Distance to coastline as well as land use type also contributes to significant variations in the air temperature. On the other hand, in homogeneous terrain direct interpolation of discrete points of air temperature work well to estimate air temperature values in un-sampled areas. In this process the estimation is solely based on discrete points of air temperature. However, this study presents that air temperature controls also play significant roles in estimating air temperature over the homogenous terrain of Peninsular Malaysia. An Inverse Distance Weighting (IDW) interpolation technique was adopted to generate continuous data of air temperature. This study compared two different datasets, observed mean monthly data of T, and estimation error of T–T’, where T’ is the estimated value from a multiple regression model. The multiple regression model considered eight independent variables. They are elevation, latitude, longitude, coastline, and four land use types consisting of water bodies, forest, agriculture and build up areas to represent the role of air temperature controls. Cross validation analysis was conducted to review the accuracy of the estimated values. Final results show that the estimated values of T–T’ produced lower errors for mean monthly mean air temperature over homogeneous terrain in Peninsular Malaysia.

Keywords:
air temperature control interpolation analysis Peninsular Malaysia regression model air temperature

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References:

[1]  C. D. Ahrens, Meteorological Today, 8th Edition. United States of America: Thompson Higher Education, 2007.
 
[2]  W. P. Cunningham and M. A. Cunningham, Priciples of environmental science inquiry and applications.United States of America: McGraw Hill International Edition, 2006.
 
[3]  A. T. DeGaetano and B. N. Belcher, “Spatial Interpolation of Daily Maximum and Minimum Air Temperature Based on Meteorological Model Analyses and Independent Observations,” Journal of Applied Meteorology and Climatology, vol. 46, p. 13, 2007.
 
[4]  R. Dodson and D. Marks, “Daily air temperature interpolated at high spatial resolution over a large mountainous region,” Climate Research, vol. 8, p. 20, 1997.
 
[5]  K. Stahl, R. D. Moore, J. A. Floyer, M. G. Asplin, and I. G. McKendry, “Comparison of Approaches for Spatial Interpolation of Daily Air Temperature in a Large Region with Complex Topography and Highly Variable Station Density,” Agricultural and Forest Meteorology, vol. 139, p. 13, 2006.
 
[6]  Z. Ustrnul and D. Czekierda, “Application of GIS for the Development of Climatological Air Temperature Maps: an Example from Poland,” Meteorological Applications, vol. 12, p. 8, 2005.
 
[7]  P. V. Bolstad, L. Swift, F. Collins, and J. Regniere, “Measured and predicted air temperature at basin to regional scales in the southern Appalachian mountains,” Agricultural and Forest Meteorology, vol. 91, p. 16, 1998.
 
[8]  F. Yunus, “Assessment of Spatial Interpolation Techniques of Temperature Elements in Peninsular Malaysia,” Master of Science Dissertation, University Putra Malaysia, 2005.
 
[9]  Fariza Yunus, Aziz Shafie, Jasmee Jaafar, and Zamalia Mahmud, “Homogeneous Climate Divisions for Peninsular Malaysia,” Geodinamica Acta, vol. 24, p. 7, 2012.
 
[10]  C.Daly, “Guidelines for Assessing the Suitability of Spatial Climate Data Sets,” International Journal of Climatology, vol. 26, p. 15, 2006.
 
[11]  S. P. Serbin and C. J. Kucharik, “Spatiotemporal Mapping of Temperature and Precipitation for the Development of a Multidecadal Climatic Dataset for Wisconsin,” International Journal of Climatology, vol. 48, p. 15, 2009.
 
[12]  C. H. Jarvis and N. Stuart, “A comparison among strategies for interpolating maximum and minimum daily air temperatures. Part I: The selection of "guiding" topographic and land cover variables,” Journal of Applied Meteorology, vol. 40, p. 15, 2001.
 
[13]  J. Choi, “Urban-effect to improve accuracy of spatially interpolated temperature estimates in Korea,” Journal of Applied Meteorology, vol. 42, p. 9, 2003.
 
[14]  K. L. Civerolo, G. Sistla, S. T. Rao, and D. J. Nowak, “The Effects of Land Use in Meteorological Modeling: Implications for Assessment of Future Air Quality Scenarios,” Atmospheric Environment, vol. 34, p. 7, 16th August, 1999.
 
[15]  K. P. Gallo, T. W. Owen, and D. R. Easterling, “Temperature trends of the U.S. historical climatology network based on satellite-designated land use/land cover,” Journal of Climate, vol. 12, p. 5, 1999.
 
[16]  H. Shudo, J. Sugiyama, N. Yokoo, and T. Oka, “A study on temperature distribution influenced by various land uses,” Energy and Buildings, vol. 26, p. 7, 1997.
 
[17]  H. Taha, “Urban climates and heat islands: albedo, evapotranspiration, and anthropogenic heat,” Energy and Buildings, vol. 25, p. 5, 1997.
 
[18]  Othman Jaafar, Sharifah Mastura, and Alias Mohd Sood, “Land use deforestation modelling of river catchments in Klang Valley, Malaysia,” Sains Malaysiana, vol. 38, p. 10, 2009.
 
[19]  C. H. Jarvis and N. Stuart, “A comparison among strategies for interpolating maximum and minimum daily air temperature. Part II: The Interaction between Number of Guiding Variables and the type of interpolation method,” Journal of Applied Meteorology, vol. 40, p. 10, 2001.
 
[20]  D. Kurtzman and R. Kadmon, “Mapping of temperature variables in Israel: a comparison of different interpolation methods,” Climate Research, vol. 13, p. 11, 1999.
 
[21]  D. E. Myers, “Spatial interpolation: an overview,” Geoderma, vol. 62, p. 12, 1994.
 
[22]  D. P. Brown and A. C. Comrie, “Spatial modeling of winter temperature and precipitation in Arizona and New Maxico, USA,” Climate Research, vol. 22, p. 13, 2002.
 
[23]  D. T. Price, D. W. M. I. A. N. M. F. Hutchinson, and J. L. Kestesen, “A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data,” Agricultural and Forest Meteorology, vol. 101, p. 14, 2002.
 
[24]  R. D. Valley, M. T. Drake, and C. S. Anderson, “Evaluation of alternative interpolation techniques for the mapping of remotely-sensed submersed vegetation abundance,” Aquatic Botani, vol. 81, p. 13, 2005.
 
[25]  L. J. Tick and Azizan Abu Samah, Weather and Climate of Malaysia, 1st Edition ed. Kuala Lumpur: University of Malaya Press, 2004.
 
[26]  B. R. A. Malmgren and A. Winter, “Climate Zonation in Puerto Rico Based on Principal Component Analysis and an Artificial Neural Network,” Journal of Climate, vol. 12, p. 9, 1999.
 
[27]  R. Ho, “Multiple regression,” in Handbook of univariate and multivariate data analysis and interpretation with SPSS ed United States of America: Chapman & Hall, 2006, p. 15.
 
[28]  J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis, Seventh Edition ed. New Jersey: Pearson Education, 2010.
 
[29]  C. J. Willmott, “On the valiation of model in physical geography,” in Spatial statistic and models, ed: D. Reidel, 1981, p. 18.
 
[30]  C. J. Willmott, S. G. Ackleson, R. E. Davis, J. J. Feddema, K. M. Klink, D. R. Legates, J.O'Donnell, and C. M. Rowe, “Statistics for the evaluation and comparison of models,” Journal of Geophysical Research, vol. 90, p. 11, 20th September 1985.