Comparison of interpolation methods for sea surface temperature data
DOI:
https://doi.org/10.21776/ub.jfmr.2018.002.02.7Keywords:
Interpolation, Sea Surface Temperature, Inverse Distance Weighted, Kriging, Natural Neighbor Interpolation, SplineAbstract
Interpolation methods have been used in many applications to produce continuous surface data based on point data. The common interpolation methods for Sea Surface Temperature (SST) data are Inverse Distance Weighted (IDW), Kriging, Natural Neighbor Interpolation (NNI), and Spline. In this study, those four interpolation methods will be reviewed and compared to find the satisfactory method. The Argo float data was chosen as SST point data and Aqua MODIS image as validation data. Each method will be reviewed and compared to Aqua MODIS data to find the best performance. The assessment for testing the best interpolation model is smooth performance, Maximum and Minimum comparison, mean comparison, Root Mean Square Error (RMSE) and Standard Deviation Difference. The result shows that IDW interpolation is the best way to make spatial interpolation for SST.
References
Anitha G., M. Ravichandran, and R. Sayanna. 2008. Surface Buoyancy Flux in Bengal and Arabian Sea. Ann. Geophys., 26, 395 – 400, 2008.
Caruso C., F. Quarta. 1998. Interpolation Method Comparison. Computer Math. Applic. Vol.35, No.12, pp. 109 – 126, 1998.
Collins, F.C. and Bolstad, P.V. 1996. A Comparison of Spatial Interpolation Techniques in Temperature Estimation. Proceedings Third International Conference/Workshop on Integrating GIS and Environmental Modeling, Santa Fe, NM. Santa Barbara, CA: National Center for Geographic Information and Analysis, Santa Barbara.
ESRI. 2003. ArcGIS 9: Using ArcGIS Geostatistical Analyst. ESRI ArcGIS user’s Guide.
ESRI. 2016. Comparing Interpolation Method. ESRI ArcGIS user’s Guide. http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/comparing-interpolation-methods.htm
Hartkamp, A.D., De Beurs, K., Stein, A. and White, J.W., 1999. Interpolation Techniques for Climate Variables, CIMMYT, Mexico, D.F.
Hoyer L. Jacob, Jun She. 2005. Optimal Interpolation of Sea Surface Temperature for North Sea and Baltic Sea. Journal of Marine Systems 65 (2007) 176 – 189.
Hofstra Nynke, Malcom Haylock, Mark New, Phil Jones, and Christoph Frei. 2008. Comparison of Six Methods for Interpolation of Daily, European Climate Data. Journal of Geophysical Research, Vol. 113.
Joseph Sudheer, Howard J. Freeland. 2005. Salinity in Arabian Sea. Geophysical Research Letters. DOI 10.1029/2005GL022972
Li Jin, Andrew D. Heap. 2008. A Review of Spatial Interpolation Methods for Environmental Scientists. Geoscience Australia Record 2008/23.
McIntosh Peter C. 1990. Oceanographic Data Interpolation: Objective Analysis and Splines. Journal of Geophysical Research.
Setianto Agung and Tamia Tiandini. 2013. Comparison of Kriging and Inverse Distance Weighted (IDW) Interpolation Methods in Lineament Extraction and Analysis. J. SE Asian Appl. Geol., Jan – Jun 2013, Vol 5(1), pp 21 – 29.
Sibson R. 1981. A Brief Description of Natural Neighbor Interpolation. Chapter 2 in Interpolating Multivariate Data, John Wiley & Sons, New York.
Susanto Dwi, Arnold L. Gordon, and Quanan Zheng. 2001. Upwelling along the coasts of Java and Sumatra and its relation to ENSO. Geophysical Research Letters, Vol. 28, No. 8, Pages 1599 – 1602.
Vinayachandran P.N, N. H. Saji. 2008, Mechanisms of South Indian Ocean Intraseasonal Cooling. Geophysical Research Letters. DOI 10.1029/2008GL035733
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