A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales

Seven methods of spatial interpolation were compared to determine their suitability for estimating daily mean wind speed surfaces, from data recorded at nearly 190 locations across England and Wales. The eventual purpose of producing such surfaces is to help estimate the daily spread of pathogens causing crop diseases as they move across regions. The interpolation techniques included four deterministic and three geostatistical methods. Quantitative assessment of the continuous surfaces showed that there was a large difference between the accuracy of the seven interpolation methods and that the geostatistical methods were superior to deterministic methods. Further analyses, testing the reliability of the results, showed that measurement accuracy, density, distribution and spatial variability had a substantial influence on the accuracy of the interpolation methods. Independent wind speed data from ten other dates were used to confirm the robustness of the best interpolation methods. © Crown copyright 2007. Reproduced with the permission of Her Majesty's Stationery Office. Published by John Wiley & Sons, Ltd.

[1]  A. H. Thiessen PRECIPITATION AVERAGES FOR LARGE AREAS , 1911 .

[2]  Hiroshi Akima,et al.  A New Method of Interpolation and Smooth Curve Fitting Based on Local Procedures , 1970, JACM.

[3]  R. Reyment,et al.  Statistics and Data Analysis in Geology. , 1988 .

[4]  G. Wahba,et al.  Some New Mathematical Methods for Variational Objective Analysis Using Splines and Cross Validation , 1980 .

[5]  D. Myers Matrix formulation of co-kriging , 1982 .

[6]  N. Lam Spatial Interpolation Methods: A Review , 1983 .

[7]  Cort J. Willmott,et al.  On the Evaluation of Model Performance in Physical Geography , 1984 .

[8]  N. Cressie Fitting variogram models by weighted least squares , 1985 .

[9]  J. Salas,et al.  A COMPARATIVE ANALYSIS OF TECHNIQUES FOR SPATIAL INTERPOLATION OF PRECIPITATION , 1985 .

[10]  Peter Lancaster,et al.  Curve and surface fitting - an introduction , 1986 .

[11]  R. H. Myers Classical and modern regression with applications , 1986 .

[12]  G. Robertson Geostatistics in Ecology: Interpolating With Known Variance , 1987 .

[13]  A. MacEachren,et al.  Sampling and Isometric Mapping of Continuous Geographic Surfaces , 1987 .

[14]  B. A. Eckstein Evalution of spline and weighted average interpolation algorithms , 1989 .

[15]  D. Legates,et al.  Mean seasonal and spatial variability in global surface air temperature , 1990 .

[16]  A. Flint,et al.  Precipitation Estimation in Mountainous Terrain Using Multivariate Geostatistics. Part II: Isohyetal Maps , 1990 .

[17]  D. Marks,et al.  A comparison of geostatistical procedures for spatial analysis of precipitation in mountainous terrain , 1992 .

[18]  Noel A Cressie,et al.  Statistics for Spatial Data. , 1992 .

[19]  Mike Rees,et al.  5. Statistics for Spatial Data , 1993 .

[20]  Noel A. C. Cressie,et al.  Statistics for Spatial Data: Cressie/Statistics , 1993 .

[21]  M. Hutchinson,et al.  Splines — more than just a smooth interpolator , 1994 .

[22]  Fred C. Collins,et al.  A comparison of spatial interpolation techniques in temperature estimation , 1995 .

[23]  M. F. Hutchinson,et al.  Interpolating Mean Rainfall Using Thin Plate Smoothing Splines , 1995, Int. J. Geogr. Inf. Sci..

[24]  R. Weisz,et al.  Map Generation in High-Value Horticultural Integrated Pest Management: Appropriate Interpolation Methods for Site-Specific Pest Management of Colorado Potato Beetle (Coleoptera: Chrysomelidae) , 1995 .

[25]  P. Burrough,et al.  Principles of geographical information systems , 1998 .

[26]  S. Fleischer,et al.  Sampling in Precision IPM: When the Objective Is a Map. , 1999, Phytopathology.

[27]  Timothy C. Coburn,et al.  Geostatistics for Natural Resources Evaluation , 2000, Technometrics.

[28]  M. Hutchinson,et al.  A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data , 2000 .

[29]  P. Goovaerts Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall , 2000 .

[30]  N. Stuart,et al.  A comparison among strategies for interpolating maximum and minimum daily air temperatures. Part II: The interaction between number of guiding variables and the type of interpolation method , 2001 .

[31]  R. Reese Geostatistics for Environmental Scientists , 2001 .

[32]  S. Vicente‐Serrano,et al.  Comparative analysis of interpolation methods in the middle Ebro Valley (Spain): application to annual precipitation and temperature , 2003 .

[33]  G. Heuvelink,et al.  A generic framework for spatial prediction of soil variables based on regression-kriging , 2004 .