Open AccessSensitivity Analysis of Quality Assurance Using the Spatial Regression Approach—A Case Study of the Maximum/Minimum Air Temperature
Author(s) -
Kenneth G. Hubbard,
Jing You
Publication year - 2005
Publication title -
journal of atmospheric and oceanic technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.774
H-Index - 124
eISSN - 1520-0426
pISSN - 0739-0572
DOI - 10.1175/jtech1790.1
Subject(s) - inverse distance weighting , acis , statistics , sensitivity (control systems) , weighting , regression , linear regression , utc offset , data set , regression analysis , mathematics , computer science , multivariate interpolation , medicine , telecommunications , botany , radiology , electronic engineering , global positioning system , bilinear interpolation , biology , engineering
Both the spatial regression test (SRT) and inverse distance weighting (IDW) methods have been applied to provide estimates for the maximum air temperature (Tmax) and the minimum air temperature (Tmin) in the Applied Climate Information System (ACIS). This is critical to the processes of estimating missing data and identifying suspect data and is undertaken here to ensure quality data in ACIS. The SRT method was previously found to be superior to the IDW method; however, the sensitivity of the performance of both methods to input parameters has not been evaluated. A set of analyses is presented for both methods whereby the sensitivity to the radius of inclusion, the regression time window, the regression time offset, and the number of stations used to make the estimates are examined. Comparisons were also conducted between the SRT and the IDW methods. The performance of the SRT method stabilized when 10 or more stations were applied in the estimates. The optimal number of stations for the IDW method varies from only a few to 30. The results indicate that the best estimates obtained using the IDW method are still inferior to the worst estimates obtained using the SRT method.