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Evaluating daily surface maximum temperature interpolation error by adding climate stations near border areas over China
Author(s) -
Ma Jianyong,
Dong Wenjie,
Wei Zhigang,
Yan Xiaodong
Publication year - 2015
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.4173
Subject(s) - climatology , environmental science , terrain , mainland china , china , interpolation (computer graphics) , climate change , elevation (ballistics) , mean radiant temperature , multivariate interpolation , geography , meteorology , physical geography , geology , statistics , mathematics , cartography , geometry , computer graphics (images) , archaeology , computer science , bilinear interpolation , animation , oceanography
Based on the observed daily surface maximum temperature data from 610 stations over mainland China and 162 sites from neighbouring countries during 1981–2010, the impacts of adding climate stations near border areas on daily surface maximum temperature interpolation errors over China were studied using the thin‐plate spline method. The results showed that large improvements of interpolation accuracy in daily surface maximum temperature were achieved in border areas by adding climate information from neighbouring countries. Mean absolute error ( MAE ) was reduced by an average of 0.6 °C year −1 and the frequency distribution of daily bias narrowed and became more peaked at 21 boundary stations that were withheld from the model‐building process. Although pronounced variation was not found in most part of mainland China at a 10 km × 10 km grid resolution, the interpolated temperature transition between different thresholds became more stable and aligned with elevation gradients in western and northern border areas of China after incorporating data from the 162 foreign stations into the spatial interpolations. Therefore, caution should be taken when generating gridded data sets only based on observed sites in study areas to investigate regional climate change, particularly if border areas with complicated terrain are insufficiently covered by data networks.