Abstract
An improved kernel regression (IKR) method based on an adaptive algorithm and particle swarm optimization is proposed. Considering the limitations of current quality control methods in different regions and on multiple time scales, the kernel regression algorithm is applied to the quality control of surface air temperature observations. Observations of 12 reference stations in Jiangsu from 1961 to 2008 and of 14 regions in China from 2010 to 2014 were selected. The analysis of surface air temperature observations was performed in terms of the mean absolute error (MAE), root mean square error (RMSE), consistency indicator (IOA), and Nash–Sutcliffe model efficiency coefficient (NSC). The results indicate that compared with the traditional IDW and SRT methods, the IKR method has a high error detection rate. Furthermore, the IKR method achieves better predictions and fitting in the single-station and multistation regression experiments in Jiangsu and in the national multistation regression prediction experiment.
Highlights
Compared with radiosonde stations, surface observation stations have a higher spatial resolution
Conclusions e improved kernel regression (IKR) method for the quality control (QC) of surface air temperature observations is introduced in this paper. e results show that the IKR method has better prediction accuracy and fitting accuracy than the traditional QC methods
Different regions of the country are selected to validate the feasibility of the KR method. e results show that application of the KR method in the regression prediction of surface air temperature observations is feasible, but its prediction accuracy and stability must be improved, and it cannot be applied to all regions
Summary
Surface observation stations have a higher spatial resolution. Surface observations, except pressure, have not yet been assimilated in the numerical weather prediction system. E study shows that the forecast skill of numerical weather prediction will be improved if surface observations are assimilated properly. 2 m temperature observations (surface air temperature observations) have more noticeable impact on the model forecast, compared with other elements [1]. Surface meteorological observations are the basic element in meteorological research and have important decision-making significance for data assimilation technology and numerical weather prediction technology [5]. E accuracy of numerical weather prediction (NWP), a key meteorological forecasting technology in the current era, is largely restricted by data assimilation technology, and the QC of surface air temperature observations in the process of data assimilation is the basis of research in this area. With the rapid improvement of the current social economy, the distribution of surface weather stations is becoming more systematic and refined, leading to geometric growth in the number of meteorological observations; the QC of surface meteorological observations is a basic and necessary task of meteorological research experiments [6]
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