A Psomef-Based Method for Quality Control of Surface Temperature Observations
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摘要: 考虑气温在空间上的相关性,提出一种基于粒子群算法(Particle Swarm Optimization,PSO)改进的多面函数内插算法(Multi-quadric Equations Fitting,MEF)的地面逐时气温观测资料的多站联网质量控制方法。为检验该方法的可行性和适用性,运用该方法对江苏省四站2006—2008年地面气温观测资料进行质量控制,并与反距离加权法(Inverse Distance Weighting,IDW)进行比较。试验结果表明,该方法相对于IDW法可以更有效地标记出人为模拟的随机误差,具有识别精度高,地区和气候适应性强等优点。Abstract: Considering the correlation between temperature in spatial, a new quality control method of surface temperature observations, based on the Multi-quadric Equations which is improved by particle swarm optimization (PSO), is introduced in detail in paper. In order to verify the feasibility and applicability of the proposed method, by using this method, the hourly air temperature in four cities of Jiangsu Province in 2008 was examined, and also comparming against inverse distance weighting method (IDW). The results show that the proposed method can more effectively check the random errors of the artificial simulation, Furthermore, the method has the advantages of high identification accuracy, strong adaptability to different regions and climate.
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Key words:
- quality control /
- Multi-quadric Equations /
- temperature /
- particle swarm optimization /
- correlation
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表 1 PSO改进MEF算法参数设置
参数 参数值或方法 核函数 高斯旋转面 核节点 系数判别法 Fitness MEF的RMSE 粒子群大小 20 xmax、vmax [1, 400][-0.5, 0.5] Tmax 40 LDIW wstart=0.9, wend=0.4 c1、c2 c1=c2=1.494 45 表 2 四站PSO-MEF质量控制法检错率
站名 2006年 2007年 2008年 淮安 0.84 0.82 0.82 常州 0.83 0.85 0.86 南通 0.85 0.87 0.84 泰州 0.81 0.84 0.85 表 3 不同年份不同季节四站的最佳f值及对应检错率
年份 月份 淮安 常州 南通 泰州 f值 检错率 f值 检错率 f值 检错率 f值 检错率 2006 1 1.2 0.80 1.2 0.76 1.2 0.79 1.2 0.76 4 1.2 0.80 1.2 0.79 1.2 0.82 1.6 0.82 7 1.6 0.81 2.0 0.80 1.6 0.88 1.6 0.76 10 1.6 0.86 1.6 0.88 1.2 0.90 1.6 0.79 2007 1 1.6 0.76 1.2 0.83 1.2 0.76 1.2 0.84 4 1.2 0.81 2.0 0.82 1.2 0.84 1.2 0.88 7 1.2 0.76 2.0 0.76 2.0 0.80 2.0 0.80 10 1.2 0.88 1.2 0.83 1.6 0.88 2.0 0.76 2008 1 1.2 0.84 1.2 0.80 1.2 0.78 1.6 0.88 4 1.2 0.76 1.2 0.81 1.2 0.83 1.2 0.95 7 1.2 0.79 2.0 0.73 1.6 0.86 1.6 0.76 10 1.2 0.93 1.2 0.84 1.2 0.92 1.2 0.82 -
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