This research was an effort to select best imputation method for missing upper air temperature data over 24 standard pressure levels. We have implemented four imputation techniques like inverse distance weighting, Bil...This research was an effort to select best imputation method for missing upper air temperature data over 24 standard pressure levels. We have implemented four imputation techniques like inverse distance weighting, Bilinear, Natural and Nearest interpolation for missing data imputations. Performance indicators for these techniques were the root mean square error (RMSE), absolute mean error (AME), correlation coefficient and coefficient of determination ( R<sup>2</sup> ) adopted in this research. We randomly make 30% of total samples (total samples was 324) predictable from 70% remaining data. Although four interpolation methods seem good (producing <1 RMSE, AME) for imputations of air temperature data, but bilinear method was the most accurate with least errors for missing data imputations. RMSE for bilinear method remains <0.01 on all pressure levels except 1000 hPa where this value was 0.6. The low value of AME (<0.1) came at all pressure levels through bilinear imputations. Very strong correlation (>0.99) found between actual and predicted air temperature data through this method. The high value of the coefficient of determination (0.99) through bilinear interpolation method, tells us best fit to the surface. We have also found similar results for imputation with natural interpolation method in this research, but after investigating scatter plots over each month, imputations with this method seem to little obtuse in certain months than bilinear method.展开更多
In current study an attempt is carried out by filling missing data of geopotiential height over Pakistan and identifying the optimum method for interpolation. In last thirteen years geopotential height values over wer...In current study an attempt is carried out by filling missing data of geopotiential height over Pakistan and identifying the optimum method for interpolation. In last thirteen years geopotential height values over were missing over Pakistan. These gaps are tried to be filled by interpolation Techniques. The techniques for interpolations included Bilinear interpolations [BI], Nearest Neighbor [NN], Natural [NI] and Inverse distance weighting [IDW]. These imputations were judged on the basis of performance parameters which include Root Mean Square Error [RMSE], Mean Absolute Error [MAE], Correlation Coefficient [Corr] and Coefficient of Determination [R2]. The NN and IDW interpolation Imputations were not precise and accurate. The Natural Neighbors and Bilinear interpolations immaculately fitted to the data set. A good correlation was found for Natural Neighbor interpolation imputations and perfectly fit to the surface of geopotential height. The root mean square error [maximum and minimum] values were ranges from ±5.10 to ±2.28 m respectively. However mean absolute error was near to 1. The validation of imputation revealed that NN interpolation produced more accurate results than BI. It can be concluded that Natural Interpolation was the best suited interpolation technique for filling missing data sets from AQUA satellite for geopotential height.展开更多
利用中国华东六省一市13个探空站1961-2017年高空温度数据,对850 h Pa、500 h Pa、200 h Pa高空温度的时间变化特征和空间变化特征进行分析,结果表明:1961-2017年中国华东区域对流层中下层增温趋势明显,向上增温趋势减弱,对流层顶增温...利用中国华东六省一市13个探空站1961-2017年高空温度数据,对850 h Pa、500 h Pa、200 h Pa高空温度的时间变化特征和空间变化特征进行分析,结果表明:1961-2017年中国华东区域对流层中下层增温趋势明显,向上增温趋势减弱,对流层顶增温趋势有所增强。850 h Pa、500 h Pa温度的年代际变化均呈现出先降低后升高的趋势,而200 h Pa温度的年代际变化则呈现持续升高的趋势。秋、冬季在各个层次上均为显著的增温趋势,冬季的增温趋势明显大于其他季节,500 h Pa春季和200 h Pa夏季有微弱的降温趋势。不同层次年平均气温的空间分布均有明显的南北差异,且随着高度的增加,南北平均温差先增大后减小。850 h Pa、500 h Pa年平均温度的空间变化趋势一定程度上呈现出华东沿海地区增温趋势大于内陆的特征,200 h Pa则呈现华东南部的增温趋势大于北部的特征。850 h Pa各季节呈现出中国华东沿海地区增温、内陆增温趋势不如沿海地区或内陆呈现降温趋势,500 h Pa的春季和200 h Pa的夏、秋季则呈现出中国华东南部地区增温、北部地区降温的趋势。展开更多
利用美国大气海洋局卫星应用和研究实验室(The Center for Satellite Applications and Research,STAR)提供的MSU/AMSU卫星微波亮温资料V3.0版本,结合三套再分析资料数据集,通过对海洋上空不同高度、不同区域及不同季节的适用性分析,来...利用美国大气海洋局卫星应用和研究实验室(The Center for Satellite Applications and Research,STAR)提供的MSU/AMSU卫星微波亮温资料V3.0版本,结合三套再分析资料数据集,通过对海洋上空不同高度、不同区域及不同季节的适用性分析,来探讨MSU/AMSU资料在热带海洋区域高空大气的温度变化特征,并通过合成分析揭示亮温资料与海洋的响应关系,从而探讨MSU/AMSU资料在热带海洋区域上的适用性和科学性。结果表明:(1)MSU/AMSU亮温资料在30°E^70°W,15°S^15°N范围的热带海洋区域适用性较好;(2)热带海洋区域对流层上层和中层大气均呈增温趋势,变化速率分别为0.045 K/(10 a)和0.107 K/(10 a),增温突变现象出现在1980年代末—1990年代初,平流层低层大气呈降温趋势,变化速率为-0.345 K/(10 a),降温突变现象出现在1990年代中期;(3)在热带海洋区域,高空大气温度的变化趋势具有较强的区域性特征,相对于中东太平洋而言,印度洋-西太平洋区域的增、降温趋势变化更显著。对流层的增温幅度随高度的升高而有所降低。平流层低层的降温趋势在季节内变化不大,而对流层则是秋、冬季的增温趋势要明显大于春、夏季,冬季的增温尤为明显;(4)MSU/AMSU亮温资料对热带海洋温度异常有很好的响应关系,能在弥补海洋区域观测资料稀缺的情况下,对海洋区域起着较好的监测作用。展开更多
文摘This research was an effort to select best imputation method for missing upper air temperature data over 24 standard pressure levels. We have implemented four imputation techniques like inverse distance weighting, Bilinear, Natural and Nearest interpolation for missing data imputations. Performance indicators for these techniques were the root mean square error (RMSE), absolute mean error (AME), correlation coefficient and coefficient of determination ( R<sup>2</sup> ) adopted in this research. We randomly make 30% of total samples (total samples was 324) predictable from 70% remaining data. Although four interpolation methods seem good (producing <1 RMSE, AME) for imputations of air temperature data, but bilinear method was the most accurate with least errors for missing data imputations. RMSE for bilinear method remains <0.01 on all pressure levels except 1000 hPa where this value was 0.6. The low value of AME (<0.1) came at all pressure levels through bilinear imputations. Very strong correlation (>0.99) found between actual and predicted air temperature data through this method. The high value of the coefficient of determination (0.99) through bilinear interpolation method, tells us best fit to the surface. We have also found similar results for imputation with natural interpolation method in this research, but after investigating scatter plots over each month, imputations with this method seem to little obtuse in certain months than bilinear method.
文摘In current study an attempt is carried out by filling missing data of geopotiential height over Pakistan and identifying the optimum method for interpolation. In last thirteen years geopotential height values over were missing over Pakistan. These gaps are tried to be filled by interpolation Techniques. The techniques for interpolations included Bilinear interpolations [BI], Nearest Neighbor [NN], Natural [NI] and Inverse distance weighting [IDW]. These imputations were judged on the basis of performance parameters which include Root Mean Square Error [RMSE], Mean Absolute Error [MAE], Correlation Coefficient [Corr] and Coefficient of Determination [R2]. The NN and IDW interpolation Imputations were not precise and accurate. The Natural Neighbors and Bilinear interpolations immaculately fitted to the data set. A good correlation was found for Natural Neighbor interpolation imputations and perfectly fit to the surface of geopotential height. The root mean square error [maximum and minimum] values were ranges from ±5.10 to ±2.28 m respectively. However mean absolute error was near to 1. The validation of imputation revealed that NN interpolation produced more accurate results than BI. It can be concluded that Natural Interpolation was the best suited interpolation technique for filling missing data sets from AQUA satellite for geopotential height.
文摘利用中国华东六省一市13个探空站1961-2017年高空温度数据,对850 h Pa、500 h Pa、200 h Pa高空温度的时间变化特征和空间变化特征进行分析,结果表明:1961-2017年中国华东区域对流层中下层增温趋势明显,向上增温趋势减弱,对流层顶增温趋势有所增强。850 h Pa、500 h Pa温度的年代际变化均呈现出先降低后升高的趋势,而200 h Pa温度的年代际变化则呈现持续升高的趋势。秋、冬季在各个层次上均为显著的增温趋势,冬季的增温趋势明显大于其他季节,500 h Pa春季和200 h Pa夏季有微弱的降温趋势。不同层次年平均气温的空间分布均有明显的南北差异,且随着高度的增加,南北平均温差先增大后减小。850 h Pa、500 h Pa年平均温度的空间变化趋势一定程度上呈现出华东沿海地区增温趋势大于内陆的特征,200 h Pa则呈现华东南部的增温趋势大于北部的特征。850 h Pa各季节呈现出中国华东沿海地区增温、内陆增温趋势不如沿海地区或内陆呈现降温趋势,500 h Pa的春季和200 h Pa的夏、秋季则呈现出中国华东南部地区增温、北部地区降温的趋势。
文摘利用美国大气海洋局卫星应用和研究实验室(The Center for Satellite Applications and Research,STAR)提供的MSU/AMSU卫星微波亮温资料V3.0版本,结合三套再分析资料数据集,通过对海洋上空不同高度、不同区域及不同季节的适用性分析,来探讨MSU/AMSU资料在热带海洋区域高空大气的温度变化特征,并通过合成分析揭示亮温资料与海洋的响应关系,从而探讨MSU/AMSU资料在热带海洋区域上的适用性和科学性。结果表明:(1)MSU/AMSU亮温资料在30°E^70°W,15°S^15°N范围的热带海洋区域适用性较好;(2)热带海洋区域对流层上层和中层大气均呈增温趋势,变化速率分别为0.045 K/(10 a)和0.107 K/(10 a),增温突变现象出现在1980年代末—1990年代初,平流层低层大气呈降温趋势,变化速率为-0.345 K/(10 a),降温突变现象出现在1990年代中期;(3)在热带海洋区域,高空大气温度的变化趋势具有较强的区域性特征,相对于中东太平洋而言,印度洋-西太平洋区域的增、降温趋势变化更显著。对流层的增温幅度随高度的升高而有所降低。平流层低层的降温趋势在季节内变化不大,而对流层则是秋、冬季的增温趋势要明显大于春、夏季,冬季的增温尤为明显;(4)MSU/AMSU亮温资料对热带海洋温度异常有很好的响应关系,能在弥补海洋区域观测资料稀缺的情况下,对海洋区域起着较好的监测作用。
