基于Global Positioning System(GPS)掩星数据在平流层具有较高准确性、稳定性的优势,本文尝试用新一代GPS掩星观测——the Constellation Observing System for Meteorology,Ionosphere,and Climate(COSMIC)资料验证不同卫星平台上先...基于Global Positioning System(GPS)掩星数据在平流层具有较高准确性、稳定性的优势,本文尝试用新一代GPS掩星观测——the Constellation Observing System for Meteorology,Ionosphere,and Climate(COSMIC)资料验证不同卫星平台上先进的微波探测仪(AMSU)的平流层观测结果.通过COSMIC大气温度廓线与AMSU辐射传输模式结合,得到模拟亮温,然后与AMSU平流层观测进行匹配比较.分析表明GPS掩星数据能够作为一个相对独立的参量检验NOAA15、16、18卫星平台内部的偏差.通过一年数据的比较验证,初步显示不同卫星平台的AMSU观测亮温在平流层低层都偏低,并且NOAA18平台的亮温偏低程度明显大于NOAA15、16.AMSU亮温偏差在极地冬季较为显著,尤其南极地区NOAA18的偏差幅度达到1.8K.结合24小时内AMSU观测亮温偏差变化及其样本分布特征,可以看到明显的太阳辐射差异可能是导致AMSU观测亮温在极地偏差显著的主要原因.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
Back propagation neural networks are used to retrieve atmospheric temperature profiles from NOAA-16 Advanced Microwave Sounding Unit-A (AMSU-A) measurements over East Asia. The collocated radiosonde observation and AM...Back propagation neural networks are used to retrieve atmospheric temperature profiles from NOAA-16 Advanced Microwave Sounding Unit-A (AMSU-A) measurements over East Asia. The collocated radiosonde observation and AMSU-A data over land in 2002-2003 are used to train the network, and the data over land in 2004 are used to test the network. A comparison with the multi-linear regression method shows that the neural network retrieval method can significantly improve the results in all weather conditions. When an offset of 0.5 K or a noise level of ±0.2 K is added to all channels simultaneously, the increase in the overall root mean square (RMS) error is less than 0.1 K. Furthermore, an experiment is conducted to investigate the effects of the window channels on the retrieval. The results indicate that the brightness temperatures of window channels can provide significantly useful information on the temperature retrieval near the surface. Additionally, the RMS errors of the profiles retrieved with the trained neural network are compared with the errors from the International Advanced TOVS (ATOVS) Processing Package (IAPP). It is shown that the network-based algorithm can provide much better results in the experiment region and comparable results in other regions. It is also noted that the network can yield remarkably better results than IAPP at the low levels and at about the 250-hPa level in summer skies over ocean. Finally, the network-based retrieval algorithm developed herein is applied in retrieving the temperature anomalies of Typhoon Rananim from AMSU-A data.展开更多
文摘基于Global Positioning System(GPS)掩星数据在平流层具有较高准确性、稳定性的优势,本文尝试用新一代GPS掩星观测——the Constellation Observing System for Meteorology,Ionosphere,and Climate(COSMIC)资料验证不同卫星平台上先进的微波探测仪(AMSU)的平流层观测结果.通过COSMIC大气温度廓线与AMSU辐射传输模式结合,得到模拟亮温,然后与AMSU平流层观测进行匹配比较.分析表明GPS掩星数据能够作为一个相对独立的参量检验NOAA15、16、18卫星平台内部的偏差.通过一年数据的比较验证,初步显示不同卫星平台的AMSU观测亮温在平流层低层都偏低,并且NOAA18平台的亮温偏低程度明显大于NOAA15、16.AMSU亮温偏差在极地冬季较为显著,尤其南极地区NOAA18的偏差幅度达到1.8K.结合24小时内AMSU观测亮温偏差变化及其样本分布特征,可以看到明显的太阳辐射差异可能是导致AMSU观测亮温在极地偏差显著的主要原因.
文摘采用支持向量机(SVM,Support Vector Machine)方法,对AMSU-A进行了临边调整试验。利用全球廓线数据集和快速辐射传输模式计算的理想亮温资料,以及AMSU-A全球实际亮温资料的分析表明,临边效应增大了窗区通道边缘视场的亮温,减小了5～14通道边缘视场亮温。临边效应对于各通道影响明显,且随着视角的增大而增大。通过理想试验分析表明,与多元线性回归方法相比,支持向量机方法对于窗区通道调整效果改进较多,对于通道5～14,同样优于多元线性回归方法。除窗区通道1、2、15边缘少数视场外,各视场调整均方根(RMS,Root Mean Square)误差在AMSU-A仪器噪声范围之内。对实际资料的试验表明,支持向量机方法调整效果同样优于多元线性回归方法。
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。
文摘Back propagation neural networks are used to retrieve atmospheric temperature profiles from NOAA-16 Advanced Microwave Sounding Unit-A (AMSU-A) measurements over East Asia. The collocated radiosonde observation and AMSU-A data over land in 2002-2003 are used to train the network, and the data over land in 2004 are used to test the network. A comparison with the multi-linear regression method shows that the neural network retrieval method can significantly improve the results in all weather conditions. When an offset of 0.5 K or a noise level of ±0.2 K is added to all channels simultaneously, the increase in the overall root mean square (RMS) error is less than 0.1 K. Furthermore, an experiment is conducted to investigate the effects of the window channels on the retrieval. The results indicate that the brightness temperatures of window channels can provide significantly useful information on the temperature retrieval near the surface. Additionally, the RMS errors of the profiles retrieved with the trained neural network are compared with the errors from the International Advanced TOVS (ATOVS) Processing Package (IAPP). It is shown that the network-based algorithm can provide much better results in the experiment region and comparable results in other regions. It is also noted that the network can yield remarkably better results than IAPP at the low levels and at about the 250-hPa level in summer skies over ocean. Finally, the network-based retrieval algorithm developed herein is applied in retrieving the temperature anomalies of Typhoon Rananim from AMSU-A data.
