利用CrIS红外高光谱卫星数据反演大气温湿度廓线的研究
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摘要
大气温湿度廓线信息是数值天气预报和气候变化评估等科学研究必不可少的基础数据,利用高光谱卫星数据准确定量地反演高精度的大气温湿度廓线,对提高天气预报和气候预测能力具有重要意义.本文利用搭载在Suomi-NPP(National Polar-orbiting Partnership)卫星上的新一代跨轨红外探测器CrIS(Cross-track Infrared Sounder)的高光谱红外辐射资料,联合欧洲中期天气预报中心ECMWF(European Centre for Medium-Range Weather Forecasts)的温湿度廓线再分析资料,利用D-R(Dual-Regression)双回归反演算法进行了大气温湿度廓线的反演研究.并利用上海宝山站点2014-2016年6-9月的实测的大气温湿度廓线探空数据和美国国家海洋与大气管理局NOAA(National Oceanic and Atmospheric Administration)官方算法NUCAPS(NOAA Unique Combined Atmospheric Processing System)提供的大气温湿度产品进行了对比与验证.结果表明:基于ECMWF的大气温湿度再分析数据作为背景场的D-R算法反演得到的大气温度廓线的总体BIAS基本保持在±1 K以内,RMSE(root mean square error)在2 K以内,与NUCAPS算法的反演精度相当;在低层,D-R算法的反演精度仍保持在2 K以内,要优于NUCAPS算法(RMSE指标).相对湿度在高度300 hPa以下时与NUCAPS算法反演的精度相当,其RMSE基本在20%以内,BIAS基本在±10%以内,反演结果较好且稳定;但在高于300 hPa时,D-R算法反演的误差明显增大到30%,精度降低.
Atmospheric temperature and humidity profile data are basic inputs for numerical weather prediction and climate change assessments, and they are considered indispensable for other scientific research. Improving weather forecast and climate prediction ability by using high spectral satellite data to accurately and quantitatively invert high precision temperature and humidity profiles is of great significance. This paper uses hyperspectral infrared radiation data from the next generation cross-track infrared detector CrIS(Cross-track Infrared Sounder) on the Suomi-NPP(National Polar-orbiting Partnership) satellite as well as reanalysis data of temperature and humidity profiles from the ECMWF(European Centre for Medium-Range Weather Forecasts). In this paper,the D-R(Dual-Regression) inversion algorithm is used to study the inversion of high temperature and humidity profiles. Then, it is compared with measured temperature and humidity profile data from June to Septemberof each year between 2014 and 2016 at the Shanghai Baoshan site and the official temperature and humidity product inversion by NOAA(National Oceanic and Atmospheric Administration)'s official NUCAPS(NOAA Unique Combined Atmospheric Processing System) algorithm. The results show that the total BIAS of the atmospheric temperature profiles retrieved by the D-R algorithm,based on the background field using ECMWF's temperature and humidity reanalysis data, is basically within 1 K, and the RMSE(root mean square error) is basically within2 K, which is equivalent to the NUCAPS algorithm's inversion accuracy. In the lowest layer of the atmosphere, the inversion accuracy of the D-R algorithm remains within 2 K,which is better than the NUCAPS algorithm(RMSE index). The relative humidity at an inversion height below 300 hPa is of the same accuracy as the NUCAPS algorithm, when the RMSE is less than 20% and the BIAS is less than 10%; hence, the inversion result is good and stable. However, when the height is above 300 hPa, the error of the inversion D-R algorithm increases to 30% and the inversion accuracy is reduced.
Atmospheric temperature and humidity profile data are basic inputs for numerical weather prediction and climate change assessments, and they are considered indispensable for other scientific research. Improving weather forecast and climate prediction ability by using high spectral satellite data to accurately and quantitatively invert high precision temperature and humidity profiles is of great significance. This paper uses hyperspectral infrared radiation data from the next generation cross-track infrared detector CrIS(Cross-track Infrared Sounder) on the Suomi-NPP(National Polar-orbiting Partnership) satellite as well as reanalysis data of temperature and humidity profiles from the ECMWF(European Centre for Medium-Range Weather Forecasts). In this paper,the D-R(Dual-Regression) inversion algorithm is used to study the inversion of high temperature and humidity profiles. Then, it is compared with measured temperature and humidity profile data from June to Septemberof each year between 2014 and 2016 at the Shanghai Baoshan site and the official temperature and humidity product inversion by NOAA(National Oceanic and Atmospheric Administration)'s official NUCAPS(NOAA Unique Combined Atmospheric Processing System) algorithm. The results show that the total BIAS of the atmospheric temperature profiles retrieved by the D-R algorithm,based on the background field using ECMWF's temperature and humidity reanalysis data, is basically within 1 K, and the RMSE(root mean square error) is basically within2 K, which is equivalent to the NUCAPS algorithm's inversion accuracy. In the lowest layer of the atmosphere, the inversion accuracy of the D-R algorithm remains within 2 K,which is better than the NUCAPS algorithm(RMSE index). The relative humidity at an inversion height below 300 hPa is of the same accuracy as the NUCAPS algorithm, when the RMSE is less than 20% and the BIAS is less than 10%; hence, the inversion result is good and stable. However, when the height is above 300 hPa, the error of the inversion D-R algorithm increases to 30% and the inversion accuracy is reduced.
引文
[1]刘延安.高光谱红外辐射资料在区域模式中的直接同化及应用研究[D].上海:华东师范大学,2015.
[2] HILTON F, ARMANTE R, AUGUST T, et al. Hyperspectral earth observation from IASI:Five years of accomplishments[J]. Bulletin of the American Meteorological Society, 2012, 93(3):347-370.
[3]余意,张卫民,曹小群,等.同化IASI资料对台风“红霞”和“莫兰蒂”预报的影响研究[J].热带气象学报,2017, 33(4):500-509.
[4]官莉.利用AIRS卫星资料反演大气廓线Ⅰ:特征向量统计反演法[J].大气科学学报,2006, 29(6):756-761.
[5]刘旸,官莉.人工神经网络法反演晴空大气湿度廓线的研究[J].气象,2011,37(3):318-324.
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[7]高路,郝璐.ERA-Interim气温数据在中国区域的适用性评估[J].亚热带资源与环境学报,2014(2):75-81.
[8]官莉.卫星红外超光谱资料及其在云检测、晴空订正和大气廓线反演方面的应用[D].南京:南京信息工程大学,2005.
[9]GAMBACORTA A. The NOAA unique CrIS/ATMS processing system(NUCAPS):Algorithm theoretical basis documentation[R]. NOAA Center for Weather and Climate Prediction(NCWCP), 2013.
[10] SUN B, REALE A, TILLEY F H, et al. Assessment of NUCAPS S-NPP CrIS/ATMS sounding products using reference and conventional radiosonde observations[J]. IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing, 2017, 99:1-11.
[11]蒋德明,董超华.大气廓线物理反演的最优化方法进展[J].地球科学进展,2010, 25(2):133-139.
[12] SR W L S, WEISZ E, KIREEV S V, et al. Dual-regression retrieval algorithm for real-time processing of satellite ultraspectral radiances[J]. Journal of Applied Meteorology&Climatology, 2012, 51(8):1455-1476.
[13] HAN Y, CHEN Y, JIN X, et al. Cross-track Infrared Sounder(CrIS)Sensor Data Record(SDR)user's guideVersion 1[R].Washington, DC:NOAA Technical ReportNESDIS 143, 2013.
[14] NALLI N R, GAMBACORTA A, LIU Q, et al. Validation of atmospheric profile retrievals from the SNPP NOAAUnique combined atmospheric processing system. Part 1:Temperature and moisture[J]. IEEE Transactions on Geoscience&Remote Sensing, 2017, 99:1-11.
[15]马鹏飞,陈良富,陶金花,等.利用红外高光谱资料CrIS反演大气温湿廓线的模拟研究[J].光谱学与光谱分析,2014, 34(7):1894-1897.
[2] HILTON F, ARMANTE R, AUGUST T, et al. Hyperspectral earth observation from IASI:Five years of accomplishments[J]. Bulletin of the American Meteorological Society, 2012, 93(3):347-370.
[3]余意,张卫民,曹小群,等.同化IASI资料对台风“红霞”和“莫兰蒂”预报的影响研究[J].热带气象学报,2017, 33(4):500-509.
[4]官莉.利用AIRS卫星资料反演大气廓线Ⅰ:特征向量统计反演法[J].大气科学学报,2006, 29(6):756-761.
[5]刘旸,官莉.人工神经网络法反演晴空大气湿度廓线的研究[J].气象,2011,37(3):318-324.
[6]官莉.星载红外高光谱资料的应用[M].北京:气象出版社,2007.
[7]高路,郝璐.ERA-Interim气温数据在中国区域的适用性评估[J].亚热带资源与环境学报,2014(2):75-81.
[8]官莉.卫星红外超光谱资料及其在云检测、晴空订正和大气廓线反演方面的应用[D].南京:南京信息工程大学,2005.
[9]GAMBACORTA A. The NOAA unique CrIS/ATMS processing system(NUCAPS):Algorithm theoretical basis documentation[R]. NOAA Center for Weather and Climate Prediction(NCWCP), 2013.
[10] SUN B, REALE A, TILLEY F H, et al. Assessment of NUCAPS S-NPP CrIS/ATMS sounding products using reference and conventional radiosonde observations[J]. IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing, 2017, 99:1-11.
[11]蒋德明,董超华.大气廓线物理反演的最优化方法进展[J].地球科学进展,2010, 25(2):133-139.
[12] SR W L S, WEISZ E, KIREEV S V, et al. Dual-regression retrieval algorithm for real-time processing of satellite ultraspectral radiances[J]. Journal of Applied Meteorology&Climatology, 2012, 51(8):1455-1476.
[13] HAN Y, CHEN Y, JIN X, et al. Cross-track Infrared Sounder(CrIS)Sensor Data Record(SDR)user's guideVersion 1[R].Washington, DC:NOAA Technical ReportNESDIS 143, 2013.
[14] NALLI N R, GAMBACORTA A, LIU Q, et al. Validation of atmospheric profile retrievals from the SNPP NOAAUnique combined atmospheric processing system. Part 1:Temperature and moisture[J]. IEEE Transactions on Geoscience&Remote Sensing, 2017, 99:1-11.
[15]马鹏飞,陈良富,陶金花,等.利用红外高光谱资料CrIS反演大气温湿廓线的模拟研究[J].光谱学与光谱分析,2014, 34(7):1894-1897.