华东师范大学学报(自然科学版) ›› 2019, Vol. 2019 ›› Issue (3): 199-210.doi: 10.3969/j.issn.1000-5641.2019.03.021

• 地理学 • 上一篇    

利用CrIS红外高光谱卫星数据反演大气温湿度廓线的研究

沈振翔1,2, 刘朝顺1,2   

  1. 1. 华东师范大学 地理信息科学教育部重点实验室, 上海 200241;
    2. 华东师范大学 地理科学学院, 上海 200241
  • 收稿日期:2018-03-16 出版日期:2019-05-25 发布日期:2019-05-30
  • 通讯作者: 刘朝顺,男,博士,副研究员,研究方向为环境遥感.E-mail:csliu@re.ecnu.edu.cn. E-mail:csliu@re.ecnu.edu.cn
  • 作者简介:沈振翔,男,硕士研究生,研究方向为大气遥感与应用.E-mail:616063531@qq.com.
  • 基金资助:
    上海市自然科学基金（17ZR1408600）

A study on the inversion of atmospheric temperature and humidity profiles by using CrIS infrared hyperspectral satellite data

SHEN Zhen-xiang1,2, LIU Chao-shun1,2   

  1. 1. Key Laboratory of Geographic Information Science(Ministry of Education), East China Normal University, Shanghai 200241, China;
    2. School of Geographic Sciences, East China Normal University, Shanghai 200241, China
  • Received:2018-03-16 Online:2019-05-25 Published:2019-05-30

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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-orbitingPartnership）卫星上的新一代跨轨红外探测器CrIS（Cross-track InfraredSounder）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-RangeWeather 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 AtmosphericAdministration）官方算法NUCAPS（NOAA Unique Combined AtmosphericProcessing 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 squareerror）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.

关键词: CrIS, 红外高光谱, 温湿度廓线, Dual-Regression算法

Abstract: 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 1K, and the RMSE (root mean square error) is basically within 2 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.

Key words: CrIS, infrared hyperspectral, temperature and humidity profile, DualRegression algorithm

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