A study on the inversion of atmospheric temperature and humidity profiles by using CrIS infrared hyperspectral satellite data
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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 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.
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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. -
图 1 高光谱红外探测仪CrIS观测方式的空间分辨率图
Fig. 1 Spatial resolution map of CrIS observation mode for the hyperspectral infrared detector
图 2 基于美国标准大气模拟晴空条件下的CrIS亮温分布
Fig. 2 CrIS brightness-temperature distribution based on American Standard atmosphere simulated clear air conditions
图 6 相对湿度廓线的RMSE (a)和BIAS(b)
Fig. 6 The RMSE (a) and BIAS (b) of the relative humidity profile
图 7 700 hPa高度层上一景的NUCAPS温度(a)、NUCAPS相对湿度(b)、D-R温度(c)、D-R相对湿度(d)、ECMWF温度(e)和ECMWF相对湿度(f)
注: 温度单位为K, 相对湿度单位为%
Fig. 7 Temperature (NUCAPS (a), D-R (c), ECMWF (e)) and relative humidity (NUCAPS (b), D-R (d), ECMWF (f)) at a location at a height levelof 700 hPa
表 1 高光谱红外探测仪CrIS的三个波段的参数
Tab. 1 Parameters for the three bands of the hyperspectral infrared detector CrIS
分辨率/cm$^{-1}$ 光谱范围/cm$^{-1}$ 通道数 长波红外 0.625 648.75$\sim $1 096.25 717 中波红外 1.25 1 207.5$\sim $1 752.5 437 短波红外 2.5 2 150$\sim $2 555 163 
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表 2 选取的ECMWF的廓线样本所对应的时间以及个数
Tab. 2 The date and number of samples of the selected ECMWF's profiles
时间 样本数 2012年6月 8 531 2012年7月 8 199 2012年8月 4 661 2012年9月 14 531 2013年6月 9 006 2013年7月 10 359 2013年8月 16 168 2013年9月 6 718 总计 78 173
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表 3 NUCAPS算法与D-R算法的对比
Tab. 3 The difference between the NUCAPS algorithm and the D-R algorithm
NUCAPS(NOAA) Dual-Regression(UW/CIMSS) 统计回归与物理解 统计回归解 反演红外与微波波段 只反演红外波段 云下也反演 深厚云层下无法反演 3$\times $3视场角(50km) 单个视场角(14km) 目前用于CrIS 用于多种传感器(AIRS、IASI、CrIS) 用于业务 用于研究
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表 4 温湿度廓线反演结果统计
Tab. 4 Statistics of inversion results from the temperature and humidity profiles
高度层/hPa 温度RMSE/K 温度BIAS/K 湿度RMSE/% 湿度BIAS/% D-R NUCAPS D-R NUCAPS D-R NUCAPS D-R NUCAPS 100 1.54 1.44 -0.77 -0.31 27.33 12.13 23.16 6.13 200 1.35 1.16 0.47 -0.15 23.19 9.99 17.77 3.50 300 1.09 1.26 -0.16 -0.70 23.30 11.95 19.54 5.51 400 1.07 1.16 -0.38 -0.37 14.26 13.70 3.92 1.25 500 1.10 1.08 -0.61 -0.16 15.44 15.27 3.99 0.63 600 0.99 1.04 -0.31 -0.28 17.96 15.13 0.70 1.70 700 1.27 1.30 -0.22 -0.44 17.90 16.19 -6.61 2.22 800 1.55 1.70 -0.36 -0.61 22.03 21.51 -8.45 1.50 900 1.89 1.90 0.21 -0.44 17.81 16.97 -12.87 -7.41 1 000 1.97 2.60 0.27 -1.41 13.64 16.27 1.77 9.63 整层平均 1.34 1.44 -0.18 -0.49 19.78 14.04 6.54 2.54
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