基于加密探空资料的上海不同云天下AIRS/Aqua相对湿度廓线反演精度研究

蒋文泽; 刘琼; 陈勇航; 黄艺伟; 赵兵科; 吴峻石; 周海江; 陈帅妤

doi:10.16032/j.issn.1004-4965.2022.027

基于加密探空资料的上海不同云天下AIRS/Aqua相对湿度廓线反演精度研究

doi: 10.16032/j.issn.1004-4965.2022.027

1.
东华大学环境科学与工程学院，上海 201620
2.
中国气象科学研究院灾害天气国家重点实验室，北京 100081
3.
中国气象局上海台风研究所，上海 200030
4.
宝山区气象局，上海 201901

基金项目:

国家重点研发计划 2018YFC1506305

国家重点研发计划 2017YFA0603502

国家自然科学基金项目 41975029

灾害天气国家重点实验室开放课题 2018LASW-B11

详细信息

通讯作者:
刘琼，女，江西省人，讲师，博士，主要从事大气环境遥感方面的研究。E-mail: lindall1987@163.com

中图分类号: P412.2
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- 被引次数: 0
出版历程
- 收稿日期: 2020-12-07
- 修回日期: 2021-10-18
- 网络出版日期: 2022-06-11
- 刊出日期: 2022-04-20

RESEARCH ON RETRIEVAL ACCURACY OF AIRS/AQUA RELATIVE HUMIDITY PROFILE UNDER DIFFERENT CLOUD CONDITIONS IN SHANGHAI BASED ON ENCRYPTED SOUNDING DATA

1.
School of Environmental Science and Engineering, Donghua University, Shanghai 201620, China
2.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
3.
Shanghai Typhoon Institute, China Meteorological Administration, Shanghai 200030, China
4.
Meteorological Service of Baoshan District, Shanghai 201901, China

摘要

摘要: 目前云对卫星相对湿度廓线反演精度的影响研究大多是针对云量，对其他云属性的影响研究尚少，云高也是影响卫星相对湿度廓线反演精度的重要因素。利用上海宝山站L波段(1型)加密探空资料，分析了上海地区7—9月不同质量控制标识、云量和云顶高度条件下大气红外探测器AIRS/Aqua (Atmospheric Infrared Sounder) 相对湿度廓线的反演精度，以期为今后开展AIRS等卫星资料的同化研究提供科学依据。结果表明：(1)AIRS相对湿度廓线反演误差随着云量的增加而逐渐增大，并且随着气压值的升高，少云与多云时的均方根误差(Root Mean Squared Error, RMSE)之差有逐渐增大的趋势；(2)云顶高度越高，AIRS相对湿度廓线反演精度越差，云顶以上湿度廓线反演精度更高，而云顶以下高度的反演误差较大；(3)高云且多云时，AIRS相对湿度廓线的反演精度最差，850 hPa处，AIRS相对湿度反演数据与探空资料绝对误差的下限达到了[-63.51%]；(4)虽然质量控制标识为0时，AIRS湿度廓线在对流层范围内的反演精度仍达不到无线电探空的水平，但是相对于质量控制标识1时，反演精度明显提高。
- AIRS/Aqua /
- 相对湿度廓线 /
- 加密探空资料 /
- 云量 /
- 云顶高度
Abstract: At present, most of the studies on the influence of cloud on the retrieval accuracy of satellite relative humidity (RH) profiles are mainly concerned with cloud fraction, while there are few studies on the influence of other cloud attributes. Cloud top height is also an important factor affecting the retrieval accuracy of satellite RH profiles. In this study, the L-band encrypted radiosonde data from Shanghai Baoshan Station was used to analyze the accuracy of AIRS/Aqua (Atmospheric Infrared Sounder) retrieved RH profiles under different quality control and cloud conditions from July to September. The purpose of this study is to make preparation for the assimilation of relevant data and to assess the influence of AIRS RH products on the accuracy of regional numerical prediction. The results show that: (1) The error of the AIRS retrieved RH profiles gradually increases with the increase of cloud fraction. With the increase of atmospheric pressure, the difference of the root mean squared errors of RH in each pressure layer tends to increase gradually when there are few clouds or it is cloudy. (2) The higher the cloud top height is, the worse the accuracy of AIRS retrieved RH profiles is. The retrieval accuracy of RH profiles above cloud top is higher, while the retrieval error below cloud top is larger. (3) Under high-level cloud and cloudy conditions, the accuracy of the AIRS retrieved RH profiles is the worst. At 850hPa, the minimum absolute error between the RH data retrieved by AIRS and the radiosonde data reaches -63.51%. (4) Although the accuracy of AIRS retrieved RH profiles in the troposphere is not as high as that of radiosonde when the quality control is 0, the retrieval accuracy is obviously improved compared with that when the quality control is 1.
- AIRS/Aqua /
- relative humidity profiles /
- encrypted sounding data /
- cloud fraction /
- cloud top height

HTML全文

图 1 50~1 000 hPa高度范围内AIRS质量控制标识为0和1时相对湿度反演数据的MB(a)和RMSE(b)

a中的误差线是各气压层统计的MB的标准差，下同。

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图 2 AIRS质量控制标识为0和1时相对湿度反演数据和与之匹配的探空观测散点图

红色实线表示两者之间的线性拟合、绿色实线表示y = x。

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图 3 不同云量条件下50~1 000 hPa高度范围内AIRS相对湿度反演数据的MB(a)和RMSE(b)

下载: 全尺寸图片幻灯片

图 4 不同云顶高度条件下50~1 000 hPa高度范围内AIRS相对湿度反演数据的MB(a)和RMSE(b)

下载: 全尺寸图片幻灯片

图 5 不同云量和云顶高度条件下50~1 000 hPa高度范围内AIRS相对湿度反演数据的箱式图

a~d为低云且少云、高云且少云、低云且多云、高云且多云时相对湿度反演情况。

下载: 全尺寸图片幻灯片

表 1 2018年7—9月AIRS和探空数据的相对湿度廓线精度比较

气压层/hPa	相关系数R	P(F检验)	相对湿度MB/%	相对湿度RMSE/%	相对湿度样本数
50	0.45	1.92x10^-3	-16.18	17.89	46
70	0.45	1.61X10^-3	-8.38	11.98	47
100	0.51	2.93x10^-4	18.85	25.80	47
150	0.52	1.23x10^-4	13.92	19.80	49
200	0.57	2.30x10^-5	10.00	16.69	49
250	0.73	3.40x10^-9	11.27	17.87	48
300	0.71	1.48x10^-8	10.49	20.64	48
400	0.82	3.64x10^-12	8.44	16.60	46
500	0.75	2.66x10^-9	6.35	16.55	45
600	0.82	1.00x10^-11	-0.60	16.50	44
700	0.76	6.31x10^-9	-8.31	20.12	42
850	0.26	0.11	-16.23	26.44	39
925	0.44	4.74x10^-3	-10.83	17.74	40
1 000	0.42	7.82x10^-3	5.17	14.46	39

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