先进技术微波探测仪（ATMS）云液态水路径算法评估

董嫦娇; 翁富忠

doi:10.11676/qxxb2022.010

先进技术微波探测仪（ATMS）云液态水路径算法评估

doi: 10.11676/qxxb2022.010

董嫦娇^1,,
翁富忠^{2, 3, ,}

1.
南京信息工程大学，南京，210044
2.
中国气象局地球系统数值预报中心，北京，100081
3.
中国气象科学研究院灾害天气国家重点实验室，北京，100081

基金项目: 国家重点研发计划项目（2018YFC1506501、2021YFB3900400）、国家自然科学基金项目（U2142212）

详细信息

作者简介:
董嫦娇，主要从事气象灾害关键参数高精度反演研究。E-mail：dongchangjiao@gmail.com

通讯作者:
翁富忠，主要从事卫星遥感、辐射传输、资料同化等方面研究。E-mail：wengfz@cma.gov.cn

中图分类号: P426
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出版历程
- 收稿日期: 2021-09-15
- 录用日期: 2022-03-11
- 修回日期: 2021-11-29
- 网络出版日期: 2021-11-30

Assessments of cloud liquid water algorithms using advanced technology microwave sounder （ATMS） observations

DONG Changjiao^1
,,
WENG Fuzhong^{2, 3
, ,}

1.
Nanjing University of Information Science and Technology，Nanjing 210044，China
2.
CMA Earth System Modeling and Prediction Centre (CEMC)，Beijing 100081，China
3.
State Key Laboratory of Severe Weather，Chinese Academy of Meteorological Sciences，Beijing 100081，China

摘要

摘要: 云液态水路径是气候和天气系统分析的重要参数，可以从卫星观测资料反演获得。目前，基于卫星微波探测仪器观测资料的云水算法可由23.8和31.4 GHz两个通道产生。本研究使用先进技术微波探测仪（ATMS）观测数据，对物理和经验两种算法反演出的云液态水路径进行验证评估。结果表明，经验算法和物理算法都可以描述云液态水在全球洋面上的分布，但是在中纬度地区数值差异较大。物理反演结果与再分析资料以及卫星可见光云图中的云分布更为一致。在中高纬度地区，经验算法受季节影响较大。灵敏度分析结果表明，物理算法误差受云层温度、海面温度和风速的影响。云层温度的不确定性可能是云液态水路径反演误差的主要来源。海面温度误差影响高液态水路径的反演，风速对低液态水路径的影响比高液态水路径更大。
- 云液态水路径 /
- ATMS探测数据集 /
- 微波遥感
Abstract: Cloud liquid water path is an important parameter in climate analysis and weather applications and is often retrieved from satellite microwave observations. The algorithm requires the data measured at two frequencies at 23.8 and 31.4 GHz. Using the Advanced Technology Microwave Sounder (ATMS) observation data, the physical and empirical algorithms for retrieving the cloud liquid water path were compared. It is shown that both algorithms can well depict the distribution of cloud phase clouds, although the magnitudes at some regions are different. The cloudy areas detected by the physical retrieval algorithm are more consistent with that shown in satellite visible images. The performance of the empirical algorithm, however, is more affected by the season, especially in the mid-high latitudes. The sensitivity analysis indicates that the errors of the physical algorithm are affected by cloud layer temperature, sea surface temperature and sea surface wind speed. The uncertainty in cloud layer temperature is likely a major source of errors in cloud liquid water retrievals. In addition, sea surface temperature errors influence the retrieval of clouds liquid water having a high amount while sea surface wind has more impacts on cloud liquid water having a low amount.
- Cloud liquid water /
- ATMS observations /
- Microwave remote sensing

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图 1 2018年7月8日ATMS全球海面云液态水反演结果（a．ATMS观测亮温经验算法， b. ERA5再分析资料，c. ATMS观测亮温物理算法）

Figure 1. Cloud liquid water retrieved from ATMS over global oceans on 8 July 2018 （a. ATMS brightness temperature using the empirical algorithm，b. ERA5 reanalysis data， c. ATMS brightness temperature using the physical algorithm）

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图 2 2018年7月8日ATMS全球海面大气总可降水反演结果（a. ATMS观测亮温经验算法， b. ERA5再分析资料，c. ATMS观测亮温物理算法）

Figure 2. Atmospheric total precipitable water retrieved from ATMS over global oceans on 8 July 2018 （a. the empirical algorithm，b. ERA5 reanalysis data， c. the physical algorithm）

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图 3 2018年7月8日全球海面云液态水路径差（a. 物理算法与ERA5再分析资料的差，b. 经验算法与ERA5再分析资料的差）

Figure 3. Differences in cloud liquid water path between retrievals and ERA5 on 8 July 2018 （a. differences between the physical retrievals and ERA5， b. differences between the empirical algorithm and ERA5）

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图 4 2018年7月8日全球海面ATMS降轨观测亮温（a. 通道1 （23.8 GHz）观测亮温，b. 通道2 （31.4 GHz）观测亮温）

Figure 4. ATMS brightness temperature from the descending orbit on 8 July 2018 （a. the observed brightness temperature of channel 1 （23.8 GHz）， b. the observed brightness temperature of channel 2 （31.4 GHz））

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图 5 2018年7月8日和ATMS降轨匹配后的全球海面温度分布

Figure 5. Global sea surface temperature distribution matched with ATMS descending orbit on 8 July 2018

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图 6 利用ATMS观测亮温反演的2021年7月20日西北太平洋云液态水路径（a. 经验算法（升轨），b. 物理算法（升轨）， c. 经验算法（降轨），d. 物理算法（降轨））

Figure 6. Cloud liquid water retrieved from ATMS in the Northwest Pacific on 20 July 2021 （a. the empirical algorithm （the ascending orbit），b. the physical algorithm （the ascending orbit），c. the empirical algorithm （the decending orbit），d. the physical algorithm （the decending orbit））

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图 7 利用ATMS观测亮温反演的2021年7月20日西北太平洋大气总可降水量（a. 经验算法（升轨），b. 物理算法（升轨）， c. 经验算法（降轨），d. 物理算法（降轨））

Figure 7. Total precipitable water retrieved from ATMS in the Northwest Pacific on 20 July 2021 （a. the empirical algorithm （the ascending orbit），b. the physical algorithm （the ascending orbit），c. the empirical algorithm （the decending orbit），d. the physical algorithm （the decending orbit））

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图 8 2021年7月20日西北太平洋ATMS观测亮温（a. 通道1 （23.8 GHz）升轨，b. 通道2 （31.4 GHz）升轨，c. 通道1 （23.8 GHz）降轨，d. 通道2 （31.4 GHz）降轨）

Figure 8. Brightness temperatures observed by ATMS in the Northwest Pacific on 20 July 2021 （a. channel 1 （23.8 GHz） ascending orbit，b. channel 2 （31.4 GHz） ascending orbit，c. channel 1 （23.8 GHz） descending orbit，d. channel 2 （31.4 GHz） descending orbit）

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Continued

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图 9 ATMS匹配的2021年7月20日西北太平洋海面风速（a. 升轨，b. 降轨）

Figure 9. Wind speed over the Northwest Pacific Ocean on 20 July 2021 matched with ATMS （a. ascending orbit，b. descending orbit）

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图 10 2021年7月20日西北太平洋可见光云图

Figure 10. VIIRS visible band image over the Northwest Pacific on 20 July 2021

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图 11 2018年云液态水随纬度带分布结果（a. 1月，b. 7月）

Figure 11. Zonal mean cloud liquid water in （a） January and （b） July，2018

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图 12 在不同大气和海洋条件下，由海面温度误差导致的云液态水误差

Figure 12. Cloud liquid water （CLW） errors caused by sea surface temperature （SST） errors under different atmospheric and surface conditions

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图 13 在不同大气和海洋条件下，由海面风速误差导致的云液态水误差

Figure 13. Cloud liquid water （CLW） errors caused by sea surface wind （SSW） speed errors under different atmospheric and surface conditions

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表 1 在AMSU-A四个通道用于总可降水和云液态水算法的计算参数

Table 1. Parameters calculated at four AMSU-A channels and used in total precipitable water and cloud liquid water algorithms

频率	23.8 GHz	31.4 GHz	50.3 GHz	89 GHz
κ_V	4.80423×10⁻³	1.93241×10⁻³	3.76950×10⁻³	1.15839×10⁻³
κ_L−a₁	1.18201×10⁻¹	1.98774×10⁻¹	4.53967×10⁻³	1.03486
κ_L−b₁	−3.48761×10⁻³	−5.45692×10⁻³	−9.68548×10⁻³	−9.71510×10⁻³
κ_L−c₁	5.01301×10⁻⁵	7.18339×10⁻⁵	8.57815×10⁻⁵	−6.59140×10⁻⁵
τ_O−a₀	3.21410×10⁻²	5.34214×10⁻²	6.26545×10⁻¹	1.08333×10⁻¹
τ_O−b₀	−6.31860×10⁻⁵	−1.04835×10⁻⁴	−1.09961×10⁻³	−2.21042×10⁻⁴

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