Assessments of cloud liquid water algorithms using advanced technology microwave sounder (ATMS) observations
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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。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.
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Key words:
- 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)
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))
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))
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)
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 κV 4.80423×10−3 1.93241×10−3 3.76950×10−3 1.15839×10−3 κL−a1 1.18201×10−1 1.98774×10−1 4.53967×10−3 1.03486 κL−b1 −3.48761×10−3 −5.45692×10−3 −9.68548×10−3 −9.71510×10−3 κL−c1 5.01301×10−5 7.18339×10−5 8.57815×10−5 −6.59140×10−5 τO−a0 3.21410×10−2 5.34214×10−2 6.26545×10−1 1.08333×10−1 τO−b0 −6.31860×10−5 −1.04835×10−4 −1.09961×10−3 −2.21042×10−4 -
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