收稿：2021-04-15，
纸质出版：2021-08-07
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作为中国发射的风云四号系列首颗卫星，FY-4A搭载了先进的静止轨道辐射成像仪AGRI。本文利用AGRI传感器的中红外到热红外波段观测数据、ERA5水汽再分析产品以及全球无线电探空数据集IGRA等数据进行晴空大气可降水量反演研究。在海洋表面，分别利用回归分析法与随机森林算法反演大气可降水量，反演结果与ERA5水汽产品相比，均方根误差为0.493 cm和0.247 cm。在陆地表面，利用随机森林模型反演大气可降水量，反演结果与ERA5水汽产品相比，均方根误差为0.155 cm；与IGRA水汽产品相比，均方根误差为0.215 cm。结果表明本文使用的随机森林算法可以有效地提升热红外遥感数据反演大气可降水量的精度。
Atmospheric water vapour
an important component of global greenhouse gases
is one of the key factors influencing global climate change
hydrologic cycle
matter-energy exchange and so on. The total water vapour content in the atmosphere is usually quantified with the parameter of Total Precipitable Water (TPW). As the first launched satellite of Chinese Fengyun-4 series
FY-4A satellite has been equipped with the Advanced Geosynchronous Radiation Imager (AGRI). This paper aims to retrieve the TPW under the clear-sky atmosphere with the combination of the middle- and thermal-infrared remote sensing observations of AGRI
ERA5 total column water vapour reanalysis product and Integrated Global Radiosonde Archive (IGRA).
On the ocean surface
the regression analysis method and random forest algorithm have been introduced to retrieve the TPW successively. In random forest modeling
the standard deviation of every regression tree’s output value is used as quality control criterion
and the retrieval results are considered as reliable only when the standard deviation is less than 0.4 cm.
On the land surface
the Split-Window Covariance-Variance Ratio (SWCVR) method and random forest algorithm have been developed to retrieve the TPW
and the elevation and Normalized Difference Vegetation Index (NDVI) estimated at the top of atmosphere have been added to the random forest model in order to reflect the information of land surface.
In terms of TPW retrieval over ocean
the determination coefficient is 0.817
the Root Mean Square Error (RMSE) is 0.493 cm
and the Mean Absolute Error (MAE) of the retrieval results is 0.393 cm compared with the ERA5 total column water vapour product when using the regression analysis method. For the random forest model
the quality control passing rate of retrieval results accounts for 84.0%
the determination coefficient is 0.956
RMSE is 0.247 cm
and MAE is 0.183 cm when compared with the ERA5 total column water vapour product.
In terms of TPW retrieval over land
for the reason that the spatial resolution of AGRI is only 4 km
the SWCVR method is unable to retrieve the TPW accurately using the observations of AGRI. For the random forest model
the passing rate of retrieval results accounts for 93.2%
the determination coefficient is 0.972
RMSE is 0.155 cm
and MAE is 0.106 cm when compared with the ERA5 total column water vapour product. In the end
the radio-sounding water vapour is introduced to evaluate the accuracy of the random forest model
and the passing rate of retrieval results accounts for 75.6%
the determination coefficient is 0.946
RMSE is 0.215 cm
and MAE is 0.163 cm.
First of all
TPW over ocean can be retrieved with the middle- and thermal-infrared remote sensing observations of AGRI by using the regression analysis method and random forest algorithm
and the retrieval accuracy of random forest model is obviously higher. Besides
the SWCVR method is unable to retrieve the TPW over land accurately due to the low spatial resolution of AGRI
meanwhile the random forest algorithm performs well. The results indicate that the random forest algorithm can effectively improve the TPW retrieval accuracy with thermal infrared remote sensing observations.
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