Evaluation of the Planetary Boundary Layer Height From ERA5 Reanalysis With MOSAiC Observations Over the Arctic Ocean

被引:0
|
作者
Xi, Xingya [1 ,2 ]
Yang, Qinghua [1 ,2 ]
Liu, Changwei [1 ,2 ]
Shupe, Matthew D. [3 ,4 ]
Han, Bo [1 ,2 ]
Peng, Shijie [1 ,2 ]
Zhou, Shaohui [5 ]
Chen, Dake [1 ,2 ]
机构
[1] Sun Yat Sen Univ, Sch Atmospher Sci, Zhuhai, Peoples R China
[2] Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai, Peoples R China
[3] Univ Colorado, Cooperat Inst Res Environm Sci, Boulder, CO USA
[4] NOAA, Phys Sci Lab, Boulder, CO USA
[5] Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteorol, Sch Atmospher Phys, Key Lab Aerosol Cloud Precipitat China Meteorol Ad, Nanjing, Peoples R China
基金
美国国家科学基金会; 中国国家自然科学基金;
关键词
planetary boundary layer height; ERA5; MOSAiC; random forest; RADIOSONDE; SURFACE; MODEL; CLOUD; LIDAR; SUMMER; DEPTH; TEMPERATURE; STABILITY; CHINA;
D O I
10.1029/2024JD040779
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
The planetary boundary layer height (PBLH) is a crucial indicator reflecting the region of the atmosphere characterized by continuous turbulence. Here, we use radiosonde and surface meteorological observations (4-7 times per day, year-round measurements) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition to derive the PBLH (PBLHMOSAiC), and further evaluate the PBLH from the ERA5 reanalysis (PBLHERA5). Comparisons between PBLHMOSAiC and PBLHERA5 from different perspectives reveal that: (a) The overestimation of PBLHERA5 when the sea ice concentration is >90% is significant with the centered root mean squared error reaching up to 201 m; (b) The difference between the two products is notably pronounced in cold seasons, while it is comparatively diminished in warm seasons; (c) In neutral boundary layers, differences in PBLHERA5 are larger compared with stable and convective boundary layers. In addition, the analysis of error sources indicates that the bias of PBLHERA5 is sensitive to the bias of vertical thermal structure and wind speed profiles in ERA5 data sets in all conditions. Finally, we find a Random Forest model effectively reduces the bias of PBLHERA5 with the index of agreement reaching up to 0.71 in the test data set, while a multiple linear regression demonstrates comparable performance to the Random Forest model.
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页数:17
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