日本地球惑星科学連合2021年大会

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セッション記号 A (大気水圏科学) » A-AS 大気科学・気象学・大気環境

[A-AS05] 大気化学

2021年6月6日(日) 13:45 〜 15:15 Ch.08 (Zoom会場08)

コンビーナ:中山 智喜(長崎大学 大学院水産・環境科学総合研究科)、齋藤 尚子(千葉大学環境リモートセンシング研究センター)、豊田 栄(東京工業大学物質理工学院)、内田 里沙(一般財団法人 日本自動車研究所)、座長:峰島 知芳(国際基督教大学)

13:45 〜 14:00

[AAS05-12] Validation and correction of TROPOMI tropospheric NO2 column density data using 4AZ-MAXDOAS at Chiba, Japan

*齊藤 輝1、入江 仁士1、Damiani Alessandro1 (1.千葉大学)

キーワード:大気、衛星観測、二酸化窒素、大気科学、大気化学

To quantify and solve the reported negative bias in tropospheric nitrogen dioxide (tropNO2) column data from the latest sensor TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite, we conducted ground-based four-different-azimuth-viewing multi-axis differential optical absorption spectroscopy (4AZ-MAXDOAS) observations at Chiba, Japan (35.63ºN, 140.10ºE, 21 m a.s.l.) in 2019. A comparison with simultaneous tropNO2 observations by 4AZ-MAXDOAS enabled an evaluation of the spatial inhomogeneity of tropNO2, which was considered responsible for a bias in results. Supported by inter-directional differences of up to 40% among 4AZ-MAXDOAS data, we found a significant horizontal spatial inhomogeneity in tropNO2 around the observation site. We then compared 4AZ-MAXDOAS data with coincident TROPOMI data. TROPOMI data had a 53% smaller tropNO2 column density as an annual average compared to 4AZ-MAXDOAS data, confirming the negative bias in TROPOMI data. However, the correlation between the magnitude of the bias and the coefficient of variance in four-azimuth directional data from 4AZ-MAXDOAS observations was not evident. This suggested that NO2 horizontal inhomogeneity couldn't fully explain the observed bias. We also applied a correction using the tropNO2 vertical profiles from 4AZ-MAXDOAS and the averaging kernels from TROPOMI. The correction reduced the bias to ~30% for annual average data, depending on the tropNO2 profiles used, indicating that there was a significant contribution of tropNO2 vertical profiles to the bias. These results not only quantified the reported negative bias but also emphasized the effectiveness of validation and correction using multi-directional ground-based data.