Deep learning bias correction of GEMS tropospheric NO2: A comparative validation of NO2 from GEMS and TROPOMI using Pandora observations

被引:4
|
作者
Ghahremanloo, Masoud [1 ]
Choi, Yunsoo [1 ]
Singh, Deveshwar [1 ]
机构
[1] Univ Houston, Dept Earth & Atmospher Sci, Houston, TX 77004 USA
关键词
Satellite remote sensing; Deep learning bias correction; Geostationary Environment Monitoring; Spectrometer (GEMS); TROPOMI; Pandora observation; Tropospheric NO 2; AIR-QUALITY; SATELLITE RETRIEVALS; EMISSIONS; ABSORPTION; CHEMISTRY; AIRBORNE; IMPACT; OZONE; SHIPS; MODEL;
D O I
10.1016/j.envint.2024.108818
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Despite advancements in satellite instruments, such as those in geostationary orbit, biases continue to affect the accuracy of satellite data. This research pioneers the use of a deep convolutional neural network to correct bias in tropospheric column density of NO2 (TCDNO2) from the Geostationary Environment Monitoring Spectrometer (GEMS) during 2021-2023. Initially, we validate GEMS TCDNO2 against Pandora observations and compare its accuracy with measurements from the TROPOspheric Monitoring Instrument (TROPOMI). GEMS displays acceptable accuracy in TCDNO2 measurements, with a correlation coefficient (R) of 0.68, an index of agreement (IOA) of 0.79, and a mean absolute bias (MAB) of 5.73321 x 1015 molecules/cm2, though it is not highly accurate. The evaluation showcases moderate to high accuracy of GEMS TCDNO2 across all Pandora stations, with R values spanning from 0.46 to 0.80. Comparing TCDNO2 from GEMS and TROPOMI at TROPOMI overpass time shows satisfactory performance of GEMS TCDNO2 measurements, achieving R, IOA, and MAB values of 0.71, 0.78, and 6.82182 x 1015 molecules/cm2, respectively. However, these figures are overshadowed by TROPOMI's superior accuracy, which reports R, IOA, and MAB values of 0.81, 0.89, and 3.26769 x 1015 molecules/cm2, respectively. While GEMS overestimates TCDNO2 by 52 % at TROPOMI overpass time, TROPOMI underestimates it by 9 %. The deep learning bias corrected GEMS TCDNO2 (GEMS-DL) demonstrates a marked enhancement in the accuracy of original GEMS TCDNO2 measurements. The GEMS-DL product improves R from 0.68 to 0.88, IOA from 0.79 to 0.93, MAB from 5.73321 x 1015 to 2.67659 x 1015 molecules/cm2, and reduces MAB percentage (MABP) from 64 % to 30 %. This represents a significant reduction in bias, exceeding 50 %. Although the original GEMS product overestimates TCDNO2 by 28 %, the GEMS-DL product remarkably minimizes this error, underestimating TCDNO2 by a mere 1 %. Spatial cross-validation across Pandora stations shows a significant reduction in MABP, from a range of 45 %-105.6 % in original GEMS data to 24 %-59 % in GEMS-DL.
引用
收藏
页数:11
相关论文
共 50 条
  • [1] NitroNet - a machine learning model for the prediction of tropospheric NO2 profiles from TROPOMI observations
    Kuhn, Leon
    Beirle, Steffen
    Osipov, Sergey
    Pozzer, Andrea
    Wagner, Thomas
    ATMOSPHERIC MEASUREMENT TECHNIQUES, 2024, 17 (21) : 6485 - 6516
  • [2] Validation of GEMS tropospheric NO2 columns and their diurnal variation with ground-based DOAS measurements
    Lange, Kezia
    Richter, Andreas
    Boesch, Tim
    Zilker, Bianca
    Latsch, Miriam
    Behrens, Lisa K.
    Okafor, Chisom M.
    Boesch, Hartmut
    Burrows, John P.
    Merlaud, Alexis
    Pinardi, Gaia
    Fayt, Caroline
    Friedrich, Martina M.
    Dimitropoulou, Ermioni
    Van Roozendael, Michel
    Ziegler, Steffen
    Ripperger-Lukosiunaite, Simona
    Kuhn, Leon
    Lauster, Bianca
    Wagner, Thomas
    Hong, Hyunkee
    Kim, Donghee
    Chang, Lim-Seok
    Bae, Kangho
    Song, Chang-Keun
    Park, Jong-Uk
    Lee, Hanlim
    ATMOSPHERIC MEASUREMENT TECHNIQUES, 2024, 17 (21) : 6315 - 6344
  • [3] Tropospheric NO2 retrieval algorithm for geostationary satellite instruments: applications to GEMS
    Seo, Sora
    Valks, Pieter
    Lutz, Ronny
    Heue, Klaus-Peter
    Hedelt, Pascal
    Molina Garcia, Victor
    Loyola, Diego
    Lee, Hanlim
    Kim, Jhoon
    ATMOSPHERIC MEASUREMENT TECHNIQUES, 2024, 17 (20) : 6163 - 6191
  • [4] Analysis of tropospheric variability of NO2 emissions over India as observed by GEMS satellite
    Varchand, Hasmukh K.
    Desai, Dhruv D.
    Dave, Jalpesh A.
    Parmar, Parthkumar N.
    Shah, Dhiraj B.
    Pathak, Vishal N.
    Singh, Manoj
    Trivedi, Himanshu J.
    Pandya, Mehul R.
    CURRENT SCIENCE, 2025, 128 (01): : 104 - 107
  • [5] Assessment of the TROPOMI tropospheric NO2 product based on airborne APEX observations
    Tack, Frederik
    Merlaud, Alexis
    Iordache, Marian-Daniel
    Pinardi, Gaia
    Dimitropoulou, Ermioni
    Eskes, Henk
    Bomans, Bart
    Veefkind, Pepijn
    Van Roozendael, Michel
    ATMOSPHERIC MEASUREMENT TECHNIQUES, 2021, 14 (01) : 615 - 646
  • [6] Long-term observations of NO2 using GEMS in China: Validations and regional transport
    Li, Yikai
    Xing, Chengzhi
    Peng, Haochen
    Song, Yuhang
    Zhang, Chengxin
    Xue, Jingkai
    Niu, Xinhan
    Liu, Cheng
    SCIENCE OF THE TOTAL ENVIRONMENT, 2023, 904
  • [7] Quality Evaluation and Improvement of Hourly Tropospheric NO2 Data from GEMS: A Case Study in East China
    Gao, Hongrui
    Qin, Kai
    He, Qin
    Kang, Junting
    ACTA OPTICA SINICA, 2024, 44 (24)
  • [8] Quantifying the diurnal variation in atmospheric NO2 from Geostationary Environment Monitoring Spectrometer (GEMS) observations
    Edwards, David P.
    Martinez-Alonso, Sara
    Jo, Duseong S.
    Ortega, Ivan
    Emmons, Louisa K.
    Orlando, John J.
    Worden, Helen M.
    Kim, Jhoon
    Lee, Hanlim
    Park, Junsung
    Hong, Hyunkee
    ATMOSPHERIC CHEMISTRY AND PHYSICS, 2024, 24 (15) : 8943 - 8961
  • [9] New observations of NO2 in the upper troposphere from TROPOMI
    Marais, Eloise A.
    Roberts, John F.
    Ryan, Robert G.
    Eskes, Henk
    Boersma, K. Folkert
    Choi, Sungyeon
    Joiner, Joanna
    Abuhassan, Nader
    Redondas, Alberto
    Grutter, Michel
    Cede, Alexander
    Gomez, Laura
    Navarro-Comas, Monica
    ATMOSPHERIC MEASUREMENT TECHNIQUES, 2021, 14 (03) : 2389 - 2408
  • [10] Assessments of the GEMS NO2 Products Using Ground-Based Pandora and In-Situ Instruments over Busan, South Korea
    Kim, Serin
    Jeong, Ukkyo
    Lee, Hanlim
    Jung, Yeonjin
    Kim, Jae Hwan
    KOREAN JOURNAL OF REMOTE SENSING, 2024, 40 (01) : 1 - 8