Mengshi Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Mengshi Li has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Fuxing Li
State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
1 shared publication - Jifu Duan
School of Geographic Sciences, Hebei Normal University, Shijiazhuang 050024, China
1 shared publication - Lihang Fan
School of Geographic Sciences, Hebei Normal University, Shijiazhuang 050024, China
1 shared publication - Wei Wang
College of Geography Science, Hebei Normal University, Shijiazhuang, China
1 shared publication - Yang Wang
School of Geographical Sciences, Hebei Normal University, Shijiazhuang, China
1 shared publication - Yi Yang
School of Geographic Sciences, Hebei Normal University, Shijiazhuang 050024, China
1 shared publication
Latest Publications
- Open Access
- 1 February 2023
- Published by MDPI AG in Sustainability
- Vol. 15 (3) , 2609
Nesting Elterman Model and Spatiotemporal Linear Mixed-Effects Model to Predict the Daily Aerosol Optical Depth over the Southern Central Hebei Plain, China
Aerosol optical depth (AOD), an important indicator of atmospheric aerosol load, characterizes the impacts of aerosol on radiation balance and atmospheric turbidity. The nesting Elterman model and a spatiotemporal linear mixed-effects (ST-LME) model, which is referred to as the ST-Elterman retrieval model (ST-ERM), was employed to improve the temporal resolution of AOD prediction. This model produces daily AOD in the Southern Central Hebei Plain (SCHP) region, China. Results show that the ST-ERM can effectively capture the variability of correlations between daily AOD and meteorological variables. After being validated against the daily Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD, the correlation coefficient between daily retrieved AOD from ST-ERM and MAIAC observations in 2017 reached 0.823. The validated Nash–Sutcliffe efficiency (Ef) of daily MAIAC AOD and ST-ERM-retrieved AOD is greater than or equal to 0.50 at 72 of the 95 stations in 2017. The relative error (Er) is less than 14% at all the stations except for Shijiazhuang (17.5%), Fengfeng (17.8%), and Raoyang (30.1%) stations. The ST-ERM significantly outperforms the conventional meteorology–AOD prediction approaches, such as the revised Elterman retrieval model (R-ERM). Thus, the ST-ERM shows great potential for daily AOD estimation in study regions with missingness of data.
- 1 February 2023
- Published by MDPI AG in Sustainability
- Vol. 15 (3) , 2609
Open AccessNesting Elterman Model and Spatiotemporal Linear Mixed-Effects Model to Predict the Daily Aerosol Optical Depth over the Southern Central Hebei Plain, China
Aerosol optical depth (AOD), an important indicator of atmospheric aerosol load, characterizes the impacts of aerosol on radiation balance and atmospheric turbidity. The nesting Elterman model and a spatiotemporal linear mixed-effects (ST-LME) model, which is referred to as the ST-Elterman retrieval model (ST-ERM), was employed to improve the temporal resolution of AOD prediction. This model produces daily AOD in the Southern Central Hebei Plain (SCHP) region, China. Results show that the ST-ERM can effectively capture the variability of correlations between daily AOD and meteorological variables. After being validated against the daily Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD, the correlation coefficient between daily retrieved AOD from ST-ERM and MAIAC observations in 2017 reached 0.823. The validated Nash–Sutcliffe efficiency (Ef) of daily MAIAC AOD and ST-ERM-retrieved AOD is greater than or equal to 0.50 at 72 of the 95 stations in 2017. The relative error (Er) is less than 14% at all the stations except for Shijiazhuang (17.5%), Fengfeng (17.8%), and Raoyang (30.1%) stations. The ST-ERM significantly outperforms the conventional meteorology–AOD prediction approaches, such as the revised Elterman retrieval model (R-ERM). Thus, the ST-ERM shows great potential for daily AOD estimation in study regions with missingness of data.