4.6 Article

Data Assimilation of AOD and Estimation of Surface Particulate Matters over the Arctic

Journal

APPLIED SCIENCES-BASEL
Volume 11, Issue 4, Pages -

Publisher

MDPI
DOI: 10.3390/app11041959

Keywords

CMAQ model; MODIS; AERONET; aerosol optical depth (τ ); optimal interpolation; Arctic; data assimilation; PMs

Funding

  1. National Research Foundation of Korea Grant from the Korean Government (MSIT
  2. the Ministry of Science and ICT) [NRF-2016M1A5A1901769, KOPRIPN20081]

Ask authors/readers for more resources

This study calculated more accurate information on aerosol optical depth (AOD) using the optimal interpolation method, and estimated more realistic levels of surface particulate matters over the Arctic. The newly inferred monthly averages of PM10 and PM2.5 showed a significant increase compared to the modeled PMs.
In this study, more accurate information on the levels of aerosol optical depth (AOD) was calculated from the assimilation of the modeled AOD based on the optimal interpolation method. Additionally, more realistic levels of surface particulate matters over the Arctic were estimated using the assimilated AOD based on the linear relationship between the particulate matters and AODs. In comparison to the MODIS observation, the assimilated AOD was much improved compared with the modeled AOD (e.g., increase in correlation coefficients from -0.15-0.26 to 0.17-0.76 over the Arctic). The newly inferred monthly averages of PM10 and PM2.5 for April-September 2008 were 2.18-3.70 mu g m(-3) and 0.85-1.68 mu g m(-3) over the Arctic, respectively. These corresponded to an increase of 140-180%, compared with the modeled PMs. In comparison to in-situ observation, the inferred PMs showed better performances than those from the simulations, particularly at Hyytiala station. Therefore, combining the model simulation and data assimilation provided more accurate concentrations of AOD, PM10, and PM2.5 than those only calculated from the model simulations.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available