# Data Assimilation of AOD and Estimation of Surface Particulate Matters over the Arctic  
[PERSON]  
1 School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea; [EMAIL_ADDRESS] (S-Y.P. ); [EMAIL_ADDRESS] (K.K. ); [EMAIL_ADDRESS] (H.S.K.) 2  
[PERSON]  
3 Department of Health Management, Kyung-in Women's University, Incheen 21041, Korea; [EMAIL_ADDRESS] 3  
[PERSON]  
4 Korea Institute of Atmospheric Prediction System, Seoul 07071, Korea; [EMAIL_ADDRESS] 4  
[PERSON]  
5 Department of Safety and Environment Research, The Seoul Institute, Seoul 06756, Korea; [EMAIL_ADDRESS] 5  
[PERSON]  
5 Department of Safety and Environment Research, The Seoul Institute, Seoul 06756, Korea; [EMAIL_ADDRESS] 5  
[PERSON]
Many observations for gaseous species, particulate matters, and clouds have been carried out at several stations of Ny-Alesund (Svalbard), Pallas-Sodankyla (Finland), Villum (Greenland), Summit (Greenland), Alert (Canada), Eureka (Canada), Oliktok Point (USA), Barrow (USA), Cherskii (Russia), Tiksi (Russia), Thule (Greenland), Hornsund (Svalbard), and Andenes (Norway) over the Arctic regions to examine the global climate changes, air pollution, and their interaction [12; 13; 14; 15; 16; 17; 18]. Since these observations were spatially sparse, there was a spatial limitation in providing detailed atmospheric information over the Arctic. However, it is hard to measure a variety of chemical/physical parameters and species in three dimensions at the same time. Additionally, in the Arctic regions, there are many restrictions on research activities due to extreme weather conditions. Nevertheless, there have been observational efforts to obtain three-dimensional and various information. The intensive field campaigns, including aircraft-based observation, have been conducted temporarily in a limited way for several weeks in spring and summer under good weather, as follows: (i) Arctic Study of Tropospheric Aerosol, Clouds and Radiation (ASTAR) [19], (ii) Aerosol, Radiation, and Cloud Processes affecting Arctic Climate (ARCPAC) [20], (iii) Arctic Research of Composition of the Troposphere from Aircraft and Satellite-Phase A and B (ARCTAS-A and B) [21; 22], and (iv) POLARCAT-Greenland Aerosol and Chemistry Experiment (POLARCAT-GRACE) [23] campaigns during the International Polar Year (IPY) in 2007 and 2008. Satellite observation and model simulation are alternatives for large and extreme environmental regions such as the Arctic [24]. However, these tools also have the following advantages and limitations. Although satellite-observed data is relatively more accurate than the model simulation, there are also limitations in spatial and temporal information. While the model simulation provides perfect spatial information for all atmospheric variables on an hourly basis, the accuracy of the simulated data is relatively low due to the uncertainties related to emissions, boundary conditions, and parameterization for aerosol calculation and atmospheric chemistry [25; 26]. In particular, since the atmospheric levels of PMs are known to be low over the Arctic, the uncertainty in (ship) emissions and information on pollutants transported from Europe, Russia, and Asia strongly links with the performance of model simulation in the Arctic. To solve the issue, many efforts have been made to conduct the data assimilation of PMs and AOD based on the variational approach (e.g., 3 DVAR and 4 DVAR) [27; 28; 29; 30; 31; 32; 33; 34; 35], sequential approach (e.g., optimal interpolation, Ensemble Kalman Filter) [36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49], and the combined method [50; 51]. For example, [PERSON] et al. developed the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) three-dimensional variational (3 DVAR) data assimilation system applied to a dust storm in East Asia [30]. [PERSON] et al.