A regional scale chemical transport modeling of Asian aerosols with data assimilation of AOD observations using optimal interpolation technique

Adhikary, Bhupesh; Kulkarni, S.; D’Allura, A.; Tang, Youhua; Chai, Tianfeng; Leung, L. Ruby; Qian, Yun; Chung, Chien; Ramanathan, V.; Carmichael, Gregory R.

doi:10.1016/j.atmosenv.2008.08.031

Cited by 112 publications

(99 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are several studies on aerosol optical properties seen from satellites and AERONET measurement stations (Ramachandran, 2007;Ramachandran and Cherian, 2008). In recent modeling studies, for instance seasonal cycle characterization and climate impact assessment has been made and satellite measurements of aerosol optical depth have been assimilated with chemical transport models Roeckner et al, 2006b;Adhikary et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Spatial distributions and seasonal cycles of aerosols in India and China seen in global climate-aerosol model

et al. 2011

View full text Add to dashboard Cite

Abstract.A climate-aerosol model is employed to study spatial and temporal variability of aerosol properties over India and China for recent (year 2006) and future conditions (year 2020) under different emission pathways. We present results for aerosol mass concentration in different size classes and optical properties for the five different aerosol species treated by the model. Aerosol mass concentration and optical depth have significant contributions from both anthropogenic and natural aerosols. Different species have maxima in different regions, with the highest anthropogenic aerosol concentrations found in Kolkata and elsewhere in the Ganges basin in India and on the northern part of the east coast and in the Sichuan basin in China. In India, natural aerosols have a maximum in the summer due to higher wind speeds, whereas anthropogenic aerosols have a maximum in the winter due to less efficient wet removal. Surface concentrations also tend to be higher in winter due to the additional reason of lower average boundary layer height. In China, seasonal cycles are weaker with natural aerosols having a maximum in the spring and sulfate contribution to the aerosol optical depth (AOD) being higher in the latter half of the year. MODIS AOD spatial distributions are reproduced well by the model, except for the Ganges valley with high absorption and for the Thar desert with high dust concentrations. Seasonal cycles compare qualitatively well with MODIS measurements.

show abstract

Section: Introductionmentioning

confidence: 99%

Spatial distributions and seasonal cycles of aerosols in India and China seen in global climate-aerosol model

et al. 2011

View full text Add to dashboard Cite

show abstract

“…Several studies have used aerosol optical depth (AOD, also referred to as aerosol optical thickness or AOT) observations along with CTM to obtain better air quality re-analyses. Some of these studies used the OI technique along with models such as STEM (Adhikary et al, 2008;Carmichael et al, 2009), CMAQ Park et al, 2014), MATCH (Collins et al, 2001), and GOCART (Yu et al, 2003). Other studies used variational approaches with models such as EU-RAD (Schroedter-Homscheidt et al, 2010;Nieradzik and Elbern, 2006) and LMDz-INCA (Generoso et al, 2007).…”

Section: Initial Conditions and Re-analysis Fieldsmentioning

confidence: 99%

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

et al. 2015

View full text Add to dashboard Cite

Abstract. Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models

show abstract

“…The OI method is described in detail in Adhikary et al (2008). Before assimilation, we put AIRNow in situ data and MODIS AOD data into their corresponding model grids.…”

Section: Model and Assimilation Setupsmentioning

confidence: 99%

“…The relative uncertainties of MODIS AOD and AIRNow PM 2.5 and O 3 were set to 0.2, 0.2, and 0.1, respectively. The cases of OI1, OI2, and OI3 were performed in a 7 × 7 grid box (Adhikary et al, 2008). OI4 was performed in an 11 × 11 grid.…”

Section: Model and Assimilation Setupsmentioning

confidence: 99%

“…The OI method assumes that the observations or modeled results in nearby locations are correlated, with the strength of that correlation inversely proportional to their distance. By combining model results and observations into an analyzed value, OI can be applied to adjust the initial conditions for an air quality model to minimize the errors in the initial condition (Adhikary, 2008). If observation data are available at near real time, this method can be used in an operational forecast.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using optimal interpolation to assimilate surface measurements and satellite AOD for ozone and PM_2.5: A case study for July 2011

Tang

Chai

Pan

et al. 2015

Journal of the Air & Waste Management Association

View full text Add to dashboard Cite

We employed an optimal interpolation (OI) method to assimilate AIRNow ozone/PM2.5 and MODIS (Moderate Resolution Imaging Spectroradiometer) aerosol optical depth (AOD) data into the Community Multi-scale Air Quality (CMAQ) model to improve the ozone and total aerosol concentration for the CMAQ simulation over the contiguous United States (CONUS). AIRNow data assimilation was applied to the boundary layer, and MODIS AOD data were used to adjust total column aerosol. Four OI cases were designed to examine the effects of uncertainty setting and assimilation time; two of these cases used uncertainties that varied in time and location, or "dynamic uncertainties." More frequent assimilation and higher model uncertainties pushed the modeled results closer to the observation. Our comparison over a 24-hr period showed that ozone and PM 2.5 mean biases could be reduced from 2.54 ppbV to 1.06 ppbV and from -7.14 µg/m 3 to -0.11 µg/m 3 , respectively, over CONUS, while their correlations were also improved. Comparison to DISCOVER-AQ 2011 aircraft measurement showed that surface ozone assimilation applied to the CMAQ simulation improves regional low-altitude (below 2 km) ozone simulation. Implications: This paper described an application of using optimal interpolation method to improve the model's ozone and PM 2.5 estimation using surface measurement and satellite AOD. It highlights the usage of the operational AIRNow data set, which is available in near real time, and the MODIS AOD. With a similar method, we can also use other satellite products, such as the latest VIIRS products, to improve PM 2.5 prediction.

show abstract

A regional scale chemical transport modeling of Asian aerosols with data assimilation of AOD observations using optimal interpolation technique

Cited by 112 publications

References 38 publications

Spatial distributions and seasonal cycles of aerosols in India and China seen in global climate-aerosol model

Spatial distributions and seasonal cycles of aerosols in India and China seen in global climate-aerosol model

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

Using optimal interpolation to assimilate surface measurements and satellite AOD for ozone and PM_2.5: A case study for July 2011

Contact Info

Product

Resources

About

A regional scale chemical transport modeling of Asian aerosols with data assimilation of AOD observations using optimal interpolation technique

Cited by 112 publications

References 38 publications

Spatial distributions and seasonal cycles of aerosols in India and China seen in global climate-aerosol model

Spatial distributions and seasonal cycles of aerosols in India and China seen in global climate-aerosol model

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

Using optimal interpolation to assimilate surface measurements and satellite AOD for ozone and PM2.5: A case study for July 2011

Contact Info

Product

Resources

About

Using optimal interpolation to assimilate surface measurements and satellite AOD for ozone and PM_2.5: A case study for July 2011