Converting Visibility to AOD

Visibility AOD

  Data download  

Data Status:


Publication State:

13 August 2020

Why are visibility data useful for aerosol inference?

Multi-decadal aerosol data are necessary to understand how aerosols affect climate and climate changes on the regional and global scales. However, high-quality satellite- or ground-based aerosol measurements are not available for such long-term studies. Surface visibility measurements at thousands of stations worldwide can provide useful information for long-term aerosol inference, complementing satellite- and ground-based measurements. Here we combine visibility measurements and a chemical transport model simulation to derive a new gridded AOD dataset, by converting near surface station-specific visibility data to gridded AOD data.

Method to converting from station-specific visibility data to gridded AOD data

Step 1: Near surface Aerosol Extinction Coefficient (AEC) is calculated from a quality-controlled 3-hourly visibility measurement in the absence of precipitation and fogs.

Step 2: A temporally and spatially coincident AOD to AEC ratio modeled by GEOS-Chem is used to convert near surface AEC to column AOD. In this way, the knowledge of model aerosol profile is involved, instead of assuming a uniform exponential vertical distribution as in many preivous studies.

Step 3: The visibility converted and GEOS-Chem simulated AOD to produce a new “merged” AOD dataset on a longitude-latitude grid (current resolution is 0.667º long. × 0.5º lat.). For each day, we find for a given grid cell all stations within a 2º radius of the grid cell center, calculate the ratios of visibility converted over GEOS-Chem AOD, and then use the median value of the ratios to scale the modeled AOD at the grid cell.

Product and validation

The visibility-model merged gridded AOD data are obtained. The new data preserve the spatial distribution of model AOD while using the visibility measurements to correct for the model bias.

The merged AOD dataset is highly consistent with AOD data from MODIS, AERONET, CARSNET and CSHNET, with a low bias (< 0.05 over East China) and high spatial and temporal (diurnal, seasonal and interannual) correlation.


Monthly data are currently available for 2004/10 through 2013/04 over East China (101.25ºE–126.25ºE, 19ºN–46ºN), and are free for non-commercial use.

Data resolution: 0.667 longitude x 0.5 latitude degree.

Animation of monthly mean visibility-model merged AOD maps: 2004/10-2013/04

Visibility-model merged monthly mean AOD data download. Please see the "readme" file inside for how to read the binary data.

Adjusted visibility-model merged monthly mean AOD data download. For comparison with monthly mean MODIS/Aqua AOD, we also provide monthly mean data adjusted based on the difference in days with valid MODIS data versus with valid visibiilty data. See our paper below for details. Please see the "readme" file inside for how to read the binary data.


Lin, J.-T. *, and Li, J.: Spatio-temporal variability of aerosols over East China inferred by merged visibility-GEOS-Chem aerosol optical depth, Atmospheric Environment, 132, 111-122, doi:10.1016/j.atmosenv.2016.02.037, 2016 (PDF)

Lin, J.-T. *, van Donkelaar, A., Xin, J., Che, H., and Wang, Y.: Clear-sky aerosol optical depth over East China estimated from visibility measurements and chemical transport modeling, Atmospheric Environment, 95, 257-267, doi:10.1016/j.atmosenv.2014.06.044, 2014 (PDF)



Temporal Range:

2018/07~ Updating

Spatial Range:



Authors (3)

  • Back to top