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Berkeley High Resolution (BEHR) OMI NO2 - Gridded pixels, monthly profiles

Cite this dataset

Laughner, Joshua; Zhu, Qindan; Cohen, Ronald (2018). Berkeley High Resolution (BEHR) OMI NO2 - Gridded pixels, monthly profiles [Dataset]. Dryad.


The BEHR OMI NO2 product reprocesses tropospheric NO2 columns from the Ozone Monitoring Instrument (OMI) satellite using high resolution a priori NO2 profiles, surface reflectivity, and surface elevation data. This product uses NO2 profiles for the day retrieved, simulated by the WRF-Chem model at 12 km spatial resolution. The use of high spatial resolution NO2 profiles has been shown to better resolve urban/rural differences in NO2 column densities, and the use of day-to-day (rather than monthly average) profiles is especially important in applications that preferentially select observations upwind or downwind of a NOx source. However, given the computation cost of simulating daily, 12 km NO2 profiles over the continental US domain and accurately representing individual lightning events, we still provide a product using monthly average profiles.


The methods for the BEHR product are described in detail in Laughner et al. (in prep). 

Usage notes

Each month of BEHR data is stored as a .tar.gz file (i.e. a tar archive compressed with gzip). These can be decompressed using 7zip or (on Mac/Linux) the command "tar -xzvf <filename>", e.g. "tar -xzvf OMI_BEHR-DAILY_US_v3-0A_200501.tar.gz" will expand OMI_BEHR-DAILY_US_v3-0A_200501.tar.gz 

In addition to downloading the full dataset or manually downloading individual files, a Python program is available at that can perform bulk downloads of a subset of the files, as well as automatically uncompress the tar files. See the Readme in that repo for further usage information.

For details on best practices for using the BEHR data, see the user guide PDF included in this repository.


National Aeronautics and Space Administration, Award: NNX14AK89H

National Aeronautics and Space Administration, Award: NNX15AE37G

Smithsonian Astrophysical Observatory, Award: SV3-83019