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Estimates of lightning NO x production based on OMI NO 2 observations over the Gulf of Mexico
Author(s) -
Pickering Kenneth E.,
Bucsela Eric,
Allen Dale,
Ring Allison,
Holzworth Robert,
Krotkov Nickolay
Publication year - 2016
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/2015jd024179
Subject(s) - lightning (connector) , ozone monitoring instrument , lightning detection , radiance , environmental science , meteorology , troposphere , satellite , air mass (solar energy) , flash (photography) , remote sensing , geography , physics , thunderstorm , statistics , mathematics , optics , representativeness heuristic , power (physics) , quantum mechanics , astronomy
We evaluate nitrogen oxide (NO x = NO + NO 2 ) production from lightning over the Gulf of Mexico region using data from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura satellite along with detection efficiency‐adjusted lightning data from the World Wide Lightning Location Network (WWLLN). A special algorithm was developed to retrieve the lightning NO x (LNO x ) signal from OMI. The algorithm in its general form takes the total slant column NO 2 from OMI and removes the stratospheric contribution and tropospheric background and includes an air mass factor appropriate for the profile of lightning NO x to convert the slant column LNO 2 to a vertical column of LNO x . WWLLN flashes are totaled over a period of 3 h prior to OMI overpass, which is the time an air parcel is expected to remain in a 1° × 1° grid box. The analysis is conducted for grid cells containing flash counts greater than a threshold value of 3000 flashes that yields an expected LNO x signal greater than the background. Pixels with cloud radiance fraction greater than a criterion value (0.9) indicative of highly reflective clouds are used. Results for the summer seasons during 2007–2011 yield mean LNO x production of ~80 ± 45 mol per flash over the region for the two analysis methods after accounting for biases and uncertainties in the estimation method. These results are consistent with literature estimates and more robust than many prior estimates due to the large number of storms considered but are sensitive to several substantial sources of uncertainty.