Parameterization retrieval of trace gas volume mixing ratios from Airborne MAX-DOAS

Abstract

Abstract. We present a parameterization retrieval of volume mixing ratios (VMRs) from differential slant column density (dSCD) measurements by Airborne Multi-AXis Differential Optical Absorption Spectroscopy (AMAX-DOAS). The method makes use of the fact that horizontally recorded limb spectra (elevation angle 0°) are strongly sensitive to the atmospheric layer at instrument altitude. These limb spectra are analyzed using reference spectra that largely cancel out column contributions from above and below the instrument, so that the resulting limb dSCDs, i.e., the column integrated concentration with respect to a reference spectrum, are almost exclusively sensitive to the atmospheric layers around instrument altitude. The conversion of limb dSCDs into VMRs is then realized by calculating box air mass factors (Box-AMFs) for a Rayleigh atmosphere and applying a scaling factor constrained by O4 dSCDs to account for aerosol extinction. An iterative VMR retrieval scheme corrects for trace gas profile shape effects. Benefits of this method are (1) a fast conversion that only requires the computation of Box-AMFs in a Rayleigh atmosphere; (2) neither local aerosol extinction nor the slant column density in the DOAS reference (SCDref) needs to be known; and (3) VMRs can be retrieved for every measurement point along a flight track, thus increasing statistics and adding flexibility to capture concentration gradients. Sensitivity studies are performed for bromine monoxide (BrO), iodine monoxide (IO) and nitrogen dioxide (NO2), using (1) simulated dSCD data for different trace gas and aerosol profiles and (2) field measurements from the Tropical Ocean tRoposphere Exchange of Reactive halogen species and Oxygenated VOC (TORERO) field experiment. For simulated data in a Rayleigh atmosphere, the agreement between the VMR from the parameterization method (VMRpara) and the true VMR (VMRtrue) is excellent for all trace gases. Offsets, slopes and R2 values for the linear fit of VMRpara over VMRtrue are, respectively (0.008 ± 0.001) pptv, 0.988 ± 0.001, 0.987 for BrO; (−0.0066 ± 0.0001) pptv, 1.0021 ± 0.0003, 0.9979 for IO; (−0.17 ± 0.03) pptv, 1.0036 ± 0.0001, 0.9997 for NO2. The agreement for atmospheres with aerosol shows comparable R2 values to the Rayleigh case, but slopes deviate a bit more from one: (0.093 ± 0.002) pptv, 0.933 ± 0.002, 0.907 for BrO; (0.0021 ± 0.0004) pptv, 0.887 ± 0.001, 0.973 for IO; (8.5 ± 0.1) pptv, 0.8302 ± 0.0006, 0.9923 for NO2. VMRpara from field data are further compared with optimal estimation retrievals (VMROE). Least orthogonal distance fit of the data give the following equations: BrOpara =  (0.1 ± 0.2) pptv + (0.95 ± 0.14)  ×  BrOOE; IOpara =  (0.01 ± 0.02) pptv + (1.00 ± 0.12)  ×  IOOE; NO2para  =  (3.9 ± 2.5) pptv + (0.87 ± 0.15)  ×  NO2OE. Overall, we conclude that the parameterization retrieval is accurate with an uncertainty of 20 % for IO, 30 % for BrO and NO2, but not better than 0.05 pptv IO, 0.5 pptv BrO and 10 pptv NO2. The retrieval is applicable over a wide range of atmospheric conditions and measurement geometries and not limited to the interpretation of vertical profile measurements in the remote troposphere.