Retrievals of tropospheric ozone profiles from the synergism of AIRS and OMI: methodology and validation

Abstract

Abstract. The Tropospheric Emission Spectrometer (TES) on the A-Train Aura satellite was designed to profile tropospheric ozone and its precursors, taking measurements from 2004 to 2018. Starting in 2008, TES global sampling of tropospheric ozone was gradually reduced in latitude, with global coverage stopping in 2011. To extend the record of TES, this work presents a multispectral approach that will provide O3 data products with vertical resolution and measurement error similar to TES by combining the single-footprint thermal infrared (TIR) hyperspectral radiances from the Aqua Atmospheric Infrared Sounder (AIRS) instrument and the ultraviolet (UV) channels from the Aura Ozone Monitoring Instrument (OMI). The joint AIRS+OMI O3 retrievals are processed through the MUlti-SpEctra, MUlti-SpEcies, MUlti-SEnsors (MUSES) retrieval algorithm. Comparisons of collocated joint AIRS+OMI and TES to ozonesonde measurements show that both systems have similar errors, with mean and standard deviation of the differences well within the estimated measurement error. AIRS+OMI and TES have slightly different biases (within 5 parts per billion) vs. the sondes. Both AIRS and OMI have wide swath widths (∼1650 km for AIRS; ∼2600 km for OMI) across satellite ground tracks. Consequently, the joint AIRS+OMI measurements have the potential to maintain TES vertical sensitivity while increasing coverage by 2 orders of magnitude, thus providing an unprecedented new data set with which to quantify the evolution of tropospheric ozone.

Balance of Emission and Dynamical Controls on Ozone During the Korea‐United States Air Quality Campaign From Multiconstituent Satellite Data Assimilation

“…The GS mode AIRS/OMI data have been produced with a spatial sampling and the retrieval characteristics of ozone profiles equivalent to TES L2 standard data product, demonstrating the feasibility of extending the TES L2 data record via a multiple spectral retrieval approach, while the RE mode processes all available AIRS+OMI measurements over the Korean Peninsula. The GS retrievals show good agreements with WOUDC global ozonesonde measurements, with seasonal and global mean biases of −0.9–14.4 ppbv at 750 hPa, 2.2–5.9 ppbv at 510 hPa, and −7.7–2.9 ppbv at 316 hPa (Fu et al, ). The retrieved ozone profile, a priori ozone profile, quality flag, averaging kernels, and the estimated uncertainty matrix for ozone profiles were used in data assimilation.…”

“…The GS retrievals show good agreements with WOUDC global ozonesonde measurements, with seasonal and global mean biases of À0.9-14.4 ppbv at 750 hPa, 2.2-5.9 ppbv at 510 hPa, and À7.7-2.9 ppbv at 316 hPa (Fu et al, 2018). The retrieved ozone profile, a priori ozone profile, quality flag, averaging kernels, and the estimated uncertainty matrix for ozone profiles were used in data assimilation.…”

“…• Atmospheric Infrared Sounder (AIRS)/OMI ozone We also assimilated observational data from the joint AIRS/OMI version 1 L2 ozone profile product (Fu et al, 2018) in sensitivity data assimilation calculations. The ozone profile retrievals were performed via applying the JPL MUlti-SpEctra, MUlti-SpEcies, Multi-Sensors (MUSES) algorithm to both AIRS and OMI level 1B (L1B) spectral radiances (Fu et al, 2018).…”

“…• Atmospheric Infrared Sounder (AIRS)/OMI ozone We also assimilated observational data from the joint AIRS/OMI version 1 L2 ozone profile product (Fu et al, 2018) in sensitivity data assimilation calculations. The ozone profile retrievals were performed via applying the JPL MUlti-SpEctra, MUlti-SpEcies, Multi-Sensors (MUSES) algorithm to both AIRS and OMI level 1B (L1B) spectral radiances (Fu et al, 2018). The methodology, characteristics, and validation of MUSES algorithm have been presented by Fu et al (2013) for joint TES/OMI ozone retrievals and joint CrIS/TROPOMI carbon monoxide (CO) profiling (Fu et al, 2016), joint TES/MLS CO retrievals (Luo et al, 2013), and AIRS alone methane, HDO, H 2 O, and CO retrievals.…”

Comparison of optimal estimation HDO∕H<sub>2</sub>O retrievals from AIRS with ORACLES measurements

“…The single-footprint AIRS HDO profile data used in this work were produced using the retrieval algorithm, named the MUlti-SpEctra, MUlti-SpEcies, MUlti-Sensors (MUSES) algorithm (D. Fu et al, , 2016Fu et al, , 2018Fu et al, , 2019Worden et al, 2019). The MUSES algorithm can use radiances from multiple instruments, including AIRS and other instruments (CrIS, TES, OMI, OMPS, TROPOMI, and MLS), to quantify geophysical observables that affect the corresponding radiance.…”