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Observing atmospheric formaldehyde (HCHO) from space: validation and intercomparison of six retrievals from four satellites (OMI, GOME2A, GOME2B, OMPS) with SEAC<sup>4</sup>RS aircraft observations over the Southeast US
摘要： Formaldehyde (HCHO) column data from satellites are widely used as a proxy for emissions of volatile organic compounds (VOCs), but validation of the data has been extremely limited. Here we use highly accurate HCHO aircraft observations from the NASA SEAC4RS campaign over the Southeast US in August–September 2013 to validate and intercompare six operational and research retrievals of HCHO columns from four different satellite instruments (OMI, GOME2A, GOME2B and OMPS) and three different research groups. The GEOS-Chem chemical transport model provides a common intercomparison platform. We find that all retrievals capture the HCHO maximum over Arkansas and Louisiana, reflecting high emissions of biogenic isoprene, and are consistent in their spatial variability over the Southeast US (r=0.4–0.8 on a 0.5o×0.5o grid) as well as their day-to-day variability (r=0.5–0.8). However, all satellite retrievals are biased low in the mean by 20–51%, which would lead to corresponding bias in estimates of isoprene emissions from the satellite data. The smallest bias is for OMI-BIRA, which has the highest corrected slant columns and the lowest scattering weights in its air mass factor (AMF) calculation. Correcting the assumed HCHO vertical profiles (shape factors) used in the AMF calculation would further reduce the bias in the OMI-BIRA data. We conclude that current satellite HCHO data provide a reliable proxy for isoprene emission variability but with a low mean bias due both to the corrected slant columns and the scattering weights used in the retrievals.
关键词： SEAC4RS,OMPS,isoprene emissions,validation,satellite retrievals,HCHO,GEOS-Chem,Formaldehyde,GOME2B,OMI,GOME2A
更新于2025-09-04 15:30:14
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Spectral analysis of atmospheric composition: application to surface ozone model-measurement comparisons
摘要： Models of atmospheric composition play an essential role in our scientific understanding of atmospheric processes and in providing policy strategies to deal with societally relevant problems such as climate change, air quality and ecosystem degradation. The fidelity of these models needs to be assessed against observations to ensure that errors in model formulations are found and that model limitations are understood. A range of approaches are necessary for these comparisons. Here, we apply a spectral analysis methodology for this comparison. We use the Lomb-Scargle Periodogram, a method similar to a Fourier transform, but better suited to dealing with the gapped data sets typical of observational data. We apply this methodology to long-term hourly ozone observations and the equivalent model (GEOS-Chem) output. We show that the spectrally transformed observational data shows a distinct power spectrum with regimes indicative of meteorological processes (weather, macroweather) and specific peaks observed at the daily and annual timescales together with corresponding harmonic peaks at half, third etc. of these frequencies. Model output shows corresponding features. A comparison between the amplitude and phase of these peaks introduces a new comparison methodology between model and measurements. We focus on the amplitude and phase of diurnal and seasonal cycles and present observational/model comparisons and discuss model performance. We find large biases notably for the seasonal cycle in the mid-latitude northern hemisphere where the amplitudes are generally overestimated by up to 16 ppb, and phases are too late on the order of 1-5 months. This spectral methodology can be applied to a range of model-measurement applications and is highly suitable for Multimodel Intercomparison Projects (MIPs).
关键词： Lomb-Scargle Periodogram,ozone,spectral analysis,GEOS-Chem,model-measurement comparisons,atmospheric composition
更新于2025-09-04 15:30:14