the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 Oct 2024
UFS-RAQMS Global Atmospheric Composition Model: TROPOMI CO Column Assimilation
Abstract. This paper describes a new version of the Real-time Air Quality Modeling System (RAQMS) which uses National Unified Operational Prediction Capability (NUOPC) coupling to combine the RAQMS chemical mechanism with the Global Ensemble Forecasting System with Aerosols (GEFS-Aerosols), the Goddard Chemistry Aerosol Radiation and Transport model (GOCART) aerosol mechanism, and NOAA’s Unified Forecast System (UFS) version 9.1 Finite Volume Cubed Sphere (FV3) dynamical core. We also present an application of TROPOMI CO column data assimilation in UFS-RAQMS with the NOAA Grid Point Statistical Interpolation (GSI) three-dimensional variational (3Dvar) analysis system to constrain UFS-RAQMS CO. We validate UFS-RAQMS control and TROPOMI CO data assimilation CO analyses for the period 15 July–30 September 2019 against independent satellite, ground based, and airborne observations. We show the largest impacts of the TROPOMI CO data assimilation are in the lower troposphere over Siberia and Indonesia. We find UFS-RAQMS biomass burning signatures in CO column are not consistent with those in AOD near the Siberian and Indonesian biomass burning source regions within our control experiment. Assimilation of TROPOMI CO improves the representation of the biomass burning AOD/CO relationship. The results also indicate that the biomass burning CO emissions from the Blended Global Biomass Burning Emissions Product (GBBEPx) used in UFS-RAQMS are too low.
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RC1: 'Comment on egusphere-2024-2501', Anonymous Referee #1, 06 Nov 2024
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Review of UFS-RAQMS Global Atmospheric Composition Model: TROPOMI CO Column Assimilation by Maggie Bruckner and co-authors.
The paper presents the development of a whole new modelling system by coupling chemistry and aerosols representation to NOAA’s UFS, in order to assimilate TROPOMI CO.
I am just having a hard time following the discussion about aerosols. You mention that sulfate aerosol formation will change with OH. Does the CO assimilation impacts aerosol formation and are the simulated AOD different between the control and the TROPOMI DA ? What about secondary organic aerosols and heterogeneous chemistry ? This is important to clarify as these should be pathways AOD would change following CO assimilation (without optimizing emissions or transport). Specifically, if AOD are not changed following TROPOMI CO, does it mean that the aerosols are well simulated but not CO (on figure 13) ?Regarding Figure 14, I don’t really follow the practice of increasing AOD by a factor of 3, why don’t you just scale the emissions of biomass burning CO and aerosols, using the information from the CO assimilation, even a rough estimate and run forward simulations ? Since you are using the same biomass burning emission inputs, AOD and CO errors must be correlated.
Similarly, with the field campaigns, you have the opportunity to evaluate the impact on other species and assess potential improvements.
The FTIR observations from NDACC uses a different remote sensing approach than TROPOMI to retrieve CO, but I think it is somehow misleading to call them profiles without caution. I would mention the degrees of freedom for signal, which are probably high enough to allow for a retrieval of a partial tropospheric column at best. If you are willing to keep the figures with the profiles, I would also show the NDACC prior and add appropriate disclaimers.
Minor comments:L33 please define the TROPOMI acronym and reference here (from line 137: Tropospheric Monitoring Instrument (TROPOMI) (Veefkind et al., 2012)).
L139: define the acronyms UV-near IR and shortwave IR
L145 (paragraph): Super observations are used to match the model grid’s spacing, to reduce noise and avoid overfitting observations, as well as for computational efficiency. Sekiya et al., (2021) found that the smoothing of the spatial variability in analysis increments with OMI NO2 assimilation and noted that the super observations were reducing the number of assimilated observations by a factor of 10, making the use of super-observation to be more relevant with TROPOMI. Note also that the smoothing is actually a desired effect by reducing noise and removing the sub-grid variability that the model cannot represent. I understand there might be a loss of signal while producing the super-observations, so it would have been desirable to actually perform the experiment. You would have been able to show whether the statement line 150 (an underestimates in localized CO column enhancements) ends up being correct or not.
L253 and L275: the precision of the Picarro instrument is about 1 ppb for 1-min averages. So, it would be more appropriate to round the biases to significant figures of 1 ppb or 0.1 ppbFigure 5: MOPITT retrievals include the dry-air atmospheric column, and it is making it easy to convert both model and observations to XCO. You could also show the bias for Control and TROPOMI DA on panels e) and f).
Citation: https://doi.org/10.5194/egusphere-2024-2501-RC1
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