Inverse modeling and mapping US air quality influences of inorganic PM_(2.5) precursor emissions using the adjoint of GEOS-Chem

Influences of specific sources of inorganic PM_(2.5) on peak and ambient aerosol concentrations in the US are evaluated using a combination of inverse modeling and sensitivity analysis. First, sulfate and nitrate aerosol measurements from the IMPROVE network are assimilated using the four-dimensional variational (4D-Var) method into the GEOS-Chem chemical transport model in order to constrain emissions estimates in four separate month-long inversions (one per season). Of the precursor emissions, these observations primarily constrain ammonia (NH_3). While the net result is a decrease in estimated US~NH_3 emissions relative to the original inventory, there is considerable variability in adjustments made to NH_3 emissions in different locations, seasons and source sectors, such as focused decreases in the midwest during July, broad decreases throughout the US~in January, increases in eastern coastal areas in April, and an effective redistribution of emissions from natural to anthropogenic sources. Implementing these constrained emissions, the adjoint model is applied to quantify the influences of emissions on representative PM_(2.5) air quality metrics within the US. The resulting sensitivity maps display a wide range of spatial, sectoral and seasonal variability in the susceptibility of the air quality metrics to absolute emissions changes and the effectiveness of incremental emissions controls of specific source sectors. NH_3 emissions near sources of sulfur oxides (SO_x) are estimated to most influence peak inorganic PM_(2.5) levels in the East; thus, the most effective controls of NH_3 emissions are often disjoint from locations of peak NH_3 emissions. Controls of emissions from industrial sectors of SO_x and NO_x are estimated to be more effective than surface emissions, and changes to NH_3 emissions in regions dominated by natural sources are disproportionately more effective than regions dominated by anthropogenic sources. NOx controls are most effective in northern states in October; in January, SO_x controls may be counterproductive. When considering ambient inorganic PM_(2.5) concentrations, intercontinental influences are small, though transboundary influences within North America are significant, with SO_x emissions from surface sources in Mexico contributing almost a fourth of the total influence from this sector

A Domain Decomposition Reduced Order Model with Data Assimilation (DD-RODA)

We present a Domain Decomposition Reduced Order Data Assimilation (DD-RODA) model which combines Non-Intrusive Reduced Order Modelling (NIROM) method with a Data Assimilation (DA) model. The NIROM is defined on a partition of the domain in sub-domains with overlapping regions and the DA is defined on a partition of the domain in sub-domains without overlapping regions. This choice allows to avoid communications among the processes during the Data Assimilation phase. However, during the balance phase, the model exploits the domain decomposition implemented in DD-NIROM which balances the results among the processes exploiting overlapping regions. The model is applied to the pollutant dispersion within an urban environment. Simulations are performed using the open-source, finite-element, fluid dynamics model Fluidity

Enhancing CFD-LES air pollution prediction accuracy using data assimilation

It is recognised worldwide that air pollution is the cause of premature deaths daily, thus necessitating the development of more reliable and accurate numerical tools. The present study implements a three dimensional Variational (3DVar) data assimilation (DA) approach to reduce the discrepancy between predicted pollution concentrations based on Computational Fluid Dynamics (CFD) with the ones measured in a wind tunnel experiment. The methodology is implemented on a wind tunnel test case which represents a localised neighbourhood environment. The improved accuracy of the CFD simulation using DA is discussed in terms of absolute error, mean squared error and scatter plots for the pollution concentration. It is shown that the difference between CFD results and wind tunnel data, computed by the mean squared error, can be reduced by up to three order of magnitudes when using DA. This reduction in error is preserved in the CFD results and its benefit can be seen through several time steps after re-running the CFD simulation. Subsequently an optimal sensors positioning is proposed. There is a trade-off between the accuracy and the number of sensors. It was found that the accuracy was improved when placing/considering the sensors which were near the pollution source or in regions where pollution concentrations were high. This demonstrated that only 14% of the wind tunnel data was needed, reducing the mean squared error by one order of magnitude

Using 3DVAR data assimilation system to improve ozone simulations in the Mexico City basin

This study investigates the improvement of ozone (O<sub>3</sub>) simulations in the Mexico City basin using a three-dimensional variational (3DVAR) data assimilation system in meteorological simulations during the MCMA-2003 field measurement campaign. Meteorological simulations from the NCAR/Penn State mesoscale model (MM5) are used to drive photochemical simulations with the Comprehensive Air Quality Model with extensions (CAMx) during a four-day episode on 13–16 April 2003. The simulated wind circulation, temperature, and humidity fields in the basin with the data assimilation are found to be more consistent with the observations than those from the reference deterministic forecast. This leads to improved simulations of plume position, peak O<sub>3</sub> timing, and peak O<sub>3</sub> concentrations in the photochemical model. The improvement in O<sub>3</sub> simulations is especially strong during the daytime. The results demonstrate the importance of applying data assimilation in meteorological simulations for air quality studies in the Mexico City basin

The Distribution of Atmospheric Pollutants in Europe: Optimal Use of Models and Observations with a Data Assimilation Approach

The research activity presented in this manuscript deals with the implementation of a methodology to merge in an optimal way atmospheric modelling and observations at different spatial scales. In particular, we approached the problem of assimilation of ground measurements and satellite columnar data and how the Data Assimilation (DA) could improve the chemical transport model (CTMs) and correct biases and errors in the chemical species forecast. The work focused on tropospheric ozone and the species linked to its formation, since they play a crucial role in chemical processes during photochemical pollution events. The study was carried out implementing and applying an Optimal Interpolation (OI) DA technique in the air quality model BOLCHEM and the CHIMERE CTM. The OI routine was chosen because it has given satisfactory results in air quality modelling and because it is relatively simple and computationally inexpensive. In the first part of the study we evaluated the improvement in the capability of regional model BOLCHEM to reproduce the distribution of tropospheric pollutants, using the assimilation of surface chemical observations. Among the many causes of uncertainties of CTMs simulations, a particular focus is given by uncertainties in emissions, that are known to be high. The scientific purpose was to analyse the efficacy of DA in correcting the biases due to perturbed emission. The work was performed using an Observing System Simulation Experiment (OSSE), which allowed the quantification of assimilation impact, through comparison with a reference state. Different sensitivity tests were carried out in order to identify how assimilation can correct perturbations on O3, induced by NOx emissions biased in flux intensity and time. Tests were performed assimilating different species, varying assimilation time window length and starting hour of assimilation. Emissions were biased quantitatively up to ± 50% and shifted temporally up to ± 2 hours. The analysis brought to the conclusions that NO2 assimilation significantly improves O3 maxima during the assimilation, making it almost independent on different emission scenarios. The assimilation impact lasts up to 36-40 hours after the end of the assimilation window. This is a considerable result, especially when it is taken into account that DA generally yields significantly better forecasts in the 6-12 hours range, but improvements vanish afterwards. The NO2 night-time chemistry has the role of maintaining the correction of O3 due to assimilation also in the following day. Assimilating NO2 and O3 simultaneously bring to rather better results, although the benefit lasts only a few hours after the end of the assimilation window. It was found that the best results are achieved assimilating observations during the photochemically active period (06-18 UTC). It was also found that temporally biased NOx emissions only slightly perturb O3 concentration during the photochemically active regime, while the perturbation is larger during night-time. Assimilation has a very low impact during the assimilation window and a negligible impact after its end. The second part of PhD research activity dealt with the evaluation of the impact of assimilation of satellite NO2 tropospheric columns on the distribution of pollutants at the ground level during photochemical pollution events at continental scale. In particular, we focused on the assimilation of observations from SCIAMACHY and from OMI, and its effect on ozone in the lowermost troposphere in Europe. For an effective improvement in assimilated fields it is particularly important the consistency between satellite and model resolution. SCIAMACHY and OMI have a considerable difference in spatial and temporal resolution, allowing to test the role of data resolution on the effectiveness of assimilation. The role of data resolution on the effectiveness of assimilation was investigated also changing the model resolutions. It was found the perturbation on NO2 field due to assimilation causes a modification on ozone field that appears more spatially variable and higher in some photochemical polluted areas. Similar effects are detected both for SCIAMACHY and OMI assimilation. Significative effects of assimilation on ozone can be appreciate in polluted areas at local scale. Focusing on specific subdomains, it was found that the effect of assimilation lasts, in general, 8 hours and in few cases until the reactivation of active photochemical period in the following day. This is a strong impact, considering that assimilation is performed at most once a day and it is probably linked to the model underestimate of ozone and its precursors in polluted areas with respect to those measured by SCIAMACHY and OMI. In wide and highly polluted areas assimilation achieves satisfactory results, comparing simulated ground ozone with independent ground measurements. In that region where OMI assimilation in the coarse and fine resolution simulations and SCIAMACHY assimilation were confronted, we could conclude that these different assimilation set-up are almost similar. Whereas, in more localised polluted areas (i.e. comparable to model and satellite resolution), OMI assimilation in the finer resolution simulation performs better with respect to OMI assimilation in the coarse resolution simulation and SCIAMACHY assimilation. As a general conclusive statement, assimilation can be an important tool to make the spatial and temporal distribution of pollutants more realistic and closer to the specific local differences with the caveat of horizontal resolution of the assimilated columns and model simulations

Ensemble-based chemical data assimilation I: An idealized setting

Data assimilation is the process of integrating observational data and model predictions to obtain an optimal representation of the state of the atmosphere. As more chemical observations in the troposphere are becoming available, chemical data assimilation is expected to play an essential role in air quality forecasting, similar to the role it has in numerical weather prediction. Considerable progress has been made recently in the development of variational tools for chemical data assimilation. In this paper we assess the performance of the ensemble Kalman filter (EnKF). Results in an idealized setting show that EnKF is promising for chemical data assimilation

Emission rate and chemical state estimation by 4-dimensional variational inversion

This study aims to assess the potential and limits of an advanced inversion method to estimate pollutant precursor sources mainly from observations. Ozone, sulphur dioxide, and partly nitrogen oxides observations are taken to infer source strength estimates. As methodology, the four-dimensional variational data assimilation technique has been generalised and employed to include emission rate optimisation, in addition to chemical state estimates as usual objective of data assimilation. To this end, the optimisation space of the variational assimilation system has been complemented by emission rate correction factors of 19 emitted species at each emitting grid point, involving the University of Cologne mesoscale EURAD model. For validation, predictive skills were assessed for an August 1997 ozone episode, comparing forecast performances of pure initial value optimisation, pure emission rate optimisation, and joint emission rate/initial value optimisation. <br><br> Validation procedures rest on both measurements withheld from data assimilation and prediction skill evaluation of forecasts after the inversion procedures. Results show that excellent improvements can be claimed for sulphur dioxide forecasts, after emission rate optimisation. Significant improvements can be claimed for ozone forecasts after initial value and joint emission rate/initial value optimisation of precursor constituents. The additional benefits applying joint emission rate/initial value optimisation are moderate, and very useful in typical cases, where upwind emission rate optimisation is essential. In consequence of the coarse horizontal model grid resolution of 54 km, applied in this study, comparisons indicate that the inversion improvements can rest on assimilating ozone observations only, as the inclusion of NO<sub>x</sub> observations does not provide additional forecast skill. Emission estimates were found to be largely independent from initial guesses from emission inventories, demonstrating the potential of the 4D-var method to infer emission rate improvements. The study also points to the need for improved horizontal model resolution to more efficient use of NO<sub>x</sub> observations

The transformation of earth-system observations into information of socio-economic value in GEOSS

The Group on Earth Observations System of Systems, GEOSS, is a co-ordinated initiative by many nations to address the needs for earth-system information expressed by the 2002 World Summit on Sustainable Development. We discuss the role of earth-system modelling and data assimilation in transforming earth-system observations into the predictive and status-assessment products required by GEOSS, across many areas of socio-economic interest. First we review recent gains in the predictive skill of operational global earth-system models, on time-scales of days to several seasons. We then discuss recent work to develop from the global predictions a diverse set of end-user applications which can meet GEOSS requirements for information of socio-economic benefit; examples include forecasts of coastal storm surges, floods in large river basins, seasonal crop yield forecasts and seasonal lead-time alerts for malaria epidemics. We note ongoing efforts to extend operational earth-system modelling and assimilation capabilities to atmospheric composition, in support of improved services for air-quality forecasts and for treaty assessment. We next sketch likely GEOSS observational requirements in the coming decades. In concluding, we reflect on the cost of earth observations relative to the modest cost of transforming the observations into information of socio-economic value

Variational Data Assimilation via Sparse Regularization

This paper studies the role of sparse regularization in a properly chosen basis for variational data assimilation (VDA) problems. Specifically, it focuses on data assimilation of noisy and down-sampled observations while the state variable of interest exhibits sparsity in the real or transformed domain. We show that in the presence of sparsity, the $\ell_{1}$-norm regularization produces more accurate and stable solutions than the classic data assimilation methods. To motivate further developments of the proposed methodology, assimilation experiments are conducted in the wavelet and spectral domain using the linear advection-diffusion equation