Observing System Simulation Experiments for air quality

Timmermans, Renske; Lahoz, W. A.; Attié, Jean-Luc; Peuch, Vincent-Henri; Curier, R.L.; Edwards, D. P.; Eskes, Henk; Builtjes, P. J. H.

doi:10.1016/j.atmosenv.2015.05.032

Cited by 43 publications

(42 citation statements)

References 45 publications

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“…The assimilation system has also been used to assess the added value of future satellite instruments through so-called Observation System Simulation Experiments (OSSEs, Timmermans et al, 2015). The added value of a future observation system is investigated by producing synthetic observations using a different model simulation (nature run), and assimilate these data in the model.…”

Section: Data Assimilation Systemmentioning

confidence: 99%

“…Baklanov et al, 2014), as well as in the design of monitoring strategies using in situ observations or new satellite instruments (e.g. Timmermans et al, 2015). Nowadays, a large number of CTMs exist with a few widely used open-source systems such as EMEP , CHIMERE (Menut et al, 2013a;Mailler et al, 2017), WRF-CHEM (Fast et al, 2006;Grell et al, 2005), CMAQ (Byun and Schere, 2006) and CAMx (Environ, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Curriculum vitae of the LOTOS–EUROS (v2.0) chemistry transport model

et al. 2017

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Abstract. The development and application of chemistry transport models has a long tradition. Within the Netherlands the LOTOS-EUROS model has been developed by a consortium of institutes, after combining its independently developed predecessors in 2005. Recently, version 2.0 of the model was released as an open-source version. This paper presents the curriculum vitae of the model system, describing the model's history, model philosophy, basic features and a validation with EMEP stations for the new benchmark year 2012, and presents cases with the model's most recent and key developments. By setting the model developments in context and providing an outlook for directions for further development, the paper goes beyond the common model description.With an origin in ozone and sulfur modelling for the models LOTOS and EUROS, the application areas were gradually extended with persistent organic pollutants, reactive nitrogen, and primary and secondary particulate matter. After the combination of the models to LOTOS-EUROS in 2005, the model was further developed to include new source parametrizations (e.g. road resuspension, desert dust, wildfires), applied for operational smog forecasts in the Netherlands and Europe, and has been used for emission scenarios, source apportionment, and long-term hindcast and climate change scenarios. LOTOS-EUROS has been a front-runner in data assimilation of ground-based and satellite observations and has participated in many model intercomparison studies. The model is no longer confined to applications over Europe but is also applied to other regions of the world, e.g. China. The increasing interaction with emission experts has also contributed to the improvement of the model's performance. The philosophy for model development has always been to use knowledge that is state of the art and proven, to keep a good balance in the level of detail of process description and accuracy of input and output, and to keep a good record on the effect of model changes using benchmarking and validation. The performance of v2.0 with respect to EMEP observations is good, with spatial correlations around 0.8 or higher for concentrations and wet deposition. Temporal correlations are around 0.5 or higher. Recent innovative applications include source apportionment and data assimilation, particle number modelling, and energy transition scenarios including corresponding land use changes as well as Saharan dust forecasting. Future developments would enable more flexibility with respect to model horizontal and vertical resolution and further detailing of model input data.Published by Copernicus Publications on behalf of the European Geosciences Union.

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Section: Data Assimilation Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Curriculum vitae of the LOTOS–EUROS (v2.0) chemistry transport model

et al. 2017

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“…As such techniques have not been applied in a CCDAS context, we only give brief summaries of the approaches that are most popular in the numerical weather prediction (Masutani et al, 2010) and chemical data assimilation (Timmermans et al, 2015) communities. OSSEs are conducted as follows: first, a "true" control vector is selected.…”

Section: T Kaminski and P J Rayner: Qndmentioning

confidence: 99%

“…Solar-induced fluorescence (SIF) is a further observational constraint from space and also presented in the contribution of Scholze et al (2017). Its assessment in a CCDAS requires a dedicated observation operator such as the SCOPE model by van der Tol et al (2009). Koffi et al (2015) coupled SCOPE with BETHY and provided a set of sensitivity tests.…”

Section: T Kaminski and P J Rayner: Qndmentioning

confidence: 99%

Reviews and syntheses: guiding the evolution of the observing system for the carbon cycle through quantitative network design

Kaminski¹

2017

Biogeosciences

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Abstract. Various observational data streams have been shown to provide valuable constraints on the state and evolution of the global carbon cycle. These observations have the potential to reduce uncertainties in past, current, and predicted natural and anthropogenic surface fluxes. In particular such observations provide independent information for verification of actions as requested by the Paris Agreement. It is, however, difficult to decide which variables to sample, and how, where, and when to sample them, in order to achieve an optimal use of the observational capabilities. Quantitative network design (QND) assesses the impact of a given set of existing or hypothetical observations in a modelling framework. QND has been used to optimise in situ networks and assess the benefit to be expected from planned space missions. This paper describes recent progress and highlights aspects that are not yet sufficiently addressed. It demonstrates the advantage of an integrated QND system that can simultaneously evaluate a multitude of observational data streams and assess their complementarity and redundancy.

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“…The true states are normally the simulated observations generated by a model run or a data assimilation system (Timmermans et al, 2015). In order to investigate the possible cause of observed mixed effects in RDA experiment, this study employed a simplified box model including the main chemical processes of NAQPMS (Xiang et al, 2010).…”

Section: Ida Experimentsmentioning

confidence: 99%

Limitations of ozone data assimilation with adjustment of NOx emissions: mixed effects on NO2 forecasts over Beijing and surrounding areas

et al. 2016

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Abstract. This study investigates a cross-variable ozone data assimilation (DA) method based on an ensemble Kalman filter (EnKF) that has been used in the companion study to improve ozone forecasts over Beijing and surrounding areas. The main purpose is to delve into the impacts of the crossvariable adjustment of nitrogen oxide (NO x ) emissions on the nitrogen dioxide (NO 2 ) forecasts over this region during the 2008 Beijing Olympic Games. A mixed effect on the NO 2 forecasts was observed through application of the cross-variable assimilation approach in the real-data assimilation (RDA) experiments. The method improved the NO 2 forecasts over almost half of the urban sites with reductions of the root mean square errors (RMSEs) by 15-36 % in contrast to big increases of the RMSEs over other urban stations by 56-239 %. Over the urban stations with negative DA impacts, improvement of the NO 2 forecasts (with 7 % reduction of the RMSEs) was noticed at night and in the morning versus significant deterioration during daytime (with 190 % increase of the RMSEs), suggesting that the negative data assimilation impacts mainly occurred during daytime. Idealdata assimilation (IDA) experiments with a box model and the same cross-variable assimilation method confirmed the mixed effects found in the RDA experiments. In the same way, NO x emission estimation was improved at night and in the morning even under large biases in the prior emission, while it deteriorated during daytime (except for the case of minor errors in the prior emission). The mixed effects observed in the cross-variable data assimilation, i.e., positive data assimilation impacts on NO 2 forecasts over some urban sites, negative data assimilation impacts over the other urban sites, and weak data assimilation impacts over suburban sites, highlighted the limitations of the EnKF under strong nonlinear relationships between chemical variables. Under strong nonlinearity between daytime ozone concentrations and NO x emissions uncertainties (with large biases in the a priori emission), the EnKF may come up with inefficient or wrong adjustments to NO x emissions. The present findings reveal that bias correction is essential for the application of the EnKF in dealing with the data assimilation problem over strong nonlinear system.

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Observing System Simulation Experiments for air quality

Cited by 43 publications

References 45 publications

Curriculum vitae of the LOTOS–EUROS (v2.0) chemistry transport model

Curriculum vitae of the LOTOS–EUROS (v2.0) chemistry transport model

Reviews and syntheses: guiding the evolution of the observing system for the carbon cycle through quantitative network design

Limitations of ozone data assimilation with adjustment of NO<sub><i>x</i></sub> emissions: mixed effects on NO<sub>2</sub> forecasts over Beijing and surrounding areas

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Observing System Simulation Experiments for air quality

Cited by 43 publications

References 45 publications

Curriculum vitae of the LOTOS–EUROS (v2.0) chemistry transport model

Curriculum vitae of the LOTOS–EUROS (v2.0) chemistry transport model

Reviews and syntheses: guiding the evolution of the observing system for the carbon cycle through quantitative network design

Limitations of ozone data assimilation with adjustment of NO&lt;sub&gt;&lt;i&gt;x&lt;/i&gt;&lt;/sub&gt; emissions: mixed effects on NO&lt;sub&gt;2&lt;/sub&gt; forecasts over Beijing and surrounding areas

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Limitations of ozone data assimilation with adjustment of NO<sub><i>x</i></sub> emissions: mixed effects on NO<sub>2</sub> forecasts over Beijing and surrounding areas