Three‐dimensional variational data assimilation of ozone and fine particulate matter observations: some results using the Weather Research and Forecasting—Chemistry model and Grid‐point Statistical Interpolation

Pagowski, Mariusz; Grell, Georg; McKeen, S. A.; Peckham, Steven E.; Dévényi, D.

doi:10.1002/qj.700

Cited by 101 publications

(101 citation statements)

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“…The use of the NWP system for data assimilation allows the use of the existing infra-structure for satellite data handling and the MACC system is able to assimilate more than one data set from a large array of satellite instruments (GOME, MIPAS, MLS, OMI, SBUV, SCIAMACHY, MOPITT, IASI, TANSO, AIRS) for O 3 , CO, NO 2 , SO 2 , HCHO, CH 4 , CO 2 and AOD. In the USA, CDA is conducted in WRF-Chem using both 3DVAR (Pagowski et al, 2010;Liu et al, 2011;Schwartz et al, 2012;Saide et al, 2012Saide et al, , 2013 and EnKF (Pagowski and Grell, 2012); there is an on-going project to assimilate surface PM 2.5 data as well as AOD using a hybrid approach that employs both EnKF and 3DVAR. Furthermore, the adjoint of WRF-Chem is currently under development with the objective of performing sensitivity analysis with a variational method in the near future and possibly CDA with inverse modelling of parameter fields later.…”

Section: Current Efforts On Cda In Online Coupled Modelsmentioning

confidence: 99%

Online coupled regional meteorology chemistry models in Europe: current status and prospects

et al. 2014

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Published by Copernicus Publications on behalf of the European Geosciences Union. A. Baklanov et al.: Online coupled regional meteorology chemistry models in EuropeAbstract. Online coupled mesoscale meteorology atmospheric chemistry models have undergone a rapid evolution in recent years. Although mainly developed by the air quality modelling community, these models are also of interest for numerical weather prediction and regional climate modelling as they can consider not only the effects of meteorology on air quality, but also the potentially important effects of atmospheric composition on weather. Two ways of online coupling can be distinguished: online integrated and online access coupling. Online integrated models simulate meteorology and chemistry over the same grid in one model using one main time step for integration. Online access models use independent meteorology and chemistry modules that might even have different grids, but exchange meteorology and chemistry data on a regular and frequent basis. This article offers a comprehensive review of the current research status of online coupled meteorology and atmospheric chemistry modelling within Europe. Eighteen regional online coupled models developed or being used in Europe are described and compared. Topics discussed include a survey of processes relevant to the interactions between atmospheric physics, dynamics and composition; a brief overview of existing online mesoscale models and European model developments; an analysis on how feedback processes are treated in these models; numerical issues associated with coupled models; and several case studies and model performance evaluation methods. Finally, this article highlights selected scientific issues and emerging challenges that require proper consideration to improve the reliability and usability of these models for the three scientific communities: air quality, numerical meteorology modelling (including weather prediction) and climate modelling. This review will be of particular interest to model developers and users in all three fields as it presents a synthesis of scientific progress and provides recommendations for future research directions and priorities in the development, application and evaluation of online coupled models.

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Section: Current Efforts On Cda In Online Coupled Modelsmentioning

confidence: 99%

Online coupled regional meteorology chemistry models in Europe: current status and prospects

et al. 2014

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“…Covariance localization forced EnSRF analysis increments to zero 1280 km from an observation in the horizontal and one scale height to reduce spurious correlations due to sampling error for all control variables, similar to Pagowski et al (2012) and Schwartz et al (2012Schwartz et al ( , 2014. In addition, posterior (after assimilation) multiplicative inflation following Whitaker and Hamill (2012) was applied aiming to maintain ensemble spread for only the concentration analysis.…”

Section: Ensemble Square Root Filter (Ensrf)mentioning

confidence: 99%

“…In addition, posterior (after assimilation) multiplicative inflation following Whitaker and Hamill (2012) was applied aiming to maintain ensemble spread for only the concentration analysis. The inflation factor α = 1.2 was chosen as in Pagowski et al (2012) and Schwartz et al (2012Schwartz et al ( , 2014. Additive or prior inflation was not employed.…”

Section: Ensemble Square Root Filter (Ensrf)mentioning

confidence: 99%

“…Peng et al: Improving PM 2.5 forecast over China et al, 2010a, b;Pagowski and Grell, 2012;Dai et al, 2014;Rubin et al, 2016;Ying et al, 2016;Yumimoto et al, 2016) and the hybrid variational-ensemble DA approach (Schwartz et al, 2014) have also been applied to aerosol predictions. All these studies have shown that DA is one of the most effective ways of improving aerosol forecasting through assimilating aerosol observations from multiple sources (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Improving PM<sub>2. 5</sub> forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter

et al. 2017

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Abstract. In an attempt to improve the forecasting of atmospheric aerosols, the ensemble square root filter algorithm was extended to simultaneously optimize the chemical initial conditions (ICs) and emission input. The forecast model, which was expanded by combining the Weather Research and Forecasting with Chemistry (WRF-Chem) model and a forecast model of emission scaling factors, generated both chemical concentration fields and emission scaling factors. The forecast model of emission scaling factors was developed by using the ensemble concentration ratios of the WRF-Chem forecast chemical concentrations and also the time smoothing operator. Hourly surface fine particulate matter (PM 2.5 ) observations were assimilated in this system over China from 5 to 16 October 2014. A series of 48 h forecasts was then carried out with the optimized initial conditions and emissions on each day at 00:00 UTC and a control experiment was performed without data assimilation. In addition, we also performed an experiment of pure assimilation chemical ICs and the corresponding 48 h forecasts experiment for comparison. The results showed that the forecasts with the optimized initial conditions and emissions typically outperformed those from the control experiment. In the Yangtze River delta (YRD) and the Pearl River delta (PRD) regions, large reduction of the root-mean-square errors (RMSEs) was obtained for almost the entire 48 h forecast range attributed to assimilation. In particular, the relative reduction in RMSE due to assimilation was about 37.5 % at nighttime when WRF-Chem performed comparatively worse. In the BeijingTianjin-Hebei (JJJ) region, relatively smaller improvements were achieved in the first 24 h forecast but then no improvements were achieved afterwards. Comparing to the forecasts with only the optimized ICs, the forecasts with the joint adjustment were always much better during the night in the PRD and YRD regions. However, they were very similar during daytime in both regions. Also, they performed similarly for almost the entire 48 h forecast range in the JJJ region.

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“…Their version of WRFChem offered a full treatment of gas-phase chemistry and PM. Pagowski et al (2010) assimilated both O 3 and PM 2.5 surface concentrations over North America. Model performance improved, but the benefits of data assimilation lasted only for a few hours.…”

Section: Data Assimilation In Coupled Chemistry Meteorology Modelsmentioning

confidence: 99%

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

et al. 2015

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Abstract. Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models

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Three‐dimensional variational data assimilation of ozone and fine particulate matter observations: some results using the Weather Research and Forecasting—Chemistry model and Grid‐point Statistical Interpolation

Cited by 101 publications

References 59 publications

Online coupled regional meteorology chemistry models in Europe: current status and prospects

Online coupled regional meteorology chemistry models in Europe: current status and prospects

Improving PM<sub>2. 5</sub> forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

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Three‐dimensional variational data assimilation of ozone and fine particulate matter observations: some results using the Weather Research and Forecasting—Chemistry model and Grid‐point Statistical Interpolation

Cited by 101 publications

References 59 publications

Online coupled regional meteorology chemistry models in Europe: current status and prospects

Online coupled regional meteorology chemistry models in Europe: current status and prospects

Improving PM&lt;sub&gt;2. 5&lt;/sub&gt; forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

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Improving PM<sub>2. 5</sub> forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter