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

Abstract

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

Evaluation of the high resolution WRF-Chem (v3.4.1) air quality forecast and its comparison with statistical ozone predictions

“…Further improvement of WRF-Chem forecasting skill could be obtained by applying one of the bias-correction methods in order to account for unresolved topographical and coastal effects, as well as emission patterns. Chemical data assimilation, although currently still in its infancy for online coupled meteorology-chemistry models (Bocquet et al, 2015), could in future also be used as an efficient method for improving prediction of chemical concentration fields. For the WRF-Chem model, a technical note on the implementation of the aerosol assimilation and a guide for prospective users has been recently published by Pagowski et al (2014).…”

Development and application of the WRFPLUS-Chem online chemistry adjoint and WRFDA-Chem assimilation system

“…While existing CTMs are capable of aerosol emission inversions, this development promises to introduce new insights into meteorologychemistry couplings. We apply this system to black carbon (BC) aerosol, because of its important implications for climate (Bond et al, 2013) and health (Grahame et al, 2014). Additionally, the widespread use and development of WRF furthers the potential for continued model improvement and a community of future users.…”

“…To address this, chemical data assimilation can be used to improve short-term forecasts. Bocquet et al (2015) review methods and applications of chemical data assimilation in CTMs and NWP-chemistry models. In WRF, three-dimensional variational data assimilation (3D-Var) (Pagowski et al, 2010;Liu et al, 2011;Schwartz et al, 2012;Saide et al, 2012Saide et al, , 2013, ensemble Kalman filter (EnKF) (Pagowski and Grell, 2012), and hybrid approaches (Schwartz et al, 2014) have all been used to improve chemical initial conditions.…”

Evaluation Criteria on the Design for Assimilating Remote Sensing Data Using Variational Approaches

“…Remote sensing makes it possible to collect data from dangerous or inaccessible areas, and meteorological satellites provide an indispensable supplement to the conventional meteorological observing system. Due to their ability of acquiring data in traditionally data-poor regions of the oceans, the stratosphere, and the Southern Hemisphere, as well as the high horizontal resolution, satellite observations have played an increasingly important role in atmospheric studies (Bocquet et al 2015;Fu et al 2017). Numerous experiments have been conducted in order to make good use of the satellite data in operational numerical weather forecasting or to improve the analysis and understanding of atmospheric phenomena and dynamics.…”