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 (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM.
Highlights
Data assimilation pertains to the combination of modeling with observational data to produce a most probable representation of the state of the variables considered
We address here data assimilation in atmospheric chemistry models, which we define as including both atmospheric chemical transport models (CTM), which use meteorological fields as inputs (e.g., Seinfeld and Pandis, 2006), and coupled chemistry meteorology models (CCMM), which simulate meteorology and atmospheric chemistry jointly (Zhang, 2008; Baklanov et al, 2014)
The treatment of interactions between aerosols and meteorology in the National Aeronautics and Space Administration (NASA) Goddard Earth Observing System (GEOS-5) model was shown to improve the simulations of the atmospheric thermal structure and general circulation during Saharan dust events (Reale et al, 2011) and the assimilation of MODIS-derived aerosol optical depth (AOD) was conducted in GEOS-5 with this interactive aerosol/meteorology treatment (Reale et al, 2014)
Summary
Data assimilation pertains to the combination of modeling with observational data to produce a most probable representation of the state of the variables considered. The objective of this review is to present the current state of the science in data assimilation in atmospheric chemistry models. The emphasis for future prospects is placed on the preferred approaches for CCMM and the challenges associated with the combined assimilation of data for meteorology and atmospheric chemistry. Potential difficulties are identified based on currently available experience and recommendations are provided on the most appropriate approaches (methods and data sets) for data assimilation in CCMM. 2 an overview of the data assimilation techniques that are used in atmospheric modeling Their applications to atmospheric chemistry are presented in Sect. 6. recommendations for future method development, method applications and pertinent data sets are provided, along with a discussion of future prospects for data assimilation in CCMM Recommendations for future method development, method applications and pertinent data sets are provided in Sect. 7, along with a discussion of future prospects for data assimilation in CCMM
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