Chemical Data Assimilation Research Articles

Chemical data assimilation of Transport and Chemical Evolution over the Pacific (TRACE‐P) aircraft measurements

In this paper, the four‐dimensional variational (4D‐Var) technique is applied to assimilate aircraft measurements during the Transport and Chemical Evolution over the Pacific (TRACE‐P) field experiment into a chemical transport model, Sulfur Transport Eulerian Model, version 2K1 (STEM‐2K1). Whether data assimilation would produce better analyzed fields is examined. It is found that assimilating ozone observations from one of two independent flights improves model prediction of the other flight ozone measurements, which are withheld as validation data. The adjusted initial fields after only assimilating the total reactive nitrogen (NOy) observations lead to better predictions of NO, NO2, and PAN, based on their agreement with the withheld measurements. One experiment simultaneously assimilating the observations of O3, NO, NO2, HNO3, PAN, and RNO3 demonstrates that the model is able to match those measurements well by changing the initial fields. In addition, the model predictions of NOy improve significantly after assimilating the aforementioned multiple observation species, which are independent of the withheld NOy measurements. In the paper, we also show that the key species whose initial mixing ratios would significantly affect the agreement between model and measurements can be identified using adjoint sensitivity analysis. Such information can be used to reduce the number of control variables in the 4D‐Var data assimilation. To speed up the optimization process in the 4D‐Var, we enforce the concentration upper bounds through the limited memory–Broyden‐Fletcher‐Goldfarb‐Shanno‐B (L‐BFGS‐B) algorithm, and this proves to be effective.

Halogens and the chemistry of the free troposphere

Abstract. The role of halogens in both the marine boundary layer and the stratosphere has long been recognized, while their role in the free troposphere is often not considered in global chemical models. However, a careful examination of free-tropospheric chemistry constrained by observations using a full chemical data assimilation system shows that halogens do play a significant role in the free troposphere. In particular, the chlorine initiation of methane oxidation in the free troposphere can contribute more than 10%, and in some regions up to 50%, of the total rate of initiation. The initiation of methane oxidation by chlorine is particularly important below the polar vortex and in northern mid-latitudes. Likewise, the hydrolysis of alone can contribute more than 35% of the production rate in the free-troposphere.

Validation of the self‐consistency of GOMOS NO3, NO2 and O3 data using chemical data assimilation

The NO3 measurement by the GOMOS instrument on board the ENVISAT platform is the first satellite measurements of this species. The simultaneous measurements of O3 and NO2, which are strongly coupled chemically to NO3, allow us to test the self‐consistency of the GOMOS measurements of the different species. In this paper, the self‐consistency of the nighttime measurements by GOMOS of O3, NO2 and NO3 are tested using chemical data assimilation. Measurements obtained between 25 and 55 km during two distinct periods are assimilated. Analyzed NO3 (i.e., NO3 calculated by the model after assimilation of GOMOS O3 and NO2 data) are then compared to corresponding GOMOS NO3 measurements in correlation plots (GOMOS NO3 versus analyzed NO3). Overall, the differences between the NO3 measurements and corresponding analyzed NO3 are found to be small, about 10% on average. The linear regressions for both periods are also found to be close to the 1‐to‐1 line with small standard errors. This agreement indicates that O3, NO2 and NO3 GOMOS measurements are self consistent chemically and that there is no substantial bias in GOMOS NO3 data. It also suggests that the nighttime NO3 chemistry is well understood.

Representativeness uncertainty in chemical data assimilation highlight mixing barriers

When performing chemical data assimilation the observational, representativeness, and theoretical uncertainties have very different characteristics. In this study, we have accurately characterized the representativeness uncertainty by studying the probability distribution function (PDF) of the observations. The average deviation has been used as a measure of the width of the PDF and of the variability (representativeness uncertainty) for the grid cell. It turns out that for long-lived tracers such as N 2O and CH 4 the representativeness uncertainty is markedly different from the observational uncertainty and clearly delineates mixing barriers such as the polar vortex edge, the tropical pipe and the tropopause.

Direct and adjoint sensitivity analysis of chemical kinetic systems with KPP: Part I—theory and software tools

The analysis of comprehensive chemical reactions mechanisms, parameter estimation techniques, and variational chemical data assimilation applications require the development of efficient sensitivity methods for chemical kinetics systems. The new release (KPP-1.2) of the kinetic preprocessor (KPP) contains software tools that facilitate direct and adjoint sensitivity analysis. The direct-decoupled method, built using BDF formulas, has been the method of choice for direct sensitivity studies. In this work, we extend the direct-decoupled approach to Rosenbrock stiff integration methods. The need for Jacobian derivatives prevented Rosenbrock methods to be used extensively in direct sensitivity calculations; however, the new automatic and symbolic differentiation technologies make the computation of these derivatives feasible. The direct-decoupled method is known to be efficient for computing the sensitivities of a large number of output parameters with respect to a small number of input parameters. The adjoint modeling is presented as an efficient tool to evaluate the sensitivity of a scalar response function with respect to the initial conditions and model parameters. In addition, sensitivity with respect to time-dependent model parameters may be obtained through a single backward integration of the adjoint model. KPP software may be used to completely generate the continuous and discrete adjoint models taking full advantage of the sparsity of the chemical mechanism. Flexible direct-decoupled and adjoint sensitivity code implementations are achieved with minimal user intervention. In a companion paper, we present an extensive set of numerical experiments that validate the KPP software tools for several direct/adjoint sensitivity applications, and demonstrate the efficiency of KPP-generated sensitivity code implementations.

Averaging kernels for DOAS total-column satellite retrievals

Abstract. The Differential Optical Absorption Spectroscopy (DOAS) method is used extensively to retrieve total column amounts of trace gases based on UV-visible measurements of satellite spectrometers, such as ERS-2 GOME. In practice the sensitivity of the instrument to the tracer density is strongly height dependent, especially in the troposphere. The resulting tracer profile dependence may introduce large systematic errors in the retrieved columns that are difficult to quantify without proper additional information, as provided by the averaging kernel (AK). In this paper we discuss the DOAS retrieval method in the context of the general retrieval theory as developed by Rodgers. An expression is derived for the DOAS AK for optically thin absorbers. It is shown that the comparison with 3D chemistry-transport models and independent profile measurements, based on averaging kernels, is no longer influenced by errors resulting from a priori profile assumptions. The availability of averaging kernel information as part of the total column retrieval product is important for the interpretation of the observations, and for applications like chemical data assimilation and detailed satellite validation studies.

Chemical data assimilation: A case study of solar occultation data from the ATLAS 1 mission of the Atmospheric Trace Molecule Spectroscopy Experiment (ATMOS)

A key advantage of using data assimilation is the propagation of information from data‐rich regions to data‐poor regions, which is particularly relevant to the use of solar occultation data such as from the Atmospheric Trace Molecule Spectroscopy Experiment (ATMOS). For the first time, an in‐depth uncertainty analysis is included in a photochemical model‐data intercomparison including observation, representativeness, and theoretical uncertainty. Chemical data assimilation of solar occultation measurements can be used to reconstruct full diurnal cycles and to evaluate their chemical self‐consistency. This paper considers as an example the measurements made by the ATMOS instrument ATLAS 1 during March 1992 for a vertical profile in flow‐tracking coordinates at an equivalent potential vorticity (PV) latitude of 38°S. ATMOS was chosen because it simultaneously observes several species. This equivalent PV latitude was chosen as it was where ATMOS observed the atmosphere's composition over the largest range of altitudes. A single vertical profile was used so that the detailed diurnal information that the assimilation utilizes could be highlighted. There is generally good self‐consistency between the ATMOS ATLAS 1 observations of O3, NO, NO2, N2O5, HNO3, HO2NO2, HCN, ClONO2, HCl, H2O, CO, CO2, CH4, and N2O and between the observations and photochemical theory.

A review on the use of the adjoint method in four‐dimensional atmospheric‐chemistry data assimilation

AbstractIn this paper we review a theoretical formulation of the adjoint method to be used in four‐dimensional (4D) chemistry data assimilation. The goal of the chemistry data assimilation is to combine an atmospheric‐chemistry model and actual observations to produce the best estimate of the chemistry of the atmosphere. The observational dataset collected during the past decades is an unprecedented expansion of our knowledge of the atmosphere. The exploitation of these data is the best way to advance our understanding of atmospheric chemistry, and to develop chemistry models for chemistry‐climate prediction. The assimilation focuses on estimating the state of the chemistry in a chemically and dynamically consistent manner (if the model allows online interactions between chemistry and dynamics). In so doing, we can: produce simultaneous and chemically consistent estimates of all species (including model parameters), observed and unobserved; fill in data voids; test the photochemical theories used in the chemistry models. In this paper, the Hilbert space is first formulated from the geometric structure of the Banach space, followed by the development of the adjoint operator in Hilbert space. The principle of the adjoint method is described, followed by two examples which show the relationship of the gradient of the cost function with respect to the output vector and the gradient of the cost function with respect to the input vector. Applications to chemistry data assimilation are presented for both continuous and discrete cases. The 4D data variational adjoint method is then tested in the assimilation of stratospheric chemistry using a simple catalytic ozone‐destruction mechanism, and the test results indicate that the performance of the assimilation method is good.

Ozone episode analysis by four‐dimensional variational chemistry data assimilation

A chemical four‐dimensional variational data assimilation system has been developed and applied for the study of an episode with enhanced summerly ozone levels. In this study the optimization parameters are the initial values of the chemical constituents. The case study focuses on an ozone episode over central Europe during August 1997. The associated observational database consists mainly of surface observations of ozone and nitrogen oxides, but also a limited number of ozone radiosonde records. The four‐dimensional data assimilation algorithm is composed of the chemistry transport model, its adjoint model with the adjoint versions of the Regional Acid Deposition Model (RADM2) chemical mechanism, horizontal and vertical advection schemes, implicit vertical diffusion, and a limited memory quasi‐newton minimization routine. The underlying model of the spatio‐temporal data assimilation scheme is the comprehensive mesoscale‐α EUropean Air pollution Dispersion model (EURAD), which is based on the RADM2 gas phase mechanism. On the basis of a 6 hours data assimilation interval, analyses of the chemical state of the atmosphere were obtained, where the skill is verified in two different ways: (1) improvements of forecasts subsequent to the assimilation procedure, and (2) validation of the analyses with observational data which is withheld from the variational data assimilation algorithm. It is demonstrated that significant improvements are achieved for short‐term forecasts including the afternoon ozone peak abundances. The analysis skill is further corroborated by examinations with retained measurement data.

PSAS and 4D-var data assimilation for chemical state analysis by urban and rural observation sites

Advanced data assimilation can be applied to retrieve as much information as possible from photooxidant measurements in the trophosphere in order to analyze the chemical state. Traditional assimilation methods have proved to be unsatisfactory due to their inability (i) to account for locally varying chemical scenarios, (ii) to exploit informations inherent in time series evolution, and (iii) to use observations measured at different times. The Physical—space Statistical Analysis System (PSAS) and a space—time data assimilation analysis are proposed aiming at the combination of both the four dimensional variational calculus (4D—var) and inhomogeneous error covariances allowing to focus on regional differences in emission rates an surface conditions. The University of Cologne, Eulerian mesoscale—α Chemistry Transport Model EURAD is part of a comprehensive 4D—var chemical data assimilation system which makes best use of available observations to provide skillful analyses. Special emphasis is placed on the locally varying representativeness of individual measurement sites.

Improvement of a 3D CTM and a 4D variational data assimilation on a vector machine CRAY J90 through a multitasking strategy

In this paper we report on the improvement of a 3D CTM and a 4D variational data assimilation (4DDA) on a vector machine CRAY J90. Significant speedup has been achieved by applying a general multitasking strategy to both a 3D CTM and a 4DDA on the shared-memory platform of a CRAY J90. The 3D CTM has been multitasked to study many complex processes involved in the troposphere. For example, annual simulation to study the interaction between the atmosphere, biosphere (e.g., terrestrial vegetation), and oceans; while the 4DDA has been multitasked to assimilate observational data from satellites (e.g., UARS and ATMOS) and other measurements (e.g., ozonesondes and aircrafts). Evaluation of the multitasked models (both 3D CTM and 4DDA) are carried out by comparing (1) required job elapsed time, and (2) spatial and temporal distribution of long-lived and short-lived chemical species, physical fields, and photolysis rates between the single-threaded and the multitasked simulations. The agreement from the later comparisons indicate a correct multitasking strategy, while the first comparison shows a significantly reduced elapsed time. This validates the need of a multitasking strategy in complex global biogeochemical modeling and 4D chemical data assimilation.

A four‐dimensional variational chemistry data assimilation scheme for Eulerian chemistry transport modeling

The inverse problem of data assimilation of tropospheric trace gas observations into an Eulerian chemistry transport model has been solved by the four‐dimensional variational technique including chemical reactions, transport, and diffusion. The University of Cologne European Air Pollution Dispersion Chemistry Transport Model 2 with the Regional Acid Deposition Model 2 gas phase mechanism is taken as the basis for developing a full four‐dimensional variational data assimilation package, on the basis of the adjoint model version, which includes the adjoint operators of horizontal and vertical advection, implicit vertical diffusion, and the adjoint gas phase mechanism. To assess the potential and limitations of the technique without degrading the impact of nonperfect meteorological analyses and statistically not established error covariance estimates, artificial meteorological data and observations are used. The results are presented on the basis of a suite of experiments, where reduced records of artificial “observations” are provided to the assimilation procedure, while other “data” is retained for performance control of the analysis. The paper demonstrates that the four‐dimensional variational technique is applicable for a comprehensive chemistry transport model in terms of computational and storage requirements on advanced parallel platforms. It is further shown that observed species can generally be analyzed, even if the “measurements” have unbiased random errors. More challenging experiments are presented, aiming to tax the skill of the method (1) by restricting available observations mostly to surface ozone observations for a limited assimilation interval of 6 hours and (2) by starting with poorly chosen first guess values. In this first such application to a three‐dimensional chemistry transport model, success was also achieved in analyzing not only observed but also chemically closely related unobserved constituents.