ALBERT

Description: This case study explores the potential for chemical state analysis at extratropical upper tropospheric – lower stratospheric (Ex-UTLS) height levels with airborne limb-images, assimilated into an advanced spatio-temporal system. The investigation is motivated by the limited capability of both, nadir- and limb-viewing satellite sensors to resolve highly filamented structures, delineated by sharp trace gas gradients on small horizontal and vertical scales. The EURAD-IM (EURopean Air pollution Dispersion – Inverse Model) is applied as assimilation system and designed to extend the flight path confined retrievals from GLORIA (Gimballed Limb Observer for Radiance Imaging of the Atmosphere) to both, larger areas and detailed vertical structures by a tomographic flight pattern. Related potential and limitations of the method are studied with the following features applied: (i) airborne limb-imaging observations of the Ex-UTLS, (ii) spatio-temporal extension by 4-dimensional variational data assimilation, (iii) correlation between ozone and potential vorticity (PV) as an indicator of airmasses and (iv) anisotropic and inhomogeneous horizontal background error correlations in the Ex-UTLS, spreading information towards unobserved regions along PV isopleths. This setup demonstrated substantial improvements to basic approaches in exploring new data on the spatial extend and alignment of airmasses down to small-scale filaments in the Ex-UTLS. Tomographic observations provide detailed insight for reconstructing filamentary foldings along airmass boundaries above the tropopause during this case study.

Description: Forecasts of biogenic trace gases in the planetary boundary layer (PBL) are highly affected by simulated emission and transport processes. The Po region during the PEGASOS campaign in summer 2012 provides challenging, yet common, conditions for simulating biogenic gases in the PBL. This study identifies and quantifies principal sources of forecast uncertainties induced by various model configurations under these conditions. Specifically, the effects of model configuration on different processes affecting atmospheric distributions of biogenic trace gas distributions are analyzed based on a priori available information. The investigation is based on the EURopean Air pollution Dispersion – Inverse Model (EURAD-IM) chemistry transport model employing the Model for Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN 2.1) biogenic emission module and Regional Atmospheric Chemistry Mechanism – Mainz Isoprene Mechanism (RACM-MIM) as the gas phase chemistry mechanism. Two major sources of forecast uncertainties are identified in this study. Firstly, biogenic emissions appear to be exceptionally sensitive to land surface properties inducing total variations in local concentrations of up to 1 order of magnitude. Moreover, these sensitivities are found to be highly similar for different gases and almost constant during the campaign, varying only diurnally. Secondly, the model configuration also highly influences regional flow patterns with significant effects on pollutant transport and mixing. This effect was corroborated by diverging source regions of a representative air mass and thus applies also to non-biogenic gases. As a result, large sensitivities to model configuration are found for surface concentrations of isoprene, as well as OH, affecting reactive atmospheric chemistry. Especially in areas with small-scale emission patterns, changes in the model configuration are able to induce significantly different local concentrations. The amount and complexity of sensitivities found in this study demonstrate the need to consider forecast uncertainties in chemical transport models with a special focus on biogenic emissions and pollutant transport.

Description: Atmospheric chemical forecasts heavily rely on various model parameters, which are often insufficiently known, such as emission rates and deposition velocities. However, a reliable estimation of resulting uncertainties with an ensemble of forecasts is impaired by the high dimensionality of the system. This study presents a novel approach, which substitutes the problem into a low-dimensional subspace spanned by the leading uncertainties. It is based on the idea that the forecast model acts as a dynamical system inducing multivariate correlations of model uncertainties. This enables an efficient perturbation of high-dimensional model parameters according to their leading coupled uncertainties. The specific algorithm presented in this study is designed for parameters that depend on local environmental conditions and consists of three major steps: (1) an efficient assessment of various sources of model uncertainties spanned by independent sensitivities, (2) an efficient extraction of leading coupled uncertainties using eigenmode decomposition, and (3) an efficient generation of perturbations for high-dimensional parameter fields by the Karhunen–Loéve expansion. Due to their perceived simulation challenge, the method has been applied to biogenic emissions of five trace gases, considering state-dependent sensitivities to local atmospheric and terrestrial conditions. Rapidly decreasing eigenvalues state that highly correlated uncertainties of regional biogenic emissions can be represented by a low number of dominant components. Depending on the required level of detail, leading parameter uncertainties with dimensions of ?(106) can be represented by a low number of about 10 ensemble members. This demonstrates the suitability of the algorithm for efficient ensemble generation for high-dimensional atmospheric chemical parameters.