Efficiency and Sensitivity Analysis of Observation Networks for Atmospheric Inverse Modelling with Emissions

The controllability of advection-diffusion systems, subject to uncertain initial values and emission rates, is estimated, given sparse and error affected observations of prognostic state variables. In predictive geophysical model systems, like atmospheric chemistry simulations, different parameter families influence the temporal evolution of the system.This renders initial-value-only optimisation by traditional data assimilation methods as insufficient. In this paper, a quantitative assessment method on validation of measurement configurations to optimize initial values and emission rates, and how to balance them, is introduced. In this theoretical approach, Kalman filter and smoother and their ensemble based versions are combined with a singular value decomposition, to evaluate the potential improvement associated with specific observational network configurations. Further, with the same singular vector analysis for the efficiency of observations, their sensitivity to model control can be identified by determining the direction and strength of maximum perturbation in a finite-time interval.Comment: 30 pages, 10 figures, 5 table

Capitalizing on Market Reforms: Facets of Legal Development in Contemporary China

A review of: Law and Justice in China’s New Marketplace by Ronald C. Keith and Zhiqiu Lin. New York: Palgrave, 2001. 315pp. and Profits and Principles: Global Capitalism and Human Rights in China by Michael A. Santoro. Ithaca, NY: Cornell University Press, 2000. 256pp

Singular vector-based targeted observations of chemical constituents: description and first application of the EURAD-IM-SVA v1.0

Measurements of the large-dimensional chemical state of the atmosphere provide only sparse snapshots of the state of the system due to their typically insufficient temporal and spatial density. In order to optimize the measurement configurations despite those limitations, the present work describes the identification of sensitive states of the chemical system as optimal target areas for adaptive observations. For this purpose, the technique of singular vector analysis (SVA), which has proven effective for targeted observations in numerical weather prediction, is implemented in the EURAD-IM (EURopean Air pollution and Dispersion – Inverse Model) chemical transport model, yielding the EURAD-IM-SVA v1.0. Besides initial values, emissions are investigated as critical simulation controlling targeting variables. For both variants, singular vectors are applied to determine the optimal placement for observations and moreover to quantify which chemical compounds have to be observed with preference. Based on measurements of the airship based ZEPTER-2 campaign, the EURAD-IM-SVA v1.0 has been evaluated by conducting a comprehensive set of model runs involving different initial states and simulation lengths. For the sake of brevity, we concentrate our attention on the following chemical compounds, O3, NO, NO2, HCHO, CO, HONO, and OH, and focus on their influence on selected O3 profiles. Our analysis shows that the optimal placement for observations of chemical species is not entirely determined by mere transport and mixing processes. Rather, a combination of initial chemical concentrations, chemical conversions, and meteorological processes determines the influence of chemical compounds and regions. We furthermore demonstrate that the optimal placement of observations of emission strengths is highly dependent on the location of emission sources and that the benefit of including emissions as target variables outperforms the value of initial value optimization with growing simulation length. The obtained results confirm the benefit of considering both initial values and emission strengths as target variables and of applying the EURAD-IM-SVA v1.0 for measurement decision guidance with respect to chemical compounds

Using Geographically Referenced Data on Environmental Exposures for Public Health Research: A Feasibility Study Based on the German Socio-Economic Panel Study (SOEP)

Background: In panel datasets information on environmental exposures is scarce. Thus, our goal was to probe the use of area-wide geographically referenced data for air pollution from an external data source in the analysis of physical health. Methods: The study population comprised SOEP respondents in 2004 merged with exposures for NO2, PM10 and O3 based on a multi-year reanalysis of the EURopean Air pollution Dispersion-Inverse Model (EURAD-IM). Apart from bivariate analyses with subjective air pollution we estimated cross-sectional multilevel regression models for physical health as assessed by the SF-12. Results: The variation of average exposure to NO2, PM10 and O3 was small with the interquartile range being less than 10µg/m3 for all pollutants. There was no correlation between subjective air pollution and average exposure to PM10 and O3, while there was a very small positive correlation between the first and NO2. Inclusion of objective air pollution in regression models did not improve the model fit. Conclusions: It is feasible to merge environmental exposures to a nationally representative panel study like the SOEP. However, in our study the spatial resolution of the specific air pollutants has been too little, yet.SOEP, Geographically Referenced Data, Feasibility Study, Air Pollution, EURAD-IM, Physical Health

Optimization of weather forecasting for cloud cover over the European domain using the meteorological component of the Ensemble for Stochastic Integration of Atmospheric Simulations version 1.0

In this study, we present an expansive sensitivity analysis of physics configurations for cloud cover using the Weather Forecasting and Research Model (WRF V3.7.1) on the European domain. The experiments utilize the meteorological part of a large ensemble framework known as the Ensemble for Stochastic Integration of Atmospheric Simulations (ESIAS-met). The experiments first seek the best deterministic WRF physics configuration by simulating over 1,000 combinations of microphysics, cumulus parameterization, planetary boundary layer physics (PBL), surface layer physics, radiation scheme and land surface models. The results on six different test days are compared to CMSAF satellite images from EUMETSAT. We then selectively conduct stochastic simulations to assess the best choice for ensemble forecasts. The results indicate a high variability in terms of physics and parameterization. The combination of Goddard, WSM6, or CAM5.1 microphysics with MYNN3 or ACM2 PBL exhibited the best performance in Europe. For probabilistic simulations, the combination of WSM6 and SBU&ndash;YL microphysics with MYNN2 and MYNN3 showed the best performance, capturing the cloud fraction and its percentiles with 32 ensemble members. This work also demonstrates the capability and performance of ESIAS-met for large ensemble simulations and sensitivity analysis.</p