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

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

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

Evaluation of atmospheric aerosols in the metropolitan area of São Paulo simulated by the regional EURAD-IM model on high-resolution

We present a high-resolution air quality study over São Paulo, Brazil with the EURopean Air Pollution Dispersion - Inverse Model (EURAD-IM) used for the first time over South America simulating detailed features of aerosols. Modeled data are evaluated with observational surface data and a Lidar. Two case studies in 2016 with distinct meteorological conditions and pollution plume features show transport (i) from central South America, associated to biomass burning activities, (ii) from the rural part of the state of São Paulo, (iii) between the metropolitan areas of Rio de Janeiro and São Paulo (MASP) either through the Paraíba Valley or via the ocean, connecting Brazil's two largest cities, (iv) from the port-city Santos to MASP and also from MASP to the city Campinas, and vice versa. A Pearson coefficient of 0.7 was found for PM10 at MASP CENTER and EURAD-IM simulations vary within the observational standard deviation, with a Mean Percentual Error (MPE) of 10%. The model's vertical distributions of aerosol layers agree with the Lidar profiles that show either characteristics of long-range transported biomass burning plumes, or of local pollution. The distinct transport patterns that agree with satellite Aerosol Optical Death and fire spot images as well as with the ground-based observations within the standard deviations, allows us exploring patterns of air pollution in a detailed manner and to understand the complex interactions between local to long-range transport sources.This study was financed in part by the Coordenaç ̃ao de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) - Finance Code 001, Brazil, for the doctoral scholarship granted. This article and the research behind it are a direct contribution to the research themes of the Klimapolis Laboratory (klimapolis.net

Intercomparison of air quality models in a megacity: Towards an operational ensemble forecasting system for São Paulo

An intercomparison of four air quality models is performed in the tropical megacity of Sao Paulo with the perspective of developing an air quality forecasting system based on a regional model ensemble. During three contrasting periods marked by different types of pollution events, we analyze the concentrations of the main regulated pollutants (Ozone, CO, SO2, NOx, PM2.5 and PM10) compared to observations of a dense air quality monitoring network. The modeled concentrations of CO, PM and NOx are in good agreement with the observations for the temporal variability and the range of variation. However, the transport of pollutants due to biomass burning pollution events can strongly affect the air quality in the metropolitan area of Sao Paulo with increases of CO, PM2.5 and PM10, and is associated with an important inter-model variability. Our results show that each model has periods and pollutants for which it has the best agreement. The observed day-to-day variability of ozone concentration is well reproduced by the models, as well as the average diurnal cycle in terms of timing. Overall the performance for ozone of the median of the regional model ensemble is the best in terms of time and magnitude because it takes advantage of the capabilities of each model. Therefore, an ensemble prediction of regional models is promising for an operational air quality forecasting system for the megacity of Sao Paulo.This article is a direct contribution to the research themes of the Klimapolis Lab-836 oratory (klimapolis.net), which is funded by the German Federal Ministry of Education837 and Research (BMBF)