The present study proposes a novel data assimilation (DA) approach for estimating emission and wind direction parameters in an advection-diffusion model. This implementation aims to improve the prediction of a chemical transport model over long distances by updating the emission operator in the model using DA techniques. As a first step, we want to test the method in a small-scale scenario. A low-dimensional advection-diffusion model was utilized to evaluate the effectiveness of the proposed approach under various sampling observation numbers. The model’s emission and wind parameters are perturbed as a source of uncertainty. The parameters are sequentially estimated with the adjoint-free Ensemble Kalman filter with an augmented state vector. These sequential DA techniques exploit the ensemble of multiple model realizations to reduce uncertainty in the state and parameter representation. An associated stream function with a divergence-free condition controls the wind fields, and the estimation of this stream function through the assimilation process allows corrections of the wind fields without violating physical laws. The technique’s performance was compared against validation observations such as the Root-Mean Square (RMS), and it was found that the number of assimilated observations had a significant impact on the parameter estimations results. This study demonstrates the potential of the proposed DA approach for improving the prediction of transport in the advection-diffusion model through parameter estimation.

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With the aim of understanding the impact of air pollution on human health and ecosystems in the tropical Andes region (TAR), we aim to couple the Weather Research and Forecasting Model (WRF) with the chemical transport models (CTM) Long-Term Ozone Simulation and European Operational Smog (LOTOS–EUROS), at high and regional resolutions, with and without assimilation. The factors set for WRF, are based on the optimized estimates of climate and weather in cities and urban heat islands in the TAR region. It is well known in the weather research and forecasting field, that the uncertainty of non-linear models is a major issue, thus making a sensitivity analysis essential. Consequently, this paper seeks to quantify the performance of the WRF model in the presence of disturbances to the initial conditions (IC), for an arbitrary set of state-space variables (pressure and temperature), simulating a disruption in the inputs of the model. To this aim, we considered three distributions over the error term: a normal standard distribution, a normal distribution, and an exponential distribution. We analyze the sensitivity of the outputs of the WRF model by employing non-parametric and robust statistical techniques, such as kernel distribution estimates, rank tests, and bootstrap. The results show that the WRF model is sensitive in time, space, and vertical levels to changes in the IC. Finally, we demonstrate that the error distribution of the output differs from the error distribution induced over the input data, especially for Gaussian distributions.@en

Chemical transport models (CTM) are crucial for simulating the distribution of air pollutants, such as particulate matter, and evaluating their impact on the environment and human health. However, these models rely heavily on accurate emission inventory and meteorological inputs, usually obtained from reanalyzed weather data, such as the European Centre for Medium-Range Weather Forecasts (ECMWF). These inputs do not accurately reflect the complex topography and micro-scale meteorology in tropical regions where air pollution can pose a severe public health threat. We propose coupling the LOTOS–EUROS CTM model and the weather research and forecasting (WRF) model to improve LOTOS–EUROS representation. Using WRF as a meteorological driver provides high-resolution inputs for accurate pollutant simulation. We compared LOTOS–EUROS results when WRF and ECMWF provided the meteorological inputs during low and high pollutant concentration periods. The findings indicate that the WRF–LOTOS–EUROS coupling offers a more precise representation of the meteorology and pollutant dispersion than the default input of ECMWF. The simulations also capture the spatio-temporal variability of pollutant concentration and emphasize the importance of accounting for micro-scale meteorology and topography in air pollution modelling.@en