Data assimilation for noise mapping using CNOSSOS - A Danish Case Study

Noise maps play a significant role in developing noise mitigation policies. However, maps based on noise simulation models are likely to be uncertain due to underlying model uncertainties and parameterization. This research work explores the feasibility of data assimilation techniques to address this issue. A noise measurement campaign was carried out at two locations in the Central Jutland Region of Denmark. Consequently, 76 days (1 September-15 November 2019) (N = 1824 hours) of measured hourly A-weighted, energy equivalent, sound pressure level data (LAeq1h in dBA) was available for this case study. In addition, traffic counting data was available. The LAeq1h (dBA) levels were then simulated using the EU noise prediction standard, CNOSSOS. The comparison of daily averaged measured and simulated noise levels reveals the Root Mean Squared Error (RMSE) in the range of 1.4 - 2.8 dBA. Subsequently, data assimilation techniques, such as the Best Linear Unbiased Estimator (BLUE) and Bayesian Inverse Inference, are used to produce improved, hourly, so-called analysis maps with the minimum error covariance. The oral presentation and short article will present details of model inputs and simulations, data assimilation as well as the strengths and limitations of this research.