Publikasjoner

Using simple particle shapes to model the Stokes scattering matrix of ensembles of wavelength-sized particles with complex shapes: possibilities and limitations.

Kahnert, M.; Stamnes, J.J.; Stamnes, K.

Using remote sensing as a support to the implementation of the European Marine Strategy Framework Directive in SW Portugal.

Cristina, S.; Icely, J.; Goela, P.C.; DelValls, T.A.; Newton, A.

Using passive air samplers to assess local sources versus long range atmospheric transport of POPs.

Halse, A.K.; Schlabach, M.; Sweetman, A.; Jones, K.C.; Breivik, K.

Using model-based screening to help discover unknown environmental contaminants.

McLachlan, M.S.; Kierkegaard, A.; Radke, M.; Sobek, A.; Malmvärn, A.; Alsberg, T.; Arnot, J.A.; Brown, T.N.; Wania, F.; Breivik, K.; Xu, S.

Using long-term air monitoring of semi-volatile organic compounds to evaluate the uncertainty in polyurethane-disk passive sampler-derived air concentrations.

Holt, E.; Bohlin-Nizzetto, P.; Boruvková, J.; Harner, T.; Kalina, J.; Melymuk, L.; Klánová, J.

Using ground station data for improving accuracy in (global) modeling. NILU F

Fiebig, M.

Using European time use data for modelling individual exposure to air pollution. NILU F

Gerharz, L.E.; Loh, M.; Yang, A.; Kuhn, A.

Using emission estimates as exposure metric: Respiratory disease and outdoor air pollution in Kanpur, India. NILU PP

Bartonova, A.; Liu, H.-Y.; Sharma, M.; Katiyar, K.; Dikshit, O.; Schindler, M.

Using dispersion models at microscale to assess long-term air pollution in urban hot spots: A FAIRMODE joint intercomparison exercise for a case study in Antwerp

Martín, F.; Janssen, S.; Rodrigues, V.; Sousa, J.; Santiago, J.L.; Rivas, E.; Stocker, J.; Jackson, R.; Russo, F.; Villani, M.G.; Tinarelli, G.; Barbero, D.; José, R. San; Pérez-Camanyo, J.L.; Sousa Santos, Gabriela; Bartzis, J.; Sakellaris, I.; Horváth, Z.; Környei, L.; Liszkai, B.; Kovács, A.; Jurado, X.; Reiminger, N.; Thunis, P.; Cuvelier, C.

In the framework of the Forum for Air Quality Modelling in Europe (FAIRMODE), a modelling intercomparison exercise for computing NO2 long-term average concentrations in urban districts with a very high spatial resolution was carried out. This exercise was undertaken for a district of Antwerp (Belgium). Air quality data includes data recorded in air quality monitoring stations and 73 passive samplers deployed during one-month period in 2016. The modelling domain was 800 × 800 m2. Nine modelling teams participated in this exercise providing results from fifteen different modelling applications based on different kinds of model approaches (CFD – Computational Fluid Dynamics-, Lagrangian, Gaussian, and Artificial Intelligence). Some approaches consisted of models running the complete one-month period on an hourly basis, but most others used a scenario approach, which relies on simulations of scenarios representative of wind conditions combined with post-processing to retrieve a one-month average of NO2 concentrations.

The objective of this study is to evaluate what type of modelling system is better suited to get a good estimate of long-term averages in complex urban districts. This is very important for air quality assessment under the European ambient air quality directives. The time evolution of NO2 hourly concentrations during a day of relative high pollution was rather well estimated by all models. Relative to high resolution spatial distribution of one-month NO2 averaged concentrations, Gaussian models were not able to give detailed information, unless they include building data and street-canyon parameterizations. The models that account for complex urban geometries (i.e. CFD, Lagrangian, and AI models) appear to provide better estimates of the spatial distribution of one-month NO2 averages concentrations in the urban canopy. Approaches based on steady CFD-RANS (Reynolds Averaged Navier Stokes) model simulations of meteorological scenarios seem to provide good results with similar quality to those obtained with an unsteady one-month period CFD-RANS simulations.

Elsevier

Using data insertion with the NAME model to simulate the 8 May 2010 Eyjafjallajökull volcanic ash cloud.

Wilkins, K.L.; Watson, I.M.; Kristiansen, N.I.; Webster, H.N.; Thomson, D.J.; Dacre, H.F.; Prata, A.J.

Using bio-optical parameters as a tool for detecting changes in the phytoplankton community (SW Portugal).

Goela, P.C.; Icely, J.; Cristina, S.; Danchenko, S.; DelValls, T.A.; Newton, A.

Using a machine learning and stochastics-founded model to provide near real-time stratospheric polar vortex diagnostics based on high-latitude infrasound data

Eggen, Mari Dahl; Midtfjord, Alise Danielle; Vorobeva, Ekaterina; Benth, Fred Espen; Hupe, Patrick; Brissaud, Quentin; Orsolini, Yvan Joseph Georges Emile G.; Pichon, Alexis Le; Listowski, Constantino; Näsholm, Sven Peter

Acoustic waves below the frequency limit of human hearing - infrasound - can travel for thousands of kilometres in the atmosphere. The global propagation signature of infrasound is highly sensitive to the wind structure of the stratosphere.

This work exploits processed continuous data from three high-latitude infrasound stations to characterize an aspect of the stratospheric polar vortex. Concretely, a mapping is developed which takes the infrasound data from these three stations as input and outputs an estimate of the polar cap zonal mean wind averaged over 60-90 degrees in latitude at the 1 hPa pressure level. This stratospheric diagnostic information is relevant to, for example, sudden stratospheric warming assessment and sub-seasonal prediction.

The considered acoustic data is within a low-frequency regime globally dominated by so-called microbarom infrasound, which is continuously radiated into the atmosphere due to nonlinear interaction between counter-propagating ocean surface waves.

We trained a stochastics-based machine learning model (delay-SDE-net) to map between a time series of five years (2014-2018) of processed infrasound data and the ERA5 (reanalysis-based) daily average polar cap wind at 1 hPa for the same period. The ERA5 data was hence treated as ground-truth. In the prediction, the delay-SDE-net utilizes time-lagged inputs and their dependencies, as well as the day of the year to account for seasonal differences. In the validation phase, the input was the 2019 and 2020 infrasound time series, and the model inference results in an estimate of the daily average polar cap wind time-series. This result was then compared to the ERA5 representation of the stratospheric diagnostic time-series for the same period.

The applied machine learning model is based on stochastics and allows for an interpretable approach to estimate the aleatoric and epistemic prediction uncertainties. It is found that the mapping, which is only informed of the trained model, the day of year, and the infrasound data from three stations, generates a 1 hPa polar cap average wind estimate with a prediction error standard deviation of around 10 m/s compared to ERA5.

Focus should be put on the winter months because this is when the coupling between the stratosphere and the troposphere can mostly influence the surface conditions and provide additional prediction skill, in particular during strong and weak stratospheric polar vortex regimes. The infrasound data is available in real-time, and we discuss how the developed approach can be extended to provide near real-time stratospheric polar vortex diagnostics.

Users manual for the WetCorr instrument. NILU TR

Danielsen, T.; Henriksen, J.F.