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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.
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The UK Toxic Organic Micro Pollutants (TOMPs) Network, which has operated since 1991, collects ambient air samples at six urban, rural, and semi-rural sites across England and Scotland, using high-volume active air samplers [1]. Furthermore, in 1994, a latitudinal sampling transect from the south of England to the north of Norway was established with eleven sampling sites, mainly in remote locations, using Semi-Permeable Membrane Devices (SPMDs) as passive air samplers [2]. Both networks provide continuous, long-term ambient air trend data for a range of Persistent Organic Pollutants (POPs), including PCBs and PBDEs, and have helped demonstrating a decline in POPs air concentrations over the last three decades. However, in recent years no further significant declines have been observed. SumPCB and SumPBDE levels in the UK are lowest at the rural sites and highest for the urban sites (TOMPs), and they generally decrease from the south of England to the north of Norway (UK/Norway) in line with expectations. Higher values at less remote sites and sites downwind from population centres show that POPs concentrations may still mainly be influenced by primary emissions. Concentrations at semi-rural sites lie between rural and urban sites; however, they can exceed the latter in some years. This can probably be attributed to short-term local effects. The data from the TOMPs network shows that concentrations of PCBs are higher in warmer than in colder months, while the seasonal patterns are less uniform for PBDEs.
2018
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2023