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2023
2023
Satellite remote sensing of Arctic fires - a literature and data review
The main aim of this report is to prepare for the proposed SGA #17 of the Caroline Herschel Framework Partnership Agreement on Copernicus User Uptake Work Programme 2020 named “Arctic peat- and forest-fire information system”. First, we summarize the scientific background of wildfires in the Arctic and the Northern boreal zone and describe observations of long-range transport of forest fire pollution. This is followed by an overview of satellite data and resources available for fire monitoring in these regions. This covers the fire ECVs, as well as smoke plume tracers. Furthermore, we list CAMS and CEMS resources, i.e., GWIS, EFFIS (including the latest country report for Norway), and GFAS, as well as other fire emission inventories. Knowledge gaps and limitations of satellite remote sen.sing, future missions, Norwegian user uptake and user groups are described.
NILU
2023
2023
Aerosol Optical Properties and Type Retrieval via Machine Learning and an All-Sky Imager
This study investigates the applicability of using the sky information from an all-sky imager (ASI) to retrieve aerosol optical properties and type. Sky information from the ASI, in terms of Red-Green-Blue (RGB) channels and sun saturation area, are imported into a supervised machine learning algorithm for estimating five different aerosol optical properties related to aerosol burden (aerosol optical depth, AOD at 440, 500 and 675 nm) and size (Ångström Exponent at 440–675 nm, and Fine Mode Fraction at 500 nm). The retrieved aerosol optical properties are compared against reference measurements from the AERONET station, showing adequate agreement (R: 0.89–0.95). The AOD errors increased for higher AOD values, whereas for AE and FMF, the biases increased for coarse particles. Regarding aerosol type classification, the retrieved properties can capture 77.5% of the total aerosol type cases, with excellent results for dust identification (>95% of the cases). The results of this work promote ASI as a valuable tool for aerosol optical properties and type retrieval.
2023
2023
Monitoring of microplastics in the Norwegian environment (MIKRONOR)
In 2021 The Norwegian Environment Agency (Miljødirektoratet) assigned the first analyses of microplastics within a national monitoring program “Microplastics in Norwegian coastal areas, rivers, lakes and air (MIKRONOR)” to NIVA. The aim of the program was to build knowledge about the background levels of microplastics in Norwegian environment, as well as identify potential sources and sinks. This is the second annual report, which presents the results from samples of 1) marine and lake/river sediments, biota and water, 2) air and deposition at two sites, including one at Svalbard, and 3) potential sources: urban runoff and effluent of wastewater treatment plants (WWTP) in two cities (Oslo and Hamar). The samples were analysed for microplastics, including tyre wear particles (TWP) from cars. The concentrations of plastic particles (mass of polymers per volume/weight unit) were calculated, using a novel formula for estimating volume of particles from the numerical analysis by spectroscopic (FTIR) analysis. The air samples were analysed for mass concentrations by mass spectrometric analysis. The main findings were the large number and concentrations of particles found in the inner Oslofjord. This included large numbers of microplastic particles resulting in high mass concentrations (μg/g dw) of plastic polymers. Particularly high mass concentrations of TWP were found in the sediments of the inner Oslofjord. TWP were also found at considerably high concentrations in blue mussels from the same area (Akershuskaia). Additionally, the urban runoff samples from both Oslo and Hamar showed high concentrations of TWP. High concentrations of TWP were also found in freshwater sediments near Hamar.
Norsk institutt for vannforskning (NIVA)
2023
2023
PM2.5 Retrieval Using Aerosol Optical Depth, Meteorological Variables, and Artificial Intelligence
Particulate matter (PM) is one of the major air pollutants that has adverse impacts on human health. The aim of this study is to present an alternative approach for retrieving fine PM (particles with an aerodynamic diameter less than 2.5 μm, PM2.5) using artificial intelligence. Ground-based instruments, including a hand-held Microtops II sun photometer (for aerosol optical depth), a PurpleAir sensor (for PM2.5), and Rotronic sensors (for temperature and relative humidity), are used for the machine learning algorithm training. The retrieved PM2.5 reveals an adequate performance with an error of 0.08 μg m−3 and a Pearson correlation coefficient of 0.84.
2023
Aerosol and dynamical contributions to cloud droplet formation in Arctic low-level clouds
The Arctic is one of the most rapidly warming regions of the globe. Low-level clouds and fog modify the energy transfer from and to space and play a key role in the observed strong Arctic surface warming, a phenomenon commonly termed “Arctic amplification”. The response of low-level clouds to changing aerosol characteristics throughout the year is therefore an important driver of Arctic change that currently lacks sufficient constraints. As such, during the NASCENT campaign (Ny-Ålesund AeroSol Cloud ExperimeNT) extending over a full year from October 2019 to October 2020, microphysical properties of aerosols and clouds were studied at the Zeppelin station (475 m a.s.l.), Ny-Ålesund, Svalbard, Norway. Particle number size distributions obtained from differential mobility particle sizers as well as chemical composition derived from filter samples and an aerosol chemical speciation monitor were analyzed together with meteorological data, in particular vertical wind velocity. The results were used as input to a state-of-the-art cloud droplet formation parameterization to investigate the particle sizes that can activate to cloud droplets, the levels of supersaturation that can develop, the droplet susceptibility to aerosol and the role of vertical velocity. We evaluate the parameterization and the droplet numbers calculated through a droplet closure with in-cloud in situ measurements taken during nine flights over 4 d. A remarkable finding is that, for the clouds sampled in situ, closure is successful in mixed-phase cloud conditions regardless of the cloud glaciation fraction. This suggests that ice production through ice–ice collisions or droplet shattering may have explained the high ice fraction, as opposed to rime splintering that would have significantly reduced the cloud droplet number below levels predicted by warm-cloud activation theory. We also show that pristine-like conditions during fall led to clouds that formed over an aerosol-limited regime, with high levels of supersaturation (generally around 1 %, although highly variable) that activate particles smaller than 20 nm in diameter. Clouds formed in the same regime in late spring and summer, but aerosol activation diameters were much larger due to lower cloud supersaturations (ca. 0.5 %) that develop because of higher aerosol concentrations and lower vertical velocities. The contribution of new particle formation to cloud formation was therefore strongly limited, at least until these newly formed particles started growing. However, clouds forming during the Arctic haze period (winter and early spring) can be limited by updraft velocity, although rarely, with supersaturation levels dropping below 0.1 % and generally activating larger particles (20 to 200 nm), including pollution transported over a long range. The relationship between updraft velocity and the limiting cloud droplet number agrees with previous observations of various types of clouds worldwide, which supports the universality of this relationship.
2023
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.
2023
2023
2023
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