Recognition and simulation of actions performable on rigidly-jointed actors such as human bodies have been the subject of our research for some time. One part of an ongoing effort towards a total human movement simulator is to develop a system to perform the actions of American Sign Language (ASL). However, one of the “channels” of ASL communication, the face, presents problems which are not well handled by a rigid model. An integrated system for an internal representation and simulation of the face is presented, along with a proposed image analysis model. Results from an implementation of the internal model and simulation modules are presented, as well as comments on the future of computer controlled recognition of facial actions. We conclude with a discussion on extensions of the system, covering relations between flexible masses and rigid (jointed) ones. Applications of this theory into constrained actions, such as across rigid nonmoving sheets of bone (forehead, eyes) are also discussed.

Recognition and simulation of actions performable on rigidly-Jointed actors such as human bodies have been the subject of our research for some time. One part of an ongoing effort towards a total human movement simulator is to develop a system to perform the actions of American Sign Language (ASL). However, one of the "channels" of ASL communication, the face, presents problems which are not well handled by a rigid model.An integrated system for an internal representation and simulation of the face is presented, along with a proposed image analysis model.Results from an implementation of the internal model and simulation modules are presented, as well as comments on the future of computer controlled recognition of facial actions.We conclude with a discussion on extensions of the system, covering relations between flexlble masses and rigid (Jointed) ones. Applications of this theory into constrained actions, such as across rigid nonmovlng sheets of bone (forehead, eyes) are also discussed.

Abstract. Following the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for COVID-19 in December 2019 in Wuhan (China) and its spread to the rest of the world, the World Health Organization declared a global pandemic in March 2020. Without effective treatment in the initial pandemic phase, social distancing and mandatory quarantines were introduced as the only available preventative measure. Despite the socioeconomic impacts, air quality improved due to lower pollutant emissions. Here we investigate the effects of the COVID-19 lockdowns on ambient black carbon (BC), which affects climate and damages health, using in-situ observations from 17 European stations in a Bayesian inversion framework. BC emissions declined by 11 % in Europe (20 % in Italy, 32 % in Germany, 20 % in Spain) during lockdown compared to the same period in the previous five years. BC temporal variation in the countries enduring the most drastic restrictions showed the most distinct lockdown impacts. Increased particle light absorption in at the beginning of the lockdown, confirmed by assimilated satellite and remote sensing data, suggests residential combustion was the dominant BC source. Accordingly, in Central and Eastern Europe, which experienced lower than average temperatures, BC was elevated compared to the previous five years. Except for the comparison of BC emissions in the lockdown with the previous five years, an immediate decrease was also seen, as compared with the period before the lockdown, which averaged about 10 % over Europe. Such a decrease was not seen in the previous years, which also confirms an impact on BC emissions from COVID-19.

<p><strong>Abstract.</strong> Methane (CH<sub>4</sub>) is a powerful greenhouse gas and atmospheric mixing ratios have been increasing since 2005. Therefore, quantification of CH<sub>4</sub> sources is essential for effective climate change mitigation. Here we report observations of the CH<sub>4</sub> mixing ratios measured at Zeppelin Observatory (Svalbard) in the Arctic and aboard the Research Vessel (RV) Helmer Hanssen over the Arctic Ocean from June 2014 to December 2016, as well as the long-term CH<sub>4</sub> trend measured at the Zeppelin Observatory (Svalbard) from 2001–2017. We investigated areas over the European Arctic Ocean to identify possible hot spot regions emitting CH<sub>4</sub> from the ocean to the atmosphere, and used state-of-the-art modelling (FLEXPART) combined with updated emissions inventories to identify CH<sub>4</sub> sources. Furthermore, we collected air samples in the region as well as samples of gas hydrates, obtained from the sea floor using a new technique developed as part of this work. Using this new methodology, we evaluated the suitability of ethane and isotopic signatures (<i>δ</i><sup>13</sup>C in CH<sub>4</sub>) as tracers for ocean-to-atmosphere CH<sub>4</sub> emission. We show that the mean atmospheric CH4 mixing ratio in the Arctic increased by 5.9 ± 0.38 parts per billion by volume (ppb) per year (yr<sup>−1</sup>) from 2001–2017. Meanwhile most large excursions from the baseline CH<sub>4</sub> mixing ratio over the European Arctic Ocean are due to long-range transport from land-based sources, lending confidence to the present inventories for high latitude CH<sub>4</sub> emissions. However, we also identify a potential hot spot region with ocean-atmosphere CH<sub>4</sub> flux North of Svalbard (80.4° N, 12.8° E) of up to 26 nmol m<sup>−2</sup> s<sup>−1</sup> from a large mixing ratio increase at the location of 30 ppb. Since this flux is highly consistent with previous constraints (both spatially and temporally), there is no evidence that the area of interest North of Svalbard is unique in the context of the wider Arctic. Rather, that the meteorology at the time of the observation was unique in the context of the measurement time series, i.e. we obtained, over the short course of the episode, measurements highly sensitive to emissions over an active seep site, without sensitivity to land based emissions.</p>

No abstract

Abstract. We document the ability of the new generation Oslo chemistry-transport model, OsloCTM3, to accurately simulate present-day aerosol distributions. The model is then used with the new Community Emission Data System (CEDS) historical emission inventory to provide updated time series of anthropogenic aerosol concentrations and consequent direct radiative forcing (RFari) from 1750 to 2014. Overall, the OsloCTM3 performs well compared with measurements of surface concentrations and remotely sensed aerosol optical depth. Concentrations are underestimated in Asia, but the higher emissions in CEDS than previous inventories result in improvements compared to observations. The black carbon (BC) treatment in OsloCTM3 gives better agreement with observed vertical BC profiles relative to the predecessor OsloCTM2. However, Arctic wintertime BC concentrations remain underestimated, and a range of sensitivity tests indicate that better physical understanding of processes associated with atmospheric BC processing is required to simultaneously reproduce both the observed features. Uncertainties in model input data, resolution and scavenging affects the distribution of all aerosols species, especially at high latitudes and altitudes. However, we find no evidence of consistently better model performance across all observables and regions in the sensitivity tests than in the baseline configuration. Using CEDS, we estimate a total net RFari in 2014 relative to 1750 of −0.17 W m−2, significantly weaker than the IPCC AR5 2010–1750 estimate. Differences are attributable to several factors, including stronger absorption by organic aerosol, updated parameterization of BC absorption, and reduced sulfate cooling. The trend towards a weaker RFari over recent years is more pronounced than in the IPCC AR5, illustrating the importance of capturing recent regional emission changes.

Figure S1: Methane mixing ratios at the RV Helmer Hanssen vs apparent wind angle relative to the bow (β), using β=arcos(W cos α + V/ √(W2+V2+2WV cos α)) where α=true pointing angle, W=true wind velocity, and V=ship velocity.