Found 10066 publications. Showing page 212 of 403:
The Zeppelin Observatory is an atmospheric monitoring station located on the northwest coast of Spitzbergen island, in the Svalbard archipelago. The station provides background air composition, meteorological and climatological data for numerous research projects. The observatory is located on a mountain ridge in a region with complex topography that affects local atmospheric circulation processes. Research question: How the seasonal data collected at the Zeppelin observatory and Ny-Ålesund station (Fig. 2b), a temporarily station in the settlement, is affected by: 1) micrometeorological conditions 2) mesoscale dynamics 3) local air pollution
2018
2011
2003
2019
2016
2019
2019
2019
2008
2014
2012
2013
Inferring surface energy fluxes using drone data assimilation in large eddy simulations
Spatially representative estimates of surface energy exchange from field measurements are required for improving and validating Earth system models and satellite remote sensing algorithms. The scarcity of flux measurements can limit understanding of ecohydrological responses to climate warming, especially in remote regions with limited infrastructure. Direct field measurements often apply the eddy covariance method on stationary towers, but recently, drone-based measurements of temperature, humidity, and wind speed have been suggested as a viable alternative to quantify the turbulent fluxes of sensible (H) and latent heat (LE). A data assimilation framework to infer uncertainty-aware surface flux estimates from sparse and noisy drone-based observations is developed and tested using a turbulence-resolving large eddy simulation (LES) as a forward model to connect surface fluxes to drone observations. The proposed framework explicitly represents the sequential collection of drone data, accounts for sensor noise, includes uncertainty in boundary and initial conditions, and jointly estimates the posterior distribution of a multivariate parameter space. Assuming typical flight times and observational errors of light-weight, multi-rotor drone systems, we first evaluate the information gain and performance of different ensemble-based data assimilation schemes in experiments with synthetically generated observations. It is shown that an iterative ensemble smoother outperforms both the non-iterative ensemble smoother and the particle batch smoother in the given problem, yielding well-calibrated posterior uncertainty with continuous ranked probability scores of 12 W m−2 for both H and LE, with standard deviations of 37 W m−2 (H) and 46 W m−2 (LE) for a 12 min vertical step profile by a single drone. Increasing flight times, using observations from multiple drones, and further narrowing the prior distributions of the initial conditions are viable for reducing the posterior spread. Sampling strategies prioritizing space–time exploration without temporal averaging, instead of hovering at fixed locations while averaging, enhance the non-linearities in the forward model and can lead to biased flux results with ensemble-based assimilation schemes. In a set of 18 real-world field experiments at two wetland sites in Norway, drone data assimilation estimates agree with independent eddy covariance estimates, with root mean square error values of 37 W m−2 (H), 52 W m−2 (LE), and 58 W m−2 (H+LE) and correlation coefficients of 0.90 (H), 0.40 (LE), and 0.83 (H+LE). While this comparison uses the simplifying assumptions of flux homogeneity, stationarity, and flat terrain, it is emphasized that the drone data assimilation framework is not confined to these assumptions and can thus readily be extended to more complex cases and other scalar fluxes, such as for trace gases in future studies.
2022
2010
2019
Industrial and public infrastructure as local sources of organic contaminants in the Arctic
Arctic pollution has been a focal point in environmental research over the past five decades. Recently, the number of pollutants identified as relevant to the Arctic has significantly increased. Consequently, the expert group on Persistent Organic Pollutants (POPs) and Chemicals of Emerging Arctic Concern (CEACs) of the Arctic Monitoring and Assessment Programme (AMAP) has prepared a series of assessments of contaminants in the Arctic, including influences of climate change. This review addresses local sources of Arctic organic pollutants associated with infrastructure in the Arctic. Industrial, military, and public infrastructures, including domestic installations, sewage treatment, solid waste management, and airports, were identified as significant local pollution sources. Additionally, operational emissions (e.g., from shipping, transportation, heating, and power production) contribute to the overall local pollution profile. Based on currently available scientific information, elevated POP and CEAC levels are mostly found in close proximity to identified local pollution sources. To date, hazardous effects have only been confirmed for a few selected chemicals, such as polycyclic aromatic compounds (PAC) and certain pharmaceutical residues. However, studies are biased in the sense that they often focus on well-known contaminants, at a risk of overlooking CEAC and their effects. The review identifies several measures to reduce human impacts on local Arctic environments, including (i) using local indicator pollutants in ongoing national monitoring schemes, (ii) harmonizing emission reduction policies and licensing of industrial activities in the region to minimize exposure risks and environmental pollution, (iii) encouraging local municipalities, industries, and related stakeholders to coordinate their activities to minimize pollutant emissions.
2025
2015
2003
Indoor wood burning and airborne particles. Model calculations for Oslo for the winter of 1998-1999. NILU OR
2001
2012