Application of ultra wide вand technology

Ultra-WideBand is a high data rate, low power short-range wireless technology, considered as a highspeed alternative to existing wireless technologies

Application of ultra wide вand technology

Ultra-WideBand is a high data rate, low power short-range wireless technology, considered as a highspeed alternative to existing wireless technologies

Multipath propagation problem analysis in data transmission systems

This article deals with the problem of multipath propagation and electromagnetic wave when it is incident on a layered medium. For the selection of tangible objects on the background of the environment in practice is usually used the reflective characteristics serving tool to optimize the electrical parameters of the probing signal

Evaluating modelled tropospheric columns of CH4_4 , CO, and O3_3 in the Arctic using ground-based Fourier transform infrared (FTIR) measurements

This study evaluates tropospheric columns of methane, carbon monoxide, and ozone in the Arctic simulated by 11 models. The Arctic is warming at nearly 4 times the global average rate, and with changing emissions in and near the region, it is important to understand Arctic atmospheric composition and how it is changing. Both measurements and modelling of air pollution in the Arctic are difficult, making model validation with local measurements valuable. Evaluations are performed using data from five high-latitude ground-based Fourier transform infrared (FTIR) spectrometers in the Network for the Detection of Atmospheric Composition Change (NDACC). The models were selected as part of the 2021 Arctic Monitoring and Assessment Programme (AMAP) report on short-lived climate forcers. This work augments the model–measurement comparisons presented in that report by including a new data source: column-integrated FTIR measurements, whose spatial and temporal footprint is more representative of the free troposphere than in situ and satellite measurements. Mixing ratios of trace gases are modelled at 3-hourly intervals by CESM, CMAM, DEHM, EMEP MSC-W, GEM- MACH, GEOS-Chem, MATCH, MATCH-SALSA, MRI-ESM2, UKESM1, and WRF-Chem for the years 2008, 2009, 2014, and 2015. The comparisons focus on the troposphere (0–7 km partial columns) at Eureka, Canada; Thule, Greenland; Ny Ålesund, Norway; Kiruna, Sweden; and Harestua, Norway. Overall, the models are biased low in the tropospheric column, on average by −9.7 % for CH$_4$, −21 % for CO, and −18 % for O$_3$. Results for CH$_4$ are relatively consistent across the 4 years, whereas CO has a maximum negative bias in the spring and minimum in the summer and O$_3$ has a maximum difference centered around the summer. The average differences for the models are within the FTIR uncertainties for approximately 15 % of the model–location comparisons

Multi-model evaluation of short-lived pollutant distributions over East Asia during summer 2008

The ability of seven state of the art chemistry-aerosol models to reproduce distributions of tropospheric ozone and its precursors, as well as aerosols over eastern Asia in summer 2008 is evaluated. The study focuses on the performance of models used to assess impacts of pollutants on climate and air quality as part of the EU ECLIPSE project. Models, run using the same ECLIPSE emissions, are compared over different spatial scales to in-situ surface, vertical profile and satellite data. Several rather clear biases are found between model results and observations including overestimation of ozone at rural locations downwind of the main emission regions in China as well as downwind over the Pacific. Several models produce too much ozone over polluted regions which is then transported downwind. Analysis points to different factors related to the ability of models to simulate VOC limited regimes over polluted regions and NOx limited regimes downwind. This may also be linked to biases compared to satellite NO2 indicating overestimation of NO2 over and to the north of the northern China Plain emission region. On the other hand, model NO2 is too low to the south and east of this region and over Korean/Japan. Overestimation of ozone is linked to systematic underestimation of CO particularly at rural sites and downwind of the main Chinese emission regions. This is likely to be due to enhanced destruction of CO by OH. Overestimation of Asian ozone and its transport downwind implies that radiative forcing from this source may be overestimated. Model-observation discrepancies over Beijing do not appear to be due to emission controls linked to the Olympic Games in summer 2008. With regard to aerosols, most models reproduce the satellite-derived AOD patterns over eastern China. Our study nevertheless reveals an overestimation of ECLIPSE model-mean surface BC and sulphate aerosols in urban China in summer 2008. The effect of the short-term emission mitigation in Beijing is too weak to explain the differences between the models. Our results rather point to an overestimation of SO2 emissions, in particular, close to the surface in Chinese urban areas. However, we also identify a clear underestimation of aerosol concentrations over northern India, suggesting that the rapid recent growth of emissions in India, as well as their spatial extension, is underestimated in emission inventories. Model deficiencies in the representation of pollution accumulation due to the Indian monsoon may also be playing a role. Comparison with vertical aerosol lidar measurements highlights a general underestimation of scattering aerosols in the boundary layer associated with overestimation in the free troposphere pointing to modeled aerosol lifetimes that are too long. This is likely linked to a too strong vertical transport and/or insufficient deposition efficiency during transport or export from the boundary layer, rather than chemical processing (in the case of sulphate aerosols). Underestimation of sulphate in the boundary layer implies potentially large errors in simulated aerosol-cloud interactions, via impacts on boundary-layer clouds. This evaluation has important implications for accurate assessment of air pollutants on regional air quality and global climate based on global model calculations. Ideally, models should be run at higher resolution over source regions to better simulate urban-rural pollutant gradients/chemical regimes, and also to better resolve pollutant processing and loss by wet deposition as well as vertical transport. Discrepancies in vertical distributions requires further quantification and improvement since this is a key factor in the determination of radiative forcing from short-lived pollutants

Global and regional trends of atmospheric sulfur

The profound changes in global SO2 emissions over the last decades have affected atmospheric composition on a regional and global scale with large impact on air quality, atmospheric deposition and the radiative forcing of sulfate aerosols. Reproduction of historical atmospheric pollution levels based on global aerosol models and emission changes is crucial to prove that such models are able to predict future scenarios. Here, we analyze consistency of trends in observations of sulfur components in air and precipitation from major regional networks and estimates from six different global aerosol models from 1990 until 2015. There are large interregional differences in the sulfur trends consistently captured by the models and observations, especially for North America and Europe. Europe had the largest reductions in sulfur emissions in the first part of the period while the highest reduction came later in North America and East Asia. The uncertainties in both the emissions and the representativity of the observations are larger in Asia. However, emissions from East Asia clearly increased from 2000 to 2005 followed by a decrease, while in India a steady increase over the whole period has been observed and modelled. The agreement between a bottom-up approach, which uses emissions and process-based chemical transport models, with independent observations gives an improved confidence in the understanding of the atmospheric sulfur budget

Arctic tropospheric ozone: assessment of current knowledge and model performance

As the third most important greenhouse gas (GHG) after carbon dioxide (CO2) and methane (CH4), tropospheric ozone (O3) is also an air pollutant causing damage to human health and ecosystems. This study brings together recent research on observations and modeling of tropospheric O3 in the Arctic, a rapidly warming and sensitive environment. At different locations in the Arctic, the observed surface O3 seasonal cycles are quite different. Coastal Arctic locations, for example, have a minimum in the springtime due to O3 depletion events resulting from surface bromine chemistry. In contrast, other Arctic locations have a maximum in the spring. The 12 state-of-the-art models used in this study lack the surface halogen chemistry needed to simulate coastal Arctic surface O3 depletion in the springtime; however, the multi-model median (MMM) has accurate seasonal cycles at non-coastal Arctic locations. There is a large amount of variability among models, which has been previously reported, and we show that there continues to be no convergence among models or improved accuracy in simulating tropospheric O3 and its precursor species. The MMM underestimates Arctic surface O3 by 5 % to 15 % depending on the location. The vertical distribution of tropospheric O3 is studied from recent ozonesonde measurements and the models. The models are highly variable, simulating free-tropospheric O3 within a range of ±50 % depending on the model and the altitude. The MMM performs best, within ±8 % for most locations and seasons. However, nearly all models overestimate O3 near the tropopause (∼300 hPa or ∼8 km), likely due to ongoing issues with underestimating the altitude of the tropopause and excessive downward transport of stratospheric O3 at high latitudes. For example, the MMM is biased high by about 20 % at Eureka. Observed and simulated O3 precursors (CO, NOx, and reservoir PAN) are evaluated throughout the troposphere. Models underestimate wintertime CO everywhere, likely due to a combination of underestimating CO emissions and possibly overestimating OH. Throughout the vertical profile (compared to aircraft measurements), the MMM underestimates both CO and NOx but overestimates PAN. Perhaps as a result of competing deficiencies, the MMM O3 matches the observed O3 reasonably well. Our findings suggest that despite model updates over the last decade, model results are as highly variable as ever and have not increased in accuracy for representing Arctic tropospheric O3.</p