Subject:
All Disciplines Arts & Humanities Biology & Life Sciences Business & Economics Chemistry & Materials Science Computer Science & Mathematics Engineering Environmental & Earth Sciences Medicine & Pharmacology Physical Sciences Public Health & Healthcare Social Sciences
Sort:
Hottest Latest Alphabetical (A-Z) Alphabetical (Z-A)
Type:
All Topic Review Biography
Topic Review
Convective Boundary Layer
The Convective Boundary layer (CBL), also known as the daytime Planetary boundary layer, is the part of the atmosphere most directly affected by solar heating of the earth's surface. This layer extends from the earth surface to a capping inversion that typically locates at a height of 1–2 km by midafternoon over land. Below the capping inversion (10-60% of CBL depth, also called entrainment zone in the daytime), CBL is divided into two sub-layers: mixed layer (35-80% of CBL depth) and surface layer (5-10% of CBL depth). The mixed layer, the major part of CBL, has a nearly constant distribution of quantities such as potential temperature, wind speed, moisture and pollutant concentration because of strong buoyancy generated convective turbulent mixing. Parameterization of turbulent transport is used to simulate the vertical profiles and temporal variation of quantities of interest, because of the randomness and the unknown physics of turbulence. However, turbulence in the mixed layer is not completely random, but is often organized into identifiable structures such as thermals and plumes in the CBL. Simulation of these large eddies is quite different from simulation of smaller eddies generated by local shears in the surface layer. Non-local property of the large eddies should be accounted for in the parameterization.
  • 864
  • 24 Nov 2022
Topic Review
Corrosion Monitoring in Atmospheric Conditions
A variety of techniques are available for monitoring metal corrosion in electrolytes. However, only some of them can be applied in the atmosphere, in which case a thin discontinuous electrolyte film forms on a surface. Traditional and state-of-the-art real-time corrosion monitoring techniques include atmospheric corrosion monitor (ACM), electrochemical impedance spectroscopy (EIS), electrochemical noise (EN), electrical resistance (ER) probes, quartz crystal microbalance (QCM), radio-frequency identification sensors (RFID), fibre optic corrosion sensors (FOCS) and respirometry.
  • 541
  • 27 Jan 2022
Topic Review
Cultural Dimensions of Climate
Cultural Dimensions of Climate means that climatic events express the dynamics of the Earth’s oceans and atmosphere, but are profoundly personal and social in their impacts, representation and comprehension. Knowledge of the climate has multiple scales and dimensions that intersect in our experience of the climate. The climate is objective and subjective, scientific and cultural, local and global, and personal and political. These divergent dimensions of the climate frame the philosophical and cultural challenges of a dynamic climate. Drawing on research into the adaptation in Australia’s Murray Darling Basin, this paper outlines the significance of understanding the cultural dimensions of the changing climate.
  • 369
  • 26 Apr 2021
Topic Review
Diurnal Extrema Timing—A New Climatological Parameter
Diurnal Extrema Timing (DET) are daily occurrence times of air temperature minimum and maximum. Although unrecognized and unrecorded as a meteorological variable, the exact timing of daily temperature extrema plays a crucial role in the characterization of air temperature variability. The results reveal the timing of daily air temperature maximum as the most vulnerable to climate change among temperature and timing extrema indices.
  • 507
  • 12 Jan 2022
Topic Review
E-Region Auroral Ionosphere Model
E-region Auroral Ionosphere Model (AIM-E) is a numerical model involving both solar EUV radiation and electron precipitation as ionization sources. The AIM-E model allows to evaluate the concentration of the main ionospheric ions N+, N2+, NO+, O2+, O+(4S), O+(2D), O+(2P), electrons and  minor neutral components NO, N(4S), N(2D), for quiet and disturbed geomagnetic conditions at specified date, time and geographic location. The model design allows to  calculate the ionospheric composition in the entire high-latitude E-region in the retrospective, nowcast and forecast modes and shows good agreement with measurements.
  • 567
  • 02 Jul 2021
Topic Review
ESA Round Robin Exercise
Motivated by the experience acquired in the ESA promoted Round Robin exercise aimed at comparing cloud detection algorithms for PROBA-V sensor, we investigate specific issues related to cloud detection when remotely sensed images comprise only a few spectral bands in the visible and near-infrared by considering a bunch of well-known classification methods. First, we demonstrate the feasibility of using a training dataset semi-automatically obtained from other accurate algorithms. In addition, we investigate the effect of ancillary information, e.g., surface type or climate, on accuracy. Then we compare the different classification methods using the same training dataset under different configurations. We also perform a consensus analysis aimed at estimating the degree of mutual agreement among classification methods in detecting Clear or Cloudy sky conditions. Results are also compared on a high-quality test dataset of 1350 reflectances and Clear/Cloudy corresponding labels prepared by ESA for the mentioned exercise.
  • 712
  • 02 Feb 2021
Topic Review
EU Policy Landscape in Climate-Related Extreme Events
Climate-related extreme events are part of disaster risk reduction policies ruled at international, EU, and national levels, covering various sectors and features such as awareness-raising, prevention, mitigation, preparedness, monitoring and detection, response, and recovery. 
  • 295
  • 24 Jan 2022
Topic Review
Flood Prediction Using ML Models
Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction of the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists.Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods. This survey can be used as a guideline for hydrologists as well as climate scientists in choosing the proper ML method according to the prediction task.
  • 10.7K
  • 04 Jan 2023
Topic Review
FluxNet
FluxNet is a global network of micrometeorological tower sites that use eddy covariance methods to measure the exchanges of carbon dioxide, water vapor, and energy between the biosphere and atmosphere. Fluxnet is a global 'network of regional networks' that serves to provide an infrastructure to compile, archive and distribute data for the scientific community. It works to ensure that different flux networks are calibrated to facilitate comparison between sites, and it provides a forum for the distribution of knowledge and data between scientists. As of April 2014, there are over 683 tower sites in continuous long-term operation. Researchers also collect data on site vegetation, soil, trace gas fluxes, hydrology, and meteorological characteristics at the tower sites.
  • 298
  • 22 Nov 2022
Topic Review
Forecasting Pollution in Urban Area
Particulate air pollution has aggravated cardiovascular and lung diseases. Accurate and constant air quality forecasting on a local scale facilitates the control of air pollution and the design of effective strategies to limit air pollutant emissions.  Accurate and constant air quality forecasting on a local scale facilitates the control of air pollution and the design of effective strategies to limit air pollutant emissions. CAMS provides 4-day-ahead regional (EU) forecasts in a 10 km spatial resolution, adding value to the Copernicus EO and delivering open-access consistent air quality forecasts. In this work, we evaluate the CAMS PM forecasts at a local scale against in-situ measurements, spanning 2 years, obtained from a network of stations located in an urban coastal Mediterranean city in Greece. Moreover, we investigate the potential of modelling techniques to accurately forecast the spatiotemporal pattern of particulate pollution using only open data from CAMS and calibrated low-cost sensors. Specifically, we compare the performance of the Analog Ensemble (AnEn) technique and the Long Short-Term Memory (LSTM) network in forecasting PM2.5 and PM10 concentrations for the next four days, at 6 h increments, at a station level. The results show an underestimation of PM2.5 and PM10 concentrations by a factor of 2 in CAMS forecasts during winter, indicating a misrepresentation of anthropogenic particulate emissions such as wood-burning, while overestimation is evident for the other seasons. Both AnEn and LSTM models provide bias-calibrated forecasts and capture adequately the spatial and temporal variations of the ground-level observations reducing the RMSE of CAMS by roughly 50% for PM2.5 and 60% for PM10. AnEn marginally outperforms the LSTM using annual verification statistics. The most profound difference in the predictive skill of the models occurs in winter, when PM is elevated, where AnEn is significantly more efficient. Moreover, the predictive skill of AnEn degrades more slowly as the forecast interval increases. Both AnEn and LSTM techniques are proven to be reliable tools for air pollution forecasting, and they could be used in other regions with small modifications.
  • 417
  • 16 Jul 2021
  • Page
  • of
  • 11
Featured Entry Collections
>>

Featured Books

>>
Encyclopedia of Social Sciences
Encyclopedia of COVID-19
Encyclopedia of Fungi
Encyclopedia of Digital Society, Industry 5.0 and Smart City
Feedback