The 1981 current research on aviation weather (bibliography)

Current and ongoing research programs related to various areas of aviation meteorology are presented. Literature searches of major abstract publications, were conducted. Research project managers of various government agencies involved in aviation meteorology research provided a list of current research project titles and managers, supporting organizations, performing organizations, the principal investigators, and the objectives. These are tabulated under the headings of advanced meteorological instruments, forecasting, icing, lightning and atmospheric electricity; fog, visibility, and ceilings; low level wind shear, storm hazards/severe storms, turbulence, winds, and ozone and other meteorological parameters. This information was reviewed and assembled into a bibliography providing a current readily useable source of information in the area of aviation meteorology

Machine Learning Modeling of Horizontal Photovoltaics Using Weather and Location Data

Solar energy is a key renewable energy source; however, its intermittent nature and potential for use in distributed systems make power prediction an important aspect of grid integration. This research analyzed a variety of machine learning techniques to predict power output for horizontal solar panels using 14 months of data collected from 12 northern-hemisphere locations. We performed our data collection and analysis in the absence of irradiation data—an approach not commonly found in prior literature. Using latitude, month, hour, ambient temperature, pressure, humidity, wind speed, and cloud ceiling as independent variables, a distributed random forest regression algorithm modeled the combined dataset with an R2 value of 0.94. As a comparative measure, other machine learning algorithms resulted in R2 values of 0.50–0.94. Additionally, the data from each location was modeled separately with R2 values ranging from 0.91 to 0.97, indicating a range of consistency across all sites. Using an input variable permutation approach with the random forest algorithm, we found that the three most important variables for power prediction were ambient temperature, humidity, and cloud ceiling. The analysis showed that machine learning potentially allowed for accurate power prediction while avoiding the challenges associated with modeled irradiation data

Real-Time Short-Term Travel Time Prediction

Real-time short-term travel time prediction is a critical component of the Intelligent Transportation System (ITS) and an important element of the Advanced Traveler Information System (ATIS). Accurate and reliable travel time prediction enables both user and system controller to be well informed of the likely future conditions on roadways, so that pre-trip plans and traffic control strategies can be made accordingly in order to reduce travel time and relieve traffic congestion. With these travel time predictions, roads may be used more efficiently with better overall network performance. This research will study short-term travel time prediction for freeway applications using various sources of real time travel time data. The integrated prediction model proposed here will put emphasis on travel time prediction under various traffic and weather scenarios and especially inclement weather conditions

Situation Awareness for Smart Distribution Systems

In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas

Visibility graphs of critical and non-critical time series for absorbing state phase transitions

It is possible to describe many real systems using time-ordered data. However, classical time series analysis is usually conditioned by data accuracy and quantity. A modern method is to map time series onto graphs and study these structures using the toolbox available in complex network analysis. An important practical problem to investigate the criticality in experimental systems is to determine whether an observed time series is associated with a critical phenomenon or not. We contribute to this problem by investigating the mapping called visibility graph (VG) of time series generated in dynamical processes with absorbing-state phase transitions. Analyzing degree correlation patterns of the VGs, we were able to distinguish between critical and non-critical regimes. One central hallmark is an asymptotic disassortative correlation on the degree for series near the critical regime in contrast with a pure assortative correlation observed for non-critical dynamics. We were also able to distinguish between continuous and discontinuous absorbing state phase transitions, which are commonly involved in catastrophic phenomena. The determination of critical behavior converges very quickly in higher dimensions, where many complex system dynamics are relevant.Comment: 9 pages, 8 figure