it could rain weather forecasting as a reasoning process

Abstract Meteorological forecasting is the process of providing reliable prediction about the future weathear within a given interval of time. Forecasters adopt a model of reasoning that can be mapped onto an integrated conceptual framework. A forecaster essentially precesses data in advance by using some models of machine learning to extract macroscopic tendencies such as air movements, pressure, temperature, and humidity differentials measured in ways that depend upon the model, but fundamentally, as gradients. Limit values are employed to transform these tendencies in fuzzy values, and then compared to each other in order to extract indicators, and then evaluate these indicators by means of priorities based upon distance in fuzzy values. We formalise the method proposed above in a workflow of evaluation steps, and propose an architecture that implements the reasoning techniques

Real-Time Urban Weather Observations for Urban Air Mobility

Cities of the future will have to overcome congestion, air pollution and increasing infrastructure cost while moving more people and goods smoothly, efficiently and in an eco-friendly manner. Urban air mobility (UAM) is expected to be an integral component of achieving this new type of city. This is a new environment for sustained aviation operations. The heterogeneity of the urban fabric and the roughness elements within it create a unique environment where flight conditions can change frequently across very short distances. UAM vehicles with their lower mass, more limited thrust and slower speeds are especially sensitive to these conditions. Since traditional aviation weather products for observations and forecasts at an airport on the outskirts of a metropolitan area do not translate well to the urban environment, weather data for low-altitude urban airspace is needed and will be particularly critical for unlocking the full potential of UAM. To help address this need, crowdsourced weather data from sources prevalent in urban areas offer the opportunity to create dense meteorological observation networks in support of UAM. This paper considers a variety of potential observational sources and proposes a cyber-physical system architecture, including an incentive-based crowdsensing application, which empowers UAM weather forecasting and operations

Proceedings of Abstracts 12th International Conference on Air Quality Science and Application

© 2020 The Author(s). This an open access work distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Final Published versio

Confronting Grand Challenges in environmental fluid mechanics

Environmental fluid mechanics underlies a wealth of natural, industrial and, by extension, societal challenges. In the coming decades, as we strive towards a more sustainable planet, there are a wide range of grand challenge problems that need to be tackled, ranging from fundamental advances in understanding and modeling of stratified turbulence and consequent mixing, to applied studies of pollution transport in the ocean, atmosphere and urban environments. A workshop was organized in the Les Houches School of Physics in France in January 2019 with the objective of gathering leading figures in the field to produce a road map for the scientific community. Five subject areas were addressed: multiphase flow, stratified flow, ocean transport, atmospheric and urban transport, and weather and climate prediction. This article summarizes the discussions and outcomes of the meeting, with the intent of providing a resource for the community going forward