Keywords
urban meteorological conditions, data assimilation, mobile measurements, crowdsourcing, urban exploration
Location
Session A4: Smart and Mobile Devices Used for Environmental Applications
Start Date
18-6-2014 9:00 AM
End Date
18-6-2014 10:20 AM
Abstract
This paper proposes a framework for assimilating urban temperature measurements with simulations of an urban micrometeorological model. Data assimilation, a technique that incorporates observations into a computer model of a real system, is commonly applied in global and regional weather forecasting and an emerging issue in urban meteorological modelling. For that purpose, we suggest applying a novel approach, the Spatiotemporal Epistemic Knowledge Synthesis (SEKS). It combines simulations of a thermodynamic urban climate model, as general knowledge base, with a model output statistics (MOS) of observations, as site-specific knowledge base, by means of Bayesian Maximum Entropy (BME) inference resulting in posterior distributions of the parameters.
Examining real meteorological situations in an urban quarter by mobile observations with instruments in a backpack and by simulations with the urban climate model ENVI-met, we illustrate how mobile meteorological measurements can considerably up-value simulated urban meteorological fields (such as air temperature). Performance measures demonstrate that (a) the output of the simulation model is improved by an assimilation of observations, and (b) the data gathered only at a path are extrapolated to a whole urban region, for which a temperature map together with information about the confidence is provided.
A conclusion is that individuals moving throughout a city are ideal explorers for the urban meteorological conditions. Recent technical developments facilitating individual-based temperature recordings, e.g. based on mobile phones, might be beneficially combined with the suggested data assimilation techniques and can help to construct urban maps of meteorological parameters. In consideration of the changing climate in cities, actual urban heat maps can be helpful for personal adaptation measures.
Included in
Civil Engineering Commons, Data Storage Systems Commons, Environmental Engineering Commons, Other Civil and Environmental Engineering Commons
A Bayesian Maximum Entropy scheme for the assimilation of mobile recordings with simulations of urban micrometeorological data
Session A4: Smart and Mobile Devices Used for Environmental Applications
This paper proposes a framework for assimilating urban temperature measurements with simulations of an urban micrometeorological model. Data assimilation, a technique that incorporates observations into a computer model of a real system, is commonly applied in global and regional weather forecasting and an emerging issue in urban meteorological modelling. For that purpose, we suggest applying a novel approach, the Spatiotemporal Epistemic Knowledge Synthesis (SEKS). It combines simulations of a thermodynamic urban climate model, as general knowledge base, with a model output statistics (MOS) of observations, as site-specific knowledge base, by means of Bayesian Maximum Entropy (BME) inference resulting in posterior distributions of the parameters.
Examining real meteorological situations in an urban quarter by mobile observations with instruments in a backpack and by simulations with the urban climate model ENVI-met, we illustrate how mobile meteorological measurements can considerably up-value simulated urban meteorological fields (such as air temperature). Performance measures demonstrate that (a) the output of the simulation model is improved by an assimilation of observations, and (b) the data gathered only at a path are extrapolated to a whole urban region, for which a temperature map together with information about the confidence is provided.
A conclusion is that individuals moving throughout a city are ideal explorers for the urban meteorological conditions. Recent technical developments facilitating individual-based temperature recordings, e.g. based on mobile phones, might be beneficially combined with the suggested data assimilation techniques and can help to construct urban maps of meteorological parameters. In consideration of the changing climate in cities, actual urban heat maps can be helpful for personal adaptation measures.
