Outil d'alerte pour identifier les périodes propices au rafraîchissement des parcs

International audienceAlert tool for identifying good periods to refresh parks. In the coolparks framework, mobile measurement campaigns will be performed to quantify the cooling induced by urban parks and the cool air diffusion within their surrounding urban areas. In order to identify the optimal conditions to implement these campaigns, fixed air temperature measurements performed during several years within parks (in Nantes, France) and their surrounding urban environment are utilized. Five interesting periods per season are identified for future campaigns : 3 are diurnal and 2 are nocturnal. For each of the {period, season} combinations, the meteorological conditions that allow to maximize the park cooling or the cool air diffusion within their close environment are investigated. Decision trees are constructed to facilitate the weather alert implementation allowing to identify the favorable days for future measurement campaigns taking place.Dans le cadre du projet CoolParks, des campagnes de mesures mobiles vont être menées pour mesurer le rafraîchissement occasionné par des parcs et la diffusion de cette fraîcheur dans leurs quartiers environnants. Afin d'identifier les conditions propices pour la mise en oeuvre de ces campagnes, des mesures fixes de température de l'air réalisées pendant plusieurs années dans des parcs nantais et leur environnement urbain sont utilisées. Cinq périodes intéressantes par saison sont identifiées pour les futures campagnes : 3 périodes diurnes et 2 périodes nocturnes. Pour chacune de ces combinaisons {période, saison}, les conditions météorologiques permettant de maximiser le rafraîchissement des parcs ou la diffusion de fraîcheur dans leur environnement proche sont étudiées. Des arbres de décision sont construits pour faciliter la mise en oeuvre d'alertes permettant d'identifier les journées propices à la tenue des futures campagnes de mesure

Towards processing chains to estimate the urban heat island intensity using FOSS tools

International audienceThis contribution proposes an original and generic method to estimate the urban heat island (UHI) intensity both spatially and temporally. This method is based on two processing chains composed of :• three data types :– air temperature data measure by some networks located in several French cities,– meteorological data (such as wind speed, solar radiation, etc.) provided by the French institute of meteorology,– geographical data (such as building shape, position and height or satellite images) provided by the French National Geographic Institute (IGN),• two free open source softwares (OFSS) :– OrbisGIS, a GIS platform used for geographical calculation and mapping,– Python, a programming language used for meteorological data manipulation and calculationA first processing chain is used to calibrate empirical models to estimate temporal and spatial variations of the UHI intensity. Temporal models are used to estimate the UHI intensity difference between two days. Air temperature measurement and meteorological data are combined using Python scripts to create multiple linear regressions explaining the temporal variations of the UHI. Spatial models are used to estimate the UHI intensity difference between two locations of the city. First, geographical indicators are calculated according to OrbisGIS scripts (SQL language) to characterize the morphological and the land type context of each of the air temperature network stations. Then air temperature measurement and geographicalindicators are combined using Python scripts to create multiple linear regressions explaining the spatial variations of the UHI.The second processing chain is used to verify the model performances. A new air temperature measurement network is used to compare estimated values to observed values. Temporal UHI variations are estimated using Python scripts, temporal model equations and meteorological data. Spatial UHI variations are estimated using Python scripts, spatial model equations and geographical indicators calculated according to OrbisGIS scripts from geographical data. The same processing chain may then be applied to any French city to estimate the urban heat island intensity for any location and any day. The scripts are run with free softwares and data available and homogeneous for any French city. It makes the method applicable by any research team (open science). This work allows several potential future developments regarding the data and the methods used. Internationally homogeneous open data may be used instead of the French one in order to apply the method to any city in the world. A user-friendly tool may be realized to connect all types of data and softwares in order to be easily applied by the operational world

Urban heat island temporal and spatial variations: Empirical modeling from geographical and meteorological data

International audienceUrban air temperature varies along time and space. This contribution proposes a methodology to model these variations from empirical models. Four air temperature networks are implemented in three west France cities. The period [1.4-1.8] of a normalized night is investigated. Three empirical models are established, in order to predict temporal and spatial air temperature variation. They are gathered under the name of model group. Urban heat island (UHI) intensity is explained according to one temporal model using meteorological variables. Temperature spatial variations are explained from two models using geographical informations averaged in a 500 m radius buffer circle. The first one represents the mean UHI value for a given location, the second the variability of the UHI around its mean value. 32 model groups are calibrated from data sampled by one network. The accuracy of their estimations is tested comparing estimated to observed values from the three other networks. The most accurate model is identified and its performances are analyzed. Night-time UHI intensity variations are explained all along the year by wind speed and nebulosity values calculated after sunset. During spring and summer season UHI variations are mainly driven by Normalized Difference Vegetation Index (NDVI) value whereas building density or surface density are predominant explicative variables during colder seasons. The model is finally applied to the city of Nantes during a summer night to illustrate the interest of this work to urban planning applications. Several warm areas located outside the city center are identified according to this method. Their high UHI value is mainly caused by their low NDVI value

Outdoor Air Temperature Measurement: A Semi-Empirical Model to Characterize Shelter Performance

Shelters used to protect air temperature sensors from solar radiation induce a measurement error. This work presents a semi-empirical model based on meteorological variables to evaluate this error. The model equation is based on the analytical solution of a simplified energy balance performed on a naturally ventilated shelter. Two main physical error causes are identified from this equation: one is due to the shelter response time and the other is due to its solar radiation sensitivity. A shelter intercomparison measurement campaign performed by the World Meteorological Organization (WMO) is used to perform a non-linear regression of the model coefficients. The regression coefficient values obtained for each shelter are found to be consistent with their expected physical behavior. They are then used to simply classify shelters according to their response time and radiation sensitivity characteristics. Finally, the ability of the model to estimate the temperature error within a given shelter is assessed and compared to the one of two existing models (proposed by Cheng and by Nakamura). For low-response-time shelters, our results reduce the root mean square error by about 15% (0.07 K) on average when compared with other compensation schemes

A semi-empirical model to characterize the error of air temperature measurement induced by the shelter used

International audienceThe air temperature measurement in outdoor spaces is very sensible to solar radiation and wind speed. In most of the cases, shelters are used to protect the air temperature sensors. However, the temperature difference between two sensors located in a same outdoor environment but within a different shelter may reach several degrees Celsius under certain meteorological conditions. The objective of this contribution is to propose a semi-empirical model to characterize the error of air temperature measurement induced by a shelter under given meteorological conditions. First, an energy balance is applied to a simplified shelter shape. The temperature difference between the air inside and outside the shelter is then isolated in the equation. Several assumptions are proposed in order to obtain an analytical solution of this error term. At the end of the day, the predictive equation of the error is a function of the shelter dimensions and thermal characteristics as well as four meteorological variables: the wind speed, outdoor air temperature, heating rate of the outdoor air temperature and global radiation. In order to verify the consistency of the predictive equation of the error, it is transformed into a semi-empirical model. The shelter dimensions and thermal characteristics information are gathered under constants that are estimated using a dataset containing shelter error and meteorological conditions along time. The data used come from a shelter comparison campaign performed by the World Meteorological Organization (WMO) in Ghardaïa (Algeria) during one year between 2008 and 2009. Eighteen shelters were equipped with the same temperature sensor in order to identify their performances in extreme meteorological conditions (desert). Seventy percent of this data are used for model calibration whereas the remaining thirty percent are used for model evaluation. Results show that the physical meaning of the model is respected: a large shelter limits its overheating but slightly delays the outdoor air temperature signal. The ability of the semi-empirical model to predict a shelter temperature error is then evaluated and compared to existing empirical models.Its performance is higher than the one of the existing models for certain shelters but not all of them

Radiative and heat storage properties of the urban fabric derived from analysis of surface forms

International audienc

Investigation of atmospheric and wake-induced turbulence within the ANR MOMENTA project

International audienc