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CORRELATION BETWEEN TEMPERATURE AND CLASSIFICATION OF URBAN FABRIC ON MARSEILLE DURING ESCOMPTE Nathalie Long
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, Grégoire Pigeon , Patrice G. Mestayer , Pierre Durand , Claude Kergomard **
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*École Centrale de Nantes, Nantes, France; Université des Sciences et Technique de Lille, Météo France, (1) Toulouse, France, Société SIRIATECH, Villeneuve d’Ascq, France. Abstract A city influences the local meteorology and ground level temperatures are, in the average, higher than in the neighboring rural area. This phenomenon, called heat island, is produced by the modification of the energy balance in built areas due to the thermal behavior of buildings and street materials. The diffusion of heat is also changed in urban space. The local scale time and space variations of temperature are linked to factors like building morphology and surface cover modes. These last parameters are the bases of a classification of the urban fabric of Marseille quarters. During the ESCOMPTE campaign, temperatures were measured over the city with a network of 20 sensors placed in different quarters of the city. The correlation between the urban fabric characteristics and the temperature has been computed to bring out the influence of the urban structure on the local climatology. Key words: urban fabric, automatic classification, temperature. 1. INTRODUCTION A city modifies the earth surface and influences the local meteorology. A phenomenon, called heat island, is characterized by higher temperatures in the city centre than in the surrounding rural area. It appears especially during the nights with low wind (Oke, 1982). Marseille is a particular city because of its geographic situation: the Mediterranean Sea borders the city on the west, and hills on the north, south and east, between 400 m and 600 m above the sea level. This topographic situation influences the meteorological conditions. Indeed, during the sunny days, a sea breeze circulation may appear and a land breeze replaces it at the sunset. This was the main weather class during the ESCOMPTE campaign of measurement, in June - July 2001. The urban fabric of Marseille is very complex and composed of very different quarters. Each quarter is characterized by the morphology of the settlements and by the land coverage. These structures have different influences on the energy balance (Arnfield et Grimmond, 1998). This is why we are attempting to analyze the temperatures spatial variability on Marseille according to weather classes and urban quarters characteristics. 2. DATA During the ESCOMPTE campaign, a network of sensors was set over Marseille area (Figure 1). These sensors were measuring air temperature and relative humidity, but only temperatures are analyzed here. 20 sensors were spread over the city centre, the suburbs, along the coast and at the border to the rural area. Measurements were recorded every 10 minutes. Here we concentrate on the period from June 14, midnight, to July 12, 23:45 UTC. The average diurnal cycle is calculated over this one-month period. The weather conditions are defined from measurements at the station Vallon Dol, located at the northern border of Marseille, out of the city. This station provided wind speed and direction. The urban fabric characteristics are defined by analyzing the BDTopo, an urban database produced by photogrammetry by the French national geographic institute. This database includes some information both on the land cover and on the elevation of the included objects, grouped by themes: buildings, vegetation, roads, hydrographic networks, among others.
Figure 1: Average temperatures over Marseille over the period, measured by the sensors network
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Corresponding author address: Nathalie Long, Laboratoire de Mécanique des Fluides, 1 rue de la Noé, BP 92101, 44321 Nantes, Cedex 3, France; e-mail:
[email protected]
A software called DFMap was developed in a partnership with the company SIRIATECH to transform the initial information of BDTopo in statistical variables expressed in raster mode. The variables describing the statistical building morphology and the urban land covers were computed onto a grid with statistical methods and recording in a Geographic Information System (GIS). The selected cell size was 200 m * 200 m and the grid covered an area of 14 km by 18 km. 3. TEMPERATURE SPATIAL VARIABILITY ON MARSEILLE 3.1 Average situation 29.00
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To analyze the spatial variability of the temperature, an automatic classification was carried out from the temperature average diurnal cycle of the 20 sensors over the studied period (Gong et Richman, 1995). A classification in 6 classes was obtained: each class is represented, here, by one representative sensor: Station 1 represents the stations close to the coast; Station 5, the stations around the city centre and close to the coast; Station 8, the sensors located in the suburbs; Station 11, the stations close to the rural area; Station 17, the stations of the city centre further away from the coast than those represented by Sensor 20.
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Figure 2: Average temperature diurnal cycle for 6 sensors in Marseille. On the average, the sensors placed downtown and close to the rural area record higher temperatures that those located close to the coast or in the suburbs (see Figure 1). The temperatures of Stations 19 and 20 are a little lower than those of Stations 16 and 17. These 4 stations are in the city centre but the first two are closer to the sea and influenced by the sea. The sensors 1, 2, and 5, located in the south of Marseille and, among which sensors 1 and 5 are close to the coast, record the lowest temperatures. The temperature diurnal cycles of the 20 stations are not identical. The daily maximum is higher for sensors 10, 11, and 12, i.e. those sensors located close to the rural area, and lowest for sensors 1, 2, 5, and 15, i.e. the sensors close to the coast. The daily minimum is higher downtown (sensors 20, 18, and 19) than in the suburbs (sensors 8, 11, and 4). Also, the thermal amplitude is higher in the suburbs than downtown and/or close to the coast. We can note the influence of the sea on temperature maxima: in sea breeze meteorological condition, the air mass which arrives at the coast, was cooled by the contact with the water. On the other hand, the air mass heats up when passing over the city because the city ground surface is warm than the sea water. As a consequence temperature maxima are higher far away from the sea. Moreover, downtown the buildings and other artificial surfaces store heat during the day and restore it during the night, which explains the higher temperature minima downtown. In the backcountry, the ground gets colder quickly when the sun sets. 3.2 Temperature spatial variability according to meteorological conditions
The breeze meteorological conditions were the main weather class during the period of measurements. The graph on the left side of Figure 3 represents the temperatures of June 26, a day characterized by a sea breeze during the day, replaced by a land breeze at sunset. We can note that the coldest temperatures during the night are recorded by the stations close to the coast or the rural area (sensors 1, 8, and 11). On the other hand, the downtown stations (sensors 20 and 17) and Station 5 record the highest temperature minima. During the day, the warmest temperatures are recorded downtown and close to the rural area while the maxima remain lower for the stations close to the coast (1 and 5). With meteorological conditions of mistral type (Figure 3), in general the temperatures are lower than in the average situation. The temperature diurnal cycle is rather similar to the preceding case. However, the temperatures of Station 11 are higher during the night than in the breeze situation. On the contrary, Station 5 records lower temperatures in mistral situation. We can observe the wind direction influence on the temperatures. The land breeze generates a larger temperature fall during the night than the mistral, because the air mass is heated when passing above the city. In land breeze condition, the air mass temperature decreases at the contact of the ground, which cools quickly when the sun sets. The thermal inertia of the sea is larger than that of a "natural" ground. For the same reason, the night temperatures are colder close to the coast in mistral condition because the air mass arriving from the sea is not heated by the ground. With land breeze condition, the lower level air circulation is from east to west: the air
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Figure 3: Temperature diurnal cycles on June 26 (left) and on June 18 (right) for 6 sensors. However, we can note that the various weather conditions have not the same influence on all the sensors in the network; this indicates that others factors are also responsible for the spatial variability of the temperature in Marseille. Indeed, the main causes of the urban heat island are radiation trapping by building vertical surfaces, heat storage, wind speed reduction and local topography. The morphology of the buildings and the land cover modes are thus parameters, which may be related with the temperature variability and the formation of urban heat island. Pinho et Manso Orgaz (2000) have analyzed these relations on a city of Portugal and shown the influence of the urban structure on the urban climate.
3.3 Temperature spatial variability according to urban quarters characteristics The wind speed and direction, measured in Vallon Dol, are not correlated with the temperature measurements of the 20 sensors. However, the proximity of the sea influences the temperature, through the wind. Pinho et Manso Orgaz (2000) have computed the distance between the coast and the sensors as a determining factor of the sea influence. In Marseille, the distance to the coast (Dist in m) and the average temperature of each sensor ( in °C) are somewhat correlated with a correlation coefficient of 0.56. A linear regression between Dist and T= 0.00006 Dist + 22.33
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allows to explain 30% of the temperature spatial variability on Marseille (R =0.30). The residues of this regression represent the differences between the observations and the values obtained with relation (1). They represent, in a way, the temperature spatial variability which is not explained by the sea influence. They are positive when the model underestimates the temperatures, as e.g. for Stations 16 and 17 which are close to the sea but which record higher temperatures than those provided by the model (Figure 4). On the contrary, the observed temperatures of Stations 1, 2, 5, and 8 are lower than the computed ones:. this shows that the presence of the sea is not the only factor influencing the temperatures variability. In a second stage, the environment of each measurement site is defined with variables deduced from the BDTopo: building area density, building frontal density for a northerly wind, average space between buildings, average building height, perimeter and volume, H/W (ratio between building height and street width), number of buildings per cell, densities of vegetation, water surface, and pavement surface, calculated on 200 m cells. An area of influence is defined with a diameter of approximately 400 m around each sensor and an average of the variables quoted above is calculated over this area. The linear correlations between the 29-days average temperatures of the 20 sensors and the variables describing those environments are all weak. Yet the residues of regression (1) present significant correlations with the building density (R=0.59), the frontal density (0.61), H/W (0.57) and the building perimeter and volume (0.58 and 0.54, respectively). These positive correlations mean that, e.g., when the built density increases, the air temperature also increases. Indeed, the more the buildings are dense on a surface, the more the radiation is trapped by vertical surfaces and the more the heat is stored. We further carry out a regression between the residual values (Res1) and the 5 variables quoted previously; building volume is removed because it is redundant with building perimeter. The equation is Res1 = -1.03 building density + 3.24 frontal density + 0.37 H/W –0.56 height + 0.004 building perimeter –0.22 (2)
The regression (2) allows to explain 40 % of the information. The residues are negative for Stations 1, 2, and 5 meaning the model provides higher temperatures than the observed temperatures (Figure 4). For downtown stations the temperatures are rather well estimated by the model and the residues are very low. With these two linear regressions, we note that the computed temperatures remain higher than the observed temperatures at the stations close to the coast, but the variations are weak. For the others stations, the temperatures are relatively well represented by one of the two models.
Figure 4: Spatial variability of residues of linear regressions 1 (left), 2 (middle) and 3 (right). Finally, a third linear regression is calculated with the average temperatures of the sensors, and all the variables of influenced as determined by the preceding two regressions. The equation is T= 0.0001 Dist + 0.240 building density + 4.334 frontal density – 0.047 height + 0.014 H/W + 0.003 building perimeter + 21.39 (3) The regression (3) allows to explain 86% of the temperature spatial variability for Marseille. The residues are low (between – 0.29 and 0.23) and are largest for Stations 6, 14, and 16 whose temperatures are underestimated and for Station 2 whose temperature is overestimated (Figure 4). 4. CONCLUSION Marseille is a particular city because of its topographic and geographical situation and of the urban fabric complexity. These parameters have an influence on the air temperature spatial variability. Indeed, the temperatures are lower close to the coast than downtown or close to the rural area. The weather conditions also have an influence on the temperature spatial variability with different situations for breeze or mistral conditions. Finally, the presence of the city modifies the energy balance and some of the morphological characteristics of the urban quarters do influence air temperature. This study is a preliminary analysis of relations between the building morphology, the land cover modes and the air temperature. Urban districts were defined from their structure; it would be interesting to analyze temperature variability according to the urban districts of a city where the influence of topography and of the proximity of the sea is not so large than in Marseille. References Arnfield A.J., Grimmond S., 1998, An urban canyon energy budget model and its application to urban storage heat flux modeling, Energy and Buildings, 27, 61-68. Gong X., Richman M.B., 1995, On the application of cluster analysis to growing season precipitation data in north America east of the Rockies, Journal of climate, 8, 897-931 Pinho O.S., Manso Orgaz M.D., 2000, the urban heat island in a small city in coastal Portugal, International Journal of biometeorology, 44, 198-203. Oke T.R., 1982, The energetic basis of the urban heat island, Quarterly Journal of the Royal Meteorological Society, 108, 1-24.
