Due to rapid development of urbanization, Urban Heat Island Effects have become serious problems in Taiwan’s urban environment, which generate higher urban temperature, consume enormous amount of electricity on air conditioning, and devastate urban climate quality. Therefore, this study combines climatic theories and investigation data on site, proposes some effective principles in mitigating urban heat island effects, which could be useful in urban planning, zoning, and land use policies.

On site investigation utilized automobiles as moving vehicles to measure urban heat island effects in 15 counties in Tainan area, in the south of Taiwan. After comparing with previous research databases, the results reveal as below:

(1). According to the climatic theories, urban heat island effects are more obvious when temperature is low. Maximum urban heat island intensity (?T) appears in Autumn (3.63?), is indeed higher than that in Summer (2.33?). Average heat island intensity (?T) in Autumn (1.90?), is also higher than others. Second is 1.59?, appeared in winter, then 1.44? in spring and 1.17? in summer.

(2). Urban heat island intensity is also more obvious in the counties with lower population (under 30000) and lower density of population (3-4 person per hectare), as in Luchia County and Kuantien County.

(3). Urban heat island intensity is closely related to population, urban density, and proportion of non-agricultural population.

(4). Through regression analysis on SPSS program, two predicting formulas are devised: Simple Predicting Formula and Accurate Predicting Formula. In the analyzing process, important variables include log of population, temperature, proportion of agricultural land ratio, and non-agricultural population ratio.

Given that mortality rates during a heat wave are a sensitive function of temperature, forecast maps of temperature anomalies within cities should be useful to the health community. An empirically based approach for predicting daily spatial variations in the Urban Heat Island has been developed for New York City. Our technique is derived from two data sets: high spatial resolution temperature data collected by multiple synchronized traverses of Manhattan by foot; and several months of high temporal resolution data collected at 10 fixed locations by instruments mounted on lamp posts. The high spatial resolution data is regressed against local characteristics such as vegetation, albedo and building height to produce a statistical model of relative temperature anomalies. The fixed instruments show local temporal variability attributed to convection, and spatial variability between instruments attributed to local surface characteristics. The magnitudes of both types of variability are regressed against weather conditions such as cloud cover, wind speed, lapse rate and humidity. When applied to the average spatial anomaly map, the amplitude of the temperature variations within the city each day can be predicted based on a weather forecast. A working model should be online by late fall of 2014, predicting temperature variations within the city 24 hours in advance. The technique should be easily portable to other cities.

The implementation of adaptation measures in cities is important to counteract the estimated increase in heat stress in the 21st century. Before establishing adaptation measures counteracting the urban heat island, city planners and officials need to know about the spatial and temporal dimensions of the meteorological conditions in a city. As city dwellers are the main target of city planners, the integral effect of air temperature, air humidity, wind speed and radiation fluxes on humans in a city has to be quantified and assessed. Hence, modern human-biometeorological methods have to be applied for the quantification of the spatial and temporal distribution of the UHI as well as to assess mitigation and adaptation measures for improving outdoor meteorological conditions.

Four urban measurement station and one rural measurement stations are used to quantify the temporal and spatial climatic characteristics in Stuttgart. Furthermore, a measurement campaign on 3rd – 4th August 2014 in Stuttgart was used as a basic data set. The state capital city Stuttgart is located in the southwestern part of Germany in complex topography. The air temperature UHI between city center and rural reference station at the airport is in average 2 °C but ranges to 12 °C.

The Physiologically Equivalent Temperature (PET) is applied in this study to quantify the integral effect of air temperature, air humidity, wind speed and radiation fluxes (expressed as mean radiant temperature) on the human energy balance.

Different statistical approaches as artificial neural network and stepwise multiple linear regression are applied and compared for mapping thermal conditions in Stuttgart in day- and nighttime. The generated maps of air temperature, urban heat island and PET have a resolution of 10 m.

The spatial distribution of air temperature and urban heat island shows a maximum in the city center and along the low-lying areas of the Neckar river at 21:00 as well as on 14:00 CET. The decrease in PET as well as in air temperature goes along with an increase in altitude, green areas and number of trees as well as a decrease in built-up ratio and sealed areas.

Artificial Neural Network allows a good estimation of the spatial distribution of PET due to its nonlinearity with an R-squared of 0.94 and a root mean square error of 2.0 K, allowing the adherence of comfort class limits.

The temperature variations in urban environment are partly related to landscape characteristics. Following the recommendation of Stewart and Oke (2012), these landscapes or climate zones are defined at the scale of few hundreds of meters. Such a spatial resolution is also relevant for urban climate models, such as the Town Energy Balance (TEB) model that simulates pretty realistic temperature maps over a given city, including a spatial variability depending on urban landscapes arrangement. Nonetheless, TEB is not able to run some finer scale simulations, because of the street canyon hypothesis on which it is based. This is a limitation for urban climate studies because temperature variability at a very local scale may be of the same order of magnitude than at city scale. Thus, the aim of the study is to determine experimentally the variability of the temperature in a neighbourhood, to deduce a statistical model based on a set of explicative variables, and then to implement in the TEB model a parametrization able to quantify the subgrid temperature variability.

With this aim, three field experiments were carried out in a neighbourhood of three French cities: Paris, Marseille and Toulouse. For each city, the area covered about 1 km x 0.5 km and was composed of different urban fabrics. Five intensive observational periods were conducted in June 2013 in Marseille, in October 2013 in Paris and in January, April and June 2014 in Toulouse. For three successive days, every three hours, mobile pedestrian measurements of temperature, humidity and wind were continuously recorded along a predefined itinerary through the neighbourhood, with a GPS recording associated. A permanent network was also set up, composed of ten weather stations recording near-surface temperature, humidity, wind speed and direction, and completed by a roof-level reference station in order to document larger scale atmospheric variables including the incoming short- and long-wave radiation.

For each city, high-resolution geospatial databases have been produced in order to obtain geographical indicators relevant for the study. Thus fifteen indicators related to land-cover fractions and morphological parameters have been calculated. Each of them is calculated around each point of measurement of each city, in buffers of different sizes. The first step of the work is to determine which size is the most relevant for each indicator. Then, statistical relationships are found in order to express the temperature variability as a function of geographical indicators and larger-scale meteorological variables. Then, these relationships are implemented in the TEB model and compared with the temperature observed in each city.

Bibliography:

Stewart, I. D., & Oke, T. R. (2012). Local climate zones for urban temperature studies. Bulletin of the American Meteorological Society, 93(12), 1879-1900.

The identification of spatiotemporal air temperature differences between urban and suburban landscape represents a complex problem. The reason is that a sufficiently dense network of meteorological stations, which represents a distinct heterogeneity of an active surface and which at the same time considers the influences of geographical environment on the urban climate, is missing. It seems necessary to acquire relevant multilevel data, or possibly to use different methods of data collection. The presented multilevel analysis of spatiotemporal regime of air temperature is based on processing stationary, mobile and surface thermal monitoring data. Olomouc represents medium-sized central European town.

First level presents stationary data measured by the special-purpose Metropolitan Station System Olomouc (MESSO) that included up to 23 measuring points in the inner city and its surrounding. The analysis proved that the mentioned relatively dense station network cannot provide satisfying detailed information on causes of spatiotemporal temperature differences. On the other hand we have obtained basic information about the air temperature field at the local scale.

Therefore the series of mobile measurements of air temperature for selected profiles (second level) namely in night time of days with radiative weather regime and in all seasons of year was carried out. The routes of mobile measuring covered both suburban and inner city areas. The results demonstrate principal differences of temperature courses between urban and suburban landscape and simultaneously enabled identification of hot/cold spots. The profile measurement also granted knowledge about the thermal stratification of urban/rural canopy layer.

Third data level presents thermal images based on surface monitoring by the use of handheld IR camera. Thermal records provided detailed values of surface temperature. Localities with various active surfaces in the time with positive/negative radiative balance and also during individual seasons of year were monitored. The results have proved extreme differences in surface temperatures for both surface type and day/year time.

It can be concluded that multilevel air temperature monitoring together with precise knowledge of local geographical conditions can yield representative findings about the character of air temperature field of middle-sized central European city.

The well known phenomenon associated with urban thermal environment is urban heat island (UHI), which urban areas are generally warmer than its surrounding rural areas. Currently, many studies have been conducted with focus on the general UHI intensity. However, the landscape complexities over urban areas may result in the non-uniformity of thermal environment. It is a critical question that whether the heterogeneity is inclined to highlight under heat extreme events. Because, as projected, global warming resulted from the raise of anthropogenic greenhouse gases will possibly increase the incidence, intensity, and duration of summertime heat wave events. Urban area, which holds large amounts of wealth and population, with large energy consumption is potentially sensitive to these related changes.

According to our previous investigation, thermal heterogeneity over urban areas was generally obvious under the condition of heat wave event, as measured by remotely sensed land surface temperature (LST), suggesting that proper and effective counter measures for heat wave events are location-dependent. However, due mainly to the uncertainties both in the quality and quantity of remotely sensed LST records, further investigation is necessary to be done more than the preliminary findings. For fully understanding the thermal heterogeneity over urban areas at summertime, especially during heat wave event, hourly records of meteorology stations were primarily collected, in addition to the MODIS remotely sensed LST products, and CRU TS and GHCN CAMS gridded land air surface temperature data. In this paper, specific attention is given to the case study of Shenzhen City in summer 2007, which located in the South China and is characterized by rapid urbanization and developed economy. Totally 140 meteorology stations were primarily included.

Findings according to monthly records show the general thermal anomalies in the whole summer 2007 over Shenzhen City, both observed by remotely sensed LST and gridded air surface temperature. However, monthly variation is significant, specifically, anomalies are more positive in July in which heat wave events occurred, compared with the anomalies in June and August. The monthly differences give a chance for detailed investigation on urban thermal heterogeneity under different conditions, to get insights into the characteristics of thermal response resulted from urban complexity during heat extreme events. The urban thermal heterogeneities over Shenzhen City were compared and discussed in view of the anomalies of LST and air temperature. Furthermore, the relationship between thermal heterogeneity and urban landscape complexity was also investigated.