Air temperature data

About 100,000 temperature values are measured for 256 points along four bus routes with a temporal resolution of mainly less than 2 hours for each measurement point along the bus routes. Data collected on 44 days between March 2010 and June 2011 are available. Of this data, only those days with precipitation less than 0.5 mm are used in order to eliminate errors from water adhering to the temperature sensor. Furthermore, data collected at a driving speed of less than 5 m/s (18 km/h) are not considered in order to ensure sufficient sensor ventilation.

For the spatial analysis with focus on thermal load, only days of the summer half-year (April-September) with a diurnal temperature amplitude of at least 7 K are taken into consideration for data evaluation. The threshold of 7 K is chosen to ensure distinctive diurnal air temperature variations. Measurement points with less than a total of 10 observations are removed. After data selection and preprocessing, data from 15 days remain for further analyses (Tab. 5.2, blue entries in the table). For these days the data from WS Aachen-Hörn are compared to those from WS DWD in order to detect strong discrepancies. This is the case for 8 September 2010 when at WS DWD precipitation of almost 2 mm is detected (Tab. 5,2, red entry in the table). In consequence, this data set is removed to avoid using improper data. In addition to clear sky conditions, which are considered in most case studies, the fixed course of the campaigns produces a certain random representation of weather situations. This yields representative results for the whole measuring period and permits a detailed analysis of the general temperature patterns throughout the day in the urban area of Aachen.

Tab. 5.2: Measurement days with corresponding meteorological conditions (data source: WS Aachen-Hörn and *DWD). Colour codes are explained in the text.

Data evaluation is mainly focused on predefined points along the traverses (256 points in total) that represent typical urban structures like urban canyons, different building densities, crossroads and green spaces (Fig. 5.4).

Fig. 5.4: Extract from locations of the predefined points along the bus routes representing various areal characteristics.

In order to specify the limiting conditions for data analysis, especially the minimum distance between the evaluation points along the bus routes, the sensors’ adaptability to changing temperatures needs to be ascertained. As defined by LILJEQUIST AND CEHAK (1984), the adaption time of thermometers can be described by a coeffcient of thermal inertia. It is the time interval that is necessary to cover 63% of a given temperature difference. For this study two test series are carried out. In each case, two loggers are cooled down and then exposed to approximately 29°C. One sensor is ventilated with a wind speed of about 5 m/s, representing the bus driving at low speed. The other sensor is not ventilated. A much faster adaption is achieved by the ventilated sensors (Fig. 5.5), which register a temperature change of about 21.7°C (18.8°C) within 10 min. Thus, 63% of the temperature range was reached within the first 48 sec (52 sec)

(average: 50 sec), which represents the coefficient of thermal inertia. This coefficient of thermal inertia is then used to determine a minimum distance between the evaluation points along the bus routes to secure accuracy and, thus, avoid spatial uncertainty.

Fig. 5.5: Temperature adaptability of ventilated (5 m/s) and non-ventilated sensors.

Taking all available data records from the bus rides into consideration, 90% of all measured temperature changes along the routes were smaller than 0.01°C/sec. Within the time of thermal inertia (50 sec.) and a driving speed of 5 m/s a distance of 250 m can be covered. Thus, 90% of all temperature measurements are expected to register a temperature change of less than 0.5°C per 250 m at a driving speed of 5 m/s. Due to thermal inertia, this value of 0.5°C represents only 63% of the real temperature change (100% = 0.79°C). The difference between measured and expected actual temperature changes results in a bias of 0.29°C. Although a bias of 0.29°C is considered to be acceptable as it is in the same order of magnitude as the accuracy of the instrument, the average bias is assumed to be much smaller. The driving speed of 5 m/s is defined as the minimum speed for data evaluation and the distance of 250 m is chosen as the typical distance between the predefined evaluation points.

Since the bus routes have different road and traffic conditions and cover different urban structures, the real distances between the points differ. In the city center, a high density of

points is favored because temperature differences are expected to be small and the driving speed often reaches the minimum value of 5 m/s. However, 90% of the predefined points have a minimum distance of 236 m, which is close to the aspired distance of 250 m. The evaluation software uses the GPS coordinates to select the closest temperature data and then interpolates them for the exact location of the nearest predefined point. These values are finally assigned to predefined points. A similar approach, but manually performed, was used by CONRADS AND VAN DER HAGE (1971) who studied the influence of crossroads and open spaces on temperature in the city of Utrecht (Netherlands).

To ensure comparability in time for measurements from different points, measured temperatures are converted into differences relative to data observed simultaneously by the reference WS Aachen-Hörn (measuring interval=10 min) for the afternoon (Fig. 5.6) and evening (Fig. 5.7).

Fig. 5.6: Temperature differences (afternoon 1 p.m.-5 p.m.) to the reference WS Aachen-Hörn (triangle).

Fig. 5.7: Temperature differences (evening 8 p.m.-12 p.m.) to the reference WS Aachen-Hörn (triangle).