2.1. Urban Climate Diversity
This research is informed by the insight that microclimatic conditions can vary considerably due to the features of the surrounding urban fabric. To illustrate this point, consider a case study for the city of Vienna, Austria. For this purpose, the notion of “Urban Unit of Observation” (U2O) is introduced to systematically address the extent of microclimatic diversity within the urban domains [
44,
45,
46,
47]. U2Os represent clearly bounded spatial segments of the urban domain. The size and features of an U2O were determined following insights formulated in previous research [
44]. Stewart [
42] further stressed the role of scale in microclimate investigations. Thus, for measured microclimatic parameters (e.g., air temperature, wind speed, humidity) to be spatially representative, the size of a sample area should not exceed a few hundred meters in radius. Therefore, a spatial dimension (diameter) of approximately 400 m was targeted for this study. The selected U2Os include both low-density and high-density typologies in and around Vienna (see
Figure 1 and
Table 1).
Each U2O contains a stationary weather station centrally positioned within the observed domain. A height of 2 m above ground is typically suggested for the elevation of air temperature sensors [
48]. However, deviations from this reference height might be occasionally unavoidable due to practical reasons, such as safety issues in the urban context (as seen in
Table 1). This circumstance is not seen as problematic, as all air temperature sensors are positioned within the urban canyon and the vertical gradients are small [
49]. All stations are operated by the Central Institution for Meteorology and Geodynamics (Zentralanstalt für Meteorologie und Geodynamik, ZAMG). The ZAMG network comprises of approximately 250 semi-automatic weather stations (Teil Automatisches Wetter Erfassungs System—TAWES), which are classified according to the SYNOP (surface synoptic observations) numerical codes [
50]. The weather stations records are stored in 10-min resolution. Once stored, quality control of meteorological observations is done using QualiMET system [
51] and according to the WMO guidelines, following five test steps: completeness check, climatological consistency check, temporal consistency check, internal consistency check, and spatial consistency check. Additionally, the correction of the hourly-based data is performed using GEKIS (Geografisches Klimainformationssystem—Geographic Climate Information System). After examining and correcting the measured data, they are stored into the main database and archived.
The collected information (air temperature, wind speed, solar radiation, and precipitation) from the aforementioned six weather stations was used to illustrate temporal and spatial variation of microclimatic conditions. To facilitate the representation, visualization, analysis, and processing of the data, we opted to represent the main prevailing “overheating” tendencies (i.e., characteristic location-dependent atmospheric warmth) for the summer season (representing meteorological summer) in terms of two distinct reference days. This intention may be realized via different methods. As there is neither a unique nor a standardized statistical method to achieve this, we opted for the following two-step procedure. In a first step, both warmest and coldest continuous one-week periods were identified for meteorological summer in 2014 (
Table 2). Thereby, “warmest” denotes that this period included the highest number of days with maximum hourly temperatures, whereas “coldest” denotes the period of minimum hourly temperatures. In a second step, data from these two one-week periods was further processed to derive respective reference days. Once the one-week period is selected, hourly temperature data is averaged over each day to derive the hourly reference day temperatures of aforementioned one-week period. It should be noted that the above mentioned data evaluation processing was conducted for the central urban area (IS) as it is the most developed part of a city. Hence, the same time frame was then applied for the other areas.
2.2. The Essential Features of an Urban Unit of Observation
The study of the variables relevant to the formation of urban climates holds great potential in generating urban resilience via the realization of effective mitigation measures. As a contribution to ongoing research efforts in this direction, we introduced a systematic framework for the representation of the urban environment and the assessment of its microclimatic variation [
44,
45,
46,
47]. The proposed methodology aims to identify essential features of the built environment that are hypothesized to influence urban microclimate variation, thus supporting the development of explanatory theories and predictive models toward the prediction of local differences in the urban climate. To realize this framework, we defined an essential set of geometric (morphological) and semantic (material-related) urban features, hereafter referred to as the U2O variables. The geometric properties (e.g., sky view factor, aspect ratio, built area fraction, built surface fraction, effective mean compactness, etc.) are applied for identification of the urban morphology in an U2O. The physical properties (e.g., albedo, emissivity, specific heat capacity, etc.) describe the thermal characteristics of urban surfaces. A more detailed description of the candidate U2O variables can be found in [
44,
52].
Once defined, U2O variables must be numerically expressed to make them comparable and accessible for further analysis. In this context, we employed the GIS technology [
53] as an operational solution for comprehensive urban form analysis. Comprehensive DEMs with non-spatial text-based attribute information (e.g., land use type, number of building floors, building construction age) provided by the city of Vienna were used as a basis for this analysis. Extensive collaborative efforts have been made to develop a Python-based framework incorporated into the GIS tool for the automated calculation of the salient geometric and physical features of an urban environment, as described in [
54,
55,
56]. It should be noted that detailed urban databases containing comprehensive spatial information are, at times, difficult to obtain. Currently, a number of local municipalities and commercial vendors provide digital datasets. However, the number of such data repositories is expected to increase in the future. Subsequently, we explored the potential relationships between U2O variable values and urban-level microclimatic patterns.
2.3. The Implications for the Buildings
Advancing the understanding of complex urban-level physical phenomena and resulting implications requires, amongst other things, the deployment of sophisticated numeric models and computational platforms [
57]. In this context, we focus on comprehensive building thermal performance simulations conducted using Thermal Analysis Simulation Software—EDSL TAS (EDSL, Milton Keynes, Buckinghamshire, United Kingdom) [
58]. This tool was selected as it has the capability to systematically asses the thermal behaviour of buildings, while considering a relatively comprehensive range of factors, such as complex building shapes, the shadow effect between buildings, multizone building modelling, and integrated natural and forced airflow [
59,
60]. To address the issue of model reliability, we have documented the potential of model calibration and validation based on empirical data in [
61,
62]. Therefore, the tool represents a valuable resource for the assessment of the building’s energy consumption, carbon emissions from buildings, operating costs, and occupant comfort. Four different buildings were selected and made subject to a systematic inquiry (
Figure 2 and
Figure 3). These buildings constitute a large fraction of total building stock in Vienna [
63] and are selected to represent different building typologies (as seen in
Table 3). Namely, MFH 1 represents a multi-family house built using todays design and standards, MFH 2 represents a multi-family house as a typical building of this category from the 1960s, MFH 3 represent a multi-family house of the “Gründerzeithaus” period (from around 1910), and an office building representing todays design and standards. The office building is freestanding, while residential buildings have adjacent buildings on both sides. All buildings have unconditioned basements, while MFH 2 and MFH 3 have unconditioned attics, as well.
Six sets of simulation runs were conducted for each building: A building was virtually placed within each location, and its performance evaluated using six obtained location-specific weather files as boundary conditions. Input assumptions, such as occupancy, internal gains, solar gains, ventilation, and thermostat settings, were based on Austrian standards: ÖNORM B 8110-3, B 8110-5, and B 8110-6 [
64,
65,
66]. The occupancy profiles were defined according to the building typology: For the residential buildings the occupancy was set on 365 days per year and 24 h per day. For the office building, a standard calendar and occupancy schedule were generated based on weekday/weekend schedules, with occupancy hours from 8 a.m. to 8 p.m. on weekdays. All simulations were conducted twice: first in a passive mode (UNCON) to investigate overheating tendencies during summer months, and then in an active operation mode (COND), with an upper and lower limit for the indoor temperature, to investigate heating and cooling loads. Internal gains are divided into equipment gains (E
g), lighting gains (L
g), and latent and sensible occupancy gains (O
l and O
s), as seen in
Table 4,
Table 5 and
Table 6. It should be noted that in case of residential buildings, the summer overheating in UNCON mode was computed with an air change rate of 1.5 h
−1 (from 9 p.m. to 9 a.m.) and 0.4 h
−1 (from 9 a.m. to 9 p.m.).
For the office building, different input assumptions were considered for the office rooms, lobby, and bathroom areas. However, the COND and UNCOND simulations for office rooms and lobby areas differ in air change rates only, thus having an air change rate of 1.5 h−1 during the summer night hours (from 9 p.m. to 9 a.m.). The air change rate for the bathroom area was assumed to be 1 h−1.
To systematically address the role of microclimatic development in thermal performance of buildings, computed performance indicators were compared and analysed across all locations. Firstly, the mean annual cooling and heating loads for the COND mode were computed with respect to the heated net floor area. Cooling and heating loads of each building were further investigated with respect to the corresponding simulations conducted with weather file obtained at non-urban location SD, thus accounting for the urban influence on thermal performance of buildings [
41]. Additionally, in order to determine the differences between locations, spatial variance (
VS,l) was calculated for the annual heating and cooling loads, as follows:
Herein, Lmax denotes maximum annual heating/cooling load for a building for each location, and Lmin denotes the corresponding minimum annual heating/cooling load.
Lastly, the overheating tendencies were analysed in terms of cooling degree hours (
CDH), as follows:
Herein,
Ti denotes the temperature in the room, and
Tb denotes the base temperature. The reference base temperature for the residential buildings is 27 °C and, for the office building, 26 °C. However, in order to make this information comparable, the weighted mean overheating (
OHm) for each building was calculated as follows:
Herein, ARn denotes the area of the room (m2), and CDHRn denotes the corresponding cooling degree hours (Kh).