The climate in urban areas is highly influenced by its building geometry (Muniz-Gäal et al., 2020; Nasrollahi et al., 2021; Xi et al., 2021), the orientation of streets (Ali-Toudert and Mayer, 2005), material characteristics (Santamouris and Yun, 2020; Adilkhanova et al., 2023; Battista et al., 2023), vegetation (Schibuola and Tambani, 2022; Stache et al., 2022) and the relatively high fraction of impermeable surfaces (Arnfield, 2003). Outdoor human thermal comfort is a consequence of the resulting microclimate. On clear, calm and warm days, the mean radiant temperature (Tmrt) is one of the most important factors in describing the outdoor thermal comfort of a human, and is used in many thermal comfort indices, e.g. the Physiological Equivalent Temperature (PET) (Mayer and Höppe, 1987; Höppe, 1999) and the Universal Thermal Climate Index (UTCI) (Blazejczyk et al., 2010). For UTCI, however, there are shortcomings when using it within the urban canopy layer because of its requirement of wind speed at 10 m above ground (Lee et al., 2025). Tmrt describes the radiant load that a human is exposed to, i.e. the exchange of short- and longwave radiation between the person and its surrounding radiative environment (Höppe, 1992; Thorsson et al., 2007). While spatial differences in air temperature (Tair) are usually very small in urban environments, the spatial differences in Tmrt between sunlit and shaded spaces can be up to 30 °C (Lau et al., 2015). For these reasons, many authors stress the importance of accurate modeling of Tmrt (Di Napoli et al., 2020; Gál and Kántor, 2020).

The shortwave radiation that a human is exposed to originates from the sun, with direct, diffuse and reflected components, whereas the longwave radiation originates from the sky, ground, vegetation and building surfaces, e.g. walls. There are numerous models to calculate Tmrt, including ENVI-met (Bruse and Fleer, 1998), RayMan (Matzarakis et al., 2007; Matzarakis et al., 2010), PALM-4U (Maronga et al., 2020) and The SOlar and LongWave Environmental Irradiance Geometry model (SOLWEIG) (Lindberg et al., 2008), all of which to some extent handle the above mentioned fluxes. Highest values of Tmrt are commonly found in front of sunlit walls (Lindberg et al., 2014) where a person would be exposed to high levels of direct and diffuse shortwave radiation as well as comparatively high amounts of reflected shortwave radiation off the wall (Wallenberg et al., 2020). Another highly influential aspect is if the wall is (or has recently been) sunlit, which increases its surface temperature (Ts) so that a person located close to the wall is exposed to higher emittance of longwave radiation.

There are several ways of estimating the Ts of a material. A common approach is with Fourier's law of diffusion (e.g. Simon, 2016; Resler et al., 2017), where the Ts of a material is calculated based on its energy balance that is estimated from heat conduction to or from the interior of the material, convection and radiative exposure. Here, thermal properties such as thermal conductivity, specific heat capacity and emissivity of the material are important, information that is not always easily accessible, especially in the interior of the walls. While this approach is accurate and dynamic, it is relatively time-consuming. Another faster approach is the force-restore method (Johnson et al., 1991). The force-restore approach uses two layers, one thin layer at the surface and one deeper layer, where the surface layer is exposed to gain or loss of energy and a deeper layer that restores or dampens the heating at the surface. Other approaches include empirical relationships, where Ts is observed and related to environmental variables, e.g. Tair or solar radiation, as in Bogren et al. (2000) and Lindberg et al. (2016). The empirical approach is only accurate for the specific material that it is calibrated on and requires logistically demanding field observations to incorporate new materials in modeling. Potentially, it is also only accurate for the time of measurements, e.g. season, used in the calibration. An additional approach that can be used to estimate the Ts of a material is step heating. Boué and Fournier (2009) used a step heating equation derived from the Dirac heat pulse to estimate Ts of materials. This is a straightforward approach, where the radiation balance at the surface of the material is used together with its thermal effusivity. Here, thermal effusivity (or thermal admittance (see Oke et al., 2017)) describes the ability of a material to absorb or return the thermal energy at its surface, for example between a wall surface and the ambient air.

As mentioned before, a common approach to estimate Ts is to use the concept of Fourier's law of diffusion and divide the wall into several layers and calculate its energy balance. The ENVI-met model uses three layers and calculates the temperature in seven nodes (Simon, 2016), whereas PALM-4U uses four layers and six nodes (Resler et al., 2017, 2021). Both of these models can simulate Ts with high accuracy. The TARGET model uses the force-restore method to estimate wall surface temperatures (Broadbent et al., 2019). Up to now, the SOLWEIG model uses the empirical approach described in Bogren et al. (2000) and Lindberg et al. (2016). The simulated Ts of ENVI-met (Simon, 2016) and PALM-4U (Resler et al., 2017, 2021) show high agreement with observations. The Ts of walls simulated with SOLWEIG has not been evaluated specifically. Gál and Kántor (2020) found that Tmrt simulated with SOLWEIG was underestimated in sunlit areas and overestimated in shaded areas, and that these offsets could be related to its wall surface temperature parameterization, i.e. the simple empirical approach (Bogren et al., 2000; Lindberg et al., 2016). Wallenberg et al. (2023a) also argued that the parameterization of wall surface temperatures in SOLWEIG could result in deviations in simulated emitted longwave radiation from building surfaces. In addition, SOLWEIG not being a full energy balance model, using simplified convection and conduction parameterizations, if included at all, should not be ignored.

The aim of this study is to present and evaluate a new step heating model approach for calculations of Ts of walls in the SOLWEIG model, and how this affects Tmrt.

Five days of simulated and observed wall Ts for the wooden and plaster brick walls are presented in Fig. 2a and b, respectively. These days are characterized by relatively high Tair, peaking at between 22–26 °C (Fig. 2c), and mainly clear weather conditions, with global shortwave radiation (K↓) at solar noon peaking at 860–914 W m⁻² (Fig. 2c). Both walls show good agreement, with the simulated wall Ts consistently following the observed values. However, some deviations are evident, particularly around the time the wall becomes sunlit and in the nighttime. Around sunrise the simulated Ts for the wooden wall are underestimated at 2–6 °C (Fig. 2a). In the afternoon the simulated cooling rate of the wall is too slow, leading to overestimations of around 1–2 °C that continue into the nighttime. For the last two days, midday temperatures were underestimated by around 5 °C.

The simulated Ts for the plaster brick wall (Fig. 2b) overestimate up to 2 °C at midday during the first three days. Contrary to the wooden wall, the plaster brick wall underestimates from the evening into nighttime, with 1–3 °C, as an effect of a too rapid simulated cooling rate.

Figure 3 shows the diurnal development for the whole study period (15 May–31 August 2023). The top panel (Fig. 3a) shows Ts for the wooden wall. The mean values of the simulation (dashed blue line) are aligned with the mean of the observations (solid line), with a small underestimation of 1–2 °C between 08:00–13:00. Before and after these hours, simulations are close to observations with a very small overestimation. The spread of the data can be explained by different weather conditions, ranging from fully overcast, shown as low daytime values, to high values correlating to clear weather conditions with high amounts of incoming solar radiation. Here, it is also noticeable that the simulations (in blue) diverge from the observations, with underestimations during intense solar radiation and overestimations in nighttime.

The plaster brick wall, presented in Fig. 3b, shows opposite patterns compared to the wooden wall. Here, simulations overestimate with 1 °C in daytime, and underestimates by 2–3 °C in nighttime. These patterns are also visible in the spread of the data, where simulations overestimate during intense solar radiation. There is a narrower spread for the plaster brick wall, compared to the wooden wall, which can be explained by their material characteristics, i.e. the wooden wall reacts much faster to changes in solar radiation.

Scatter plots of 10 min simulated and observed Ts of the wooden wall, classified into five classes of mean K↓ between 11:00–14:00 are presented in Fig. 4a–e, with all values given in Fig. 4f. For example, an entire day will be classified into K↓<150 W m⁻² if the mean K↓ between 11:00–14:00 on this day is below 150 W m⁻². The five classes are K↓<150 W m⁻² (9 d), 150–300 W m⁻² (14 d), 300–450 W m⁻², (14 d), 450–600 W m⁻² (19 d) and K↓>600 W m⁻² (51 d).

The first class (K↓<150 W m⁻²) (Fig. 4a) shows high correlation between simulations and observations (R2=0.93) and low deviation of simulations (RMSE 0.82 °C). The three following classes (Fig. 4b–d) have lower correlations, partly because days in these classes are subject to partly cloudy weather, compared to the first class that is subject to overcast conditions. The last class (Fig. 4e) shows higher agreement between simulations and observations (R2=0.95). Overall, with 15 394 observation points (Fig. 4f), the simulations for the wooden wall correlate well with observed values (R2=0.93) and show relatively low RMSE (2.09 °C), indicating that the model performs well.

Similar scatter plots to those in Fig. 4, but for the plaster brick wall, are presented in Fig. 5. Correlations are generally good for all five classes. Best agreement can be seen in K↓ > 600 W m⁻² (R2=0.95) (Fig. 5e), whereas lowest is evident in K↓ < 150 W m⁻² (Fig. 5a) and K↓ 300–450 W m⁻² (Fig. 5c) classes with R2=0.86. Highest deviation is found in K↓ 450–600 W m⁻² (RMSE = 2.09 °C) (Fig. 5d), whereas lowest deviation is evident for K↓ < 150 W m⁻² (RMSE = 1.46 °C) (Fig. 5a), which can be explained by low incoming shortwave radiation during overcast weather conditions, resulting in a Ts of the wall that is possibly close to Tair. All observations combined (Fig. 5f), simulated Ts show good agreement with observations (R2=0.94) and small deviations (RMSE = 1.94 °C).

Comparisons in Tmrt at different timesteps are presented in Fig. 6 to assess the effect of the new parameterization scheme for wall Ts with the previous method (Lindberg et al., 2008). The simulations are for 12 June 2023 (as shown in Fig. 2) and the different timesteps compared are (a) 00:00, (b) 03:00, (c) 06:00, (d) 09:00, (e) 12:00, (f) 15:00, (g) 18:00 and (h) 21:00.

The effect of the new scheme on nighttime Tmrt in the courtyard is small but noticeable, around 0.3 °C lower compared to the old version (v2022a) of SOLWEIG (Fig. 6a–b). At 06:00 in the morning (Fig. 6c), when the sun is above the horizon but still at a low elevation, more differences in Tmrt appear, with some areas having slightly higher values (0.4 °C), while others have slightly lower (-0.2 °C). At 09:00 (Fig. 6d), more pronounced differences appear, with areas close to the northwestern corner of the courtyard being up to 2.5 °C warmer compared to SOLWEIG v2022a. Here, the sun reaches most of the areas in this corner as the sun is in the east at around 35° elevation, meaning that the direct shortwave component is close to perpendicular to the walls, resulting in high amounts of radiation reaching these surfaces. This becomes evident when looking at the differences at 12:00 (Fig. 6e), when incoming shortwave radiation is higher but differences are smaller, as this is close to solar noon (∼ 55°) and therefore far from perpendicular to the wall. Nevertheless, differences are about 2.0 °C. At 15:00 when the sun is in the southwest, differences are up to 1.5–2.0 °C in the northeastern corner (Fig. 6f). Here, it is also evident that parts of the walls are shaded by the trees in this corner, which results in lower Tmrt (∼-1.5 °C). When the sun is in west (θ ≈ 270°) at 18:00, parts of the southeastern corner are sunlit. This results in ∼1.0 °C higher Tmrt with the new scheme, compared to the old one. In the last example (21:00), Tmrt is lower, ∼-0.6 °C, compared with the old scheme. The sun is close to setting, at an elevation ∼5–6°. Thus, the courtyard and its walls are shaded.

Since the new wall temperature scheme presented here enables calculation of the variation in Ts at different heights, visualization in 3D provides important information on the distribution of Ts throughout the model domain. As an example, Fig. 7 presents a 3D visualization of the surface model used in SOLWEIG (Fig. 7a) and wall Ts in central Gothenburg on 12 June 2023 17:00 UTC+1 (Fig. 7b). The shadow-casting algorithm in SOLWEIG captures shadows from trees, which are visible along the wall in the top right of the figure. In this area, lower wall temperatures (blue) in shade can be observed, contrasting with the sunlit parts of the wall.

To analyze the effect of the input variables of the model on the resulting Ts, a sensitivity test was conducted. The test is based on an unobstructed wall facing south forced with meteorological data from 13 June 2023 (see Fig. 2c). Default values are given in Table 2. Default albedo and emissivity were 0.5 and 0.95, respectively.

As mentioned in the methods, specific heat capacity and density has no effect on the resulting Ts as any changes in these two variables are cancelled out by corresponding changes in t. The remaining input variables, emissivity, albedo, thermal conductivity and wall thickness, influence Ts to different extents. The three first variables are depicted in Fig. 8. The influence of wall emissivity is relatively small (Fig. 8a), and Ts increases with a decrease in emissivity, as expected. There is less radiative cooling and therefore Ts increases. Albedo (Fig. 8b), on the other hand, influences the amount of absorbed shortwave radiation. As such, Ts increases with a decrease in albedo as more shortwave radiation is absorbed by the wall. A decrease in thermal conductivity (Fig. 8c), likewise, leads to an increase in Ts. Lower thermal conductivity means that less heat is conducted through the material and instead is used to heat up the surface.

The influence of wall thickness on Ts differs from the previous variables as it also depends on the other thermal properties of the material. Three examples are shown in Fig. 9: wood (Fig. 9a), brick (Fig. 9b) and concrete (Fig. 9c). The wooden wall is very sensitive to changes in wall thickness. As evident in Fig. 9a, Ts deteriorates already between 0.04 and 0.06 m. For the brick wall, in Fig. 9b, Ts starts to deteriorate after 0.2 m, indicating that it is less sensitive to changes in thickness. In the last example (Fig. 9c) the concrete wall starts to deteriorate after 0.5 m.

The previous figure (Fig. 9) indicates that Ts starts to deteriorate at different wall thickness depending on material. For example, Ts for wood starts to deteriorate at wall thickness < 0.06 m, brick < 0.3 m and concrete at 0.5–0.6 m. The ratio R=1etπ (K m² W⁻¹) against wall thickness is visualized in Fig. 10. Here it is visible that these wall thicknesses correspond to R≈0.06 K m² W⁻¹ for wood and brick. Concrete, that is a substantially more inert material, potentially has a higher threshold for its R, but is nevertheless close to 0.06. The ratio R is a good indicator for when the model might start to behave erratically and can be used to warn potential users of too thick walls. Because we are using a characteristic time t with our semi-infinite step heating approach, the estimated R reduces to a simple function of wall thickness over thermal conductivity, R=Lππλ, that can be used to estimate the maximum wall thickness Lmax=ππλR. Using a conservative R=0.05 K m² W⁻¹ instead of 0.06 K m² W⁻¹ gives Lmax=0.047, 0.234 and 0.326 m for wood, brick and concrete, respectively.

A new and simple method for simulations of wall Ts has been presented. This new parameterization scheme, based on the works of Boue and Fournier (2009), enables estimations of wall Ts for individual vertical pixels described as voxels. This can be compared to the previous method where wall Ts was estimated for either sunlit or shaded façades (Lindberg et al., 2008).

The five-day examples presented in Fig. 2 showcase that simulations follow observations. High agreement between simulations and observations is also evident from the statistics in Figs. 4 and 5, even though correlations decrease with cloudiness, i.e. neither overcast nor clear weather conditions. The lower correlations under cloudy conditions can easily be explained by the fact that the SOLWEIG model does not solve for explicit cloud cover but depends on observations of shortwave radiation, and that observations originate from a weather station located approximately 2.3 km from the wall of interest for the model evaluation. Nevertheless, R2 values are high (0.93 and 0.94 for wood and plaster brick respectively), and based on 15 394 observation points, covering the entire summer of 2023. RMSE are also low (2.09 and 1.94 °C). These can be compared to RMSE values for simulated Ts of walls on traditional (3.3 °C) and contemporary (7.4 °C) buildings in Dejvice, Prague, Czech Republic, with the PALM-4U model (Resler et al., 2021) and the Ts simulated with the ENVI-met model for a brick wall covered with plaster (R2=0.98–0.99, RMSE = 1.03–1.25) (Simon, 2016). The better performance of the ENVI-met simulations compared to our method could potentially be explained by the fact that (i) ENVI-met make use of a 7-node conduction model and (ii) their observations are for a controlled building test site, whereas our results are from a real-world example, where we have less knowledge of the thermal properties of the walls. Other examples of models where our results compare are e.g. TUF3D (Krayenhoff and Voogt, 2007) and SOLENE (Hénon et al., 2012). The temperature range for the wooden wall is larger than that of the plaster brick wall, which could be explained by the material characteristics. The wooden wall has a lower thermal effusivity compared to the plaster brick wall, meaning that the wooden wall is more sensitive to changes in radiation. Thus, the Ts of the wooden wall will fluctuate more, especially during cloudy conditions. The lower thermal effusivity also explains why the Ts of the wooden wall is higher compared to the plaster brick wall.

The present simulation of wall Ts may look simple since there are only two variables involved (Tair and net radiation of the wall). However, there are several steps to get the net radiation of the wall. The shortwave radiation depends on the angle between the direct solar radiation and the wall but also reflected radiation from the surrounding surfaces and shadow patterns. The estimation of the outgoing longwave radiation is problematic since it requires information on Ts of the wall, i.e. the same variable we aim to simulate. As a proxy we use an iterative process starting with the Ts from the previous timestep. Already after the first iteration the wall Ts is stabilized within a few tenths of a degree, which we consider to be close enough to calculate the outgoing longwave radiation.

The wall surface energy balance is of course not only influenced by the net radiation but also by the sensible heat flux to/from the air and the heat flow within the wall. Both these processes are forced by the wall Ts as well as other atmospheric conditions (e.g. wind/turbulence). However, the close connection between the observed and simulated Ts in our study shows that net radiation dominates heating and cooling of the wall surfaces. A similar conclusion was made by Resler et al. (2021). They found it striking that the agreement was better between observed and modelled wall Ts during summer episodes with strong radiative forcing. However, the observed wall Ts of plastered brick walls both in our study and in the study by Resler et al. (2021) were higher than the simulated after the daytime maximum, which indicates an influence of heat stored in the wall earlier during the day. Higher observed than simulated minimum wall Ts, as also noted by Resler et al. (2021), point to an addition of stored heat to the wall surface. On the other hand, the thinner wooden wall with less ability to store heat was cooler than the simulation during the night.

The new parameterization scheme has enabled better estimations of wall Ts and introduced estimations of shading on walls from surrounding buildings and trees (e.g. Fig. 7b). This results in differences in Tmrt between this new version and previous versions of SOLWEIG. Some of these differences are presented in Fig. 6 and show that the location of the sun has an effect on Tmrt. This is explained by the estimation of wall Ts for individual voxels, where each voxel has its own relation to the location of the sun because of its material characteristics (thermal effusivity, albedo, emissivity) but not the least aspect. The aspect of the wall, i.e. its orientation, influences how much of the direct shortwave radiation that it is exposed to. Previous studies have concluded that the old parameterization in SOLWEIG led to deviations in simulated wall Ts that affected Tmrt (Gál and Kántor, 2020; Wallenberg et al., 2023a).

Gál and Kántor (2020), using SOLWEIG, saw deviations in simulated Tmrt in proximity to wall surfaces and suggested a separate surface temperature parameterization scheme. Some of the overestimations were potentially reduced by around 3 °C with the introduction of patches for short- and longwave radiation described in Wallenberg et al. (2020, 2023a). The new parameterization scheme for wall Ts should improve these deviations further. For example, underestimations were seen in vicinity of sunlit areas (Gál and Kántor, 2020). Here, we show (e.g. Fig. 6d) that the new wall surface scheme calculates higher Tmrt close to sunlit walls (up to 2.5 °C for our case). A small decrease is also noticeable under the trees (Fig. 6f–g). These differences come from the directionality of the walls and accounting for whether they are sunlit or shaded, which was previously not possible.

Kim and Ham (2024) found deviations in simulated Ts of asphalt, plywood and soil, and suggested improvements in thermophysical attributes of construction materials in calculations in SOLWEIG. Although their study referred to horizontal surfaces (e.g. ground and roofs) the suggestions are in line with what has been presented in this paper. The old parameterization scheme for wall Ts is based on the parameterization scheme for ground surface temperatures utilized in SOLWEIG. This scheme was initially developed by Bogren et al. (2000) and refined by Lindberg et al. (2016) and is based on a sinusoidal curve that peaks at a set time. For ground surfaces, this peak in time might be accurate as it occurs not long after solar noon. For walls, however, one peak time for the entire model domain will lead to differences in Ts, depending on the aspect of the wall. Peak time of a wall facing east will for example occur in the morning, whereas it will occur in the afternoon or evening for a wall facing west. The Ts scheme presented here, thus, is an improvement in SOLWEIG, but for wall surfaces. The method presented could, however, be useful for ground surfaces as well.

Even though the model performs well, there are some shortcomings. For example, the spatial input data is pixel based. This leads to deviations when estimating wall aspects which could be seen in Fig. 7b (stripe effect). Although the accuracy of aspect calculations is high in mid-sections of the walls, deviations occur close to corners. Other potential sources of error could be the material characteristics used here, i.e. thermal conductivity, wall thickness, emissivity and albedo. In addition to this, there is a possibility that there are deviations based on longwave radiation reflected from the background, i.e. the opposite of our sensors (Apogee Instruments, 2024b), something that is difficult to estimate in urban areas with many different surfaces that have different emissivity. In relation to the sensors, there is also a possibility that there are micro-shadows on e.g. the wooden wall from the microscale spacing between the boards. Other shortcomings that are likely to influence our results are that we are modeling environmental radiative forcings at the surface of the wall, i.e. input short- and longwave radiation. As they are simulated, they are not perfect, with impacts on the resulting Ts. This also goes for the properties of the wall surfaces, e.g. albedo, emissivity, thermal conductivity and wall thickness. Similar conclusions are described in Johnson et al. (1991), where their simulated rural and urban surface temperatures performed better when the specification of input thermal properties and environmental forcings were most complete and accurate. Similar conclusions can be given about the basis of our step heating approach in Boue and Fournier (2009), where they have complete control over the forcing (heat pulse), thermal properties and not the least environmental conditions of their lab setting.

It is evident from the sensitivity analysis that there are constraints in the model. For example, there are limits to how thick a wall can be, depending on its thermal conductivity (Fig. 9). The presented solution, using R to estimate Lmax to get an indication on when the model might become unstable can hopefully guide users (e.g. Fig. 10). In addition to this, the sensitivity analysis shows that albedo and thermal conductivity have largest effects on Ts and emissivity least (Fig. 8). These results are expected. An increase in albedo should lead to a decrease in Ts as less shortwave radiation is absorbed. An increase in thermal conductivity leads to more energy propagating through the material and less energy at its surface, i.e. lower Ts. A material with higher emissivity has higher radiant cooling and therefore a lower Ts. Thermal conductivity and emissivity are material properties that should affect the thermal comfort of a human, i.e. if they are changed, so is the exposure to a human. A change in albedo, on the other hand, leads to less absorption of heat by a surface (if it is increased), i.e. decrease in longwave radiation, and increases the reflectivity of shortwave radiation. Thus, a human would be exposed to less longwave radiation but more shortwave radiation (Erell et al., 2014).

The new scheme has only introduced four new input parameters to the SOLWEIG model: thermal conductivity, density, specific heat capacity and thickness of the wall. These parameters are easily accessible in literature (e.g. CIBSE, 2015). Three materials are available in the new publicly available SOLWEIG model: brick, concrete and wood. Other materials can simply be added to the parameter file in SOLWEIG. One drawback with the new scheme is the computational complexity. Running the model for one day at 10 min resolution (144 time steps) took approximately 5 min with the newly implemented wall scheme parameterization, compared to around 1 min without it, on an Intel i7-7700 CPU @ 3.6 GHz with 16 GB RAM. However, this is still faster than most other models designed for similar purposes.

Future efforts will include improvements of the ground surface calculations. Initial results indicate that the force-restore method is a well-suited framework for this application. Moreover, the parameterization scheme should be evaluated for other wall surfaces and urban environments, as well as a deeper evaluation of the behavior of walls and their influence on the microclimate and thermal comfort of humans.

We have presented a new parameterization scheme for calculations of wall Ts. The scheme is simple and fast, with only four new input parameters: thermal conductivity, density, specific heat capacity and wall thickness, together with a heat flux density, in this case the heat balance of the wall surface. The performance of the scheme is high for the two assessed wall surfaces, wood (R2=0.93, RMSE = 2.09 °C) and plaster brick (R2=0.94, RMSE = 1.94 °C), making it comparable to heat conductivity models utilized in other models for microclimate.

The implementation of the scheme in the SOLWEIG model has enabled more realistic estimations of wall Ts by incorporating wall aspects and material characteristics. In the examples presented here this has led to differences in Tmrt of up to 2.5 °C compared to the previous version of SOLWEIG.

With this new scheme SOLWEIG can now be used to understand how different building materials influence outdoor thermal comfort. The high accuracy indicated by our results combined with the computational speed suggests that this approach can be used in other areas where Ts of walls are important, for example building energy and urban energy balance models.

The new version of SOLWEIG presented here and its associated datasets are available in Wallenberg et al. (2025a, b).