The micro-scale simulations were performed using the PALM model system in version 25.04 (see Data availability section). The aforementioned model is a turbulence-resolving LES micro-scale meteorological model developed to support urban boundary-layer and climate research . By employing the non-hydrostatic, filtered, Boussinesq-approximated incompressible Navier–Stokes equations together with a suite of specially developed components (see e.g., ), this numerical framework serves as a robust tool for theoretical and practical evaluation of the urban atmosphere at the street scale. Due to its modular architecture, PALM can be configured for specific applications; for example, when wind dynamics are not required, as in simulations focused solely on short-wave solar radiation, the model can be run in spin-up mode. This arrangement greatly reduces computational requirements while still enabling the evaluation and execution of a larger number of configurations and parameterisations. In this mode, the surface energy balance is solved using the Land Surface Model LSM;, Building Surface Model BSM;, Radiative Transfer Model and Plant Canopy Model RTM and PCM;, with the static driver, without the need for LES. Depending on the configuration, the primary radiative forcing is provided either by a model integrated in PALM (such as RRTMG) or, as in this study, by the prescribed external forcing from the dynamic driver file. The dynamic driver values are usually obtained either from a meso-scale model such as WRF, or from measured values. As a result, any biases in the simulated short-wave radiation will be caused by driver errors, geometry effects (sky view factor), or incorrect input parameters. The present experimental setup utilises a pre-validated urban simulation domain of Dejvice, Prague . Micro-scale simulations were performed using the spin-up methodology, with the LSM, BSM, RTM, and MESO modules employed in this study. Furthermore, it should be noted that PALM does not explicitly resolve cloud processes; instead, their radiative impact is represented through externally specified forcing. A comprehensive description of the spin-up methodology follows below.

For the radiation initial and boundary conditions, the Weather Research and Forecasting (WRF) model in versions 4.4 (WRF-CNU setup; denoted interchangeably as CNU) and 4.6.1 (WRF-FU setup, denoted interchangeably as FU) was employed. WRF is a widely used, state-of-the-art meso-scale numerical weather prediction system designed for both atmospheric research and operational forecasting. It comprises two dynamical cores, a data assimilation system, and a software architecture that supports parallel computation and system extensibility. The model serves a wide range of meteorological applications across scales, from tens of meters to thousands of kilometres, and, together with field measurements, can serve as input forcing for micro-scale simulations.

The radiative forcing used in this study is derived from two different WRF model setups: a coarse-resolution non-urban setup (WRF-CNU; 3 km horizontal resolution), and a fine-resolution urban setup (WRF-FU; 1 km horizontal resolution). The specific technical configurations for the WRF grid nesting are fully detailed in Fig. S14 in the Supplement. Although WRF output is processed through PALM-meteo to generate a “complete” PALM dynamic driver file, the spin-up simulations employed in this study utilise only the WRF-derived short-wave and long-wave radiation fields. The remaining meteorological variables contained in the dynamic driver are not dynamically coupled to the PALM simulations and therefore do not contribute to the analysed radiative response. Both WRF simulation setups were initialised using the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis, ERA5 data . A comparative overview of the physics schemes for both WRF configurations is summarised in Table . Typically, the main WRF output files contain data at 1 h intervals. However, when addressing radiation modelling in the context of changes in solar irradiance and the likelihood of cloud development and movement, it is preferable to configure WRF to generate auxiliary output files containing only radiation-related variables at a finer temporal resolution (e.g., 10 min). Furthermore, the WRF files for the PALM-meteo the preprocessor of meso-scale meteorology which generates the PALM dynamic driver; see need to contain short-wave and long-wave horizontal irradiance (marked in WRF as downward radiation); the diffuse component of the short-wave horizontal irradiance is optional. If this component is absent, it is skipped during dynamic driver preprocessing, and the PALM radiation module uses its internal estimate of the direct-to-diffuse ratio. When utilising a high-resolution model such as PALM, it is preferable to provide high-quality input data . Hence, for radiation, this study uses 10 min WRF radiation data to drive the PALM model. A schematic representation of the modelling chain showing the sequential data flow from the meso-scale WRF forcing through the PALM-meteo pre-processor, the dynamic driver to PALM/RTM, is presented in Appendix .

The study area is located in Dejvice, a district of Prague, the capital of the Czech Republic. As presented in , Prague is in a temperate continental climate zone.

The average annual air temperature recorded at the meteorological station Praha-Klementinum (WMO ID 11515, located in the city centre) was 11.5 °C, according to the WMO's Climatological Standard Normal for 1991–2020. The mean air temperature in July, typically the warmest month, was 21.6 °C. The coldest month, with a temperature of 1.8 °C, is typically January. Average annual precipitation is 453.9 mm, and mean monthly sunshine hours range from 46.2 (December) to 229.1 (July).

There are four major types of built-up areas in Dejvice (Fig. ) with the following Local Climate Zone LCZ; classification: (1) historical residential areas in south-east, classified as LCZ 2 Compact midrise, (2) a combination of old and new buildings with a variety of other urban components (west and north-west), predominantly classified as LCZ 5 Open midrise, (3) a residential area with green gardens (north-east) that corresponds to LCZ 6 Open lowrise, and (4) lightly wooded landscape of deciduous trees, with a grass sublayer (LCZ B Scattered trees; more precisely in the case of Hanspaulka (HAN) observational station (see Fig. ), the transitional class LCZ B_D).

Measurement locations (see Fig. ) were situated in the north-west of Dejvice, mainly in the Czech Technical University campus. There were two calibrated stations, each producing 10 min averages for two years (2017–2018). At the end of the year 2017, both stations were moved to a new position (four locations in total; see Table ) . The first location, opened in 2017, was situated on the asphalt surface (FSV) and was primarily influenced by the surrounding buildings. The effect of the plant canopy, distanced ≈ 20 m with an approximate crown perimeter of 2 m, was negligible in most cases. The second position in 2017 was in a public orchard with fruit trees (HAN). The station surroundings were affected during the vegetation season by various grass heights (0.2–1.0 m) and available soil moisture. The majority of fruit trees were lower than 4 m and were distanced more than 5 m from the measurement location. The third location, first in 2018, was in a small park known as Fleming Square (FLE). The station was situated on a homogeneous grass surface and was significantly impacted by neighbouring trees. The last location (NTK), second in 2018, was situated on light grey coloured concrete, between the National Technical Library building and a tree alley (height 8–10 m) on the south-west. An additional note pertains to the curved glassy walls of the neighbouring library, which, under specific conditions, can reflect incoming short-wave radiation.

Each measurement station was equipped with several sensors: two RVT 11 for air temperature and relative humidity (at heights of 0.3 and 2.0 m), three Pt₁₀₀ for soil temperature (at various depths), a “Tlusťák W2” anemometer for wind speed and direction, and two Kipp&Zonen CMP3 pyranometers with the spectral range from 300 to 2800 nm for incoming and reflected solar radiation. The pyranometer's interval covers most of the short-wave radiation spectrum, although not the entire spectrum; in the following text, its results as a short-wave radiation measurement will be considered. Details about sensors are available in Table  or .

In addition to the dedicated observation stations described above, meteorological observations-particularly short-wave radiation measurements-from the Czech Hydrometeorological Institute (CHMI) stations Praha-Libuš (WMO ID 11520) and Praha-Karlov (WMO ID 11519) were also employed. The Praha-Libuš station (50.0077° N, 14.4467° E; 302 m a.s.l.), located in the southern part of Prague, is the reference aerological and meteorological observatory. Owing to its long-term operational record and high-quality measurements, it serves as one of the principal reference stations for meteorological measurements in the Prague region. The Praha-Karlov station (50.0691° N, 14.4276° E; 261 m a.s.l.) is situated closer to the central urban area of Prague and represents conditions typical of a more densely urbanised environment. In this study, both stations provide independent short-wave radiation observations used to evaluate the radiative conditions associated with the analysed episodes. While these stations could, in principle, provide observational radiation forcing data for the PALM model, in the present study, they were used exclusively as out-of-domain reference evaluation stations, rather than as direct model forcing.

Atmospheric conditions with no cloud cover, or the so-called “clear-sky” days, characterised by a smooth, parabolic-shaped curve, are an ideal testbed for evaluating radiation physics in micro-scale models like PALM, thanks to a high confidence in the incoming solar radiation. Nevertheless, high-solar-irradiance weather conditions with a completely cloud-free sky are relatively rare; more realistic conditions involve heterogeneous variations of thin cirrus, scattered cumulus, or cloud fragments. To cover a series of typical summer heat wave periods and the corresponding atmospheric radiation regimes, a total of 16 episodes with various types of synoptic and cloud cover conditions during the summers of 2017 and 2018 were selected for model evaluation. Since the chosen episodes differ in cloud cover, they are categorised into two groups: predominantly cloud-free episodes, referred to as “clear-sky” (i.e., clear-sky proxies), and episodes with pronounced cloudiness, referred to as “non-clear-sky” episodes. The selection of “clear-sky” episodes was based on a qualitative inspection of the observed incoming short-wave radiation to identify periods exhibiting a continuous, smooth, “bell-shaped” diurnal curve. To formalise this for future protocols, we define such episodes as days during which the measured short-wave radiation does not deviate from the theoretical clear-sky envelope, indicating the absence of transient cloud shading. For “non-clear-sky” episodes, we selected days during which the observed short-wave radiation showed high-frequency fluctuations relative to the potential clear-sky maximum. Simulation episodes cover a range of large-scale weather systems and fronts crossing Prague (e.g., cyclonic and anticyclonic), providing a realistic set of meteorological conditions (see Table S1). In addition, the maximum solar elevation was calculated for each of the simulation episodes, ranging from 44.41° during the autumn to 63.37° at the summer peak. Six of the 16 episodes (e1–e6) serve as a sample for the direct WRF setup-to-setup intercomparison (i.e., CNU vs. FU). To further validate the WRF-FU's setup generalisability and transferability and to deliver a more robust statistical evaluation, the analysis was extended to 10 additional episodes (e7–e16) simulated using the WRF-FU setup only. Comprehensive details on the simulation episodes, maximum solar elevation, cloud conditions, and associated WRF driving configurations are provided in Table .

The subsequent evaluation is designed to quantify the influence of WRF mesoscale driving data on PALM-simulated incoming and outgoing short-wave radiation and to identify the conditions under which PALM accurately reproduces observed radiative processes or exhibits systematic biases. To achieve this, PALM outputs from two WRF configurations (CNU and FU) were compared with observations using complementary statistical and radiative-process-oriented analyses.

PALM output data are produced every 60 s relative to the simulation start. For each simulation episode, incoming and outgoing short-wave radiation fluxes (SWin and SWout) are extracted at the locations of the observation stations and averaged into hourly mean values. Similarly, measurements (recorded at 10 min intervals) were aggregated into hourly means. The obtained datasets for both variables are further distinguished by WRF driver (CNU and FU) and station. All subsequent analyses (statistical metrics, summary mean bias and root mean square error heatmaps, and scatter plots) are derived from these model/observation hourly data. The evaluation was designed to address several aspects of micro-scale model performance. First, the overall impact of the WRF driving configurations on the PALM-simulated short-wave radiation was assessed. Second, the ability of PALM to reproduce the magnitude and temporal variability of observed radiative fluxes was evaluated. Third, the PALM's capability to resolve local radiative processes associated with urban morphology and vegetation was examined. Finally, the robustness of PALM under non-clear-sky atmospheric conditions was investigated.

Firstly, the overall model performance was evaluated using absolute and relative differences between modelled and observed values. Absolute differences provide a direct measure of the magnitude of model errors. Relative differences are expressed as percentages relative to the observed values and are used to assess the magnitude of model deviations independent of absolute radiation intensity. These metrics were calculated for each station, radiation variable, simulation episode, and WRF driving configuration, and presented to facilitate comparisons between stations, sky conditions, and driving-data setups.

To further quantify model performance, we employ a multi-level evaluation procedure. First, we calculate overall and station-specific Mean Bias Error (MBE) and Root Mean Square Error (RMSE) using twofold averaging: first, per episode (single episode–station–variable–WRF driver value), and then averaging across all episodes. These are visualised as summary heatmaps. Second, point-by-point comparisons between modelled and observed values are presented in scatter plots, where each point represents an hourly value at a specific station and WRF driver. Performance is further assessed using statistical metrics including Pearson's correlation coefficient (r), coefficient of determination (R2), Spearman's ρ, Kendall's τ, and Willmott's index of agreement (d). Results are stratified by WRF driver (CNU vs FU), sky conditions (clear-sky vs non-clear-sky episodes), and station type (urban vs vegetated).

In addition to bulk statistical evaluation, we quantitatively analyse diurnal cycles of SWin and SWout. This approach allows the assessment of the model's ability to capture key radiative processes such as shading patterns induced by urban morphology, the influence of surface characteristics (e.g., vegetation), and performance under complex cloudy conditions. Specific episodes are selected to highlight these aspects: clear-sky cases for shading and surface representation issues, and non-clear-sky episodes for cloud–radiation interaction.

The influence of sky conditions and the configuration of driving data on PALM simulations is reflected in both absolute and relative metrics defined in Sect. S1 in the Supplement (see the differences in Table ). Under non-clear-sky episodes (see, e.g., column Non-clear (FU) in Table ), and for all stations considered, SWin exhibits consistently large errors, with average absolute differences above 80 Wm-2 and average relative differences exceeding 100 %. The strongest degradation occurs at the NTK station, with a relative difference of 276.6 %, and at the FLE station, with an increase of over 100 %. These are station-specific anomalies that are not observed at the other two locations. The modelling of SWout is only slightly affected, as the increase in discrepancies is not substantial, except at the NTK station. On the other hand, PALM's fidelity improves for the clear-sky scenario simulated with the FU configuration, with both examined variables exhibiting comparatively small biases. However, the HAN station exhibits large relative and absolute differences, a pattern that holds across all sky conditions and driving data configurations. A more detailed analysis of the HAN station is presented in Sect. . The metrics quantifying the impact of the WRF configurations on SWin indicate that the FU outperforms the CNU, as the overall absolute difference decreases from 67.9 Wm-2 (CNU) to 39.8 Wm-2 (FU; corresponding to a reduction of roughly 40 %). Compared with SWin, WRF-FU improves SWout only at urban stations (FSV and NTK), whereas biases persist at vegetated sites (HAN and FLE), resulting in similar overall relative and absolute differences.

Given the performance metrics indicated by the Person's correlation coefficient r, Spearman's ρ, and Willmott's index of agreement d, the timing and magnitude of the modelled and observed values for SWin show a satisfactory agreement for both of the driving data configurations considered (r≈0.985, ρ≈0.984, d≥0.980; see Table ; metrics are defined in Supplement Sect. S1). An evaluation of all indicators, however, suggests that the FU configuration performs somewhat better than the CNU setup, particularly at urban stations (i.e., FSV and NTK). Outgoing short-wave radiation (SWout) shows a greater spread in performance metrics than SWin. There is a pronounced systematic bias in SWout at the HAN site, as indicated by low correlation and agreement metrics, and the strongly negative coefficient of determination (R2≈-1.4). This bias is consistent across the full analysis and in every tested configuration, indicating that the dominant source of error lies in the surface representation at HAN rather than in the driving data forcings alone. When examining PALM's performance under different sky conditions (see Table ; FU Clear-Sky and FU Non-Clear-Sky), the contrast is clear and expected: the model reproduces SWin with high accuracy during clear-sky periods. This level of accuracy is not achieved in episodes with cloud cover, where performance metrics degrade noticeably for both variables. PALM's overall performance shows that SWin is represented with high fidelity under clear-sky conditions when using the updated FU-driving data. Although PALM's simulation of SWout is not poor, a clear deterioration in the evaluation metrics is evident, particularly at vegetated sites. Under conditions other than clear skies, PALM struggles to model radiation, and its performance remains inconsistent.

Figure  illustrates PALM performance and accuracy through the MBE and RMSE error distributions for both incoming and reflected short-wave radiation across four stations. The arrangement in Fig.  is to isolate the impact of driving data configurations and sky conditions on the model's performance. In the CNU-driven setup, the incoming short-wave radiation (SWin-CNU) is underestimated across all evaluated stations. The negative biases span from -13 to -46 Wm-2, most pronounced at the FSV and NTK stations (Fig. a). Conversely, for SWin-FU, the MBE values are significantly reduced, approaching 0, and at the HAN and FLE stations, they become slightly positive. For outgoing short-wave radiation, both SWout-CNU and SWout-FU exhibit negative biases, which are reduced when using the FU configuration. The only exception is the HAN station, where both setups show a strong negative bias greater than -30 Wm-2. This bias pattern is further supported by the RMSE values (Fig. d), which decrease for the FU configuration for both incoming and outgoing short-wave radiation, although the reduction is less pronounced for the outgoing component.

SWin-FU biases (Fig. b) display a site-specific performance pattern comparable to that in Fig. a, with negative biases at urban stations (FSV and NTK) and positive biases at vegetated stations (HAN and FLE). SWout MBEs are generally small, except for the pronounced negative bias at the HAN station (≈-60 Wm-2), which remains evident and aligns with earlier analyses. The RMSE values are generally moderate; however, for SWout at the HAN station, they are notably higher than at the other stations. Finally, under non-clear-sky conditions, the model performance degrades notably, as indicated by large MBE and RMSE values, particularly for SWin (Fig. c, d). In the case of SWin, MBEs indicate model overestimation, with the highest value exceeding 120 Wm-2 at the NTK station. SWout biases are negative, except for the NTK station, which has a positive bias.

To separate the effect of the driving model setup on the PALM-simulated short-wave radiation, we perform a direct, point-by-point comparison, as shown in Figs.  and . Firstly, in the CNU-driven PALM simulations, SWin is consistently and systematically underestimated for irradiance values exceeding 600 Wm-2 (see Fig. a). This deviation is evident in the relatively large MBE and RMSE values of -32.05 and 62.21 Wm-2, respectively. The consistent underestimation suggests that the deficiencies likely stem from the WRF-CNU driving setup and cannot be fully mitigated by PALM's high-resolution local radiative transfer calculations. In contrast, a clear improvement is observed for the WRF-FU-driven PALM simulations, in which the MBE is reduced by roughly one order of magnitude and the RMSE decreases by about 30 % compared to the CNU-driven PALM simulations (see Fig. b). However, the FU-based outputs at radiation levels below 300 Wm-2 show a slight spread around the 1:1 line. A noticeable deterioration in PALM's performance (also for the FU-based outputs) is evident for outgoing short-wave radiation, as illustrated in Fig. . An initial assessment common to both configurations is a systematic underestimation of outgoing short-wave radiation, which is somewhat more pronounced when using the CNU driver. This underestimation results in a negative bias of -16.15 Wm-2 for CNU and -12.61 Wm-2 for the FU configuration, corresponding to an approximate reduction of 22 % in favour of the FU setup. As indicated by the RMSE values, PALM's predictions remain moderately accurate, though slightly improved in the FU configuration. Furthermore, the evaluated stations exhibit more variability for outgoing radiation. For instance, FLE (blue) and NTK (purple) align relatively well with the observations (Fig. b), whereas the HAN (green) station exhibits significant deviations and has the poorest correspondence (Fig. a, b). Although the results show that the FU driver reduces bias relative to the CNU, discrepancies with observations remain relatively high. It implies that the model's inadequate representation of surfaces near problematic sites (i.e., HAN) influences reflected radiation and is the primary cause of these biases.

Addressing the main model outputs through diurnal cycles of incoming and outgoing short-wave radiation components is a well-established benchmark for testing the overall fidelity of micro-scale models. The significance of urban meteorology studies, in particular, is underscored by the ability to capture the relationship between the moving Sun and surrounding physical objects, radiation intensity, surface albedos, and the integration of dynamic driver data. Although significant, statistical summaries cannot fully capture the specifics of the model's radiative process treatment (e.g., resolving morning and afternoon shading patterns, midday radiation intensity offsets or magnitude discrepancies, location-specific sensitivities, surface-type biases). Hence, the following subsections are organised into three complementary evaluations that demonstrate the model's fidelity (Sect. ), reveal instances where the performance degradation is most clearly evident (Sect. ), and validate the model's predictive skills under more complex radiative regimes (Sect. ).

Shadow patterns of urban morphology in modelling incoming short-wave radiation. One of the key assets of PALM, identified in the study, is its ability to realistically simulate the magnitude of incoming short-wave radiation at morning and afternoon times as observed from street-level stations. Buildings, trees, and other obstacles cast the longest and broadest shadows during the hours when the Sun is near the horizon (i.e., early morning and late afternoon). Correctly reproducing the impact of urban obstructions (i.e., shadow patterns) on the detected SWin amount at the urban station requires explicitly integrating information on building and tree size, height, orientation, and spacing into the micro-scale model. At this point, the robustness of the RTM and PALM becomes evident: they can explicitly resolve sunlit and shaded surfaces and energy redistribution based on the calculated local sky view factors and shading geometry. Beyond their ability to resolve shading patterns, a further key aspect addressed by the micro-scale model is the correct translation of this information into short-wave flux magnitudes at the 1 m scale. Moreover, the visible delay and attenuated magnitude of the PALM-simulated SWin are corrected relative to the WRF driving data, which most likely results from the WRF assuming unobstructed surfaces and therefore further overestimates SWin during these hours.

However, certain disagreements between the micro-scale outputs and the observation data, resulting from incorrect urban canopy representation in the micro-scale model, are also identified (see Fig.  FLE station around 15:00 UTC, for example, PALM's tree height is shorter than the real one, and SWin is not attenuated enough, and Fig. S12 NTK around 04:00 UTC, where the tree is not represented accurately in PALM as well). As said, incident short-wave radiation is highly sensitive to the placement and dimensions of urban obstacles, so even small inaccuracies in the PALM static driver data can produce excess attenuation or an increase in the incoming radiation in the PALM's outputs.

A primary outcome of the driving-data impact evaluation on micro-scale simulations is the added value of the FU and the lower accuracy of the CNU configuration for PALM micro-scale simulations. Although both setups reproduced temporal SWin evolution well relative to the observed values (with FU being more accurate), differences between them are reflected in the magnitude of bias and in overall statistical metrics of the evaluated PALM outputs.

The central differences between the two WRF setups are in their horizontal resolutions and physics parameterisation bundles as presented in Table . The CNU configuration does not include information on urban canopy, geometry, or three-dimensional objects (i.e., buildings) and has a coarser 3 km resolution. A combination of these WRF-CNU features produces smoother radiation fields and overestimates morning and late-afternoon incoming SW radiation by failing to account for urban physical structures and shadowing effects. The inability to represent building-involved shadowing effects is evident in the qualitative analysis, e.g., Fig. , where the WRF-CNU setup predicts an earlier and accelerated morning increase, as well as a delayed afternoon reduction in SW incoming radiation compared to the observation.

Another prominent distinction visible in Fig. a is that the PALM-CNU In to WRF-CNU In ratio differs substantially from the PALM-FU In to WRF-FU In ratio. The differences in the PALM-to-WRF short-wave radiation ratios at the FLE station during the evaluated episode are closely related to differences between the diffuse (see Fig. S15) and direct short-wave radiation components (see Fig. S16). Namely, the WRF-CNU simulations exhibited substantially larger diffuse short-wave radiation (larger PALM / WRF ratio) than the WRF-FU simulations (smaller PALM / WRF ratio). These results, therefore, highlight that accurate representation of the diffuse and direct shortwave radiation components is crucial for reproducing total radiation fluxes, and that PALM simulations are sensitive to the diffuse/direct partitioning inherited from the mesoscale forcing model.

The improved resolution and more explicit treatment of urban morphology in the FU configuration benefit the FU-driven PALM simulation outputs. This is evidenced by improved overall statistical metrics (i.e., reduced MBE and RMSE and higher agreement metrics) and a lower difference between peak solar noon insolation and observations. Even though the WRF-FU setup's departure from observation in SWin is evident in morning and afternoon street-level shadowing patterns, its midday maxima are more realistic, and this configuration demonstrates a higher degree of agreement with observations from both CHMU and dedicated measurement stations. Finally, the FU's overall superiority is seen in reduced dispersion of PALM's SWin and SWout values (see Figs.  and ).

Given these findings, the WRF-CNU driving dataset impairs PALM's performance due to biased radiation input fields, whereas the WRF-FU setup provides PALM with more accurate radiation fields. As previously demonstrated in and for meteorological variables (i.e., temperature and wind speed), this study emphasises both the importance of highly accurate radiation input fields and PALM's sensitivity to the quality of meso-scale radiation forcing. Since PALM's and the RTM's limited capability to simulate complex radiative transfer processes is insufficient to correct biases in the driving fields, supplying the micro-scale model with realistic and robust meso-scale radiation forcing is imperative.

Undoubtedly, this study demonstrates clear performance degradation of the micro-scale model under non-clear-sky conditions relative to the clear-sky conditions, where PALM's RTM exhibits high-quality performance. During clear-sky conditions, the Sun's position relative to the Earth and the city's structure predominantly control the radiation field. In such cases, PALM's RTM modelling, with meter-scale resolution, explicitly resolves the detailed processes that affect radiation fluxes and accurately simulates the spatio-temporal dynamics of incoming short-wave radiation (i.e., exhibiting high temporal coherence with observations, good magnitude and phase alignment, and strong statistical performance). When the variability in incoming radiative fluxes is caused by intermittent cloud cover, PALM's framework is unable to capture rapid spatiotemporal fluctuations in radiative fluxes because it depends on information from the meso-scale model. The performance degradation is more pronounced in PALM's SWin outputs, as evidenced by overestimated incoming radiation, low correlation, and generally poor statistical performance. PALM's ability to accurately capture and redistribute radiation based on finely detailed, high-resolution geometry is insufficient for capturing the effects of cloud pattern shifts. Under cloud-cover conditions, the PALM is driven by the input conditions given by WRF. Temporal mismatches in cloud passages lead to large discrepancies in radiation values, thereby further reducing correlation and increasing relative errors. However, it is important to keep in mind that PALM's limited fidelity under non-clear-sky conditions does not represent a failure of the model itself and that its predictive skill remains reasonably good.

Short-wave radiative fluxes play a critical role in urban surface energy balance and strongly control urban climate, thermal comfort, and energy consumption in urbanised areas. If they are not captured accurately, errors and uncertainties in the radiative fluxes computed by PALM's RTM will propagate through the rest of the model framework and, in turn, affect all other variables that depend on them, thereby diminishing the overall reliability of the simulation. Establishing reliable simulations is especially important when the micro-scale model is applied to human biometeorological studies or to the development of urban adaptation or mitigation scenarios.

In human biometeorology, particularly in cities, SWin and SWout are crucial for estimating the thermal exposure and evaluating human thermal comfort. In essence, these two variables govern the MRT, the most important variable for calculating thermal comfort indices such as UTCI and PET. The incoming short-wave radiation determines the level of solar exposure within cities (e.g., in sunlit areas such as open squares, wide streets, large public squares and plazas, and similar spaces). In contrast, reflected short-wave radiation contributes to the amount of radiative load imposed by buildings, roads, and surrounding surfaces on urban dwellers. Inaccurate calculations of these radiative variables can lead to misidentification and mapping of heat-vulnerable locations within cities, as well as to incorrect assessments of both the magnitude and the spatial distribution of heat stress.

From an application perspective, precise short-wave flux calculations are crucial for urban planning and design purposes, as well as for the development of mitigation strategies. Since the amount of SWin cannot be controlled, its precise and high-resolution modelling is fundamental to urban planning and design; without it, the strategic arrangement and optimisation of building orientation and placement, street layouts, shade sail installation, and tree positioning would be less effective. In certain situations, inappropriate mitigations may fail, leading to increased heat stress, distorted heating and cooling loads, and reduced overall urban sustainability. On the other hand, the SWout is a variable that urban planners can “control” to a certain extent, as it can be altered by choosing urban materials that, for example, promote cooling. Our results emphasise the high sensitivity of the SWout to the input static driver data. Hence, effectively simulating any pavement-based mitigation strategy or evaluating pavement albedos requires both reliable urban static driver datasets and fidelity in PALM's SWout calculation procedures. Only if these two conditions are met can the predicted benefits and drawbacks of a designed strategy be considered realistic and applicable to urban sustainability planning (e.g., urban heat mitigation, thermal comfort improvements, and the long-term resilience of cities).

Static driver data accuracy constraints. Despite substantial efforts to ensure data quality see Sect. 3.2 in, PALM's static driver dataset is inherently prone to inaccuracies in urban morphology, land cover, and vegetation characteristics. This makes it a weak link in the micro-scale simulation framework and a major source of internally driven uncertainty, because it is practically impossible to map and keep all urban surfaces up to date. Due to the high heterogeneity of urban elements within the micro-scale simulation domain, uncertainties can arise from any component defined in the static dataset . They can lead to systematic errors that directly affect both SWin and SWout. Static driver data that may be erroneous includes, for example, building height and location, roof geometry, surface material, surface optical properties (e.g., albedo), vegetation height and spatial placement, and leaf area density. Spherical photography at the measurement location can be used to identify these discrepancies as potential sources of errors, as shown in Fig. .

Simplified or idealised albedos and their deviations from actual “real-world” values for roofs, walls, pavements, and vegetated surfaces are the main contributors to incorrect estimates of reflected radiation (as illustrated at the HAN station). In addition, these incorrectly represented albedos also affect the final value of the modelled SWin, owing to the multiple-reflection processes represented and permitted by PALM's RTM. In reality, urban objects, surfaces and vegetation are not “static” – they are prone to change due to ageing, season, and temporary or permanent human modification. Since such limitations inevitably reduce simulation accuracy, future work should prioritise continuous updates to urban morphology datasets and their utilisation in micro-scale models, such as PALM.