Representation Of Physical Processes Research Articles

The delivery of water ice to the Martian surface by passive degassing

Passive degassing, or volatile release from persistent, non-eruptive volcanic activity, may have contributed significantly to the Martian ice budget over the planet's history by providing a pathway for volatile species, such as water vapor, to outgas from the interior into the atmosphere and accumulate on the surface. Passive degassing from Martian volcanoes can result in ice accumulations in unusual locations (e.g., outside of the typical cold traps like the Tharsis Rise and polar ice caps). Such locally derived water could cover the volcanic source in an ice-rich veneer that may later serve as a source of ice for subsequent interactions between impactors or lava. Using the Laboratoire de Météorologie Dynamique Generic Planetary Climate Model, we model the spatial distribution of ice that results from passive degassing from five major Martian volcanic centers, Cerberus Fossae, Apollinaris Mons, Elysium Mons, Hadriacus Mons, and Pityusa Patera, and assess the sensitivity of these results to a range of volcanic, atmospheric, orbital, environmental, and numerical parameters. Previous studies have placed estimates on the amount of water released from volcanic outgassing (e.g., Carr, 1987; Craddock and Greeley, 2009; Grott et al., 2011; Carr and Head, 2015), but this is the first study to track the water released from specific volcanoes and to determine the spatial distribution of resulting ice deposits. We find that volcanic variables, such as mass flux and duration of degassing, primarily drive the thickness of volcanogenic ice fields –– higher mass fluxes create the thickest ice deposits (up to ~210 mm around the volcanic center for a mass flux 106 kg s−1) and durations of >6 months are required for ice to reach near-global distributions. Ice accumulation around the degassing source is maximized if the latitude of passive degassing occurs near the south pole (i.e., Pityusa Patera) due to its exposure to circulation patterns that promote ice deposition in that region. Changes in the season, dust visible optical depth, and eccentricity can also impact the thickness of ice, with ice accumulation around the volcanic source being maximized in the spring, with higher dust visible optical depths, and when the eccentricity is low, due to more favorable temperatures and atmospheric circulation. Variances in the obliquity can also influence ice fields by modulating which latitudes receive more surface ice accumulation. The amount of activated cloud condensation nuclei (CCN) in the atmosphere has a lesser impact on the thickness and spatial distribution of ice fields than other parameters, with lower amounts of CCN leading to narrower, but slightly thicker deposits compared to higher amounts of CCN. The mean surface pressure and longitude of perihelion both affect the timing of ice fields. The surface pressure influences how long water lingers in the atmosphere before depositing to the surface, while the longitude of perihelion controls the time of year in which the thickest ice deposits accumulate around the degassing source. Ice fields are highly sensitive to the model resolution. Higher model resolutions allow for a more detailed representation of physical processes, such as cloud formation and ice deposition, while lower resolution model runs provide a more approximate guide to the representation of these processes and may underestimate surface ice thicknesses around the degassing source. The thickest deposits of ice form around the source of passive degassing, but later migrates and restabilizes in cold traps after degassing has ceased, unless it is protected against sublimation by the deposition of dust or pyroclastic material. Assuming the ice is protected, recurrent episodes of passive degassing could have facilitated the gradual accumulation of ice deposits several meters thick over time.

Entropic velocity distribution for sediment-laden flows with a modified dip estimation model at data-scarce river sites

This study proposes an entropic velocity distribution and dip-position (y max) model for sediment-laden (SL) flows. Unlike previous studies, the proposed entropic distribution incorporates the effect of suspended-sediment (SS) and y max, to improve velocity predictions near-bed and water surface in SL flows. A new dampened mixing length for SL flows is proposed in terms of SS concentration. Moreover, the effect of channel bed roughness (k s), slope (S), SS, width (B) and flow depth (H) on y max is studied. Further, a comprehensive model for y max is proposed as a bivariate function of B/H and k s/H for natural channels. A comparative analysis is presented with the existing models, which shows the proposed equations provide higher accuracy with reduced parameters and a better representation of physical processes in SL flows. Subsequently, the proposed equations are applied at three gauged sites (Mahanadi River, India), showing its suitability to obtain velocity at data-scarce river sites.

Parameterizing Raindrop Formation Using Machine Learning

Abstract Raindrop formation processes in warm clouds mainly consist of condensation and collision–coalescence of small cloud droplets. Once raindrops form, they can continue growing through collection of cloud droplets and self-collection. In this study, we develop novel emulators to represent raindrop formation as a function of various physical or background environmental conditions by using a sophisticated aerosol–cloud model containing 300 droplet size bins and machine learning methods. The emulators are then implemented in two microphysics schemes in the Weather Research and Forecasting Model and tested in two idealized cases. The simulations of shallow convection with the emulators show a clear enhancement of raindrop formation compared to the original simulations, regardless of the scheme in which they were embedded. On the other hand, the simulations of deep convection show a more complex response to the implementation of the emulators, in terms of the changes in the amount of rainfall, due to the larger number of microphysical processes involved in the cloud system (i.e., ice-phase processes). Our results suggest the potential of emulators to replace the conventional parameterizations, which may allow us to improve the representation of physical processes at an affordable computational expense. Significance Statement Formation of raindrops marks a critical stage in cloud evolution. Accurate representations of raindrop formation processes require detailed calculations of cloud droplet growth processes. These calculations are often not affordable in weather and climate models as they are computationally expensive due to their complex dependence on cloud droplet size distributions and dynamical conditions. As a result, simplified parameterizations are more frequently used. In our study we trained machine learning models to learn raindrop formation rates from detailed calculations of cloud droplet evolutions in 1000 parcel-model simulations. The implementation of the developed models or the emulators in a weather forecasting model shows a change in the total rainfall and cloud characteristics, indicating the potential improvement of cloud representations in models if these emulators replace the conventional parameterizations.

Brief communication: Surface energy balance differences over Greenland between ERA5 and ERA-Interim

Abstract. We compare the main atmospheric drivers of the melt season over the Greenland Ice Sheet (GrIS) in ERA5 and ERA-Interim (ERAI) in their overlapping period 1979–2018. In summer, ERA5 differs significantly from ERAI, especially in the melt regions. Small-scale ERA5 − ERAI differences near the ice sheet's margins and over steep slopes can be explained by the different resolution, while the large-scale differences indicate a different representation of physical processes in the two reanalyses: averaged over the lower parts of the GrIS, the mean near-surface air temperature is 1 K lower, while the mean downward shortwave radiation at the surface is on average 15 W m−2 higher than in ERAI. Comparison with observational weather station data shows a significant warm bias in ERAI and, for ERA5, a significant positive bias in downward shortwave radiation. Consequently, methods that previously estimated the GrIS surface mass balance from the ERAI surface energy balance need to be carefully recalibrated before converting to ERA5 forcing.

Simulations of idealised 3D atmospheric flows on terrestrial planets using LFRic-Atmosphere

Abstract. We demonstrate that LFRic-Atmosphere, a model built using the Met Office's GungHo dynamical core, is able to reproduce idealised large-scale atmospheric circulation patterns specified by several widely used benchmark recipes. This is motivated by the rapid rate of exoplanet discovery and the ever-growing need for numerical modelling and characterisation of their atmospheres. Here we present LFRic-Atmosphere's results for the idealised tests imitating circulation regimes commonly used in the exoplanet modelling community. The benchmarks include three analytic forcing cases: the standard Held–Suarez test, the Menou–Rauscher Earth-like test, and the Merlis–Schneider tidally locked Earth test. Qualitatively, LFRic-Atmosphere agrees well with other numerical models and shows excellent conservation properties in terms of total mass, angular momentum, and kinetic energy. We then use LFRic-Atmosphere with a more realistic representation of physical processes (radiation, subgrid-scale mixing, convection, clouds) by configuring it for the four TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI) scenarios. This is the first application of LFRic-Atmosphere to a possible climate of a confirmed terrestrial exoplanet. LFRic-Atmosphere reproduces the THAI scenarios within the spread of the existing models across a range of key climatic variables. Our work shows that LFRic-Atmosphere performs well in the seven benchmark tests for terrestrial atmospheres, justifying its use in future exoplanet climate studies.

Identifying climate model structural inconsistencies allows for tight constraint of aerosol radiative forcing

Abstract. Aerosol radiative forcing uncertainty affects estimates of climate sensitivity and limits model skill in terms of making climate projections. Efforts to improve the representations of physical processes in climate models, including extensive comparisons with observations, have not significantly constrained the range of possible aerosol forcing values. A far stronger constraint, in particular for the lower (most-negative) bound, can be achieved using global mean energy balance arguments based on observed changes in historical temperature. Here, we show that structural deficiencies in a climate model, revealed as inconsistencies among observationally constrained cloud properties in the model, limit the effectiveness of observational constraint of the uncertain physical processes. We sample the uncertainty in 37 model parameters related to aerosols, clouds, and radiation in a perturbed parameter ensemble of the UK Earth System Model and evaluate 1 million model variants (different parameter settings from Gaussian process emulators) against satellite-derived observations over several cloudy regions. Our analysis of a very large set of model variants exposes model internal inconsistencies that would not be apparent in a small set of model simulations, of an order that may be evaluated during model-tuning efforts. Incorporating observations associated with these inconsistencies weakens any forcing constraint because they require a wider range of parameter values to accommodate conflicting information. We show that, by neglecting variables associated with these inconsistencies, it is possible to reduce the parametric uncertainty in global mean aerosol forcing by more than 50 %, constraining it to a range (around −1.3 to −0.1 W m−2) in close agreement with energy balance constraints. Our estimated aerosol forcing range is the maximum feasible constraint using our structurally imperfect model and the chosen observations. Structural model developments targeted at the identified inconsistencies would enable a larger set of observations to be used for constraint, which would then very likely narrow the uncertainty further and possibly alter the central estimate. Such an approach provides a rigorous pathway to improved model realism and reduced uncertainty that has so far not been achieved through the normal model development approach.

Impacts of Uncontrolled Operator Splitting Methods on Parameter Identification, Prediction Uncertainty, and Subsurface Flux Representation in Conceptual Hydrological Models

AbstractThe proper numerical representation of physical processes in mechanistic hydrological models is essential to produce robust predictions. A common problem with numerical schemes in hydrological models is that multiple concurrent fluxes are calculated sequentially. Although the importance of errors introduced by inappropriate numerical schemes is well recognized in the literature, many hydrological models calculate concurrent fluxes sequentially. Here, two versions of the HYPE model are used to investigate the limitations of sequential calculations. A fourth order Gear‐Nordsieck solution of the continuous state‐space formulation of HYPE (I‐HYPE) is developed to provide a robust solution, and a fixed‐step implicit Euler scheme (IE‐HYPE) is implemented to provide a computationally efficient and robust approximation of the I‐HYPE simulations. In contrast to I‐HYPE, results show that the original HYPE and the sequential calculation implemented in the continuous state‐space formulation of HYPE (SQ‐HYPE) typically simulate no interflow when soil moisture levels exceed the field capacity. The discrepancy between SQ‐HYPE and I‐HYPE grows with the size of the computation time step, and this implies a compromised representation of flow paths by sequential schemes. IE‐HYPE provides responses comparable with I‐HYPE for both daily and hourly time steps. IE‐HYPE and SQ‐HYPE are compared in terms of their groundwater representation, parameter identifiability, and predictive skills for two catchments. The sequential models have larger groundwater contributions to flow than IE‐HYPE because the splitting errors in SQ‐HYPE limit the interflow flux. IE‐HYPE estimates of the groundwater flux are more consistent with literature values of groundwater contributions to flow for the basins studied.

Complex motion of Greenland Ice Sheet outlet glaciers with basal temperate ice.

Uncertainty associated with ice sheet motion plagues sea level rise predictions. Much of this uncertainty arises from imperfect representations of physical processes including basal slip and internal ice deformation, with ice sheet models largely incapable of reproducing borehole-based observations. Here, we model isolated three-dimensional domains from fast-moving (Sermeq Kujalleq/Store Glacier) and slow-moving (Isunnguata Sermia) ice sheet settings in Greenland. By incorporating realistic geostatistically simulated topography, we show that a spatially highly variable layer of temperate ice (much softer ice at the pressure-melting point) forms naturally in both settings, alongside ice motion patterns which diverge substantially from those obtained using smoothly varying BedMachine topography. Temperate ice is vertically extensive (>100meters) in deep troughs but thins notably (<5meters) over bedrock highs, with basal slip rates reaching >90 or<5% of surface velocity dependent on topography and temperate layer thickness. Developing parameterizations of the net effect of this complex motion can improve the realism of predictive ice sheet models.

Effect of land–atmosphere process parameterizations on the PM simulation of a river valley city with complex topography

Realistic representation of land–atmosphere processes (LAPs) plays a significant role in meteorology and consequent air quality simulations. In this study, 15 series setups, different combinations of five planetary boundary layers (PBL) with compatible surface layer (SL) schemes, and three land surface models (LSMs) were designed in the WRF-Chem model to perform a sensitivity study assessing the influence of LAPs on simulations. Sensitivity tests were conducted over the river valley city at 1 km resolution under synoptically quiescent conditions on December 1–6, 2019. The results were evaluated via comparison with five meteorological and 29 air quality stations. Statistical indicators were used to understand the physical mechanisms of various LAPs which impact air pollution diffusion and to identify the preferred configuration. Our studies revealed that, under synoptically quiescent conditions over complex topography, adequate representations of physical processes occurred in lower atmosphere have critical implications on numerical simulation of regional meteorology and air quality. The simulations seemed more sensitive to the LSMs than to PBL schemes. The NoahMP land surface model exhibited better performance than the RUC and SLAB models, likely because it effectively captured the daily variations in near-surface temperature and wind direction. LAPs had a significant effect on the air quality simulations. The differences in correlation coefficients and daily particulate matter (PM) concentrations between the model simulations in response to the LAP configurations reached 86%–94% and 42%–48% respectively. The MYN_N configuration, namely the NoahMP land surface model coupled with the MYNN PBL scheme, improved the accuracy of the modeled PM concentration and constituted a reference for air quality prediction over complex topography under calm synoptic conditions.

Parameterizing the JULES land surface model for different land covers in the tropical Andes

ABSTRACT Physically based hydrological models such as the Joint UK Land Environment Simulator (JULES) are increasingly used for hydrological assessments because of their state-of-the-art representation of physical processes and versatility. Generating parameter sets for a larger variety of land cover types may be an appropriate approach to simplify setting up JULES for operating simulations beyond the default parameterizations. Here we explore the possibilities of this approach using a case study in the tropical Andes. First, we evaluate to what extent the standard JULES land cover configurations can simulate the hydrological response of dominant soil and land cover types of the region. Next, we adjust the soil water retention parameters on a regional basis using experimental soil data from representative sites. We find that the adjusted parameters result in substantial alteration for the flow partition. Such parameterizations may increase the configurations to implement JULES for a larger variety of land cover types and assess soil disturbance’s potential impact more precisely.

Static, Dynamic, and Signal-to-Noise Analysis of a Solid-State Magnetoelectric (Me) Sensor with a Spice-Based Circuit Simulator

Modeling the non-electrical processes by equivalent electrical circuits is a widely known and successfully used technique in research and development. Although finite element methods software development has supplanted electrical analogy techniques due to greater accuracy and intuitiveness in recent decades, the modeling of physical processes based on analogies has several advantages in some cases. Representation of physical processes in the form of lumped circuits and graphs allows researchers to estimate the system with an alternative view, use standardized methods for solving electrical circuits for non-electrical systems, and, most importantly, allows us to use electrical circuit simulators with their unique capabilities. Of particular interest for using the analogy technique are systems that include electronic components along with components belonging to other physical domains, such as mechanical, thermal, magnetic, and others. A solid-state magnetoelectric (ME) sensor equipped with a charge amplifier is proposed in this study as an example of analysis using the equivalent electrical circuit and simulating these circuits using SPICE-based circuit simulators. Sensor analysis is conducted with an emphasis on noise budgeting and optimizing the sensor’s signal-to-noise ratio and resolution. In addition, the steady state, the phasor, and transient types of analyses were employed to study the static and dynamic behavior of the system. Validation of the model using analytical calculations and comparison with experimental data demonstrated superior results.

Evaluating the Atibaia River hydrology using JULES6.1

Abstract. Land surface models such as the Joint UK Land Environment Simulator (JULES) are increasingly used for hydrological assessments because of their state-of-the-art representation of physical processes and versatility. Unlike statistical models and AI models, the JULES model simulates the physical water flux under given meteorological conditions, allowing us to understand and investigate the cause and effect of environmental changes. Here we explore the possibility of this approach using a case study in the Atibaia River basin, which serves as a major water supply for metropolitan regions of Campinas and São Paulo, Brazil. The watershed is suffering increasing hydrological risks, which could be attributed to environmental changes, such as urbanization and agricultural activity. The increasing risks highlight the importance of evaluating the land surface processes of the watershed systematically. We explore the use of local precipitation collection in conjunction with data from a global meteorological reanalysis to simulate the basin hydrology. Our results show that key hydrological fluxes in the basin can be represented by the JULES model simulations.

Illustration of an object‐based approach to identify structural differences in tropical cyclone wind fields

AbstractThe meteorology community primarily assesses tropical cyclone (TC) forecast skill using track and intensity errors. These metrics are frequently uncorrelated and can offer conflicting information about model performance. Continued improvements in intensity forecasting require improved representation of physical processes over multiple scales, and model verification of TC spatial structure can contribute to these improvements. To date, there are limited studies into forecast model representation of wind fields. More work is needed to better understand model deficiencies in skillfully predicting TC size metrics. In this study, we demonstrate an object‐based approach that can reveal structural differences in TC wind fields. Object‐based methods have been underutilized, and these methods, along with spatial metrics, can serve as additional verification methods for assessing storm structure in both observations and model simulations. Specifically, we illustrate this approach by examining a major difference between the Tiedtke and Kain–Fritsch cumulus parametrization schemes: The Tiedtke scheme includes convective momentum transport while the Kain–Fritsch scheme does not. We create three experiments of Hurricane Isabel (2003) using the Weather Research and Forecasting model using the Kain–Fritsch and Tiedtke cumulus parametrization schemes and an altered Tiedtke scheme with convective momentum transport turned off. Within the three experiments, we generate a small ensemble of four simulations to avoid drawing erroneous conclusions due to growth of numerical noise. Then, we use object‐based methods to measure and compare spatial attributes of the low‐level wind fields to confirm the dominant influence of momentum transport in influencing the TC spatial structure. Our spatial metric approach offers an objective suite of structural attributes that could be useful in diverse applications. A future goal is to use spatial metrics in systematic verification studies of TCs in operational model forecasts and climate model simulations, which may offer great benefit to operational forecasters and numerical model developers.

Supporting Information for "Limitations in representation of physical processes prevents successful simulation of PM2.5 during KORUS-AQ "

Supporting Information for "Limitations in representation of physical processes prevents successful simulation of PM2.5 during KORUS-AQ "

Modeling of bodies with spherical pores by generalized linear interpolation

The article offers a description of parametric objects with spherical pores by generalized linear interpolation. Increasing the volume of high-resolution image data requires the development of algorithms capable of processing large images with reduced computational costs. Numerical data on the geometry of the pores of the object under study are transformed into the geometry of bodies consisting of octagonal portions of cubic shape. Parametric porous objects can model both the shape and the isoparametric interior. Often, this type of parametric bodies is used as initial or boundary conditions in numerical modeling to demonstrate internal modeling. To form a body of complex shape, parametric solid-state elements can be connected together. The continuity between the elements can be determined in the same way as when modeling cubic parametric splines. A lot of research is devoted to the reconstruction of the geometric structure of porous materials based on digital images of objects for a better understanding and representation of physical processes in a porous medium. A detailed understanding of the microstructure can be used to determine physical properties, and then to evaluate and improve the characteristics of simulated objects and processes in them. The article presents the results of the proposed algorithm in the MathCAD environment and software processing of a porous body based on digital images.

The use of information and educational resources in the educational process

Currently, the training of specialists in credit technology, according to which students are taught at the University, determines the relevance of developing new conceptual foundations in the organization of educational and methodological work and the introduction of modern, information and communication technologies in the educational process. The above suggests the widespread use of information technology and personal computers to simulate various physical processes, both in the learning process and current control. The use of computers activates the process of studying the discipline by students, facilitates and accelerates the assimilation of new material and control, which ultimately improves the quality of education and deepens students’ knowledge. Therefore, the study of methods for modeling physical processes using modern application packages is currently the most relevant. In the article, for a visual representation of physical processes using the MathCad program, modelling of complex systems described by ordinary differential equations of the second order is given. Using this Mathcad package, a graph of forced oscillations without resistance in the presence of an external periodic force is obtained using the resolve function. The paper also talks about the use of physical models in the educational process.

Towards the development of an automated electrical self-potential sensor of melt and rainwater flow in snow

AbstractTo understand snow structure and snowmelt timing, information about flows of liquid water within the snowpack is essential. Models can make predictions using explicit representations of physical processes, or through parameterization, but it is difficult to verify simulations. In situ observations generally measure bulk quantities. Where internal snowpack measurements are made, they tend to be destructive and unsuitable for continuous monitoring. Here, we present a novel method for in situ monitoring of water flow in seasonal snow using the electrical self-potential (SP) geophysical method. A prototype geophysical array was installed at Col de Porte (France) in October 2018. Snow hydrological and meteorological observations were also collected. Results for two periods of hydrological interest during winter 2018–19 (a marked period of diurnal melting and refreezing, and a rain-on-snow event) show that the electrical SP method is sensitive to internal water flow. Water flow was detected by SP signals before it was measured in conventional snowmelt lysimeters at the base of the snowpack. This initial feasibility study shows the utility of the SP method as a non-destructive snow sensor. Future development should include combining SP measurements with a high-resolution snow physics model to improve prediction of melt timing.

Multi‐Scale Hydrologic Evaluation of the National Water Model Streamflow Data Assimilation

AbstractStreamflow predictions derived from a hydrologic model are subjected to many sources of errors, including uncertainties in meteorological inputs, representation of physical processes, and model parameters. To reduce the effects of these uncertainties and thus improve the accuracy of model prediction, the United States (U.S.) National Water Model (NWM) incorporates streamflow observations in the modeling framework and updates model‐simulated values using the observed ones. This updating procedure is called streamflow data assimilation (DA). This study evaluates the prediction performance of streamflow DA realized in the NWM. We implemented the model using WRF‐Hydro® with the NWM modeling elements and assimilated 15‐min streamflow data into the model, observed during 2016–2018 at 140 U.S. Geological Survey stream gauge stations in Iowa. In its current DA scheme, known as “nudging,” the assimilation effect is propagated downstream only, which allows us to assess the performance of streamflow predictions generated at 70 downstream stations in the study domain. These 70 locations cover basins of a range of scales, thus enabling a multi‐scale hydrologic evaluation by inspecting annual total volume, peak discharge magnitude and timing, and an overall performance indicator represented by the Kling–Gupta efficiency. The evaluation results show that DA improves the prediction skill significantly, compared to open‐loop simulation, and the improvements increase with areal coverage of upstream assimilation points.

Model inter-comparison for short-range forecasts over the southern African domain

Numerical weather prediction (NWP) models have been increasing in skill and their capability to simulate weather systems and provide valuable information at convective scales has improved in recent years. Much effort has been put into developing NWP models across the globe. Representation of physical processes is one of the critical issues in NWP, and it differs from one model to another. We investigated the performance of three regional NWP models used by the South African Weather Service over southern Africa, to identify the model that produces the best deterministic forecasts for the study domain. The three models – Unified Model (UM), Consortium for Small-scale Modelling (COSMO) and Weather Research and Forecasting (WRF) – were run at a horizontal grid spacing of about 4.4 km. Model forecasts for precipitation, 2-m temperature, and wind speed were verified against different observations. Snow was evaluated against reported snow records. Both the temporal and spatial verification of the model forecasts showed that the three models are comparable, with slight variations. Temperature and wind speed forecasts were similar for the three different models. Accumulated precipitation was mostly similar, except where WRF captured small rainfall amounts from a coastal low, while it over-estimated rainfall over the ocean. The UM showed a bubble-like shape towards the tropics, while COSMO cut-off part of the rainfall band that extended from the tropics to the sub-tropics. The COSMO and WRF models simulated a larger spatial coverage of precipitation than UM and snow-report records.Significance: Extreme weather events, such as tornadoes, floods, strong winds and heat waves, have significant impacts on society, the economy, infrastructure, agriculture and many other sectors. These impacts may be mitigated or even prevented through early warning systems which depend on the use of weather forecasts and information from NWP models. As South Africa depends on models from developed countries, these models may have shortcomings in capturing extreme weather events over the southern African region.

Validation of the PALM model system 6.0 in a real urban environment: a case study in Dejvice, Prague, the Czech Republic

Abstract. In recent years, the PALM 6.0 modelling system has been rapidly developing its capability to simulate physical processes within urban environments. Some examples in this regard are energy-balance solvers for building and land surfaces, a radiative transfer model to account for multiple reflections and shading, a plant-canopy model to consider the effects of plants on flow (thermo)dynamics, and a chemistry transport model to enable simulation of air quality. This study provides a thorough evaluation of modelled meteorological, air chemistry, and ground and wall-surface quantities against dedicated in situ measurements taken in an urban environment in Dejvice, Prague, the Czech Republic. Measurements included monitoring of air quality and meteorology in street canyons, surface temperature scanning with infrared cameras, and monitoring of wall heat fluxes. Large-eddy simulations (LES) using the PALM model driven by boundary conditions obtained from a mesoscale model were performed for multiple days within two summer and three winter episodes characterized by different atmospheric conditions. For the simulated episodes, the resulting temperature, wind speed, and chemical compound concentrations within street canyons show a realistic representation of the observed state, except that the LES did not adequately capture night-time cooling near the surface for certain meteorological conditions. In some situations, insufficient turbulent mixing was modelled, resulting in higher near-surface concentrations. At most of the evaluation points, the simulated surface temperature reproduces the observed surface temperature reasonably well for both absolute and daily amplitude values. However, especially for the winter episodes and for modern buildings with multilayer walls, the heat transfer through walls is not well captured in some cases, leading to discrepancies between the modelled and observed wall-surface temperature. Furthermore, the study corroborates model dependency on the accuracy of the input data. In particular, the temperatures of surfaces affected by nearby trees strongly depend on the spatial distribution of the leaf area density, land surface temperatures at grass surfaces strongly depend on the initial soil moisture, wall-surface temperatures depend on the correct setting of wall material parameters, and concentrations depend on detailed information on spatial distribution of emissions, all of which are often unavailable at sufficient accuracy. The study also points out some current model limitations, particularly the implications of representing topography and complex heterogeneous facades on a discrete Cartesian grid, and glass facades that are not fully represented in terms of radiative processes. Our findings are able to validate the representation of physical processes in PALM while also pointing out specific shortcomings. This will help to build a baseline for future developments of the model and improvements of simulations of physical processes in an urban environment.