Accurate modelling of soil and grass temperatures is essential for improving weather prediction models. The soil and grass temperatures are used to determine the surface temperature, which is a key parameter in latent and sensible heat flux calculations.

The surface temperature is often estimated using land surface parametrisation schemes, such as empirical skin resistance models. These parametrisations often lead to deviations and temporal shifts in the heat flux at the surface, causing a discrepancy in the closure of the surface energy balance (SEB) on short time scales. Addressing these inconsistencies requires a more refined approach to model heat transfer processes within the vegetation-soil continuum.

This research investigates the accuracy of a two-layer diffusive model with uniform thermal parameters in capturing temperature dynamics within the vegetation-soil continuum. The results indicate that a purely diffusive model accurately describes temperature dynamics within the soil. However, this approach is too simplistic to capture the complexity of heat transfer within the vegetation layer. Within the soil, the thermal diffusivity remains relatively constant over time. An optimal value is determined as $\kappa_{soil} = 3.0 \pm 0.3 \cdot 10 ^{-7} \text{ m}^2 \text{ s}^{-1}$, in line with values reported in previous research. In contrast, heat transfer within the grass is influenced by additional processes beyond pure diffusion. Preliminary analysis shows an improvement in the model performance with the introduction of a linear source term, likely accounting for radiative effects.

A diffusive approach to in-canopy heat transfer, combined with a source term, presents a promising step in describing the vegetation layer in surface heat transfer models. However, further research is necessary to refine the formulation of the source term, whether through a physically motivated or data-driven approach.

From a broader perspective, further additional observational and numerical research into the physical processes behind heat transfer within the grass layer is advised to assess their influence. Additionally, generalisation of the model will enhance its applicability in weather forecasting models to improve the prediction of thermal effects near the surface.

This study investigates pollutant dispersion from residential wood burning in a neighborhood in Utrecht, Netherlands, employing the Dutch Atmospheric Large-Eddy Simulation (DALES) model under various atmospheric conditions.
Residential wood combustion is a major source of urban air pollution, especially during winter months. This research aims to quantify the distribution and distance traveled by pollutants from their release source. By using DALES, detailed analyses of atmospheric variables and pollutant concentration fields are conducted, providing valuable insights into how atmospheric stability influences pollutant spread.
The results show that atmospheric stability significantly affects pollutant dispersion. Higher pollutant concentrations were generally observed near the surface under stable and very stable conditions as compared to neutral conditions, due to restricted vertical motions that limit the vertical dispersion of pollutants. Additionally, under stable conditions, pollutant concentrations remained elevated farther from the source, affecting residents who do not live close to the emission source. The study also compared the performance of DALES with commonly used Gaussian plume models (GPMs) to evaluate their performance in urban environments and under different atmospheric conditions. Three schemes that provide the dispersion parameters for the GPMs are tested to determine their accuracy in representing the DALES results. The comparison reveals that, while GPMs offer a general overview of pollutant distribution, they often fail to accurately capture concentration decay rates or the spatial extent of the plume.
The study concludes that further research should investigate the impact of atmospheric stability on air pollutant dispersion in urban environments. It also highlights the limitations of Gaussian Plume Models (GPMs), which often simplify processes occurring in the boundary layer. In urban settings, where the urban geometry plays a significant role in pollutant dispersion, DALES proves to be more effective than GPMs, which cannot accurately capture the effects of buildings on the plume.

In recent years, record-breaking wildfires have occurred globally, with projections indicating a dramatic increase in their frequency and intensity in the future. These wildfires present serious risks to the environment by releasing harmful pollutants and various greenhouse gases, which significantly contribute to air pollution and climate change. To accurately predict emissions of such gases, a comprehensive understanding of combustion efficiency is essential. Due to TROPOMI’s capability to measure trace gases such as NO2 and CO with high spatial resolution and global coverage, it has been used in various studies to analyse combustion efficiency. The study used NO2 and CO column concentrations measured by TROPOMI to estimate Mole Density Ratio (MDR), which is a proxy of combustion efficiency, over two devastating wildfires that occurred in California in 2020. By using TROPOMI data, aggregated to various resolutions using the super-observation approach, the study assessed the spatial and temporal limits of TROPOMI-derived MDR. It evaluated changes in MDR values across various vegetation types by integrating higher resolution land classification data from MODIS. Additionally, it explored the relationship between MDR and environmental indicators such as drought conditions and soil moisture. Super-observations resulted in significantly different MDR values with those estimated at TROPOMI resolution. The findings indicated that there was loss of information regarding MDR when super-observations were used. Furthermore, there was no clear link found on the impact of environmental factors such as soil moisture and drought conditions on MDR. Finally, a detailed land use characterisation provided deeper insights into the effect of burning various types of vegetation on the MDR. However, to be able to fully interpret the effect of super-observations and environmental factors
on MDR, a more extensive analysis is suggested.

This thesis contributes to the scientific underpinning of the battle against fruit frost. Fruit frost is the freezing damage to blossoms when in the growing season the night temperature drops below 0±C. This results in damaged or undeveloped fruits, and a yield loss for the fruit farmer. Several techniques against fruit frost have been developed, including sprinkling and wind machines, often in combination with meteorological models, for example, to predict air temperature. However, the contribution of heat exchange with the soil to moderate orchard temperatures is often not included. In this thesis, this heat transfer is investigated, as an increase of heat transfer from the soil to the orchard during the night is a potential remedy against fruit frost. The research is based on measurements for soil temperature, soil heat flux, and soil moisture from two locations (1. Haarweg (Gelderland), The Netherlands 2. Bushland (Texas), The U.S.A.). First, a numerical model is developed to calculate the temperature and soil heat flux profiles for a soil layer. The results are compared to the results of an already developed analytical model. Second, the thermal parameters, that are of influence on the heat transfer, are analyzed by assessing a) their robustness in relation to the model and b) their relation to soil moisture. Because a numerical model is more flexible for shorter periods of data compared to an analytical model (because of underlying assumptions), it can be used to relate the parameters to (daily) varying soil moisture. Third, the numerical model for heat transfer is extended to the vegetation layer, and, again, the results are compared to analytical results. The model is created by assuming homogeneity in both separate layers and by discretizing the governing heat equation over the domain. The results show that the model reproduces temperature and soil heat flux in the soil layer with similar accuracy as the analytical, harmonic model. One thermal parameter, the diffusivity, is robust and does not show a clear dependency on soil moisture. The model is however sensitive to deviations in the other parameter, the heat conductivity. The model shows a clear relation between conductivity and soil moisture, and from this, a site-specific quantitative relation is determined. This relation however is only valid in the investigated region of moisture variation and we recommend future research to cover data in a broader range of soil moisture. Overall, we conclude that the model successfully reproduced the temperature and soil heat flux throughout the full vegetation-soil continuum.