Les modèles de prévision opérationnelle du temps résolvent numériquement les équations de la mécanique des fluides en calculant l'évolution de champs (pression, température, humidité, vitesses) définis comme moyennes horizontales à l'échelle des mailles d'une grille (et à différents niveaux verticaux). Les processus d'échelle inférieure à la maille jouent néanmoins un rôle essentiel dans les transferts et les bilans de chaleur, humidité et quantité de mouvement. Les paramétrisations physiques visent à évaluer les termes de source correspondant à ces phénomènes, et apparaissant dans les équations des champs moyens aux points de grille. Lorsque l'on diminue la taille des mailles afin de représenter plus finement l'évolution des phénomènes atmosphériques, certaines hypothèses utilisées dans ces paramétrisations perdent leur validité. Le problème se pose surtout quand la taille des mailles passe en dessous d'une dizaine de kilomètres, se rapprochant de la taille des grands systèmes de nuages convectifs (systèmes orageux, lignes de grain). Ce travail s'inscrit dans le cadre des développements du modèle à mailles fines ARPÈGE ALADIN, utilisé par une douzaine de pays pour l'élaboration de prévisions à courte échéance (jusque 48 heures). Nous décrivons d'abord l'ensemble des paramétrisations physiques du modèle. Suit une analyse détaillée de la paramétrisation actuelle de la convection profonde. Nous présentons également notre contribution personnelle à celle ci, concernant l'entraînement de la quantité de mouvement horizontale dans le nuage convectif. Nous faisons ressortir les principaux points faibles ou hypothèses nécessitant des mailles de grandes dimensions, et dégageons les voies pour de nouveaux développements. Nous approfondissons ensuite deux des aspects sortis de cette discussion: l'usage de variables pronostiques de l'activité convective, et la prise en compte de différences entre l'environnement immédiat du nuage et les valeurs des champs à grande échelle. Ceci nous conduit à la réalisation et la mise en œuvre d'un schéma pronostique de la convection profonde. A ce schéma devraient encore s'ajouter une paramétrisation pronostique des phases condensées suspendues (actuellement en cours de développement par d'autres personnes) et quelques autres améliorations que nous proposons. Des tests de validation et de comportement du schéma pronostique ont été effectués en modèle à aire limitée à différentes résolutions et en modèle global. Dans ce dernier cas l'effet du nouveau schéma sur les bilans globaux est également examiné. Ces expériences apportent un éclairage supplémentaire sur le comportement du schéma convectif et les problèmes de partage entre la schéma de convection profonde et le schéma de précipitation de grande échelle. La présente étude fait donc le point sur le statut actuel des différentes paramétrisations du modèle, et propose des solutions pratiques pour améliorer la qualité de la représentation des phénomènes convectifs. L'utilisation de mailles plus petites que 5 km nécessite enfin de lever l'hypothèse hydrostatique dans les équations de grande échelle, et nous esquissons les raffinements supplémentaires de la paramétrisation possibles dans ce cas.

A well-represented description of convection in weather and climate models is essential since convective clouds strongly influence the climate system. Convective processes interact with radiation, redistribute sensible and latent heat and momentum, and impact hydrological processes through precipitation. Depending on the models’ horizontal resolution, the representation of convection may look very different. However, the convective scales not resolved by the model are traditionally parameterized by an ensemble of non-interacting convective plumes within some area of uniform forcing, representing the “large scale”. A bulk representation of the mass-flux associated with the individual plumes in the defined area provide the statistical effect of moist convection on the atmosphere. Studying the characteristics of the ECMWF ensemble prediction system it is found that the control forecast of the ensemble system is not variable enough in order to yield a sufficient spread using an initial perturbation technique alone. Such insufficient variability may be addressed in the parameterizations of, for instance, cumulus convection where the sub-grid variability in space and time is traditionally neglected. Furthermore, horizontal transport due to gravity waves can act to organize deep convection into larger scale structures which can contribute to an upscale energy cascade. However, horizontal advection and numerical diffusion are the only ways through which adjacent model grid-boxes interact in the models. The impact of flow dependent horizontal diffusion on resolved deep convection is studied, and the organization of convective clusters is found very sensitive to the method of imposing horizontal diffusion. However, using numerical diffusion in order to represent lateral effects is undesirable. To address the above issues, a scheme using cellular automata in order to introduce lateral communication, memory and a stochastic representation of the statistical effects of cumulus convection is implemented in two numerical weather models. The behaviour of the scheme is studied in cases of organized convective squall-lines, and initial model runs show promising improvements. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Submitted. </p>

This thesis presents an investigation of ensemble Gaussianity, the effect of non- Gaussianity on covariance structures, storm-centered data assimilation techniques, and the relationship between commonly used data assimilation variables and the underlying dynamics for the case of Hurricane Humberto. Using an Ensemble Kalman Filter (EnKF), a comparison of data assimilation results in Storm-centered and Eulerian coordinate systems is made. In addition, the extent of the non-Gaussianity of the model ensemble is investigated and quantified. The effect of this non-Gaussianity on covariance structures, which play an integral role in the EnKF data assimilation scheme, is then explored. Finally, the correlation structures calculated from a Weather Research Forecast (WRF) ensemble forecast of several state variables are investigated in order to better understand the dynamics of this rapidly intensifying cyclone. Hurricane Humberto rapidly intensified in the northwestern Gulf of Mexico from a tropical disturbance to a strong category one hurricane with 90 mph winds in 24 hours. Numerical models did not capture the intensification of Humberto well. This could be due in large part to initial condition error, which can be addressed by data assimilation schemes. Because the EnKF scheme is a linear theory developed on the assumption of the normality of the ensemble distribution, non-Gaussianity in the ensemble distribution used could affect the EnKF update. It is shown that multiple state variables do indeed show significant non-Gaussianity through an inspection of statistical moments. In addition, storm-centered data assimilation schemes present an alternative to traditional Eulerian schemes by emphasizing the centrality of the cyclone to the assimilation window. This allows for an update that is most effective in the vicinity of the storm center, which is of most concern in mesoscale events such as Humberto. Finally, the effect of non-Gaussian distributions on covariance structures is examined through data transformations of normal distributions. Various standard transformations of two Gaussian distributions are made. Skewness, kurtosis, and correlation between the two distributions are taken before and after the transformations. It can be seen that there is a relationship between a change in skewness and kurtosis and the correlation between the distributions. These effects are then taken into consideration as the dynamics contributing to the rapid intensification of Humberto are explored through correlation structures.

No description available.

Accurate, fast detection of clouds in satellite imagery has many applications, for example Numerical Weather Prediction (NWP) and climate studies of both the atmosphere and of the Earth’s surface temperature. Most operational techniques for cloud detection rely on the differences between observations of cloud and of clear-sky being more or less constant in space and in time. In reality, this is not the case - different clouds have different spectral properties, and different cloud types are more or less likely in different places and at different times, depending on atmospheric conditions and on the Earth’s surface properties. Observations of clear sky also vary in space and time, depending on atmospheric and surface conditions, and on the presence or absence of aerosol particles. The Bayesian approach adopted in this project allows pixel-specific physical information (for example from NWP) to be used to predict pixel-specific observations of clear sky. A physically-based, spatially- and temporally-specific probability that each pixel contains a cloud observation is then calculated. An advantage of this approach is that identification of ambiguously classed pixels from a probabilistic result is straightforward, in contrast to the binary result generally produced by operational techniques. This project has developed and validated the Bayesian approach to cloud detection, and has extended the range of applications for which it is suitable, achieving skills scores that match or exceed those achieved by operational methods in every case. High temperature gradients can make observations of clear sky around ocean fronts, particularly at thermal wavelengths, appear similar to cloud observations. To address this potential source of ambiguous cloud detection results, a region of imagery acquired by the AATSR sensor which was noted to contain some ocean fronts, was selected. Pixels in the region were clustered according to their spectral properties with the aim of separating pixels that correspond to different thermal regimes of the ocean. The mean spectral properties of pixels in each cluster were then processed using the Bayesian cloud detection technique and the resulting posterior probability of clear then assigned to individual pixels. Several clustering methods were investigated, and the most appropriate, which allowed pixels to be associated with multiple clusters, with a normalized vector of ‘membership strengths’, was used to conduct a case study. The distribution of final calculated probabilities of clear became markedly more bimodal when clustering was included, indicating fewer ambiguous classifications, but at the cost of some single pixel clouds being missed. While further investigations could provide a solution to this, the computational expense of the clustering method made this impractical to include in the work of this project. This new Bayesian approach to cloud detection has been successfully developed by this project to a point where it has been released under public license. Initially designed as a tool to aid retrieval of sea surface temperature from night-time imagery, this project has extended the Bayesian technique to be suitable for imagery acquired over land as well as sea, and for day-time as well as for night-time imagery. This was achieved using the land surface emissivity and surface reflectance parameter products available from the MODIS sensor. This project added a visible Radiative Transfer Model (RTM), developed at University of Edinburgh, and a kernel-based surface reflectance model, adapted here from that used by the MODIS sensor, to the cloud detection algorithm. In addition, the cloud detection algorithm was adapted to be more flexible, making its implementation for data from the SEVIRI sensor straightforward. A database of ‘difficult’ cloud and clear targets, in which a wide range of both spatial and temporal locations was represented, was provided by M´et´eo-France and used in this work to validate the extensions made to the cloud detection scheme and to compare the skill of the Bayesian approach with that of operational approaches. For night land and sea imagery, the Bayesian technique, with the improvements and extensions developed by this project, achieved skills scores 10% and 13% higher than M´et´eo-France respectively. For daytime sea imagery, the skills scores were within 1% of each other for both approaches, while for land imagery the Bayesian method achieved a 2% higher skills score. The main strength of the Bayesian technique is the physical basis of the differentiation between clear and cloud observations. Using NWP information to predict pixel-specific observations for clear-sky is relatively straightforward, but making such predictions for cloud observations is more complicated. The technique therefore relies on an empirical distribution rather than a pixel-specific prediction for cloud observations. To try and address this, this project developed a means of predicting cloudy observations through the fast forward-modelling of pixel-specific NWP information. All cloud fields in the pixel-specific NWP data were set to 0, and clouds were added to the profile at discrete intervals through the atmosphere, with cloud water- and ice- path (cwp, cip) also set to values spaced exponentially at discrete intervals up to saturation, and with cloud pixel fraction set to 25%, 50%, 75% and 100%. Only single-level, single-phase clouds were modelled, with the justification that the resulting distribution of predicted observations, once smoothed through considerations of uncertainties, is likely to include observations that would correspond to multi-phase and multi-level clouds. A fast RTM was run on the profile information for each of these individual clouds and cloud altitude-, cloud pixel fraction- and channel-specific relationships between cwp (and similarly cip) and predicted observations were calculated from the results of the RTM. These relationships were used to infer predicted observations for clouds with cwp/cip values other than those explicitly forward modelled. The parameters used to define the relationships were interpolated to define relationships for predicted observations of cloud at 10m vertical intervals through the atmosphere, with pixel coverage ranging from 25% to 100% in increments of 1%. A distribution of predicted cloud observations is then achieved without explicit forward-modelling of an impractical number of atmospheric states. Weights are applied to the representation of individual clouds within the final Probability Density Function (PDF) in order to make the distribution of predicted observations realistic, according to the pixel-specific NWP data, and to distributions seen in a global reference dataset of NWP profiles from the European Centre for Medium Range Weather Forecasting (ECMWF). The distribution is then convolved with uncertainties in forward-modelling, in the NWP data, and with sensor noise to create the final PDF in observation space, from which the conditional probability that the pixel observation corresponds to a cloud observation can be read. Although the relatively fast computational implementation of the technique was achieved, the results are disappointingly poor for the SEVIRI-acquired dataset, provided by M´et´eo-France, against which validation was carried out. This is thought to be explained by both the uncertainties in the NWP data, and the forward-modelling dependence on those uncertainties, being poorly understood, and treated too optimistically in the algorithm. Including more errors in the convolution introduces the problem of quantifying those errors (a non-trivial task), and would increase the processing time, making implementation impractical. In addition, if the uncertianties considered are too high then a PDF flatter than the empirical distribution currently used would be produced, making the technique less useful.

In 2006 a Numerical Weather Prediction (NWP) model known as the Unified Model (UM) from the United Kingdom Meteorological Office (UK Met Office) was installed at the South African Weather Service (SAWS). Since then it has been used operationally at SAWS, replacing the Eta model that was previously used. The research documented in this dissertation was inspired by the need to verify the performance of the UM in simulating and predicting weather over South Africa. To achieve this aim, three model configurations of the UM were compared against each other and against observations. Verification of rainfall as well as minimum and maximum temperature for the year 2008 was therefore done to achieve this. 2008 is the first year since installation, where all the configurations of the UM used in the study are present. For rainfall verification the model was subjectively verified using the eyeball verification for the entire domain of South Africa, followed by objective verification of categorical forecasts for rainfall regions grouped according to standardized monthly rainfall totals obtained by cluster analysis and finally objective verification using continuous variables for selected stations over South Africa. Minimum and maximum temperatures were subjectively verified using the eyeball verification for the entire domain of South Africa, followed by objective verification of continuous variables for selected stations over South Africa, grouped according to different heights above mean sea level (AMSL). Both the subjective and objective verification of the three model configurations of the UM (for both rainfall as well as the minimum and maximum temperatures) suggests that 12km UM simulation with DA gives better and reliable results than the 12km and 15km UM simulations without DA. It was further shown that although there was no significant difference between the model outputs from the 12km and the 15km UM without DA, the 15km UM simulation without DA, proved to me more reliable and accurate than the 12km UM simulation without DA in simulating minimum and maximum temperatures over South Africa, on the other hand the 12km UM simulation without DA is more reliable and accurate than the 15km UM simulation without DA in simulating rainfall over South Africa. / Dissertation (MSc)--University of Pretoria, 2013. / gm2014 / Geography, Geoinformatics and Meteorology / unrestricted

The topography of South Africa (SA) shows complex variations and is one the main factors that determine the daily weather patterns and climate characteristics. It affects for example temperature, winds and rainfall (intensity and distribution). Mesoscale numerical weather prediction (NWP) models are used to simulate atmospheric motions with high horizontal grid resolution using appropriate cumulus parameterisation schemes. They also allow users to investigate the effects of topography and surface heating on the development of convective systems. The Weather Research and Forecasting (WRF) model was applied over the complex terrain of SA to simulate extreme weather events and evaluate the influence of topography and grid resolution on the accuracy of weather simulations. This includes heavy precipitation event that lead to floods over Limpopo region of SA which was caused by the tropical depression Dando for the period 16 -18 January 2012; the heat wave events over Limpopo region for the period 22-26 October 2011 and also over Cape region for the period 15-18 January 2012. The Grell-Devenyi Ensemble (GDE) cumulus parameterization scheme was applied. The WRF model was run at a horizontal resolution of 9 km with 3 km nests, one over Limpopo and another over Cape region respectively. A total of 210 South African Weather Service (SAWS) synoptic stations data were used to verify the model, with 37 stations located over Limpopo and 88 over Cape region. The WRF model simulations are able to capture the spatial and temporal distribution of the heat wave over Limpopo and Cape regions respectively. The model verification with observational data showed that the performance statistics are in the expected range. The experiments without topography give unrealistic verification scores. The increase of model grid resolution from 9 to 3 km improved the spatial and temporal distribution and performance statistics. The above findings are in general similar for the two heat wave events, although the influence of topography over Cape region is not too pronounced. This can be attributed to different topographic variations over the Cape region as compared to the Limpopo region. The WRF model captured well the spatial and temporal distribution of rainfall patterns; verification statistics shows over-prediction of its intensity in simulation with topography. The simulation without topography shows unrealistic space and intensity of rain distribution. An increase in model grid resolution from 9 to 3 km shows improved spatial and temporal distribution of rainfall. The importance of high grid resolution and the use of non-hydrostatic equations are confirmed by the analysis of the vertical velocity distribution and moisture fluxes. The overall findings proved that topography plays a major role to weather and climate over SA. The high grid resolution allows for a better topography representation and capturing convective activities by the use of nonhydrostatic approximations. Therefore the WRF model proved to be useful forecasting tool for weather and climate simulations and can be used for operational weather forecasting over South Africa. / Dissertation (MSc)--University of Pretoria, 2014. / lk2014 / Geography, Geoinformatics and Meteorology / MSc / Unrestricted

<p>Urban meteorology has developed in parallel to other sub-fields in the science, but in many ways remains poorly described. In particular, the study of urban rainfall modification remains behind compared to other comparable features. Urban rainfall modification refers to the change of a precipitation feature as it crosses an urban area. Typically, this manifests as rainfall initiation, local suppression, local invigoration, and/or storm morphology changes. Research in the prior decades have shown urban rainfall modification to arise from a combination of land-atmosphere and aerosol-cloud interaction. Urban areas create a greater surface roughness, which produces local convergence and divergence, modifying local thunderstorm inflow and morphology. The land surface also generates vertical velocity perturbations which can act to initiate or modify existing convection. Urban aerosols act as CCN to perturb existing cloud and precipitation characteristics. Higher CCN narrows the cloud droplet distribution, creating more smaller cloud droplets, and initially reducing precipitation efficiency by keeping more liquid water in the cloud than what would form into rain. The CCN-cloud interaction eventually increasing heavy rainfall production as graupel riming is enhanced by the narrower cloud droplet distribution, leading to more larger raindrops and higher rain in areas.</p><p>This dissertation addresses the observation and modeling of urban thunderstorm interaction from both the land surface and aerosol perspective. It reassesses the original urban rainfall anomaly: The La Porte Anomaly. First analyzed in the late 1960s, the La Porte Anomaly was ultimately dismissed by 1980 as either a temporary, biased, or otherwise unexplainable observation, as the process level understanding had yet to be explained. The contemporary analysis utilizes all existing data and objective optimal interpolation to show that a rainfall anomaly downwind of Chicago has indeed existed at least since the 1930s. The current rainfall anomaly exists as a broad region of warm season rainfall downwind of Chicago that is 20-30% greater than the regional average. Using synoptic parameters, the rainfall anomaly is shown to be independent of wind direction and most closely associated with local land surface forcing. Weekdays, where local aerosol loading has been measured at 40% or more greater than weekends, have up to 50% more warm season rainfall than weekends. The analysis is able to show that there is a land surface and aerosol contribution to the rainfall anomaly, but cannot unambiguously separate them.</p><p>In order to separate the land surface and aerosol effects on urban rainfall distribution, a numerical model was improved to better handle urban weather interaction. The Regional Atmospheric Modeling System (RAMS 6.0) was chosen for its base land surface and cloud physics parameterization. The Town Energy Budget (TEB) urban canopy model was coupled to RAMS to handle the urban land surface. The Simple Photochemical Module (SPM) was coupled with the cloud physics to handle conversion of surface emissions to CCN. The model utilized an external traffic simulation to create a realistic diurnal and weekly cycle of surface emissions, based on human behavior. The new Urban RAMS was used to study the land surface sensitivity of city size and of aerosol loading in two studies using the Real Atmosphere Idealized Land surface (RAIL) method, by which all non-urban features of the land surface are removed to isolate the urban effects. The city size study determined that the land surface of a given city eventually has a maximum effect on thunderstorm modifying potential, and that rainfall does not continue to increase or decrease locally for cities larger than a certain size based on that storm’s own motion. The aerosol-cloud analysis corroborated previous observations on the non-linear effects of aerosol loading on clouds. It also demonstrated that understanding the aerosol effect in an urban environment requires high resolution observations of precipitation change. In a single thunderstorm, regions can be both impacted by local rainfall rate increases and decreases from urban aerosols, leading to little total change in precipitation. But the rainfall rate changes can significantly affect soil moisture and drought potential in and around urban areas.Following the idealized studies, the historical and current La Porte Anomaly was simulated to separate the land surface from the aerosol factors near the Chicago area. The Urban RAMS model was deployed on a real land surface with full model physics. Simulations with 1932, 1962, 1992, and 2012 land covers were run over an exceptionally wet Aug. 2007 to approximate the rain variability for an entire summer season. Surface emissions were also varied in the 2012 land cover for variable aerosol loading. The simulations successfully reproduced the location of the downwind rainfall anomaly in each land cover scenario: farther east toward La Porte in 1932, moving southwestward to its current location by 2012. Doubling surface emissions eliminated the downwind anomaly, as was observed during the highest pollution decade of the 1970s. Eliminating surface emissions also decreased the downwind anomaly. As the land cover at the upwind edge of Chicago became more connected from the 1932 to 2012 land cover scenarios, a local upwind rainfall anomaly developed, moving westward with urban expansion. The results of these simulations enabled the conclusions that a) at the upwind edge, the land surface dominates urban rainfall modification, b) the aerosol loading sustains and increases the locally downwind rainfall increase, and c) that the total modification distance is static on given day and given urban footprint. A more expansive city does not produce a rainfall anomaly more distantly downwind, but rather the distance of rainfall modification moves to where the upwind edge of the city begins.</p><p></p><p>The modeling work ends with a two-city simulation in the southeast United States, of a bow-echo forming near Memphis, TN and crossing Birmingham, AL before splitting. Simulations were performed on different surface emissions rates, land covers where Birmingham did not exist, and a novel approach with two inner emitting grids over both Birmingham and Memphis. A storm tracking algorithm enabled one-to-one comparisons of point simulated storm characteristics between scenarios. The results of most scenarios only corroborated previous research, showing how increased aerosol loading changes cloud and rainfall characteristics until the highest aerosol loading shuts down riming and rainfall enhancement. However, the two most accurate simulations, where the storm forms and splits over Birmingham, were a non-urban higher rural aerosol scenario and the scenario with Memphis also emitting pollution. In order to split the storm over Birmingham, the upwind cloud characteristics were primed by higher upwind aerosols, either from a realistic city upwind or unrealistically high rural aerosols. The conclusions produced by this study demonstrated the importance of aerosol cloud interaction, perhaps equal with land surface, but also the need for far upwind information for a storm in a given city. Memphis and Birmingham are separated by over 300km, far exceeding the threshold thought to connect two cities by mutual rainfall modification.</p><p>The overall conclusions of the research presented in this dissertation shows a more unified approach to the effects of urban rainfall modification. The upwind edge of a city is a fixed location, and a thunderstorm begins modifying at that point. The thunderstorm usually produces a local rainfall maximum at the upwind edge, due to the vertical velocity of the urban land surface. The urban aerosols proceed to narrow the cloud droplet distribution, locally reducing rainfall as the storm passes over the urban area. Eventually the enhanced rainfall from enhanced riming produces a maximum somewhere downwind. However, “downwind” is a location relative to the storm’s motion and could exist anywhere over the urban footprint or downwind in a rural region. The climatological location of increased rainfall is an average of every storm in a season and beyond. The results of each part of the study provide a way to continue the research presented here.</p><br>

<p>Storms in the SE-US often evolve in different environments than those in the central Plains. Many poorly understood aspects of these differing environments may impact the tornadic potential of SE-US storms. Among these differences are potential variations in the CCN concentration owing to differences in land cover, combustion, industrial and urban activity, and proximity to maritime environments. The relative influence of warm and cold rain processes is sensitive to CCN concentration, with higher CCN concentrations producing smaller cloud droplets and more efficient cold rain processes. Cold rain processes result in DSDs with relatively larger drops from melting ice compared to warm rain processes. Differences in DSDs impact cold pool and downdraft size and strength, that influence tornado potential. This study investigates the impact of CCN concentration on DSDs in the SE-US by comparing DSDs from ARPS-EnKF model analyses and forecasts to observed DSDs from portable disdrometer-equipped probes collected by a collaboration between Purdue University, the University of Oklahoma (OU), the National Severe Storms Laboratory (NSSL), and the University of Massachusetts in a tornadic QLCS on 30 April 2017 during VORTEX-SE.</p><p>The ARPS-EnKF configuration, which consists of 40 ensemble members, is used with the NSSL triple-moment microphysics scheme. Surface and radar observations are both assimilated. Data assimilation experiments with CCN concentrations ranging from 100 cm^-3 (maritime) to 2,000 cm^-3 (continental) are conducted to characterize the variability of DSDs and the model output DSDs are verified against the disdrometer observations. The sensitivity of the DSD variability to CCN concentrations is evaluated. Results indicate continental CCN concentrations (close to CCN 1,000 cm³) produce DSDs that align closest to the observed DSDs. Other thermodynamic variables also accord better to observations in intermediate CCN concentration environments.</p>

Numerical weather prediction (NWP) and climate models have shown to have a challenge to correctly simulate stable boundary layers and diurnal cycles. This aim of this study is to evaluate, describe and give suggestions for improvements of the descriptions of stable boundary layers in operational NWP models. Two papers are included. Paper I focuses on the description of the surface and the interactions between the surface and the boundary layer in COAMPSR, a regional NWP model. The soil parametrization showed to be of great importance to the structure of the boundary layer. Moreover, it showed also that a low frequency of radiation calculations caused a bias in received solar energy at the surface. In paper II, the focus is on the formulation of the turbulent transport in stable boundary layers. There, an implementation of a diffusion parametrization based on the amount of turbulent kinetic energy (TKE) is tested in a single column model (SCM) version of the global NWP model Integrated Forecast System (IFS). The TKE parametrization turned out to behave similarly as the currently operational diffusion parametrization in convective regimes and neutral regimes, but showed to be less diffusive in weakly stable and stable conditions. The formulations of diffusion also turned out to be very dependent on the length scale formulation. If the turbulence and the gradients of wind temperature and wind are weak, the magnitude of turbulence can enter an oscillating mode. This oscillation can be avoided with the use of a lower limit of the length scale. / Det har visat sig att det är en stor utmaning för numeriska väderprognosmodeller (NWP-modeller) att simulera stabilt skiktade atmosfäriska gränsskikt och gränsskiktets dygnscykel på ett korrekt sätt. Syftet med denna studien är att utvärdera, beskriva och ge förslag på förbättringar av beskrivningen av gränsskiktet i NWP-modeller. Studien innehåller två artiklar. Den första fokuserar på beskrivningen av markytan och interaktionen mellan marken och gränsskiktet i den regionala NWP-modellen COAMPS R . Det visade sig att beskrivningen av markytan har en signifikant inverkan på gränsskiktets struktur. Det framkom också att strålningsberäkningarna endast görs en gång i timmen vilket bland annat orsakar en bias i inkommande solinstrålning vid markytan. Den andra artikeln fokuserar på beskrivningen av den turbulenta transporten i stabila skiktade gränsskikt. En implemenering av en diffusionsparametrisering som bygger på turbulent kinetisk energy (TKE) testas i en endimensionell version av NWP-modellen Integrated Forecast System (IFS), utvecklat vid European Center for Medium Range Weather Forecasts (ECMWF). Den TKE-baserade diffussionsparametriseringen är likvärdigt med den nuvaran de operationella parametriseringen i neutrala och konvektiva gränsskikt, menär mindre diffusivt i stabila gränsskikt. Diffusionens intensitet är beroende påden turbulenta längdskalan. Vidare kan turbulensen i TKE-formuleringen hamna i ett oscillerande läge om turbulensen är svag samtidigt som temperatur- och vindgradienten är kraftig. Denna oscillation kan förhindras om längdskalans minsta tillåtna värde begränsas.