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Understanding the physical processes that affect the turbulent structure of the nocturnal urban boundary layer is essential for improving forecasts of air quality and the air temperature in urban areas. Low-level jets have been shown to affect turbulence in the nocturnal urban boundary layer. We investigate the interaction of a mesoscale low-level jets with the urban boundary layer during a 60-h case study. We use observations from two Doppler lidars and results from two high-resolution numerical-weather-prediction models (Weather Research and Forecasting model, and the Met Office Unified Model for limited-area forecasts for the U.K.) to study differences in the occurrence frequency, height, wind speed, and fall-off of low-level jets between an urban (London, U.K.) and a rural (Chilbolton, U.K.) site. The low-level jets are elevated (≈ 70 m) over London, due to the deeper urban boundary layer, while the wind speed and fall-off are slightly reduced with respect to the rural low-level jets. Utilizing two idealized experiments in the WRF model, we find that topography strongly affects low-level jets characteristics, but there is still a substantial urban influence. Finally, we find that the increase in wind shear under the low-level jets enhances the shear production of turbulent kinetic energy and helps to maintain the vertical mixing in the nocturnal urban boundary layer.Reference:Tsiringakis, A., Theeuwes, N.E., Barlow, J.F., GJ. Steeneveld, 2022: Interactions Between the Nocturnal Low-Level Jets and the Urban Boundary Layer: A Case Study over London. Boundary-Layer Meteorol 183, 249–272. https://doi.org/10.1007/s10546-021-00681-7

WG3 discussed both the pros and cons of existing schemes as Working group 1 considered the treatment of model uncertainty (MU) in high-resolution ensembles, at grid spacings of order 1-5 km. These systems are often run for regional weather forecasting, perhaps over a single country, and for lead times of up to 5 days. Looking ahead, ECMWF’s strategy seeks to deliver global medium-range ensemble forecasts with 3-4 km grid spacings by 2030. It is questionable for what grid spacing we should dispense with a deep convection parameterization, but it will be either switched off or damped in these systems, such that deep convection can be assumed to be dominated by explicit motions. One of the problems with limited-area ensemble systems at this scale is that spread depends not only on the modelling system itself but also on the variability inherited from the large-scale boundary conditions. There is often thought to be a lack of spread in our high-resolution EPS (ensemble prediction systems), but this could reflect a lack of diversity on larger scales. The relative importance of lateral-boundary diversity and the model uncertainty mechanisms is regime dependent. The lateral boundaries will generally be more important in midlatitude winter but less so for summertime convection in relatively weak synoptic flow.

In an ensemble prediction system (EPS) the uncertainty in the initial atmospheric conditions is usually represented via perturbation of the initial atmospheric state and different boundary conditions at the beginning and throughout the duration of the forecast. These approaches exclude the uncertainty due to the representation of physical processes within the parameterization schemes of a numerical weather prediction model (NWP). Much of the uncertainty in the presentation of physical process arises from uncertain parameter values regulating key physical processes in the boundary-layer and microphysics schemes. This uncertainty can be represented with a Stochastically Perturbed Parameterization (SPP) scheme, where parameter values for the different model grid points are randomly selected from a defined probability density function. The SPP scheme can improve model performance and increase ensemble spread, but may lead to unrealistic parameter values, which can introduce additional model bias. A potential solution is to use coupled/correlated perturbations for relevant SPP parameters to increase the model performance and ensemble spread, while maintaining physically realistic ranges for the parameters. In this study, we investigate the impact of coupled perturbations in key parameters within the boundary-layer and microphysics schemes of the HarmonEPS model using the new SPP scheme. The performance of the coupled perturbations experiment is evaluated against HarmonEPS experiments using independent parameter perturbations, and perturbations in the initial atmospheric state and boundary conditions for both a winter and a summer period. We find that coupled perturbations in the SPP scheme can decrease model bias and increase the ensemble spread for the 2m temperature and relative humidity, 10m-wind speed and total cloud cover.

The surface energy balance is different over cities compared to the surrounding rural areas due to differences in heat storage, evapo-transpiration and anthropogenic activities. Consequently, the urban boundary layer varies substantially in its structure from its rural counterpart. This has implications for the interactions between urban areas and meso-scale atmospheric circulations. Understanding these interactions is essential to improve the representation of urban surface process in urban canopy models (UCMs) and their coupling with numerical weather prediction (NWP) models. This can lead to better weather forecasts for urban areas, with consequences for human health and economic activities in cities.The skill of a UCM in quantifying the surface energy balance strongly depends on the accurate prescription of many surface parameters, including some that are notoriously difficult to estimate (albedo, thermal conductivity, heat capacities etc.). Therefore, offline optimisation approaches for these parameters are often used, but these optimizations exclude the effects of the urban surface-atmosphere feedback mechanisms.In Chapter 1 we identify the feedback mechanisms and their importance on the surface energy balance using two modelling setup, the off-line single-layer urban canopy model (SLUCM) and the SLUCM coupled to the single-column version of the Weather Research and Forecasting (1D-WRF) model. The response of the modelled energy balanced to changes in uncertain surface parameters is investigated, during 2 summer days over London. The model responds differently to changes in surface parameter when coupled to the atmosphere. The turbulent heat flux shows up to 22% lower variability in the on-line setup, because near surface potential temperature gradient and atmospheric stability are altered. Consequently, the energy is directed toward the urban fabric changing the variability of the storage heat flux up to 50% compared to the off-line model. Moreover, entrainment of heat and moisture affect the near surface humidity altering the latent heat flux an effect that does not occur when the model is in offline mode.Once a UCM is coupled to the atmosphere, forcing provided by other parts of the atmospheric model is needed to compute the surface energy balance. This forcing comes in the form of incoming short- and long-wave radiation and advection of heat moisture and momentum. However, the atmospheric forcing can carry biases in the UCM, thus affecting the surface energy balance, near surface meteorology and the atmospheric stability with consequences for turbulent mixing and boundary-layer dynamics.In Chapter 3 the same case study as Chapter 2 is utilized to investigate the impact of the uncertainty in atmospheric forcing using a 1D WRF-SLUCM modelling setup. Uncertainties in the advection of potential temperature, aerosol optical depth and exchange coefficients of heat and momentum strongly affect model performance by altering either the radiative input at the surface (aerosols), or the near surface temperature and moisture gradients (advection and exchange coefficients). The resulting model responses are similar in magnitude to the changes induced by uncertainty in surface parameters. Moreover, a feedback mechanism between high daytime temperature and increased nocturnal radiative cooling was identified. This mechanism is triggered by an increase in nocturnal atmospheric stability that originates from changes in heat advection and the exchange coefficient of heat.Low-level jets (LLJs) are local maxima in the wind profile occurring above the nocturnal boundary layer, usually between 100-500m above ground. They are important phenomena affecting turbulent mixing, atmospheric stability and horizontal advection in the nocturnal boundary layer. These processes are important for the ’ventilation’ of heat and pollutants from urban areas, with consequences for human thermal comfort and street-level air quality. However, LLJ are also affected by turbulent mixing in the nocturnal boundary layer.In Chapter 4, the interactions between LLJ and the urban area using a 2-day case study between 14-16 May 2019 over London are investigated. Two Doppler Lidars and two numerical weather prediction models are used to identify differences in LLJ speed, height and fall-off between London and Chilbolton, a rural south west of London. The LLJs over London are elevated by 80 m compared to LLJs over Chilbolton, due to a combination of increased vertical mixing in the nocturnal UBL and orographic drag by the topography around London. Using a series of idealised experiments we find that urban and orographic effects contribute equally towards the reported differences over London. Moreover, we find that in areas with deeper UBL, the LLJ appears to be elevated and has lower speed. Finally, the presence of LLJ increases the shear induced turbulent kinetic energy in the UBL maintaining part of the vertical mixing many hours after the collapse of the daytime UBL.In Chapter 5 initial onset stages of the LLJ are investigated, to identify the effects of turbulent mixing and momentum advection in the formation of LLJ over urban areas. Two modelling setups are used, the 1D-WRF and the state-of-the-art PALM LES model in an idealised case study inspired by the meteorological situation during 15 May 2019 over London. The reported increase in turbulent mixing, induced by a large urban fraction, delays the onset of the LLJ up to two hours due to a later decoupling of the flow over the city. The height of the LLJ increases (up to 100m) while its speed is reduced (up to 2 m/s) with increasing urban fractions. Yet these effects are strongly dependent on the city size. The delay in the onset of the LLJ and its decreased wind speed over and downwind of the urban areas result in strong momentum advection from the already formed LLJ in the surroundings rural areas. The momentum advection contributes substantially to the formation of LLJs over urban areas, especially in the upwind part and its contributions positively scales with the size of the city.Overall, the findings of this thesis highlight the importance of accurately capturing the interactions between the urban surface, boundary layer and external meso-scale flows (i.e. advection processes, LLJ) for the quantification of the surface energy balance and turbulent exchange over urban areas.

Instructions for participants of the Urban-PLUMBER evaluation project for modelling inurban areas. This protocol sets out: background and scope of the Urban-PLUMBER project expected project outputs experiments to be undertaken format of provided data (forcing and site description) format of requested model output For further projectinformation seehttps://urban-plumber.github.io/

Urban canopy models (UCMs) are parametrization schemes that are used to improve weather forecasts in urban areas. The performance of UCMs depends on understanding potential uncertainty sources that can generally originate from the (a) urban surface parameters, (b) atmospheric forcing, and (c) physical description. Here, we investigate the relative importance of surface and atmospheric driven model sensitivities of the single‐layer urban canopy model when fully interactive with a 1‐D configuration of the Weather Research and Forecasting model (WRF). The impact of different physical descriptions in UCMs and other key parameterization schemes of WRF is considered. As a case study, we use a 54‐h period with clear‐sky conditions over London. Our analysis is focused on the surface radiation and energy flux partitioning and the intensity of turbulent mixing. The impact of changes in atmospheric forcing and surface parameter values on model performance appears to be comparable in magnitude. The advection of potential temperature, aerosol optical depth, exchange coefficient and roughness length for heat, surface albedo, and the anthropogenic heat flux are the most influential. Some atmospheric forcing variations have similar impact on the key physical processes as changes in surface parameters. Hence, error compensation may occur if one optimizes model performance using a single variable or combinations that have potential for carryover effects (e.g., temperature). Process diagrams help differences to be understood in the physical description of different UCMs, boundary layer, and radiation schemes and between the model and the observations.

At present atmospheric models for weather and climate use enhanced turbulent drag under stable conditions, because these empirically provide the necessary momentum drag for accurate forecast of synoptic systems. The enhanced mixing (also known as the "long-tail"), introduces drag that can not be physically justified and deteriorates the score for near surface temperature, wind and boundary-layer height, and deteriorates fog and frost forecasting. This study hypothesises that the insufficient representation of small-scale orographic gravity wave drag in the stable boundary layer may explain the need for the enhanced drag formulation. Hence, we introduce a new scheme in the WRF model that accounts for this drag as a superposition on the turbulent drag induced by a so-called short-tail mixing function. The latter is consistent with boundary-layer observations and large eddy simulations. We evaluate this scheme, against a short-tail and a long-tail scheme for sixteen 8-day forecasts over the Atlantic Ocean and Europe in winter. The new scheme outperforms the short and long tail schemes on sea level pressure, height of the 500 hPa field, 10 m wind and the cyclonic core pressure. Cyclonic core pressure bias is reduced by approximately 45% to 80% compared to the short-tail scheme. Sea level pressure bias is reduced by up to 0.48 hPa (50%) over the whole domain compared to the short-tail run. The new scheme has even smaller biases than the long-tail scheme, supporting our hypothesis that small-scale gravity wave drag may explain the need for a long-tail function. Near surface wind bias is reduced by up to 40% compared to the long-tail and up to 32% compared to the short-tail scheme, while the 2 m temperature bias is only slightly increased (19%).

Models for weather and climate have been actively populated with urban canopy models in the last decade. Urban canopy models are available with different levels of complexity. In an earlier study several urban canopy models have been evaluated in offline mode (Grimmond et al. 2011). However, in reality these schemes operate within a numerical weather prediction model, and are coupled with the atmospheric boundary layer. Within the SUBLIME model intercomparison study, single-column models equipped with urban canopy models are evaluated against observations for a clear sky case over London. As such we aim to unravel whether model sensitivity for urban morphological parameters is similar in coupled and uncoupled model. Moreover, the SUBLIME project provides a benchmark for future model evaluation and further development. The SUBLIME experiment consists of a forecast task over a 54 hour period (23-25 July 2012), during which clear sky conditions persisted over London. It consists of two main stages, firstly an offline urban canopy model run, to determine how the surface scheme performs. This is followed by a run in which the urban canopy model is coupled to a single-column model to simulate the coupling to the urban boundary layer. Model forcing data were provided by flux tower, LIDAR and radiosonde observations. Additional external forcings for geostrophic wind speed and advection of heat, moisture and momentum which could not be directly observed were simulated using, 3-D WRF (Weather Research and Forecasting model) model runs. This presentation will discuss the modelling results using the new revised external forcings. We evaluate model outcomes against surface radiation and energy balance observations for both stages. For the second stage, modelled vertical profiles of wind, temperature and humidity as well as boundary-layer height are compared against observations and between models. Finally, differences in model results are identified and the physical processes responsible for these are explored using process diagrams.

Background The effects of motor control training with pressure biofeedback on neck pain and disability of patients with neck pain is a field that has drawn a lot of research interest. Therefore, the aim of this study was to conduct, for the first time, a systematic review and meta-analysis for examining whether motor control training of deep neck flexors with pressure biofeedback improves pain and disability in patients with neck pain. Methods The systematic review and meta-analysis were performed according to PRISMA guidelines. The databases PubMED, MEDLINE, EBSCOhost, Google scholar, SportDiscus, COCHRANE and EMBASE were searched for studies related to the research question. PEDro scale was used for examining methodological quality. A random effects model was used for data meta-analysis. Results Seventeen studies were included for qualitative synthesis and 10 were selected for further quantitative synthesis. Motor control training of deep neck flexors with pressure biofeedback was found to have equal or better effectiveness on neck pain intensity and disability in comparison to other control interventions. The meta-analyses show that motor control training of deep neck flexors with pressure biofeedback is more effective than strength-endurance training of cervical muscles for improving pain (Hedges’ g = 0.323, 95% CI 0.04–0.60, I2 = 50.94%) and disability (Hedges g = 0.401, 95% CI 0.12–0.68, I2 = 53.44%) in patients with neck pain. Conclusions Motor control training of deep neck flexors with pressure biofeedback is an effective intervention for improving pain intensity and disability in patients with neck pain and preferable to strength-endurance training of cervical muscles.

Urban canopy models are essential tools in forecasting weather and air quality in cities. However, they require many surface \ud parameters, which are uncertain and can reduce model performance if inappropriately prescribed. Here, we evaluate the model \ud sensitivity of the Single-Layer Urban Canopy Model (SLUCM) in the Weather Research and Forecasting model (WRF) to \ud surface parameters in two different configurations, one coupled to the overlying atmosphere (on-line) in a 1D configuration and one without coupling (off-line). A 2-day summertime period in London is used as a case study, with clear skies and low wind speeds. Our sensitivity tests indicate that SLUCM reacts differently, when coupled to the atmosphere. For certain surface parameters, atmospheric feedback effects can outweigh the variations caused by surface parameter settings. Hence to fully understand model sensitivity atmospheric feedbacks should be considered.

Urban canopy models are essential tools for forecasting weather and air quality in cities. However, they require many surface parameters, which are uncertain and can reduce model performance if inappropriately prescribed. Here, we evaluate the model sensitivity of the single-layer urban canopy model (SLUCM) in the Weather Research and Forecasting (WRF) model to surface parameters in two different configurations, one coupled to the overlying atmosphere (on-line) in a 1D configuration and one without coupling (off-line). A two-day summertime period in London is used as a case study, with clear skies and low wind speeds. Our sensitivity tests indicate that the SLUCM reacts differently when coupled to the atmosphere. For certain surface parameters, atmospheric feedback effects can outweigh the variations caused by surface parameter settings. Hence, in order to fully understand the model sensitivity, atmospheric feedback should be considered.

At present atmospheric models for weather and climate use enhanced turbulent drag under stable conditions, because these empirically provide the necessary momentum drag for accurate forecast of synoptic systems. The enhanced mixing (also known as the "long-tail"), introduces drag that can not be physically justified and deteriorates the score for near surface temperature, wind and boundary-layer height, and deteriorates fog and frost forecasting. This study hypothesises that the insufficient representation of small-scale orographic gravity wave drag in the stable boundary layer may explain the need for the enhanced drag formulation. Hence, we introduce a new scheme in the WRF model that accounts for this drag as a superposition on the turbulent drag induced by a so-called short-tail mixing function. The latter is consistent with boundary-layer observations and large eddy simulations. We evaluate this scheme, against a short-tail and a long-tail scheme for sixteen 8-day forecasts over the Atlantic Ocean and Europe in winter. The new scheme outperforms the short and long tail schemes on sea level pressure, height of the 500 hPa field, 10 m wind and the cyclonic core pressure. Cyclonic core pressure bias is reduced by approximately 45% to 80% compared to the short-tail scheme. Sea level pressure bias is reduced by up to 0.48 hPa (50%) over the whole domain compared to the short-tail run. The new scheme has even smaller biases than the long-tail scheme, supporting our hypothesis that small-scale gravity wave drag may explain the need for a long-tail function. Near surface wind bias is reduced by up to 40% compared to the long-tail and up to 32% compared to the short-tail scheme, while the 2 m temperature bias is only slightly increased (19%).