Simon Vosper

Various studies have shown that low-level drag in the atmosphere is parametrised inconsistently across the world’s numerical weather prediction and climate models, ultimately due to a lack of constraints on the underlying physical processes and the overlap in scale between them. Trapped lee waves (TLWs) are not parametrised in most models but have been shown in theoretical and case studies to produce significant drag (necessarily at low levels) on the atmosphere under the right conditions. To investigate whether TLWs contribute to low-level drag consistently, the resolved momentum fluxes in the archived analyses of the TLW-resolving UKV model are calculated and compared to the resolved plus parametrised gravity wave fluxes in the coarse-resolution, global version of the Met Office Unified Model (MetUM), which does not resolve TLWs.The comparison between the models reveals that gravity wave momentum fluxes in the UKV model are about double that of the global MetUM in the mid-troposphere and up to four times that in the boundary layer. Only a portion of this discrepancy in momentum fluxes can be explained by the presence of trapped lee wave modes, which are found using a numerical solver of the Taylor-Goldstein equation. The other part is likely to be caused by orographic gravity waves that are reflected due to the general decrease of the Scorer parameter with altitude (and are distinct from the resonant TLWs). This work therefore demonstrates that the inclusion of the drag produced by both reflected and trapped lee waves would alleviate the current issues with low-level drag parametrisation.

The third version of the Regional Atmosphere and Land (RAL3) science configuration is documented. Developed through international partnerships, RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator (JULES) when applied across timescales with kilometre and sub-kilometre-scale model grids. The RAL3 configuration represents a major advance compared to previous versions by delivering a common science definition suitable for application to tropical and mid-latitude regions. Developments within RAL3 include the introduction of a double-moment microphysics scheme and a bimodal cloud scheme, replacing use of a single-moment scheme and different cloud schemes for mid-latitudes and tropics in previous versions. Updates have been implemented to the boundary layer scheme and a consolidation of land model settings to be more consistent with global atmosphere and land (GAL) science configurations. Physics developments aimed to address priorities for model performance improvement identified by users. This paper documents the RAL3 science configuration, including a series of iterative revisions delivered since its first release, and their characteristics. Evidence is provided from the variety of assessments of RAL3, relative to the previous version (RAL2). Collaborative development and evaluation across organizations have enabled evaluation across a range of domains, grid spacing and timescales. The analysis indicates more realistic precipitation distributions, improved representation of clouds and of visibility, a continued trend to more realistic representation of convection, and reduced near-surface wind speeds but a persistent cold-temperature bias. Overall the convective-scale verification scores and climatological model distributions relative to observations improve for the majority of variables. Ensemble results show improvements to the spread–error relationship. User feedback from subjective assessment activities has also been positive. Differences between RAL3 revisions and RAL2 are further illustrated through a process-based analysis of a convective system over the UK. The latest RAL3 configuration (RAL3.3) is therefore recommended for research, operational numerical weather prediction, and climate production at kilometre and sub-kilometre scales.

Although various lee wave trapping mechanisms have been studied theoretically since Lyra (1940), not much is known about the relative occurrence of these trapping mechanisms in the real world. For this purpose, vertical atmospheric profiles associated with trapped lee waves are clustered here using self-organising maps.Because in-situ observations of trapped lee waves are scarce, these vertical profiles are extracted from the Met Office’s convective-scale UKV model (which encompasses the UK and Ireland). To demonstrate that this model accurately represents the conditions relevant to trapped lee wave generation, the wavelength and orientation of trapped lee waves visible in satellite imagery are compared to those in the model. The model is found to reproduce these observed characteristics well.Subsequently, we use the trapped lee wave identification model developed by Coney et al. (2023) and a linear Taylor-Goldstein equation solver to determine which vertical profiles are associated with trapped lee wave activity. We confirm that high low-level wind speeds are a necessary condition for the generation of trapped lee waves of substantial amplitude. We find that wind speeds increasing with height contribute to wave trapping in most cases. Temperature inversions are present in roughly one-third of trapped lee wave cases. The implications of these results for the development of a trapped lee wave drag parametrisation scheme are discussed. References:Lyra, G. (1943) Theorie der stationären Leewellenströmung in freier Atmosphäre. Z. Angew. Math. Mech., 23, 1-28.Coney, J. et al. (2023) Identifying and characterising trapped lee waves using deep learning techniques. Quarterly Journal of the Royal Meteorological Society, 150, 213–231.

Trapped lee waves exert frictional forces on the atmosphere which, due to the small scale of the orography that causes the waves, must be parametrised in coarser‐resolution weather and climate models. Because of the scarcity of in situ observations, numerical model data have been used in previous climatological studies of trapped lee waves, which necessitates model evaluation. While the occurrence of trapped lee waves in models has been evaluated in a systematic manner before, their characteristics have not. To this end, the wavelength and orientation of trapped lee waves in the (UK) Met Office's convective‐scale variable‐resolution model (UKV) are validated against geostationary satellite imagery for the year 2023. The validation is performed using a newly developed wavelet transform technique for the objective estimation of wavelengths and orientations from pre‐selected satellite images and corresponding model‐derived vertical velocity fields. The model is shown to be skilful in reproducing trapped lee‐wave wavelengths, but consistently overestimates them, especially when these wavelengths are below 9 km ( six times the model grid spacing). Anisotropy in the satellite's resolution complicates the analysis of wave orientations between the model and the satellite imagery, but they are nonetheless found to be correlated. There is no evidence for a decline in trapped lee‐wave production in short‐range forecasts and most of the model's skill in wavelength and orientation prediction is retained at a two‐day lead time. Overall, the wavelength and orientation of trapped lee waves are generally found to be well‐reproduced, which, due to the high sensitivity of trapped lee‐wave characteristics to flow parameters, suggests that the UKV provides an accurate representation of the conditions relevant to trapped lee‐wave formation. The model may therefore be considered a suitable substitute for observations in climatological studies of trapped lee waves and in the future development of a trapped lee‐wave drag parametrisation.

The third version of the Regional Atmosphere and Land (RAL3) science configuration is documented. Developed through international partnership, RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator when applied across timescales with kilometre and sub-km scale model grids. The RAL3 configuration represents a major advance compared to previous versions by delivering a common science definition suitable for application to tropical and mid-latitude regions. Developments within RAL3 include the introduction of a double-moment microphysics scheme and a bi-modal cloud scheme, replacing use of a single-moment scheme and different cloud schemes for mid-latitudes and tropics in previous versions. Updates have been implemented to the boundary layer scheme and a consolidation of land model settings to be more consistent with Global Atmosphere and Land (GAL) science configurations. Physics developments aimed to address priorities for model performance improvement identified by users. This paper documents the RAL3 science configuration, including a series of iterative revisions delivered since its first release, and their characteristics. Evidence is provided from the variety of assessments of RAL3, relative to the previous version (RAL2). Collaborative development and evaluation across organisations has enabled evaluation across a range of domains, grid-spacing and timescales. The analysis indicates more realistic precipitation distributions; improved representation of clouds and of visibility; a continued trend to more realistic representation of convection; and reduced near-surface wind speeds, but a persistent cold temperature bias. Overall the convective-scale verification scores and climatological model distributions relative to observations improve for the majority of variables. Ensemble results show improvements to the spread-error relationship. User feedback from subjective assessment activities has also been positive. Differences between RAL3 revisions and RAL2 are further illustrated through process-based analysis of a convective system over the UK. The latest RAL3 configuration (RAL3.3) is therefore recommended for research, operational numerical weather prediction and climate production at km and sub-km scales.

Trapped lee waves and turbulent rotor activity are a hazard for aviation, so forecasters must take into account the likelihood of lee wave activity when advising aviation personnel. The UK Met Office’s operational high resolution Numerical Weather Prediction model, UKV, resolves lee wave activity but there is currently no automated operational way to recognise such regions directly from the high resolution UKV data, short of a forecaster looking at the data themselves. If lee waves can be detected from the UKV data, we can use this output to gain a better understanding of lee waves and their impact over Britain and Ireland. For example, over which regions are lee waves likely to form? Which atmospheric conditions are the most conducive to lee wave propagation?  A machine learning (ML) model, a U-Net, was trained to identify and segment lee waves, and then retrained to derive characteristics about the waves (orientation, wavelength) from the model data. The wave amplitudes are also extracted using the wave mask from the segmentation model. These ML models were then applied to a 30 year dataset (1982 – 2012) of high resolution NWP output, driven by ERA-Interim data. From this, the location and characteristics of lee waves over Britain and Ireland from within this time period were extracted. In this presentation, we explore the climatology of lee waves in the data over this period, including the relationship between waves and the orography; frequency of waves; seasonal effects; the effects of weather patterns on waves; and diurnal effects. 

The parametrization of orographic drag processes is a major source of circulation uncertainty in models. The COnstraining ORographic Drag Effects (COORDE) project makes a coordinated effort to narrow this uncertainty by bringing together the modeling community to: explore the variety of orographic drag parametrizations employed in current operational models; assess the resolution sensitivity of resolved and parametrized orographic drag across models; and to validate the parametrized orographic drag in low‐resolution simulations using explicitly resolved orographic drag from high‐resolution simulations. Eleven models from eight major modeling centers are used to estimate resolved orographic drag from high‐resolution (km‐scale) simulations and parametrized orographic drag from low‐resolution simulations, typically used for seasonal forecasting (∼40 km) and climate projections (∼100 km). In most models, at both seasonal and climate resolutions, the total (resolved plus parametrized) orographic gravity wave drag over land is shown to be underestimated by a considerable amount (up to 50%) over the Northern and Southern Hemisphere and by more than 60% over the Middle East region, with respect to the resolved gravity wave drag estimated from km‐scale simulations. The km‐scale simulations also provide evidence that the parametrized surface stress and the parametrized low‐level orographic drag throughout the troposphere are overestimated in most models over the Middle East region, particularly at climate resolutions. Through this process‐based evaluation, COORDE provides model developers new valuable information on the current representation of orographic drag at seasonal and climate resolutions and the vertical partitioning of orographic low‐level and gravity wave drag.

Atmospheric gravity waves are key drivers of the transfer of energy and momentum between the layers of the Earth’s atmosphere. The accurate representation of these waves in General Circulation Models (GCMs) however has proved very challenging. This is because large parts of the gravity wave spectrum are at scales that are near or below the resolution of global GCMs. This is especially relevant for small isolated mountainous islands such as South Georgia (54° S, 36° W) in the Southern Ocean. Observations reveal the island to be an intense source of stratospheric gravity waves, but their momentum fluxes can be under-represented in global models due to its small size. This is a crucial limitation, since the inadequate representation of gravity waves near 60° S during winter has been linked to the long-standing "cold-pole problem", where the southern stratospheric polar vortex breaks up too late in spring by several weeks. Here we address a fundamental question: when a model is allowed to run at very high spatial resolution over South Georgia, how realistic are the simulated gravity waves compared to observations? To answer this question, we present a 3-D comparison between satellite gravity wave observations and a high resolution model over South Georgia. We use a dedicated high-resolution run (1.5 km horizontal grid, 118 vertical levels) of the Met Office Unified Model over South Georgia and coincident 3-D satellite observations from NASA AIRS/Aqua during July 2013 and June–July 2015. First, model winds are validated with coincident radiosonde observations. The AIRS observational filter is then applied to the model output to make the two data sets comparable. A 3-D S-transform method is used to measure gravity-wave amplitudes, wavelengths, directional momentum fluxes and intermittency in the model and observations. Our results show that although the timing of gravity wave activity in the model closely matches observations, area-averaged momentum fluxes are generally up to around 25 % lower than observed. Further, we find that 72 % of the total flux in the model region is located downwind of the island, compared to only 57 % in the AIRS measurements. Directly over the island, the model exhibits higher individual flux measurements but these fluxes are more intermittent than in observations, with 90 % of the total flux carried by just 22 % of wave events, compared to 32 % for AIRS. Observed gravity wave fluxes also appear to dissipate more quickly with increasing height than in the model, suggesting a greater role for wave-mean flow interactions in reality. Finally, spectral analysis of the wave fields suggests that the model over-estimates gravity wave fluxes at short horizontal scales directly over the island, but under-estimates fluxes from larger horizontal scale non-orographic waves in the region, leading to a lower average value overall. Our results indicate that, although increasing model resolution is important, it is also important to ensure that variability in the background wind vector and role of non-orographic waves are accurately simulated in order to achieve realistic gravity wave activity over the Southern Ocean in future GCMs.