The Spanish plume is a synoptic pattern associated with deep moist convective storms in western and central Europe. A large-amplitude trough or cut-off low in the jet stream extending to low latitudes produces a long fetch of southerly or southwesterly flow in the lower troposphere across the Iberian Peninsula and into Europe. The preconvective environment is traditionally characterized by an elevated mixed layer of hot dry air with steep lapse rates (i.e., the Spanish plume airstream) overtop a warm surface layer and capping inversion, resembling the loaded-gun convective sounding. A literature review of 102 peer-reviewed journal articles mentioning the Spanish plume is performed (of which 84 have only passing mentions). Some articles correctly employ the original definition of the Spanish plume airstream as the dry elevated mixed layer, whereas others incorrectly apply the term to the surface (sometimes humid) airstream. The origin of the airstream is variously described as the Iberian Peninsula, northern Africa, or both, often unevidenced. Some air in so-called Spanish plumes does not even cross Spain. Descriptions of convective storms in Spanish plume synoptic patterns also are largely unevidenced, with release of instability attributed to various synoptic-scale and mesoscale processes. This review reveals these and other issues with the literature on the Spanish plume, painting a sometimes unevidenced, inconsistent, unclear, and inaccurate picture. The goals are to recommend proper usage of the term Spanish plume and articulate future research questions, specifically related to quantifying interactions with terrain through diurnal sensible heat fluxes and orographic flow modification to produce favorable environments for convective storms.

The canonical Spanish plume is a summertime southerly airstream that is heated while travelling across the Meseta Central on the Iberian Peninsula, developing into an elevated mixed layer farther poleward. A recent review of the literature paints a sometimes unevidenced, inconsistent, unclear, and inaccurate picture of the Spanish plume, raising questions about the evidence supporting this canonical conceptual model. A case of a Spanish-plume synoptic pattern from 1 to 2 July 2015 is studied, using air-parcel trajectories calculated from a mesoscale model simulation to identify four distinct airstreams responsible for the unstable thermodynamic profile over the United Kingdom: (1) a near-surface continental airstream transporting hot, moist boundary-layer air from France to the United Kingdom; (2) a lid (850-hPa) airstream descended from the mid-troposphere over the eastern North Atlantic and western Iberian Peninsula, initially warm and dry, but gradually cooling, moistening, and rising while travelling northward, forming a layer of convective inhibition; (3) a subtropical airstream of hot, dry air with steep lapse rates travelling poleward from North Africa and the Mediterranean then ascending near the United Kingdom; and (4) a middle-latitude upper-level trough airstream travelling eastward from the North Atlantic Ocean then ascending near the United Kingdom. These results challenge the canonical Spanish-plume synoptic pattern in three ways. First, most air reaching the United Kingdom did not travel over the Iberian Peninsula, particularly in the near-surface and lid airstreams. Second, steep lapse rates were pre-existing from the subtropics rather than created when passing over the Iberian Peninsula. Third, the lid resulted from subsidence rather than surface heating. Thus, the synoptic-scale pattern appeared to exert a larger control over the thermodynamics of the Spanish-plume airstream than heating from the Iberian Peninsula. A new conceptual model is proposed that does not require surface heating over the Iberian Peninsula.

Large language models afford opportunities for using computers for intensive tasks, realizing research opportunities that have not been considered before. One such opportunity could be a systematic interrogation of the scientific literature. Here, we show how a large language model can be used to construct a literature review of 2699 publications associated with microphysics parametrizations in the Weather and Research Forecasting (WRF) model, with the goal of learning how they were used and their systematic biases, when simulating precipitation. The database was constructed of publications identified from Web of Science and Scopus searches. The large language model GPT-4 Turbo was used to extract information about model configurations and performance from the text of 2699 publications. Our results reveal the landscape of how nine of the most popular microphysics parameterizations have been used around the world: Lin, Ferrier, WRF Single-Moment, Goddard Cumulus Ensemble, Morrison, Thompson, and WRF Double-Moment. More studies used one-moment parameterizations before 2020 and two-moment parameterizations after 2020. Seven out of nine parameterizations tended to overestimate precipitation. However, systematic biases of parameterizations differed in various regions. Except simulations using the Lin, Ferrier, and Goddard parameterizations that tended to underestimate precipitation over almost all locations, the remaining six parameterizations tended to overestimate, particularly over China, southeast Asia, western United States, and central Africa. This method could be used by other researchers to help understand how the increasingly massive body of scientific literature can be harnessed through the power of artificial intelligence to solve their research problems.

High‐resolution mesoscale model simulations of two archetypal quasi‐linear convective systems producing outbreaks of three or more tornadoes in the United Kingdom are performed to determine vortexgenesis mechanisms. Type 1 events are associated with north–south‐oriented cold fronts with regularly spaced misocyclones along them. In one type 1 event, a near‐surface vortex sheet broke down into near‐equally spaced misovortices having a wavelength of about 7.5 times the width of the shear zone. Rayleigh's and Fjørtoft's instability criteria were met preceding the development of the vortices, suggesting the presence of horizontal shearing instability (HSI). Lagrangian calculations of vorticity tendency showed that parcels entered the misovortices at lower heights, acquired their vorticity via tilting, before being further enhanced by stretching as the parcel ascended. These results implied HSI was the initial mechanism for the amplification of perturbations along the vortex sheet in the type 1 event. In contrast, type 2 events are associated with west–east‐oriented cold fronts with disorganized, elongated cyclonic–anticyclonic vorticity couplets evolving into a small number of cyclonic and anticyclonic misovortices with irregular misovortices. In one type 2 event, Fjørtoft's instability criterion was not met. Lagrangian vorticity‐tendency calculations showed that parcels acquired vorticity similar to type 1 events, where parcels entered the misovortices at lower heights, acquired their vorticity via tilting, before being further enhanced by stretching as the parcel ascended. However, the magnitude of tilting was typically larger in the type 2 event. Comparing these two events showed two possible mechanisms for misovortexgenesis in UK tornado outbreaks: misovortices in type 1 events form and grow via HSI along the front, whereas misovortices in type 2 events are not due to HSI.

One‐third of the fresh water in the Adriatic Sea originates from the Po River, influencing the heat, salt budget, and circulation (or hydrodynamics) of the Adriatic. Although many studies highlight the Po River's impact on the Adriatic, uncertainty remains in understanding the potential consequences if the Po River were to dry up due to climate change. Here, we show the dependence of the heat and salt budgets, as well as the hydrodynamics of the Adriatic, on the discharge of the Po River. We use an ocean model of the Adriatic Sea under two scenarios: The control simulation WITHPO reflecting natural conditions during 2018 and incorporating the Po River's freshwater input and an experimental simulation NOPO with the freshwater input turned off. WITHPO generally results in the surface waters in the northern basin being as much as 1.5°C warmer throughout the year, but colder in the spring by 2°C. WITHPO shows a salinity decrease ranging from 0.35 to 1 PSU at the surface. The sea surface height is 5 cm higher in the WITHPO, with the greatest effects observed along the western coast of the Adriatic Sea. The Po River's inflow increases surface outflow and inflow near the seabed between the Adriatic and Ionian Seas. Our results underscore the Adriatic Sea's dependency on Po River discharge and highlight the potential consequences of climate change producing reduced or zero discharge.

The performance of six subgrid-scale (SGS) models is analyzed for large-eddy simulations (LES) of wind-farm flows under stable (SBL) and conventionally-neutral (CNBL) atmospheric conditions. A precursor–concurrent technique is employed to provide fully developed turbulent inflow for simulations of a 40-turbine wind farm. Turbines are represented using the actuator-disc method, employing a baseline grid of 12 cells across the turbine diameter. The SBL precursor flow poses a challenge for LES, as it may not be able to resolve the small turbulent scales featured in this flow if the grid is coarse. For these precursor flows, the baseline grid results of all six SGS models are assessed relative to coarser and finer grids, with 6 and 45 cells across the diameter, respectively. The wall-adapting local eddy-viscosity (WALE) and Lagrangian-averaged scale-dependent dynamic (LASDD) models exhibit high grid sensitivity, while the standard Smagorinsky (Smag.), anisotropic minimum-dissipation (AMD), one-equation turbulent kinetic energy (TKE), and stability-dependent Smagorinsky (SDS) models show low sensitivity. For the wind-farm simulations conducted with the baseline grid, the AMD and SDS models predict similar wind-farm performance. In contrast, the WALE and LASDD models predict nearly 30

Although many people believe their pain fluctuates with weather conditions, both weather and pain may be associated with time spent outside. For example, pleasant weather may mean that people spend more time outside doing physical activity and exposed to the weather, leading to more (or less) pain, and poor weather or severe pain may keep people inside, sedentary, and not exposed to the weather. We conducted a smartphone study where participants with chronic pain reported daily pain severity, as well as time spent outside. We address the relationship between four weather variables (temperature, dewpoint temperature, pressure, and wind speed) and pain by proposing a three-step approach to untangle their effects: (i) propose a set of plausible directed acyclic graphs (also known as DAGs) that account for potential roles of time spent outside (e.g., collider, effect modifier, mediator), (ii) analyze the compatibility of the observed data with the assumed model, and (iii) identify the most plausible model by combining evidence from the observed data and domain-specific knowledge. We found that the data do not support time spent outside as a collider or mediator of the relationship between weather variables and pain. On the other hand, time spent outside modifies the effect between temperature and pain, as well as wind speed and pain, with the effect being absent on days that participants spent inside and present if they spent some or all of the day outside. Our results show the utility of using directed acyclic graphs for studying causal inference.

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Large hail (greater than 2 cm in diameter) can cause devastating damage to crops and property and can even cause loss of life. Because hail reports are often collected by individual countries, constructing a Europe-wide large-hail climatology has been challenging to date. However, the European Severe Storm Laboratory's European Severe Weather Database provides the only pan-European dataset for severe convective-storm reports. The database is comprised of 62 053 large-hail reports from 40 CE to September 2020, yet its characteristics have not been evaluated. Thus, the purpose of this study is to evaluate hail reports from this database for constructing a climatology of large hail. For the period 2000–2020, large-hail reports are most prominent in June, whereas large-hail days are most common in July. Large hail is mostly reported between 13:00–19:00 local time, a consistent pattern since 2010. The intensity, as measured by maximum hail size, shows decreasing frequency with increasing hailstone diameter and little change over the 20-year period. The quality of reports by country varies, with the most complete reporting being from central European countries. Thus, results suggest that despite its short record, many indications point to the dataset representing some reliable aspects of the European large-hail climatology, albeit with some limitations.

Spinal cord stimulation is a powerful tool that can be used to treat neuropathic pain. This treatment has increased in popularity because it can provide ongoing, durable pain relief for patients. Spinal cord stimulation trial and implantation is extremely safe, especially in the ideal patient. This chapter summarizes published and practical recommendations for addressing complications and more complex patients.

A narrow cold-frontal rainband (NCFR) and wide cold-frontal rainband (WCFR) over the United Kingdom on 24 January 2018 are selected to evaluate the sensitivity of cold-season precipitation to three bulk cloud microphysics schemes in the Weather Research and Forecasting (WRF) Model. The three simulations tend to overpredict the intensity and the areal coverage of NCFRs but underpredict those of WCFRs against the Met Office radar imagery. Nonetheless, the WRF double-moment 6-class (WDM6) simulation produces the most realistic NCFR and WCFR. More specifically, the intensity of the NCFR in the WDM6 simulation is overpredicted in the developing stage and underpredicted in the dissipating stage when compared to the Thompson and Morrison simulations. This phenomenon is mainly attributed to the rain mixing ratio at 1 km. The more intense and larger area of the WCFR in the WDM6 simulation is associated with more low-level snow, graupel, and rain than the other two simulations. In the WDM6 simulation, the larger low-level snow mixing ratios are due to the higher rates of aggregation between cloud ice and snow, whereas the larger rain mixing ratio is due to higher melting rates. Thus, differences in low-level ice-phase processes between microphysics schemes are large, strongly influencing the intensity of WCFRs, but the differences in warm-rain processes are smaller. Additionally, the small size of raindrops in the WDM6 simulation leads to wider and more intense WCFR as these raindrops are preferentially sent rearward relative to the cold front.

The urgency to mitigate the effects of climate change necessitates an unprecedented global deployment of offshore renewable-energy technologies mainly including offshore wind, tidal stream, wave energy, and floating solar photovoltaic. To achieve the global energy demand for terawatt-hours, the infrastructure for such technologies will require a large spatial footprint. Accommodating this footprint will require rapid landscape evolution, ideally within two decades. For instance, the United Kingdom has committed to deploying 50 GW of offshore wind by 2030 with 90–110 GW by 2050, which is equivalent to four times and ten times more than the 2022 capacity, respectively. If all were 15 MW turbines spaced 1.5 km apart, 50 GW would require 7500 km ^2 and 110 GW would require 16 500 km ^2 . This review paper aims to anticipate environmental impacts stemming from the large-scale deployment of offshore renewable energy. These impacts have been categorised into three broad types based on the region (i.e. atmospheric, hydrodynamic, ecological). We synthesise our results into a table classifying whether the impacts are positive, negative, negligible, or unknown; whether the impact is instantaneous or lagged over time; and whether the impacts occur when the offshore infrastructure is being constructed, operating or during decommissioning. Our table benefits those studying the marine ecosystem before any project is installed to help assess the baseline characteristics to be considered in order to identify and then quantify possible future impacts.

In a large-eddy simulation (LES) approach, the sub-grid scale (SGS) model accounts for the contribution of eddies and their fluxes whose length scales are smaller than the filter width. In wind turbine and farm simulations, different SGS models have been adopted, but their impact on turbine performance and wake prediction remains unknown for non-neutrally stable atmospheric boundary layers. Here, large-eddy simulations of an NREL-5MW wind turbine in stable atmospheric conditions are performed with six SGS models: standard Smagorinsky, Lagrangian-Averaged Scale-Dependent Dynamic (LASDD), Wall-Adapting Local Eddy-Viscosity, Turbulent Kinetic Energy, Stability Dependent Smagorinsky, and Anisotropic Minimum-Dissipation (AMD) models. The resolved flow field and turbine loading have shown limited sensitivity to the SGS model with some deviations from the LASDD in wind speed and turbulence intensity at the turbine elevation. This limited sensitivity is owed to the adopted high-resolution grid necessary to provide an acceptable resolution for the actuator-line method. Regarding the computational costs, the LASDD model has the highest compute overhead to the LES compared to the other five SGS models. The AMD model is simple to implement and provides three-dimensional variation of the SGS eddy-viscosity without any parameter tuning, thus it has the highest potential to be used in LES of wind turbines and farms operating in stable conditions.

Spinal cord stimulation (SCS) is a well-established treatment for chronic neuropathic pain. However, over- or under-delivery of the SCS may occur because the spacing between the stimulating electrodes and the spinal cord is not fixed; spacing changes with motion and postural shifts may result in variable delivery of the SCS dose, and in turn a sub-optimal therapy experience for the patient. The evoked compound action potential (ECAP)—a measure of neural activation — may be used as a control signal to adapt SCS parameters in real-time to compensate for this variability. In this prospective, multicenter, randomized, single-blind, crossover trial, reduction in overstimulation intensity was used as a perceptual measure to evaluate a novel ECAP-controlled, closed-loop (CL) SCS algorithm relative to traditional open-loop (OL) SCS. The primary outcome used a Likert scale to assess sensation during activities of daily living with CL versus OL SCS. Of the 42 subjects in the Intent-to-Treat Analysis set, 97.6% had a reduction in sensation with CL versus OL SCS. The primary objective was met as the lower confidence limit (87.4%) exceeded the performance goal of 50% (p < 0.001). A total of 88.1% (37/42) of subjects preferred CL and 11.9% (5/42) preferred OL SCS. SCS dose consistency during CL SCS was demonstrated by the reduced variability in ECAP amplitude with CL SCS (SD: 8.72 µV) relative to OL SCS (SD: 19.95 µV). Together, these results demonstrate that the ECAP-controlled, CL algorithm reduces or eliminates unwanted sensation, and thereby provides a more preferred and consistent SCS experience. Perspective Patients with chronic pain need durable and dependable options for pain relief. SCS is an important therapy option, and new technology advancements could improve long-term therapy use. Closed-loop SCS offers a preferred and more consistent therapy experience for patients that could lead to increased therapy utilization and reliable therapy outcomes.

Understanding the temporal relationship between key events in an individual’s infection history is crucial for disease control. Delay data between events, such as infection and symptom onset times, is doubly censored because the exact time at which these key events occur is generally unknown. Current mathematical models for delay distributions are derived from heuristic justifications. Here, we derive a new model for delay distributions, specifically for incubation periods, motivated by bacterial-growth dynamics that lead to the Burr family of distributions being a valid modelling choice. We also incorporate methods within these models to account for the doubly censored data. Our approach provides biological justification in the derivation of our delay distribution model, the results of fitting to data highlighting the superiority of the Burr model compared to currently used models when the mode of the distribution is clearly defined or when the distribution tapers off. Under these conditions, our results indicate that the derived Burr distribution is a better-performing model for incubation-period data than currently used methods, with the derived Burr distribution being 13 times more likely to be a better-performing model than the gamma distribution for Legionnaires’ disease based on data from a known outbreak.