Numerical Prediction Research Articles

A hybrid machine learning-based model for predicting failure of water mains under climatic variations: A Hong Kong case study

Effective functioning of water systems is critical to ensure the quality of human life. Therefore, failure prediction of water mains under climatic variations is necessary to avoid socio-economic and environmental losses. This paper aims to propose a hybrid model named STL-GC-LSTM for an accurate failure prediction of water mains under the impact of climatic variations. Firstly, the seasonal-trend decomposition based on Loess (STL) method is employed to decompose the failure time series. Next, significant climate variables are selected from the Granger causality (GC) test. Lastly, the final predicted failure of water mains is acquired by adding up the predictive results of the three components which are learned by Long Short-Term Memory (LSTM) models. Several evaluation metrics are used to assess the prediction performance. The results from a case study in Hong Kong imply that STL decomposition is promising for fully mining intrinsic properties of failure series. The developed hybrid models are effective in specifically identifying which component climatic variations exert influence on, and the final failure predictions show satisfactory agreement compared with peer models. This paper could provide an accurate estimation for failures of water mains ahead of time and be used as an essential complement to other numerical prediction models.

Evaluation of the Coupled Arctic Forecast System’s representation of the Arctic atmospheric boundary layer vertical structure during MOSAiC

Observations from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) were used to evaluate the Coupled Arctic Forecast System (CAFS) model’s ability to simulate the atmospheric boundary layer (ABL) structure in the central Arctic. MOSAiC observations of the lower atmosphere from radiosondes, downwelling longwave radiation (LWD) from a pyranometer, and near-surface wind conditions from a meteorological tower were compared to 6-hourly CAFS output. A self-organizing map (SOM) analysis reveals that CAFS reproduces the range of stability structures identified by the SOM trained with MOSAiC observations of virtual potential temperature (θv) profiles, but not necessarily with the correct frequency or at the correct time. Additionally, the wind speed profiles corresponding to a particular θv profile are not consistent between CAFS and the observations. When categorizing profiles by static stability, it was revealed that CAFS simulates all observed stability regimes, but overrepresents the frequency of near-surface strong stability, and underrepresents the frequency of strong stability between the top of the ABL and 1 km. The 10 m wind speeds corresponding to each stability regime consistently have larger values in CAFS versus observed, and this offset increases with decreasing stability. Whether LWD is over or underestimated in CAFS is dependent on stability regime. Both variables are most greatly overestimated in spring, leading to the largest near-surface θv bias, and the greatest underrepresentation of strong stability in spring. The results of this article serve to highlight the positive aspects of CAFS for representing the ABL and reveal impacts of misrepresentations of physical processes dictating energy, moisture, and momentum transfer in the lower troposphere on the simulation of central Arctic ABL structure and stability. This highlights potential areas for improvement in CAFS and other numerical weather prediction models. The SOM-based analysis especially provides a unique opportunity for process-based model evaluation.

A Hybrid Extended Kalman Filter Based on Parametrized ANNs for the Improvement of the Forecasts of Numerical Weather and Wave Prediction Models

A hybrid optimization filter for weather and wave numerical models is proposed and tested in this study. Parametrized Artificial Neural Networks are utilized in conjunction with Extended Kalman Filters to provide a novel postprocess strategy for 10 m wind speed, 2 m air temperature, and significant wave height simulations. The innovation of the developed model is the implementation of Feedforward Neural Networks and Radial Basis Function Neural Networks as estimators of an exogenous parameter that adjusts the covariance matrices of the Extended Kalman Filter process. This hybrid system is evaluated through a time window process leading to promising results, thus enabling a decrease in systematic errors alongside the restriction of the error variability and the corresponding forecast uncertainty. The obtained results showed that the average reduction of the systematic error exceeded 75%, while the corresponding nonsystematic part of that error decreased by 35%.

Theoretical analysis of using multiple borehole heat exchangers for production of heating and cooling energy in shallow geothermal reservoirs with underground water flow

In this work the feasibility of using the double-U borehole heat exchangers (BHE) in multiple arrangements for production of heating and cooling energy has been investigated.Theoretical case studies are given for a few hypothetical lithological cases corresponding to the lithology that can be found in City of Zagreb. The results show interference between the borehole heat exchangers and limits of use of shallow reservoir with respect to slow convection and loss of accumulated energy in the reservoir. Special emphasis is placed on the description of the groundwater flow determined by the parameters of the shallow geothermal reservoir, such as permeability, water saturation, thermal conductivity, specific heat capacity and soil density, topography, precipitation, etc. Simulations have been performed in FeFlow coupled with Python user-scripts for individual control of the BHE’s, and the surface equipment is modelled using RES2GEO. To assess the accuracy of numerical predictions in simulating thermal responses, FeFlow software was utilized to conduct Thermal Response Tests (TRT) for individual Borehole Heat Exchangers (BHEs). These numerical results were then juxtaposed with empirical TRT data collected from a site near Velika Gorica, a town within the City of Zagreb. For each case simulation is done for one year with one hour resolution. The temperature distribution analysis highlights notable differences between scenarios, where the temperature field with lower permeability experiences significant variations, particularly near the BHE. Here, temperatures can soar to 20 °C during cooling periods and plummet to 4 °C in heating seasons. These fluctuations cause overheating and subcooling of the reservoir, impairing the heat pump’s efficiency and necessitating increased electricity consumption. By using Multivariate linear regression and Multi-layer perceptron regressor method, the main parameter for determination of COP of ground source heat pump. The analysis reveals that the majority of errors fall within the range of ± 5 %, although the MLA prediction method exhibits slightly more frequent errors. Additionally, the R2 scores demonstrate strong performance: the MLA procedure achieves 0.95 for heating and 0.99 for cooling, while the MLP procedure attains 0.99 for both heating and cooling phases.

Implementation and tests of the NLT3D scheme in the ICON model

AbstractThe main goal of this article is to test the long‐term performance of the three‐dimensional non‐local turbulence (NLT) parameterization scheme at different grid sizes in the so‐called gray zone between classical mesoscale modeling ( several km) and large eddy simulations (LES: several 100 m). For this, NLT has been implemented in the numerical weather prediction Icosahedral Nonhydrostatic model (ICON) of Deutscher Wetterdienst (DWD). Results are compared with a one‐dimensional version of NLT (NLT) and with two operational turbulence schemes available in ICON. Comparisons with observations from radiosondes, the operational surface synoptic (SYNOP) station network, and RAdar‐OnLine‐ANeichung (RADOLAN) radar data of DWD indicate that all turbulence schemes investigated perform reasonably well. Nonetheless, a more detailed study of the model results reveals several interesting differences between the turbulence parameterizations to be discussed in detail. Median absolute errors (MAE) from point‐to‐point comparisons between numerical results and SYNOP observations tend to be smaller than those from comparisons with averaging simulated fields over an environment around each station location. This behavior indicates an information loss caused by the averaging process. For the 2‐m temperature () and the hourly precipitation sums (), MAEs decrease with decreasing grid sizes, thus suggesting an information gain for finer grids. The nighttime MAEs of and obtained with NLT and NLT are similar to or lower than those of the operational turbulence schemes of ICON. Moreover, during a shallow warm‐air intrusion, NLT and especially NLT yield a more realistic representation of the horizontal structures of and, during nighttime stable boundary‐layer situations, also . Radiosonde profiles of the potential temperature confirm a reasonable vertical mixing as obtained with NLT and NLT.

Compliant frame geometry for DEMES-based gripper and flapping wing actuators: A comprehensive design study

Dielectric elastomer minimum energy structures (DEMES) have attracted significant attention in the recent past because of their ability to switch between multiple equilibrium states. The DEMES is formed when a pre-stretched elastomer film adheres to an inextensible frame and is further allowed to attain an equilibrium configuration as a result of energy minimization. While several researchers have investigated; both theoretically and experimentally, the underlying mechanics of DEMES, a majority of them deploy simplistic boundary frames (square/rectangular/circular) to obtain the requisite minimum energy configuration. In this paper, we demonstrate that this seemingly restrictive choice of using simplistic frame geometries can be given away by designing more complex-shaped compliant frames capable of producing useful modes of deformation. To demonstrate this idea, two prototypes; namely, the four-arm gripper actuator and the flapping-wing actuator, are studied numerically and experimentally. In both cases, a single compliant frame is used to realize the respective configuration, thus circumventing the need to assemble multiple MES on a common platform. In tandem, we investigate the role of reinforcements in (a) controlling the warping in MES and (b) maximizing actuation in the desired mode of deformation. Finite element analysis are carried out using the ABAQUS to determine the equilibrium configuration of the actuators, and subsequently their electromechanical behavior. Experimental investigations involve the utilization of the commercially available VHB 4910 acrylic tape in conjunction with PET frames and 3D-printed reinforcements. Excellent qualitative agreement is achieved between the numerical predictions and the experimental observations. Finally, we allude to a few innovative frame architectures leading the MES of engineering interest.

Intelligent RCS Extrapolation Technology of Target Inspired by Physical Mechanism Based on Scattering Center Model

In this paper, a technology named SCM−ANN combining physical scattering mechanisms and artificial intelligence is proposed to realize radar cross-section (RCS) extrapolation of non-cooperative conductor targets with higher efficiency. Firstly, an adaptive scattering center (SC) extraction algorithm is used to construct the scattering center model (SCM) for non-cooperative targets from radar echoes in the low-frequency band (LFB). Secondly, an artificial neural network (ANN) is constructed to capture the nonlinear relationship between the real LFB echoes and those reconstructed from the SCM. Finally, the SCM is used to reconstruct echoes in the high-frequency band (HFB), and these reconstructions, together with the trained ANN, optimize the extrapolated HFB RCS. For the SCM−ANN technology, physical mechanistic modes are used for trend prediction, and artificial intelligence is used for regression optimization based on trend prediction. Simulation results show that the proposed method can achieve a 50% frequency extrapolation range, with an average prediction error reduction of up to 40% compared with the traditional scheme. By incorporating physical mechanisms, this proposed approach offers improved accuracy and an extended extrapolation range compared with the RCS extrapolation techniques relying solely on numerical prediction.

A new global daily sea‐surface temperature analysis system at Environment and Climate Change Canada

AbstractA new global daily sea‐surface temperature (SST) analysis system has been developed at Environment and Climate Change Canada (ECCC). All components of the new SST analysis system are implemented within the Modular and Integrated Data Assimilation System (MIDAS) software. MIDAS is already used for the data assimilation component of the main operational numerical weather prediction (NWP) systems at ECCC. The new SST analysis system, integrated together with the global sea‐ice analysis, will be part of the combined ocean surface analysis used for all operational prediction systems at ECCC. The data assimilation method used to compute the new SST analyses is two‐dimensional variational method with a diffusion operator for representing the horizontal background‐error correlations. A new algorithm for satellite data bias estimation has also been developed employing gridded bias estimates computed from a spatial averaging of the differences between collocated satellite and in‐situ data. New algorithms for quality control and thinning of satellite data have also been implemented, making each type of observational dataset more evenly distributed over the globe. The performance of the new SST system is examined relative to the current operational SST system by using independent data. The impact of using the new SST analysis within NWP and ocean prediction systems is also evaluated. When compared with the operational system currently in use, the experiments employing the new SST analysis system produce a nearly neutral impact on the NWP and ocean prediction systems. This validation of the new system is an important first step towards the ability to use MIDAS to perform ensemble‐based three‐dimensional ocean and coupled ocean‐ice–atmosphere data assimilation.

Hydrodynamic analysis of three-module semi-submersible platform and its mooring design

The floating platform consisting of three interconnected semi-submersible modules deployed in shallow water with severe environmental conditions will be subject to very high environmental loads during storms and its position keeping will become a huge challenge. Therefore, it is important to investigate the hydrodynamic characteristics of the platform and design a robust mooring system to meet different demands during the service period of the platform. In this study, the hydrodynamic and motion analyses of the three-module semi-submersible platform were conducted by using both numerical simulation and experimental tests. In addition, a distinctive X-shaped mooring system was designed, which is characterized by relatively low stiffness in the direction of beam sea in which the environmental loads are the highest, and better load sharing among different mooring lines so that the extreme tension is curbed. Model tests were performed in a wave basin to acquire the platform motions and mooring tensions, and comparisons were made between the measurements and numerical predictions. The results of the study verify the feasibility of the three-module connected platform and its mooring system.

Aerosol impacts on summer precipitation forecast over the North China Plain by using Thompson aerosol aware scheme in WRF: Process analysis and response sensitivity to aerosol concentrations during a Heavy Rainfall Event in Beijing

Aerosol-Cloud-Interactions (ACIs) are possibly important factors affecting precipitation while are usually not considered in numerical weather prediction systems (NWP) due to the complexities and uncertainties involved by considering aerosol information. This research focused on the use of the Thompson aerosol-aware scheme in WRF (Weather Research and Forecasting model). This paper summarized the process analysis and response sensitivity to aerosol concentrations during a heavy rainfall event in Beijing. The July 16, 2018 rainfall event -a typical precipitation case involving both warm and cold-phase clouds was thoroughly analyzed, including cloud fraction, downward shortwave radiation, water vapor consumption, cloud microphysical processes, net latent heat etc., which verified that the aerosol-cloud interaction paths were accurately and adequately considered in Thompson aerosol-ware scheme. By tracking key parameter changes, it is confirmed that the CCN/IN activation and relative roles in changing precipitation at different stages are reasonable in Thompson aerosol-aerosol scheme. According to the rainfall characteristics, the precipitation was divided into two phases. And the difference in the aerosol effect on precipitation in the two precipitation phases is mainly due to the complexity of cold rain process and hypothesis. Sensitivity simulations also revealed that the current concentrations of background aerosol in Beijing are actually high enough and further ten-times of aerosol concentration increase didn't bring significant additional precipitation response.

Sundowner Winds at Montecito during the Sundowner Winds Experiment

This study investigates the predictability of downslope windstorms located in Santa Barbara County, California, locally referred to as Sundowner winds, from both observed relationships and a high-resolution, operational numerical weather prediction model. We focus on April 2022, during which the Sundowner Winds Experiment (SWEX) was conducted. We further refine our study area to the Montecito region owing to some of the highest wind measurements occurring at or near surface station MTIC1, situated on the coast-facing slope overlooking the area. Fires are not uncommon in this area, and the difficulty of egress makes the population particularly vulnerable. Area forecasters often use the sea-level pressure difference (ΔSLP) between Santa Barbara Airport (KSBA) and locations to the north such as Bakersfield (KBFL) to predict Sundowner windstorm occurrence. Our analysis indicates that ΔSLP by itself is prone to high false alarm rates and offers little information regarding downslope wind onset, duration, or magnitude. Additionally, our analysis shows that the high-resolution rapid refresh (HRRR) model has limited predictive skill overall for forecasting winds in the Montecito area. The HRRR, however, skillfully predicts KSBA-KBFL ΔSLP, as does GraphCast, a machine learning weather prediction model. Using a logistic regression model we were able to predict the occurrence of winds exceeding 9 m s−1 with a high probability of detection while minimizing false alarm rates compared to other methods analyzed. This provides a refined and easily computed algorithm for operational applications.

Spatial degradation characteristics and numerical prediction for life of MWCNTs-reinforced concrete by salt freezing

Nanotechnology is of great importance towards the multiscale enhancement of mechanical strength, anti-cracking, corrosion and/or abrasion resistance of concrete. To optimize the durability of concrete structure in a frigid and saline environment, the multi-walled carbon nanotubes (MWCNTs) were introduced along with the evaluation of spatial deterioration characteristics and numerical prediction for its service life in this work. The degree and mechanism of salt freeze-thaw damage relief of MWCNTs-reinforced concrete (CNTC) were investigated by establishing relief rate (Rf, RE and Rp) and damage parameters (Df, DE and Dp) in terms of compressive strength, relative dynamic elastic modulus and porosity. Macroscopic results showed that the presence of MWCNTs substantially delayed the evolution of spatial damage of concrete. The optimum dosage of MWCNTs for the degradation mitigation was also determined to be 0.05 wt% from the microscale scanning electron microscopy, mercury intrusion and mesoscale X-CT scanning techniques, which was responsible to the well-dispersed MWCNTs without entanglement. The synergic effects of filling, nucleation and bridging of MWCNTs in hydrating concrete decreased the porosity and effectively inhibited micro-crack propagation of concrete under the salt freezing effects. Besides, the evolutionary relationships between Df, DE and Dp were established, and a three parameters-dependent prediction index D had been established on the basis of the entropy method and grey system theory GM(1,1) model. The experimental results were in good agreement with the prediction model, and further showed that the salt- freeze-thaw damage of CNTC was significantly reduced.

Systematic experimental and model-based evaluation of the synergistic effects of alloy composition and damage rate on the formation of Cr-rich precipitates in Fe–Cr–Al alloys under ion irradiation

Fourteen Fe–Cr–Al alloys with systematically varied Cr and Al compositions were irradiated at 350∘C to 0.24 dpa by 10.5 MeV self-ions as model alloys for the Fe–Cr–Al (oxide dispersion strengthened: ODS) alloys being developed as accident tolerant fuel (ATF) cladding for light water reactors. Three dose rates (8×10−6, 8×10−5, and 8×10−4 dpa/s) were selected to predict the formation behavior of Cr-rich precipitates (CrRP) under neutron irradiation conditions. The effects of Cr, Al composition, and dose rate on the CrRP formation behavior were evaluated using a three-dimensional atom probe (3DAP) analysis. The results showed that the CrRP number density, volume fraction, and Cr concentration increase with increasing Cr composition, decreasing Al composition, and decreasing dose rate. Multiple regression analysis showed that in addition to these primary effects, there was an interaction between them on the CrRP volume fraction: (1) the higher the Al composition, the smaller the increase in the CrRP volume fraction with increasing Cr composition, and (2) the lower the Cr composition, the smaller the increase in the CrRP volume fraction with the decreasing Al composition and dose rate. It was also highlighted that to understand the dose rate effect on the CrRP formation behavior under neutron irradiation, it is useful to examine the irradiation time dependence, including the effective use of thermal aging data as a limit to the zero dose rate, in addition to the dose-dependency perspective. In addition to the systematic database of CrRP-related quantities established in this study, these considerations should contribute to further developing and validating numerical prediction models.

Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models

Abstract. Accurate and continuous estimates of the thermodynamic structure of the lower atmosphere are highly beneficial to meteorological process understanding and its applications, such as weather forecasting. In this study, the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval is used to retrieve temperature and humidity profiles from various combinations of input data collected by passive and active remote sensing instruments, in situ surface platforms, and numerical weather prediction models. Among the employed instruments are microwave radiometers (MWRs), infrared spectrometers (IRSs), radio acoustic sounding systems (RASSs), ceilometers, and surface sensors. TROPoe uses brightness temperatures and/or radiances from MWRs and IRSs, as well as other observational inputs (virtual temperature from the RASS, cloud-base height from the ceilometer, pressure, temperature, and humidity from the surface sensors) in a physical iterative retrieval approach. This starts from a climatologically reasonable profile of temperature and water vapor, with the radiative transfer model iteratively adjusting the assumed temperature and humidity profiles until the derived brightness temperatures and radiances match those observed by the MWR and/or IRS instruments within a specified uncertainty, as well as within the uncertainties of the other observations, if used as input. In this study, due to the uniqueness of the dataset that includes all the abovementioned sensors, TROPoe is tested with different observational input combinations, some of which also include information higher than 4 km above ground level (a.g.l.) from the operational Rapid Refresh numerical weather prediction model. These temperature and humidity retrievals are assessed against independent collocated radiosonde profiles under non-cloudy conditions to assess the sensitivity of the TROPoe retrievals to different input combinations.

RisingTides: An Analytic Modeling Code of Tidal Effects in Binary Neutron Star Mergers

Gravitational waves produced by binary neutron star mergers offer a unique window into matter behavior under extreme conditions. In this context, we analytically model the effect of matter on gravitational waves from binary neutron star mergers. We start with a binary black hole system, leveraging the post-Newtonian formalism for the inspiral and the Backwards-one-Body model for the merger. We combine the two methods to generate a baseline waveform and we validate our results against numerical relativity simulations. Next, we integrate tidal effects in phase and amplitude to account for matter and spacetime interaction using the NRTidal model and test its accuracy against numerical relativity predictions for two equations of state, finding a mismatch around the merger. Subsequently, we lift the restriction on the coefficients to be independent of the tidal deformability and recalibrate them using the numerical relativity predictions. We obtain better fits for phase and amplitude around the merger and are able to extend the phase modeling beyond the merger. We implement our method in new open-source, user-friendly Python code, steered by a Jupyter Notebook, named RisingTides. Our research offers new perspectives on analytically modeling the effect of tides on the gravitational waves from binary neutron star mergers.

Discovering chiral auxetic structures with near-zero Poisson's ratio using an active learning strategy

In this paper, a set of hexachiral auxetic structural designs with near zero Poisson's ratio (ZPR) characteristics is discovered via the combination of machine learning and experimentally validated finite element simulation. An active learning-enhanced Gaussian process model is utilized to generate multiple designs with near-ZPR properties and discover the boundary of the positive and negative Poisson's ratio. The results show that active learning successfully constructs a probabilistic estimation of the ZPR boundary. A comprehensive analysis of the identified ZPR contour is performed to extract crucial design insights. The findings indicate that the near-ZPR characteristic can be attained through various combinations of geometric parameters. This offers users the flexibility to select the configuration that best aligns with their specific requirements. Additionally, an investigation of the various ZPR configurations that have been discovered is carried out to understand the mechanism that yields near-ZPR property. One discovered near-ZPR design was subsequently fabricated using 3D printing for validation purposes. The experimental outcomes demonstrated a good agreement with the numerical predictions, underscoring the effectiveness of the active learning strategy in uncovering designs that closely approach ZPR conditions.

A climatology of stratospheric gravity waves induced by tropical cyclones on the northwest Pacific Ocean

A climatology of stratospheric gravity waves (SGWs) induced by tropical cyclones (TC-SGWs) is necessary for stratospheric aviation safety and accurate numerical weather prediction. Few previous works have characterized the climatology of TC-SGWs, and the relative importance of the source and background wind for TC-SGWs has not been clearly established. Here we present the distribution of TC-SGWs formed over the northwest Pacific Ocean based on 30 years of ERA5 reanalysis data. Using explainable artificial intelligence, we found the background wind in the lower stratosphere to be more important than TCs in controlling the distribution pattern of TC-SGWs. The background wind shear can influence the direction of TC-SGW propagation by refraction and filtering, which influences the distribution pattern of the TC-SGWs. The TC intensity only influences the TC-SGW intensity. Our results provide information on TC-SGW hotspots and suggest the importance of the background wind shear.

Special Observing Period (SOP) data for the Year of Polar Prediction site Model Intercomparison Project (YOPPsiteMIP)

Abstract. The rapid changes occurring in the polar regions require an improved understanding of the processes that are driving these changes. At the same time, increased human activities such as marine navigation, resource exploitation, aviation, commercial fishing, and tourism require reliable and relevant weather information. One of the primary goals of the World Meteorological Organization's Year of Polar Prediction (YOPP) project is to improve the accuracy of numerical weather prediction (NWP) at high latitudes. During YOPP, two Canadian “supersites” were commissioned and equipped with new ground-based instruments for enhanced meteorological and system process observations. Additional pre-existing supersites in Canada, the United States, Norway, Finland, and Russia also provided data from ongoing long-term observing programs. These supersites collected a wealth of observations that are well suited to address YOPP objectives. In order to increase data useability and station interoperability, novel Merged Observatory Data Files (MODFs) were created for the seven supersites over two Special Observing Periods (February to March 2018 and July to September 2018). All observations collected at the supersites were compiled into this standardized NetCDF MODF format, simplifying the process of conducting pan-Arctic NWP verification and process evaluation studies. This paper describes the seven Arctic YOPP supersites, their instrumentation, data collection and processing methods, the novel MODF format, and examples of the observations contained therein. MODFs comprise the observational contribution to the model intercomparison effort, termed YOPP site Model Intercomparison Project (YOPPsiteMIP). All YOPPsiteMIP MODFs are publicly accessible via the YOPP Data Portal (Whitehorse: https://doi.org/10.21343/a33e-j150, Huang et al., 2023a; Iqaluit: https://doi.org/10.21343/yrnf-ck57, Huang et al., 2023b; Sodankylä: https://doi.org/10.21343/m16p-pq17, O'Connor, 2023; Utqiaġvik: https://doi.org/10.21343/a2dx-nq55, Akish and Morris, 2023c; Tiksi: https://doi.org/10.21343/5bwn-w881, Akish and Morris, 2023b; Ny-Ålesund: https://doi.org/10.21343/y89m-6393, Holt, 2023; and Eureka: https://doi.org/10.21343/r85j-tc61, Akish and Morris, 2023a), which is hosted by MET Norway, with corresponding output from NWP models.

Examining the factors behind the new UK January maximum temperature record, 28 January 2024

AbstractLarge‐scale subsidence and associated adiabatic warming through compression allowed exceptionally mild air to reach western Scotland at low elevations on 28 January 2024. Marked foehn effects were then able to mix this air mass into the boundary layer across the Scottish Highlands, where an official observation from Achfary recorded a new UK January maximum temperature of 19.9°C. Observations and some numerical weather prediction data were used to assess the likely contributions of various foehn mechanisms to this event, with findings suggesting that mechanical mixing/turbulence most likely played the dominant role.

Biomechanical analysis of spinal cord injury during scoliosis correction surgery.

Introduction: Surgical correction is a common treatment for severe scoliosis. Due to the significant spinal deformation that occurs with this condition, spinal cord injuries during corrective surgery can occur, sometimes leading to paralysis. Methods: Such events are associated with biomechanical changes in the spinal cord during surgery, however, their underlying mechanisms are not well understood. Six patient-specific cases of scoliosis either with or without spinal complications were examined. Finite element analyses (FEA) were performed to assess the dynamic changes and stress distribution of spinal cords after surgical correction. The FEA method is a numerical technique that simplifies problem solving by replacing complex problem solving with simplified numerical computations. Results: In four patients with poor prognosis, there was a concentration of stress in the spinal cord. The predicted spinal cord injury areas in this study were consistent with the clinical manifestations of the patients. In two patients with good prognosis, the stress distribution in the spinal cord models was uniform, and they showed no abnormal clinical manifestations postoperatively. Discussion: This study identified a potential biomechanical mechanism of spinal cord injury caused by surgical correction of scoliosis. Numerical prediction of postoperative spinal cord stress distribution might improve surgical planning and avoid complications.