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This study describes a method that combines synthetic aperture radar (SAR) data with shallow-water modeling to estimate flood hazards at a local level. The method uses particle filtering to integrate flood probability maps derived from SAR imagery with simulated flood maps for various flood return periods within specific river sub-catchments. We tested this method in a section of the Severn River basin in the UK. Our research involves 11 SAR flood observations from ENVISAT ASAR images, an ensemble of 15 particles representing various pre-computed flood scenarios, and 4 masks of spatial units corresponding to different river segmentations. Empirical results yield maps of maximum flood extent with associated return periods, reflecting the local characteristics of the river. The results are validated through a quantitative comparison approach, demonstrating that our method improves the accuracy of flood extent and scenario estimation. This provides spatially distributed return periods in sub-catchments, making flood hazard monitoring effective at a local scale. [ABSTRACT FROM AUTHOR]

Background: Individuals with rare kidney diseases account for 5-10% of people with chronic kidney disease, but constitute more than 25% of patients receiving kidney replacement therapy. The National Registry of Rare Kidney Diseases (RaDaR) gathers longitudinal data from patients with these conditions, which we used to study disease progression and outcomes of death and kidney failure., Methods: People aged 0-96 years living with 28 types of rare kidney diseases were recruited from 108 UK renal care facilities. The primary outcomes were cumulative incidence of mortality and kidney failure in individuals with rare kidney diseases, which were calculated and compared with that of unselected patients with chronic kidney disease. Cumulative incidence and Kaplan-Meier survival estimates were calculated for the following outcomes: median age at kidney failure; median age at death; time from start of dialysis to death; and time from diagnosis to estimated glomerular filtration rate (eGFR) thresholds, allowing calculation of time from last eGFR of 75 mL/min per 1·73 m 2 or more to first eGFR of less than 30 mL/min per 1·73 m 2 (the therapeutic trial window)., Findings: Between Jan 18, 2010, and July 25, 2022, 27 285 participants were recruited to RaDaR. Median follow-up time from diagnosis was 9·6 years (IQR 5·9-16·7). RaDaR participants had significantly higher 5-year cumulative incidence of kidney failure than 2·81 million UK patients with all-cause chronic kidney disease (28% vs 1%; p<0·0001), but better survival rates (standardised mortality ratio 0·42 [95% CI 0·32-0·52]; p<0·0001). Median age at kidney failure, median age at death, time from start of dialysis to death, time from diagnosis to eGFR thresholds, and therapeutic trial window all varied substantially between rare diseases., Interpretation: Patients with rare kidney diseases differ from the general population of individuals with chronic kidney disease: they have higher 5-year rates of kidney failure but higher survival than other patients with chronic kidney disease stages 3-5, and so are over-represented in the cohort of patients requiring kidney replacement therapy. Addressing unmet therapeutic need for patients with rare kidney diseases could have a large beneficial effect on long-term kidney replacement therapy demand., Funding: RaDaR is funded by the Medical Research Council, Kidney Research UK, Kidney Care UK, and the Polycystic Kidney Disease Charity., Competing Interests: Declaration of interests ERM reports support for the current manuscript from VHL UK/Ireland and consulting fees from MSD. SM is chair of OxalEurope. MS reports support for the current manuscript from a Medical Research Council UK Precision Medicine programme grant (MR/R013942/1) and consulting fees from Travere Therapeutics. RJC reports support for the current manuscript from Kidney Research UK. JAS reports support for the current manuscript from Kidney Research UK, Northern Counties Kidney Research Fund, and the Medical Research Council UK (all payments to institution). JAS is Academic Vice President of the UK Kidney Association. FWKT reports support from the National Institute for Health and Care Research Imperial Biomedical Centre. DN is the UK Kidney Association Director of Informatics Research. DPG reports support for the current manuscript from St Peter's Trust for Kidney Bladder and Prostate Research, Medical Research Council, Kidney Research UK, Kidney Care UK, and Polycystic Kidney Disease Charity (all payments to institution). DPG chairs the Rare Diseases Committee of the UK Kidney Association and reports fees for consulting and presenting from Novartis, Alexion, Calliditas, Sanofi, Britannia, and Travere. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Tidal flats are some of the most dynamic coastal environments in the world, where traditional coastal mapping and monitoring provide insufficient temporal resolution to reliably map channels and sand flats. Satellite-based Synthetic Aperture Radar (SAR) enables regular cloud-penetrating detection of water flowing through channels within the tidal flats, referred to as tidal channels. This paper presents a method for detecting a path through tidal channels, using satellite imagery, that supports our understanding and safe exploitation of this valuable coastal environment. This approach is the first proposed to identify navigable paths in all conditions, with SAR images susceptible to variation due to weather and tidal conditions. Tidal channels are known to vary in SAR presentation, and we find that tidal flat presentation is also influenced by conditions. The most influential factor is the wind, with high winds causing an inversion in how both tidal flats and tidal channels present in SAR images. The presented method for the automatic detection of tidal channels accounts for this variability by using previous channel paths as a reference to reliably correct imagery and detect the latest path. The final algorithm produces paths with minor errors in 17.6% of images; the error rate increases to 71.7%, with an almost tenfold increase in errors, when the SAR image and paths are not adjusted to account for conditions. This capability has been used to support the Nith Inshore Rescue in attending call-outs from their base in Glencaple, UK, while the insights from monitoring tidal channels for a year demonstrate how periods of high river flow preceded major changes in the channel path. [ABSTRACT FROM AUTHOR]

Coastal sediment grain size is an important factor in determining coastal morphodynamics. In this study, we explore a novel approach for retrieving the median sediment grain size (D50) of gravel-dominated beaches using Synthetic Aperture Radar (SAR) spaceborne imagery. We assessed this by using thirty-six Sentinel-1 (C-band SAR) satellite images acquired in May and June 2022 and 2023, and three NovaSAR (S-band SAR) satellite images acquired in May and June 2022, for three different training sites and one test site across England (the UK). The results from the Sentinel-1 C-band data show strong positive correlations (R2 ≥ 0.75 ) between the D50 and the backscatter coefficients for 15/18 of the resultant models. The models were subsequently used to derive predictions of D50 for the test site, with the models which exhibited the strongest correlations resulting in Mean Absolute Errors (MAEs) in the range 2.26–5.47 mm. No correlation (R2 = 0.04) was found between the backscatter coefficients from the S-band NovaSAR data and D50. These results highlight the potential to derive near-real time estimates of coastal sediment grain size for gravel beaches to better inform coastal erosion and monitoring programs. [ABSTRACT FROM AUTHOR]

For the physical model testing of wave energy converters (WECs) in the wave basin, it is necessary to test the models in a small number of sea states. Previously, the H – T binning method was widely used to determine the sea states that are representative of an ocean area. However, it omitted much useful information such as the wave directionality. In this paper, a novel method, the K -means clustering technique is used in combination with High Frequency (HF) radar measured data from Wave Hub, UK. The results show that K -means clustering method better preserves the characteristics of the ocean area than the binning method. Furthermore, the impact of different regrouping methods on assessing the annual energy output of the model is investigated, by applying the K -means clustering method to a 1:25 two-body hinged raft WEC. It is found that although non-linear performance can be clearly observed in the model both physically and numerically. Due to the fact that most sea states from Wave Hub are out of the non-linearity range of the model, the non-linear effect on the overall performance of the WEC model in this ocean area is limited. It allows the annual energy output to be accurately predicted by using only a small number of representative sea states (defined as K) ≤15, based on K -means clustering method. • K -means method selected representative sea states tested on a physical WEC model. • K -means method is effective in selecting the sea states for WEC model testing. • Representative sea states can obtain accurate annual energy output estimation. • Non-linearity of WEC tested had limited influence on annual energy output estimation. [ABSTRACT FROM AUTHOR]

Background: Disease recurrence remains one of the biggest concerns in patients after resection of pancreatic ductal adenocarcinoma (PDAC). Despite (neo)adjuvant systemic therapy, most patients experience local and/or distant PDAC recurrence within 2 years. High-level evidence regarding the benefits of recurrence-focused surveillance after PDAC resection is missing, and the impact of early detection and treatment of recurrence on survival and quality of life is unknown. In most European countries, recurrence-focused follow-up after surgery for PDAC is currently lacking. Consequently, guidelines regarding postoperative surveillance are based on expert opinion and other low-level evidence. The recent emergence of more potent local and systemic treatment options for PDAC recurrence has increased interest in early diagnosis. To determine whether early detection and treatment of recurrence can lead to improved survival and quality of life, we designed an international randomized trial., Methods: This randomized controlled trial is nested within an existing prospective cohort in pancreatic cancer centers in the Netherlands (Dutch Pancreatic Cancer Project; PACAP) and the United Kingdom (UK) (Pancreas Cancer: Observations of Practice and survival; PACOPS) according to the "Trials within Cohorts" (TwiCs) design. All PACAP/PACOPS participants with a macroscopically radical resection (R0-R1) of histologically confirmed PDAC, who provided informed consent for TwiCs and participation in quality of life questionnaires, are included. Participants randomized to the intervention arm are offered recurrence-focused surveillance, existing of clinical evaluation, serum cancer antigen (CA) 19-9 testing, and contrast-enhanced computed tomography (CT) of chest and abdomen every three months during the first 2 years after surgery. Participants in the control arm of the study will undergo non-standardized clinical follow-up, generally consisting of clinical follow-up with imaging and serum tumor marker testing only in case of onset of symptoms, according to local practice in the participating hospital. The primary endpoint is overall survival. Secondary endpoints include quality of life, patterns of recurrence, compliance to and costs of recurrence-focused follow-up, and the impact on recurrence-focused treatment., Discussion: The RADAR-PANC trial will be the first randomized controlled trial to generate high level evidence for the current clinical equipoise regarding the value of recurrence-focused postoperative surveillance with serial tumor marker testing and routine imaging in patients after PDAC resection. The Trials within Cohort design allows us to study the acceptability of recurrence-focused surveillance among cohort participants and increases the generalizability of findings to the general population. While it is strongly encouraged to offer all trial participants treatment at time of recurrence diagnosis, type and timing of treatment will be determined through shared decision-making. This might reduce the potential survival benefits of recurrence-focused surveillance, although insights into the impact on patients' quality of life will be obtained., Trial Registration: Clinicaltrials.gov, NCT04875325 . Registered on May 6, 2021., (© 2024. The Author(s).)

Differential reflectivity (ZDR$$ {Z}_{\mathrm{DR}} $$) columns were observed using a Met Office three‐dimensional radar composite. An algorithm for automatic detection of ZDR$$ {Z}_{\mathrm{DR}} $$ columns was developed, based on ZDR≥1.0$$ {Z}_{\mathrm{DR}}\ge 1.0 $$ dB and ZH≥10$$ {Z}_{\mathrm{H}}\ge 10 $$ dBZ. Across three case days, detected ZDR$$ {Z}_{\mathrm{DR}} $$ columns were found to precede severe convection in tracked convective cells with a range of lead times from 0 to 20 min depending on the case day. Requiring maxima above 1.4 dB and 30 dBZ of ZDR$$ {Z}_{\mathrm{DR}} $$ and ZH$$ {Z}_{\mathrm{H}} $$ respectively was an appropriate second condition for all three cases although the skill in the early detection of severe convection varied across case days. Despite the high probability of detections, the high false alarm rate accompanied by low critical success index and data latency limit performance based on the three cases considered in this study. Nevertheless, the ability to detect ZDR$$ {Z}_{\mathrm{DR}} $$ columns in operational radar data with a useful lead time prior to severe convection in certain conditions is a promising development towards advancing nowcasting of severe convection in the United Kingdom. [ABSTRACT FROM AUTHOR]

Object-based radar rainfall nowcasting is a widely used technique for convective storm prediction. Currently, most existing object-based nowcasting methods primarily focus on predicting cell movements, neglecting the temporal evolution of cell properties such as size, shape, and intensity. Incorporating this evolution is critical for improving predictability in convective storms. While previous studies have used three-dimensional (3D) radar observations to capture vertical changes during convective cell formation, these efforts often analyse or reconstruct specific convective events. Integrating 3D radar information into operational object-based radar rainfall nowcasting remains an open challenge. This research addresses this challenge using deep learning (DL) techniques. More specifically, a DL-based prediction model is developed, which uses 2D and 3D cells' properties retrieved from 3D radar reflectivity data at the current time and across the past 15 min to predict the evolution of these properties over the next 15 min. This model could eventually be integrated into existing object-based nowcasting models. A total of 4708 cell lifecycles, extracted from high-resolution (5-min, 1-km, 24 levels at 0.5 km intervals) 3D radar data across the UK, are used to train the model, and a total of 1177 lifecycles are used for testing. The proposed model is shown to predict the evolution of single-core convective cells effectively, including changes in 2D projected geometry and mean 2D and 3D reflectivity. In particular, by incorporating information on the vertical evolution of convective cores, the prediction errors of mean reflectivity (in both 2D and 3D) can be reduced by approximately 50% at 15-min forecast lead time, as compared to a persistence forecast. Keywords: radar, tracking, convective cell, nowcasting, 3D, deep learning, lstm. • A deep-learning model that predicts single-core convective cell evolution. • Convective core altitudes aid cell mean reflectivity prediction. • Prediction errors of cell reflectivity are reduced by 50% at 15-min lead time. [ABSTRACT FROM AUTHOR]

In recent years, trees in European countries have been increasingly endangered by emerging infectious diseases (EIDs). In the United Kingdom (U.K.), this has been observed to affect whole woodlands and forests, threatening the existence of some types of trees. Although quarantine measures have been taken to limit the spreading of such diseases, this has not yet been effectively controlled leading to millions of trees affected by EIDs. Ground-penetrating radar (GPR) has proven effective in identifying critical features on diseased trees for detection of EIDs spread. However, the irregular shape of tree trunks and their complex internal structure represent real challenges for conventional GPR measurements and data processing methodologies. In this research, a dual-polarized GPR system is used to detect internal decay in tree trunks using novel signal processing methodologies. A polarimetric correlation filter based on Bragg scattering on a 3-D Pauli feature vector and an arc-shaped Kirchhoff migration are discussed in detail. The proposed polarimetric correlation filter is utilized to enhance the signal-to-noise ratio (SNR) of B-scans due to bark and tree trunk high-loss properties of tree trunks. Meanwhile, an arc-shaped Kirchhoff migration algorithm is performed to counteract the influence of the bark irregularity. The proposed data processing framework is successfully validated with measurements on a real tree trunk, where cross sections were subsequently cut for comparison purposes. Outcomes from the proposed methodology demonstrate a high consistency with the features observed on the tree trunk cross sections, indicating the reliability of the proposed detection scheme for assessing tree-decay associated with EIDs. [ABSTRACT FROM AUTHOR]

Fireworks and other pyrotechnics are acknowledged as sources of disturbance to wildlife, with evidence that many species react adversely to their sight and sound at discharge. However, how firework releases impact wildlife within a city landscape is poorly understood. Here, we explore the effect of fireworks on urban birds using an L-band staring radar (90-degree sector out to a 5 km range) to capture bird activity derived from flight tracks (i.e. 3D visualisation of individual flying birds built from radar detections) within the city of Birmingham, UK. Comparing the tracks between baseline periods with no fireworks and periods where fireworks are commonly discharged using a null model indicated that birds flew at higher elevations during firework periods (standardised effect sizes of 17.11, 26.54 and 5.83, for Diwali, Bonfire Night, and New Year's Eve, respectively). Birds also flew in more significant numbers (standardised effect sizes of 23.41, 7.98 and 7.19 for Diwali, Bonfire Night, and New Year's Eve, respectively). Therefore, bird activity was elevated during firework events at a time of night when many would otherwise be roosting. Such disturbance may have implications for avian biology since large public firework events occur at colder times of the year in the UK when birds have elevated thermoregulatory costs. [ABSTRACT FROM AUTHOR]

Accurate precipitation estimation with weather radars is essential for hydrological and meteorological applications. The differential reflectivity (ZDR) is a crucial weather radar measurement that helps to improve quantitative precipitation estimates using polarimetric weather radars. However, a system bias between the horizontal and vertical channels generated by the radar produces an offset in ZDR. Existing methods to calibrate ZDR measurements rely on the intrinsic values of the ZDR of natural targets (e.g. drizzle or dry snow) collected at high elevation angles (e.g. higher than 40 ∘ or even at 90 ∘), in which ZDR values close to 0 dB are expected. However, not all weather radar systems can scan at such high elevation angles or point the antenna vertically to collect precipitation measurements passing overhead. Therefore, there is a need to develop new methods to calibrate ZDR measurements using lower-elevation scans. In this work, we present and analyse a novel method for correcting and monitoring the ZDR offset using quasi-vertical profiles computed from scans collected at 9 ∘ elevations. The method is applied to radar data collected through 1 year of precipitation events by two operational C-band polarimetric weather radars in the UK. The proposed method shows a relative error of 0.1 dB when evaluated against the traditional approach based on ZDR measurements collected at 90 ∘ elevations. Additionally, the method is independently assessed using disdrometers located near the radar sites. The results showed a reasonable agreement between disdrometer-derived and radar-calibrated ZDR measurements. [ABSTRACT FROM AUTHOR]

This paper proposes a human activity recognition (HAR) method for frequency-modulated continuous wave (FMCW) radar sensors. The method utilizes a multi-domain feature attention fusion network (MFAFN) model that addresses the limitation of relying on a single range or velocity feature to describe human activity. Specifically, the network fuses time-Doppler (TD) and time-range (TR) maps of human activities, resulting in a more comprehensive representation of the activities being performed. In the feature fusion phase, the multi-feature attention fusion module (MAFM) combines features of different depth levels by introducing a channel attention mechanism. Additionally, a multi-classification focus loss (MFL) function is applied to classify confusable samples. The experimental results demonstrate that the proposed method achieves 97.58% recognition accuracy on the dataset provided by the University of Glasgow, UK. Compared to existing HAR methods for the same dataset, the proposed method showed an improvement of about 0.9–5.5%, especially in the classification of confusable activities, showing an improvement of up to 18.33%. [ABSTRACT FROM AUTHOR]

The first Doppler spectra ever acquired by an atmospheric radar at 200 GHz (G‐band) are presented. The observations were taken during a light precipitation event in May (rain rates <2 mm hr−1) at Chilbolton Observatory, UK, with coincident Ka‐band and W‐band Doppler radar measurements. The collected rain spectra agree with Mie theory predictions: at G‐band they show significant reductions in the spectral power return—as compared to theoretical Rayleigh scattering—corresponding to high Doppler velocities (i.e., large raindrops) with the presence of multiple peaks and "Mie notches" in correspondence to the maxima and minima of the raindrop backscattering cross sections. The first two G‐band Mie troughs correspond to smaller velocities/sizes than the first W‐band Mie notch. These features offered by G‐band radars pave the way toward applying, in rain, Mie notch vertical wind retrievals and multifrequency drop size distribution microphysical retrievals to smaller rain rates and smaller characteristic sizes than ever before. Plain Language Summary: The first Doppler velocity measurements ever acquired by an atmospheric radar at 200 GHz (in the so‐called G‐band) are presented. The observations were taken during a light precipitation event in May (rain rates <2 mm hr−1) at the STFC Chilbolton Observatory, UK, with coincident measurements at 35 and 94 GHz. At high Doppler velocities (i.e., in correspondence to large raindrops) the collected G‐band Doppler velocity spectra show significant non‐Rayleigh effects, as expected from scattering Mie theory when the wavelength is much larger than the scattering target dimension. This is highlighted by the presence of multiple peaks and minima in the spectral power return and by a significant reduction of the spectral power at G‐band compared to the other frequencies. The first two G‐band minima both correspond to smaller velocities/sizes than the first W‐band minimum, a direct consequence of the reduced wavelength of the G‐band radar. These novel features observed by G‐band radars pave the way toward applying, in rain, vertical wind retrievals and multifrequency drop size distribution retrievals to smaller rain rates and smaller characteristic drop sizes than ever before. Key Points: The first Doppler spectra ever acquired by an atmospheric radar at 200 GHz (G‐band) are presentedThe G‐band spectra in rain show the presence of Mie notches, as expected from Mie scattering theoryThe G‐band spectra pave the way toward applying vertical wind and multifrequency DSD retrievals at smaller rain rates than ever before [ABSTRACT FROM AUTHOR]

In this work, we present a new quantitative precipitation estimation (QPE) quality-control (QC) algorithm for the U.K. weather radar network. The real-time adaptive algorithm uses a neural network (NN) to select data from the lowest useable elevation scan to optimize the combined performance of two other radar data correction algorithms: ground-clutter mitigation [using Clutter Environment Analysis using Adaptive Processing (CLEAN-AP)] and vertical profile of reflectivity (VPR) correction. The NN is trained using 3D tiles of observed uncontaminated weather signals that are systematically combined with ground-clutter signals collected under dry weather conditions. This approach provides a way to simulate radar signals with a wide range of clutter contamination conditions and with realistic spatial structures while providing the uncontaminated "truth" with respect to which the performance of the QC algorithm can be measured. An evaluation of QPE products obtained with the proposed QC algorithm demonstrates superior performance as compared to those obtained with the QC algorithm currently used in operations. Similar improvements are also illustrated using radar observations from two periods of prolonged precipitation, showing a better balance between overestimation errors from using clutter-contaminated low-elevation radar data and VPR-induced errors from using high-elevation radar data. [ABSTRACT FROM AUTHOR]

The understanding of large‐scale rainfall microphysical characteristics plays a significant role in meteorology, hydrology and natural hazards managements. Traditional instruments for estimating raindrop size distribution (DSD), including disdrometers and ground dual‐polarization radars, are available only in limited areas. However, the development of space‐based radars and mesoscale numerical weather prediction models would allow for DSD estimation on a large scale. This study investigated the performance of the weather research and forecasting (WRF) model and the global precipitation measurement mission (GPM) dual‐frequency precipitation radar for DSD retrieval under different conditions. The DSD parameters (Dm and Nw), rain rate (R), rainfall kinetic energy (KE) and radar reflectivity (Z) were estimated in Chilbolton, United Kingdom, by using long‐term disdrometer observations for validation. The rainfall kinetic energy–rain rate (KE–R) and radar reflectivity–rain rate (Z–R) relationships were explored using a disdrometer, the WRF model and GPM. It was found that the DSD parameter distribution trends of the three approaches are similar although the WRF model has larger Dm and smaller Nw values. In terms of the rainfall microphysical relationship, GPM performs better when both Ku‐ and Ka‐band precipitation radars (KuPR and KaPR) observe precipitation simultaneously (R > 0.5 mm h−1), while the WRF model shows high accuracy in light rain (R < 0.5 mm h−1). The fusion of GPM and WRF model is recommended for the improved understanding of rainfall microphysical characteristics in ungauged areas. [ABSTRACT FROM AUTHOR]

The UK's tourism and hospitality industries, having the highest concentration of migrant workers compared to other industries, face complex challenges in managing the risks of labour exploitation and modern slavery (MS); issues largely neglected in academic research. New employment models risk weakening workers' power in employment relations, potentially leading to increased vulnerability and exploitation risk. Based on analysing primary and secondary sources, this article examines the nature of these risks. Our findings show the following drivers: power imbalances; possible enforcement gaps; normalisation and moralisation of possible exploitation; and potential threats posed by macro-level political and socioeconomic issues/events; these, including more competitive labour markets, paradoxically also present opportunities. We develop significant, novel theoretical contributions and propose the new '(in)conspicuous exploitariat' concept, encapsulating their (dis)empowerment, and potential future risks/opportunities. The research advances theory, with implications for policy and practice, for both the UK and the international tourism and hospitality industries. [ABSTRACT FROM AUTHOR]

Weather radars produce high spatial and temporal resolution observations of precipitation events. Over the years, weather radar operators have updated their radar networks to exploit the latest technological advancements. One of the most significant improvements in this matter was providing the radars with polarimetric capabilities, as this allows the radars to gather more detailed information about the precipitation targets' shape, size, phase, and orientation. The radar research community has been working along with these advancements to develop robust algorithms that boost the radar data applications, such as radar quantitative precipitation estimation (QPE), rain microphysics analysis, nowcasting of precipitation or numerical weather prediction based on radar measurements. Some of these algorithms have been implemented in open-source toolboxes that aim to facilitate the processing and quality control of radar data produced by different radar systems. However, these open-source toolboxes have not yet included a chain process tailored to the UK radar research context. This paper presents a TOolbox to process WEather Radar data in PYthon (Towerpy). Towerpy can read, process and display polarimetric radar data from different radar systems, but it is specially customised for data produced by the UK Met Office radar network. Towerpy is built upon robust algorithms that cover various aspects of the radar data quality control (e.g., calibration of radar measurements, identification of non-meteorological echoes, attenuation correction, among others) and the computation of radar rainfall rates. Additionally, a radar processing chain was devised using the Towerpy modules to produce radar rainfall estimates. Raw polarimetric radar measurements collected by the UK Met Office radar network throughout the UK's wettest day on record were used as input for this processing chain. The results confirm that Towerpy is a powerful radar research tool and demonstrate its ability to generate functional radar QPE that can be used to improve operational radar rainfall products. • We present Towerpy, a Python toolbox to process polarimetric weather radar data. • Towerpy provides robust algorithms for radar data quality control and radar QPE. • We set a processing chain that estimates rain rates to show Towerpy's capabilities. [ABSTRACT FROM AUTHOR]

Operational forecasting centres increasingly rely on convection‐permitting NWP simulations to assist in their forecasting of convective events. The evaluation of upgrades in the underlying NWP modelling system normally happens through routine verification using traditional metrics on two‐dimensional fields, such as gridded rainfall data. Object‐ and process‐based evaluation can identify specific physical mechanisms for model improvement, but such evaluation procedures normally require targeted and expensive field campaigns. Here, we explore the potential use of the UK operational radar network observations and its derived 3D composite product for evaluating the representation of convective storms in the Met Office Unified Model. A comparison of the 1 × 1 × 0.5 km 3D radar composites against observations made with the research‐grade radar at Chilbolton in the southern UK indicates that the 3D radar composite data can reliably be used to evaluate the morphology of convective storms. The 3D radar composite data are subsequently used to evaluate the development of convective storms in the Met Office Unified Model. Such analysis was previously unavailable due to a lack of 3D radar data of high temporal frequency. The operational nature of the UK radar data makes these 3D composites a valuable resource for future studies of the initiation, growth, development, and organisation of convective storms over the UK. [ABSTRACT FROM AUTHOR]

Llandegla research station. [ABSTRACT FROM AUTHOR]

Introduction: Multiple myeloma is a plasma cell malignancy that accounts for 1%-2% of newly diagnosed cancers.At diagnosis, approximately 20% of patients can be identified, using cytogenetics, to have inferior survival (high-risk). Additionally, standard-risk patients, with detectable disease (minimal residual disease (MRD)-positive) postautologus stem cell transplant (ASCT), fare worse compared with those who do not (MRD-negative). Research is required to determine whether a risk-adapted approach post-ASCT could further improve patient outcomes., Methods: RADAR is a UK, multicentre, risk-adapted, response-guided, open-label, randomised controlled trial for transplant-eligible newly diagnosed multiple myeloma patients, using combinations of lenalidomide (R), cyclophosphamide (Cy), bortezomib (Bor), dexamethasone (D) and isatuximab (Isa).Participants receive RCyBorD(x4) induction therapy, followed by high-dose melphalan and ASCT. Post-ASCT, there are three pathways as follows:A phase III discontinuation design to assess de-escalating therapy in standard-risk MRD-negative patients. Participants receive 12 cycles of Isa maintenance. Those who remain MRD-negative are randomised to either continue or stop treatment.A phase II/III multiarm multistage design to test treatment strategies for treatment escalation in standard-risk MRD-positive patients. Participants are randomised to either; R, RBorD(x4) +R, RIsa, or RBorIsaD(x4) + RIsa.A phase II design to assess the activity of intensive treatment strategies in high-risk patients. Participants are randomised to RBorD(x4) +R or RBorIsaD(x4) + RIsa.1400 participants will be registered to allow for 500, 450 and 172 participants in each pathway. Randomisations are equal and treatment is given until disease progression or intolerance., Ethics and Dissemination: Ethical approval was granted by the London-Central Research Ethics Committee (20/LO/0238) and capacity and capability confirmed by the appropriate local research and development department for each participating centre prior to opening recruitment. Participant informed consent is required before trial registration and reconfirmed post-ASCT. Results will be disseminated by conference presentations and peer-reviewed publications., Trial Registration Number: ISCRTN46841867., Competing Interests: Competing interests: ABC, KLR, DAC, AH, CO, LBai, LBar, AP and RL report grants and non-financial support from BMS/Celgene, grants and non-financial support from Merck Sharpe & Dohme, grants and non-financial support from Amgen, grants and non-financial support from Takeda, during the conduct of the trial. DAC also reports travel support from Celgene Corporation. KY, RDT, CB, GJ, GP, MC, BD, DM, GC, HA, KR,SA and AC have no conflicting interests to declare. MC declares, Bristol Myers Squibb- employee, Honoraria/travel support in the last 3 years from, Amgen, BMS/Celgene, Janssen, Takeda, Abbvie. CP declares BMS/Celgene Ad boards and speaker fees, Sanotif—Ad board, speaker fees, conference registration fees. JS reports Carrying out consultancy work (Advisory Board) for Sanofi. And an educational speaking engagement for Celgene/BMS RP declares; Honoraria—Jannsen, BMS, Abbvie, GSK. Consultancy: GSK, Janssen. Meeting support: Janssen, Takeda, BMS. RO declares- Janssen - advisory board, honoraria, Celegene— honoraria, Beigene - advisory board, honoraria, Astra Zeneca—honoraria. MK declares inter-relationships: AbbVie: consultancy; Amgen: honoraria; BMS/Celgene: consultancy, research funding (institution); GSK: consultancy; Janssen: consultancy, research funding (institution); Karyopharm: consultancy; Pfizer: consultancy; SeattleGenetics: consultancy; Takeda: consultancy; Sanofi: honoraria. MD reports owning stock in Abingdon Health. SQ is the founder and CSO of Achilles therapeutics a company developing T cell therapies for solid tumours., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

A 5-yr climatology and composite study of precipitation bands associated with extratropical cyclones over the British Isles from April 2017 to March 2022 is constructed. A total of 249 single bands were manually identified from radar network mosaics in association with 167 cyclones identified from surface maps. More bands formed over water near the coast than over inland areas, and most had a meridional orientation. The average lengths of bands at the times of formation and maximum length were 290 and 460 km, respectively; only 20% of bands reached a maximum length exceeding 600 km. The number of bands decreased with increasing duration, with 31% of bands lasting for 2–3 h, with bands lasting more than 10 h uncommon. The bands were classified into six categories, with occluded-frontal bands (19 yr−1), warm-frontal bands (11 yr−1), and cold-frontal bands (10 yr−1) being the most frequent. Occluded-frontal and warm-frontal bands commonly occurred west of Scotland and in the east quadrant relative to their parent cyclones. In contrast, cold-frontal bands commonly occurred southwest of Great Britain and in the south quadrant relative to their parent cyclones. Composites for northwest–southeast occluded-frontal and warm-frontal bands west of Scotland, and southwest–northeast cold-frontal bands southwest of Great Britain, show the different synoptic environments that favor bands. The low-level jet transports moisture into the band and is similar to the location and scale of the composite bands, similar to that of an atmospheric river. These results are compared to previous studies on bands from the United States. Significance Statement: Precipitation bands are lines of heavy precipitation as seen on weather radar. Most studies of bands in extratropical cyclones have occurred in the United States. We examine 5 years of bands in extratropical cyclones over the British Isles to better understand their characteristics. Bands form in preferred geographic regions: offshore of the west coasts of Scotland, Wales, and southwest England. The most common bands are associated with occluded fronts (37% of all bands). The average scale of the bands is associated with the average scale of wind maxima 1–2 km above ground. These results provide a better understanding of the typical characteristics and conditions under which bands form and their geographical variability compared to the United States. [ABSTRACT FROM AUTHOR]

A MIMO GB-SAR system called MELISSA was put in place to monitor landslides in Italy and the sinking of the Costa Concordia cruise liner in France. It comprises 12 pyramidal horn antennas placed in a linear geometry for transmission, and these are used in the detection of the motion of a target (for example a landslide or other terrestrial deformation). The low half power beam width (19.76° at θ = 90°) of the transmitting radiation pattern of MELISSA results in low coverage area of the target. This paper proposes two alternative types of horn antenna for the current transmitter module of MELISSA, namely the cantenna and coaxial cavity horn antenna, for installation in a 2×6 planar antenna array. A higher value of the 3 dB beamwidth is observed using these arrays (38.320 at θ = 90° and 104.80 at φ = 0° for the cantenna array and 410 at θ = 90° and 140.40 at φ = 0° for the coaxial cavity horn antenna array). The overall gain of the proposed systems is around 10 dBi, and the efficiencies are between 85% and 90%. Using the Dolph Chebyshev beamforming technique on the proposed antenna arrays yields a zero sidelobe level, which improves the overall peak sidelobe ratio of the system and in turn the quality of the images obtained. Our proposed design for the transmitting section of the MELISSA system has applications terrestrial deformation monitoring with higher area coverage. [ABSTRACT FROM AUTHOR]

This work presents studies of naturally enhanced ion acoustic lines and the correlation they have with the electron temperature fluctuations on the eve and after the NEIAL events. The methodology used in this work is based on the analysis of European Incoherent Scatter Scientific Association raw data, archived at Rutherford- Appleton Laboratory (Harwell Oxford Campus) in the United Kingdom. The EISCAT Svalbard radar raw data were analyzed using two specialized programs: the Real Time Graph and the Grand Unified Incoherent Scatter Data Analysis Program, version 8.6. Our findings suggest a strong correlation between the NEIALs phenomenon and plasma parameter fluctuations such as the electron temperature in the Earth ionosphere. Our findings show that the plasma electron temperature progressively increases before the event, and progressively decreases after the event. [ABSTRACT FROM AUTHOR]

There is an urgent need for high-quality and high-spatial-resolution hourly precipitation products around the globe, including the UK. Although hourly precipitation products exist for the UK, these either contain large errors, or are insufficient in spatial resolution. An efficient way to solve this is to develop a merged precipitation product that combines the information and benefits from multiple data sources, improving both the spatial resolution and accuracy of hourly precipitation estimates over the UK. In this study, we develop a UK high-resolution gauge–radar–satellite merged hourly precipitation analysis: the UKGrsHP. It covers the UK from 12.5° W to 3.5° E, 49° N–60° N, with a spatial resolution of 0.01° × 0.01° in latitude/longitude (equivalent to 1 km resolution in the mid-latitudes). An optimal interpolation (OI)–based multi-source merging scheme with compound strategy is developed and tested for producing the UKGrsHP. Three input data sources are used: gauge analysis data interpolated from 1903 quality-controlled hourly observations, the UK Nimrod radar precipitation analysis and the GSMaP global satellite precipitation analysis. Using independent tests against ~ 220 independent gauge observations on 1 year's experimental UKGrsHP, covering the period from January to December 2014, we find that the final merged data performs better than three individual precipitation analyses used as inputs. A full version of the UKGrsHP starting in April 2004 is now under production, which will have wide applications in climate services and scientific research across multiple disciplines. [ABSTRACT FROM AUTHOR]

Probabilistic flood mapping offers flood managers, decision makers, insurance agencies, and humanitarian relief organizations a useful characterization of uncertainty in flood mapping delineation. Probabilistic flood maps are also of high interest for data assimilation into numerical models. The direct assimilation of probabilistic flood maps into hydrodynamic models would be beneficial because it would eliminate the intermediate step of having to extract water levels first. This paper introduces a probabilistic flood mapping procedure based on synthetic aperture radar (SAR) data. Given a SAR image of backscatter values, we construct a total histogram of backscatter values and decompose this histogram into probability distribution functions of backscatter values associated with flooded (open water) and non-flooded pixels, respectively. These distributions are then used to estimate, for each pixel, its probability of being flooded. The new approach improves on binary SAR-based flood mapping procedures, which do not inform on the uncertainty in the pixel state. The proposed approach is tested using four SAR images from two floodplains, i.e., the Severn River (U.K.) and the Red River (U.S.). In all four test cases, reliability diagrams, with error values ranging from 0.04 to 0.23, indicate a good agreement between the SAR-derived probabilistic flood map and an independently available validation map, which is obtained from aerial photography. [ABSTRACT FROM PUBLISHER]

Progress toward habitat protection goals can effectively be performed using satellite imagery and machine-learning (ML) models at various spatial and temporal scales. In this regard, habitat types and landscape structures can be discriminated against using remote-sensing (RS) datasets. However, most existing research in three-dimensional (3D) habitat mapping primarily relies on same/cross-sensor features like features derived from multibeam Light Detection And Ranging (LiDAR), hydrographic LiDAR, and aerial images, often overlooking the potential benefits of considering multi-sensor data integration. To address this gap, this study introduced a novel approach to creating 3D habitat maps by using high-resolution multispectral images and a LiDAR-derived Digital Surface Model (DSM) coupled with an object-based Random Forest (RF) algorithm. LiDAR-derived products were also used to improve the accuracy of the habitat classification, especially for the habitat classes with similar spectral characteristics but different heights. Two study areas in the United Kingdom (UK) were chosen to explore the accuracy of the developed models. The overall accuracies for the two mentioned study areas were high (91% and 82%), which is indicative of the high potential of the developed RS method for 3D habitat mapping. Overall, it was observed that a combination of high-resolution multispectral imagery and LiDAR data could help the separation of different habitat types and provide reliable 3D information. [ABSTRACT FROM AUTHOR]

• Precipitation data from an X-band radar in northern Scotland are assessed. • Assessment against river flow using a distributed hydrological model brings fresh insights. • Lowest usable beam elevation is the main limit on performance in this mountainous area. • Use of dual-polarisation estimators improves performance during summer months. • The X-band radar is suited for filling gaps in the existing C-band radar network. A set of Quantitative Precipitation Estimates (QPEs) from a dual-polarisation X-band radar observation campaign in a mountainous area of Northern Scotland is assessed with reference to observed river flows as well as being compared to estimates from the UK C-band radar and raingauge networks. Employing estimation methods of varying complexity, the X-band QPEs are trialled as alternative inputs to Grid-to-Grid (G2G), a distributed hydrological model, to produce simulated river flows for comparison with observations. This hydrological assessment complements and extends a previous meteorological assessment that used point raingauge data only. Precipitation estimates for two periods over the observation campaign in 2016 (March to April and June to August) are assessed. During the second period, increased incorporation of dual-polarisation variables into the radar processing chain is found to be of considerable benefit, whereas during the first period the low height of the melting layer often restricts their use. As a result of the complex topography in Northern Scotland, the Lowest Usable Elevation (LUE) of the X-band radar observations is found to be a stronger indicator of the hydrological model performance than range from the radar. For catchments with an LUE of less than 3 km, the best X-band QPE typically performs better for modelling river flow than using an estimate from the UK C-band radar network. The hydrological assessment framework used here brings fresh insights into the performance of the different QPEs, as well as providing a stimulus for targeted improvements to dual-polarisation radar-based QPEs that have wider relevance beyond the case study situation. [ABSTRACT FROM AUTHOR]

Across the UK, sandy beaches and dunes protect coastal infrastructure from waves and extreme water levels during large‐scale storms, while providing important habitats and recreational opportunities. Understanding their long‐term evolution is vital in managing their condition in a changing climate. Recently, ground‐penetrating radar (GPR) methods have grown in popularity in geomorphological applications, yielding centimetre‐scale resolution images of near‐surface stratigraphy and structure, thus allowing landscape evolution to be reconstructed. Additionally, abrupt changes in palaeo‐environments can be visualized in three dimensions. Although often complemented by core data, GPR allows interpretations to be extended into areas with minimal ground‐truth control. Nonetheless, GPR data interpretation can be non‐intuitive and ambiguous, and radargrams may not initially resemble the expected subsurface geometry. Interpretation can be made yet more onerous when handling the large 3D data volumes that are facilitated with modern GPR technology. Here we describe the development of novel semi‐automated GPR feature‐extraction tools, based on 'edge detection' and 'thresholding' methods, which detect regions of increased GPR reflectivity which can be applied to aid in the reconstruction of a range Quaternary landscapes. Since reflectivity can be related to lithological and/or pore fluid changes, the 3D architecture of the palaeo‐landscape can be reconstructed from the features extracted from a geophysical dataset. We present 500 MHz GPR data collected over a buried Holocene coastal dune system in North Wales, UK, now reclaimed for use as an airfield. Core data from the site, reaching a maximum depth 2 m, suggest rapid vertical changes from sand to silty‐organic units, and GPR profiles suggest similar lateral complexity. By applying thresholding methods to GPR depth slices, these lateral complexities are effectively and automatically mapped. Furthermore, automatic extraction of the local reflection power yields a strong correlation with the depth variation of organic content, suggesting it is a cause of reflectivity contrast. GPR‐interpolated analyses away from core control thus offer a powerful proxy for parameters derived from invasive core logging. The GPR data collected at Llanbedr airfield highlight a complex dune system to a depth of 2.8 m, probably deposited in several phases over ~700 years, similar to elsewhere in North Wales. [ABSTRACT FROM AUTHOR]

Water is a fundamental control on the deterioration of historic stone masonry, of which wind-driven rain (WDR) is an important source in the UK. Non-destructive testing methods for moisture measurement can characterise the response of masonry to short (but intense) periods of wind-driven rain. An important part of this response is how masonry functions as a system of stone units and mortar joints, in which mortar can act as a conduit for moisture. While non-destructive techniques are common in moisture surveying of built heritage, there are no agreed best practice methods for collection, handling, and visual representation of data. This study explores the comparative advantages of microwave and radar measurements in two field experiments of exposure to short (but intense) simulated wind-driven rain exposure to demonstrate when and how they are most effectively employed. A novel method of representing data as percentiles is explored to facilitate effective communication of moisture measurements. In the case of the granite wall (e.g. with components of strongly contrasting hygric properties), microwave and radar provided similar information. The average travel time of the radar signal (from the back wall reflection) demonstrated that radar can non-destructively identify water penetration through mortar joints. In the sandstone tower, the microwave measurements were able to clearly identify four different moisture regimes as a result of different intensities of WDR exposure. The radar measurements were suited to identifying distinctions between localised moisture contents within masonry units and mortar joints, which characterised how the masonry was functioning as a holistic system. The measurements on both the granite wall and the sandstone tower demonstrated that the radar is influenced by environmental conditions which influence surface condensation and equilibrium moisture contents. Representing the measurements as percentiles improved visual representation of measurements with colour scales and minimised potential skewing of normalisation and scales from extreme values/outliers. This paper demonstrates that both microwave and radar techniques can be useful for monitoring moisture in stone masonry systems. Material characteristics of the masonry system and the objective(s) of the investigation should be considered during selection of the appropriate technique(s). [ABSTRACT FROM AUTHOR]

ABSTRACT Atkinson, J.; Esteves, L.S.; Williams, J.W.; McCann D.L., and Bell, P.S., 2018. The Application of X-Band Radar for Characterisation of Nearshore Dynamics on a Mixed Sand and Gravel Beach. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 281–285. Coconut Creek (Florida), ISSN 0749-0208. Remote sensing using X-band radar allows the estimation of wave parameters, near surface currents and the underlying bathymetry. This paper explores the use of radar to derive nearshore bathymetry at a complex site, at Thorpeness in Suffolk, UK. The site has a history of sporadic and focused erosion events along the beach frontage and as part of the X-Com project (X-band Radar and Evidence-Based Coastal Management Decisions) a radar system was deployed with the aim of further understanding the complex nearshore sediment processes influencing erosion. Initially, the bathymetric variation at the site is quantified through analysis of current and historic multibeam surveys. These indicate depth changes approaching 3 m. Subsequently, validation of the radar data against concurrent multibeam survey data has been undertaken. Results show that the radar derived bathymetry has a precision of ±1m at the site, with the largest errors being associated with areas of more complex bathymetry and where wave data quality was less suitable for analysis by the X-band radar bathymetry algorithms. It is concluded that although the accuracy of radar-derived bathymetry is lower than traditional multibeam survey, the low cost for high temporal coverage can be utilised for long-term monitoring of coastal sites where a cost-effective means of quantifying large-scale bathymetric changes is required. [ABSTRACT FROM AUTHOR]

For the past eight years the authors have been engaged in a debate with Dr. Brian N. Hall regarding the role of British continuous wave (CW) wireless in WWI. Dr. Hall contended the British army did as well as could be expected in applying this technology to warfare. The authors compared British developments to much faster, although later, American development. The French army, however, deployed this technology much earlier than either. New evidence shows that though aware of the French achievement the British chose not to exploit it. [ABSTRACT FROM AUTHOR]

The National Registry of Rare Kidney Diseases (RaDaR) is a Renal Association (now the UK Kidney Association) initiative to improve patient and clinician knowledge of rare kidney diseases. The RaDaR is the largest rare kidney disease registry in the world and it is a globally unique and extremely rich resource with incredible potential to improve the understanding of these rare conditions [ABSTRACT FROM AUTHOR]

Nowadays, an increasing amount of attention is being devoted towards passive and non-intrusive sensing methods. The prime example is healthcare applications, where on-body sensors are not always an option or in other applications which require the detection and tracking of unauthorized (non-cooperative) targets within a given environment. Therefore, in this paper we present a dataset consisting of measurements obtained from Radio-Frequency (RF) devices. Essentially, the dataset consists of Ultra-Wideband (UWB) data in the form of Channel Impulse Response (CIR), acquired via a Commercial Off-the-Shelf (COTS) UWB equipment. Approximately 1.6 hours of annotated measurements are provided, which are collected in a residential environment. This dataset can be used to passively track a target's location in an indoor environment. Additionally, it can also be used to advance UWB-based Human Activity Recognition (HAR) since three basic human activities were recorded, namely, sitting, standing and walking. We anticipate that such datasets may be utilized to develop novel algorithms and methodologies for healthcare, smart homes and security applications. Measurement(s) Human physical activity • Human location Technology Type(s) Ultra-wideband impulse radar Factor Type(s) Human location • Human physical activity • Monitoring environment • Participant demographics • Radio-frequency signals Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment residential building Sample Characteristic - Location United Kingdom [ABSTRACT FROM AUTHOR]

Creating native woodland is a policy goal globally, and one strategy to maximize woodland creation benefits in limited space is to target efforts to extend existing woodlands. There is evidence to support spatially targeting habitat creation for biodiversity, however, there is little evidence of how this affects a habitat's structural development. Here, a space‐for‐time study using light detection and ranging (LiDAR) data assesses how the structure of recently created woodlands, are affected by the presence of an adjacent older woodland. Recently created native woodlands were identified across the Isle of Wight UK using historical maps and satellite imagery. Canopy height and foliage height diversity were derived for all woodlands from LiDAR data collected at two different time points (2011 and 2021), and linear models were used to test for any differences in these structural metrics between sites with an adjacent older woodland, and those without. The percentage change in woodland height between the two time points was also tested. In woodlands created adjacent to older woodlands, canopy height was found to be higher by an average of nearly 2 m, and foliage height diversity was found to be on average 4.7% higher, using the 2021 data. Growth rates between 2011 and 2021 were not significantly different between the groups, although young adjacent woodlands grew the most on average. This research shows that creating woodlands adjacent to existing older woodlands reduces the time taken to create tall and to a lesser extent structurally diverse habitat, which may lead to early biodiversity benefits. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive dataset intended to evaluate passive Human Activity Recognition (HAR) and localization techniques with measurements obtained from synchronized Radio-Frequency (RF) devices and vision-based sensors. The dataset consists of RF data including Channel State Information (CSI) extracted from a WiFi Network Interface Card (NIC), Passive WiFi Radar (PWR) built upon a Software Defined Radio (SDR) platform, and Ultra-Wideband (UWB) signals acquired via commercial off-the-shelf hardware. It also consists of vision/Infra-red based data acquired from Kinect sensors. Approximately 8 hours of annotated measurements are provided, which are collected across two rooms from 6 participants performing 6 daily activities. This dataset can be exploited to advance WiFi and vision-based HAR, for example, using pattern recognition, skeletal representation, deep learning algorithms or other novel approaches to accurately recognize human activities. Furthermore, it can potentially be used to passively track a human in an indoor environment. Such datasets are key tools required for the development of new algorithms and methods in the context of smart homes, elderly care, and surveillance applications. Measurement(s) Human physical activity • Human location Technology Type(s) WiFi sensing device • ultra-wideband impulse radar • passive WiFi radar • Kinect motion sensor Factor Type(s) Human location • Human physical activity • Room geometry • Participant demographics • Contactless sensing devices Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment Office building Sample Characteristic - Location United Kingdom [ABSTRACT FROM AUTHOR]

Framed by a literature on media coverage of welfare, "poverty porn" and "poverty propaganda," and its emphasis on political messaging and ideology, this article explores reporting of benefit fraud in the United Kingdom. In a comprehensive and systematic assessment, it illuminates the structure and extremely large volume of benefit fraud coverage, 2008–2017, across national, sub‐national, regional and local newspapers, mainstream television, TV documentaries, BBC web output, and BBC radio. With the aid of the 2016 British Social Attitude survey, and utilizing the most current conceptualizations of the processes underpinning media influence, it argues that this tsunami of coverage will have had a significant impact on the public. The results are explored in relation to their political and ideological implications, and their relevance to a much broader array of media discourses. [ABSTRACT FROM AUTHOR]

The start of the shift then involved lively discussions as to how long into the day the snowfall would persist, then amending the forecast temperatures for the next couple of days markedly downwards in view of the snow cover. Although the snow was well forecast for southwest Britain, N. S. Grahame recalls that heavy snow was forecast to extend right into southeast England, but in the end never made it that far east. Greater than expected deepening of the system at short notice led to heavier than anticipated precipitation, meaning that the forecast rain (with wintry precipitation largely confined to hills), rapidly and unexpectedly turned to widespread heavy snow late in the night, with the resulting snow cover persisting for many days subsequently. (a) NWP model forecast, (b) subjective 24-h forecast and (c) rerun of the NWP forecast in research mode after the event. [Extracted from the article]

Complex spatial heterogeneity in riverbed and floodplain sediments control the spatio-temporal exchange of groundwater and surface water in the hyporheic zone, inducing hot spots of microbial activity and biogeochemical cycling. However, the characterization of hyporheic exchange dynamics has thus far failed to adequately account for the complex subsurface heterogeneity of river bed sediments in a spatial explicitly manner, in particular for highly complex lowland river bed sediments. Here we demonstrate the ability of ground penetrating radar (GPR) to efficiently map floodplain and river bed sediment structures within a lowland meandering river. The aim of this study was to delineate the type and spatial extent of complex, texturally heterogeneous facies of high and low conductive streambed materials. GPR surveys in this study involved not only state-of-the-art terrestrial applications but also an aquatic survey conducted from a floating rig. The surveys revealed substantial sub-surface heterogeneity of depositional materials in the streambed and riparian zone. Eight characteristic radar facies were identified through the floodplain and ground-truthed against core samples and exposures of bank deposits. The majority of GPR profiles were dominated by trough-shaped depositional elements with erosional, curved, concave upward bounding surfaces, indicative of abandoned and chute stream channel structures. The identified abandoned channel structure was found to extend into the riverbed and to be filled by suspension fall-out fine-grained deposits (mud with organic matter and interbedded clay as indicated by observed signal attenuations). GPR proved to be a successful method to identify the spatial patterns of low conductivity peat and clay structures in the streambed and riparian zone of the investigated meander bend, highlighting its potential for larger scale analysis of these structures that have shown to control the exchange flow patterns between groundwater and surface water in lowland rivers. • We used a combination of novel in-stream GPR in conjunction with riparian surveys. • Identifying the spatial patterns and extent of low conductivity structures in the streambed and riparian zone. • The architectures of the fluvial settings have been found to control groundwater – surface water exchange fluxes. [ABSTRACT FROM AUTHOR]

Aiming at the key problem of low precision of the deformation data due to the inaccurate correction of the original image interference phase error based on the ground-based synthetic aperture radar(GB-SAR) in open pit slope， the sources and distribution characteristics of interference phase error in original image were analyzed with the original GB-SAR image of Malanzhuang open-pit iron mine as the data source. The method of extracting PS points and high-quality PS points by using triple threshold was also proposed. In addition， a multi-regression model was established based on the high-quality PS point phase and pixel coordinates. According to the model， the PS point phase was corrected to obtain the accurate deformation phase and the single image deformation， and the superposition deformation analysis was performed on the time series. The results show that the GB-SAR deformation monitoring error correction method based on multiple regression model can accurately correct the GB-SAR deformation monitoring error and improve the accuracy of deformation monitoring data. [ABSTRACT FROM AUTHOR]

The preservation of natural assets is nowadays an essential commitment. In this regard, root systems are endangered by fungal diseases which can undermine the health and stability of trees. Within this framework, ground penetrating radar (GPR) is emerging as a reliable non-destructive method for root investigation. A coherent GPR-based root-detection framework is presented in this paper. The proposed methodology is a multi-stage data analysis system that is applied to semi-circular measurements collected around the investigated tree. In the first step, the raw data are processed by applying several standard and advanced signal processing techniques in order to reduce noise-related information. In the second stage, the presence of any discontinuity element within the survey area is investigated by analysing the signal reflectivity. Then, a tracking algorithm aimed at identifying patterns compatible with tree roots is implemented. Finally, the mass density of roots is estimated by means of continuous functions in order to achieve a more realistic representation of the root paths and to identify their length in a continuous and more realistic domain. The method was validated in a case study in London (UK), where the root system of a real tree was surveyed using GPR and a soil test pit was excavated for validation purposes. Results support the feasibility of the data processing framework implemented in this study. [ABSTRACT FROM AUTHOR]

Turbulent mixing processes are important in determining the evolution of convective clouds, and the production of convective precipitation. However, the exact nature of these impacts remains uncertain due to limited observations. Model simulations show that assumptions made in parametrizing turbulence can have a marked effect on the characteristics of simulated clouds. This leads to significant uncertainty in forecasts from convection‐permitting numerical weather prediction (NWP) models. This contribution presents a comprehensive method to retrieve turbulence using Doppler weather radar to investigate turbulence in observed clouds. This method involves isolating the turbulent component of the Doppler velocity spectrum width, expressing turbulence intensity as an eddy dissipation rate, ϵ. By applying this method throughout large datasets of observations collected over the southern United Kingdom using the (0.28° beam‐width) Chilbolton Advanced Meteorological Radar (CAMRa), statistics of convective cloud turbulence are presented. Two contrasting case days are examined: a shallow "shower" case, and a "deep convection" case, exhibiting stronger and deeper updraughts. In our observations, ϵ generally ranges from 10−3 to 10−1 m2/s3, with the largest values found within, around and above convective updraughts. Vertical profiles of ϵ suggest that turbulence is much stronger in deep convection; 95th percentile values increase with height from 0.03 to 0.1 m2/s3, compared to approximately constant values of 0.02–0.03 m2/s3 throughout the depth of shower cloud. In updraught regions on both days, the 95th percentile of ϵ has significant (p < 10−3) positive correlations with the updraught velocity, and the horizontal shear in the updraught velocity, with weaker positive correlations with updraught dimensions. The ϵ‐retrieval method presented considers a very broad range of conditions, providing a reliable framework for turbulence retrieval using high‐resolution Doppler weather radar. In applying this method across many observations, the derived turbulence statistics will form the basis for evaluating the parametrization of turbulence in NWP models. Turbulent mixing processes are important in determining the evolution of convective clouds, and the production of convective precipitation. To investigate turbulence in observed clouds, this contribution provides a comprehensive method to retrieve turbulence using Doppler weather radar. Statistics of convective cloud turbulence are presented through the analysis of retrievals (example in picture) over many observations in two contrasting case‐studies. [ABSTRACT FROM AUTHOR]

Soil moisture is important in many hydrological and ecological processes. However, data sets which are currently available have issues with accuracy and resolution. To translate remotely-sensed data to an absolute measure of soil moisture requires mapped estimates of soil hydrological properties and estimates of vegetation properties, and this introduces considerable uncertainty. We present an alternative methodology for producing daily maps of soil moisture over the UK at 2-km resolution ('SMUK'). The method is based on a simple empirical model, calibrated with five years of daily data from cosmic-ray neutron sensors at ~40 sites across the country. The model is driven by precipitation, humidity, a remotely-sensed 'soil water index' satellite product, and soil porosity. The model explains around 70 % of the variance in the daily observations. The spatial variation in the parameter describing the soil water retention (and thereby the response to precipitation) was estimated using daily water balance data from ~1200 catchments with good coverage across the country. The model parameters were estimated by Bayesian calibration using a Markov chain Monte Carlo method, so as to characterise the posterior uncertainty in the parameters and predictions. We found that the simple model could emulate the behaviour of a more complex process-based model. Given the high resolution of the inputs in time and space, the model can predict the very detailed variation in soil moisture which arises from the sporadic nature of precipitation events, including the small-scale and short-term variations associated with orographic and convective rainfall. Predictions over the period 2016 to 2023 demonstrated realistic patterns following the passage of weather fronts and prolonged droughts. The model has negligible computation time, and inputs and predictions are updated daily, lagging approximately one week behind real time. [ABSTRACT FROM AUTHOR]

Aggressive fungal and insect attacks have reached an alarming level, threatening a variety of tree species, such as ash and oak trees, in the United Kingdom and beyond. In this context, Ground Penetrating Radar (GPR) has proven to be an effective non-invasive tool, capable of generating information about the inner structure of tree trunks in terms of existence, location, and geometry of defects. Nevertheless, it had been observed that the currently available and known GPR-related processing and data interpretation methods and tools are able to provide only limited information regarding the existence of defects and anomalies within the tree inner structure. In this study, we present a microwave tomographic approach for improved GPR data processing with the aim of detecting and characterising the geometry of decay and cavities in trees. The microwave tomographic approach is able to pinpoint explicitly the position of the measurement points on the tree surface and thus to consider the actual geometry of the sections beyond the classical (circular) ones. The robustness of the microwave tomographic approach with respect to noise and data uncertainty is tackled by exploiting a regularised scheme in the inversion process based on the Truncated Singular Value Decomposition (TSVD). A demonstration of the potential of the microwave tomography approach is provided for both simulated data and measurements collected in controlled conditions. First, the performance analysis was carried out by processing simulated data achieved by means of a Finite-Difference Time-Domain (FDTD) in three scenarios characterised by different geometric trunk shapes, internal trunk configurations and target dimensions. Finally, the method was validated on a real trunk by proving the viability of the proposed approach in identifying the position of cavities and decay in tree trunks. [ABSTRACT FROM AUTHOR]

In the UK, tree, hedgerow, and woodland (THaW) habitats are key havens for biodiversity and support many related ecosystem services. The UK is entering a period of agricultural policy realignment with respect to natural capital and climate change, meaning that now is a critical time to evaluate the distribution, resilience, and dynamics of THaW habitats. The fine‐grained nature of habitats like hedgerows necessitates mapping of these features at relatively fine spatial resolution—and freely available public archives of airborne laser scanning (LiDAR) data at <2 m spatial resolution offer a means of doing so within UK settings. The high cost of LiDAR prohibits use for regular monitoring of THaW change, but space‐borne sensors such as Sentinel‐1 Synthetic Aperture Radar (SAR at ca. 10 m resolution) can potentially meet this need once baseline distributions are established. We address two aims in this manuscript—(1) to rapidly quantify THaW across UK landscapes using LiDAR data and (2) to monitor canopy change intra‐ and inter‐annually using SAR data. We show that workflows applied to airborne LiDAR data can deliver THaW baselines at 2 m resolution, with positional accuracy of >90%. It was also possible to combine LiDAR mapping data and Sentinel‐1 SAR data to rapidly track canopy change through time (i.e., every 3 months) using, cloud‐based processing via Google Earth Engine. The resultant toolkit is also provided as an open‐access web app. The results highlight that whilst nearly 90% of the tallest trees (above 15 m) are captured within the National Forest Inventory (NFI) database only 50% of THaW with a canopy height range of 3–15 m are recorded. Current estimates of tree distribution neglect these finer‐grained features (i.e., smaller or less contiguous THaW canopies), which we argue will account for a significant proportion of landscape THaW cover. [ABSTRACT FROM AUTHOR]

Cybercrime is recognized as one of the top threats to UK economic security. On a daily basis, the computer networks of businesses suffer security breaches. A less explored dimension of this problem is cybercrimes committed by insiders. This paper provides a criminological analysis of corporate insider victimization. It begins by presenting reviews of insider criminal threats and routine activities theory as applied to cybercrime. Analysis of the nationally representative Cardiff University UK Business Cybercrime Survey then informs statistical models that predict the likelihood of businesses suffering insider cyber victimization, using routine activities and guardianship measures as predictors. [ABSTRACT FROM AUTHOR]

Information about the presence and movement of water is crucial to understanding stone deterioration and rock weathering but hard to obtain. Non-destructive, non-invasive measurements of electromagnetic phenomena can provide proxy data on water contents within porous stone and rock. Commercial geophysical devices, such as radar and microwave moisture sensors, produce raw data or readings in arbitrary units, but can be related to absolute water contents through gravimetric calibration. Calibration procedures typically either equilibrate samples to a set of relative humidities (RH%) using salt solutions or environmental chambers (requiring specialised equipment), or monitor ambient drying which yields less homogenous moisture distributions and takes time. This study proposes and tests a cost- and time-effective 'isolated diffusion' gravimetric calibration procedure in which a set of samples are sealed at specific water contents and equilibrated. The procedure is compared to ambient drying over 120 h for three United Kingdom building stones and evaluated with modelled reflection coefficients and relative permittivities. The calibrations determined from isolated diffusion more closely follow modelled behaviour than those from ambient drying, as the calibrations developed from the latter were affected by uneven distributions of moisture. Calibrations for radar measurements developed from two types of back interfaces (air and metal) were very similar to one another, suggesting that measurements are consistent regardless of the type of back interface used. The isolated diffusion calibration procedure provides a cost-effective and simple method to facilitate comparison between different non-destructive testing methods and enables accurate measurement of water contents in porous geomaterials. • A procedure is proposed for 'isolated diffusion' gravimetric calibration. • Calibrations were developed for radar and microwave sensors for UK building stones. • Dielectric constants and reflection coefficients were modelled. • 'Isolated diffusion' calibrations more closely matched models than ambient drying. • The procedure produces low-cost, accurate calibrations without specialised equipment. [ABSTRACT FROM AUTHOR]

In this paper, an efficient and accurate imaging algorithm is presented for Ground-Based Synthetic Aperture Radar (GB-SAR) or other radar systems that could be formed by a physical or synthetic linear aperture. The imaging algorithm is based on the fractional Fourier transform (FrFT) for the azimuth compression. A mathematical framework is derived according to the projection of a sample reflectivity image onto the pseudopolar coordinate, and its implementation was presented. With the data acquisition geometry and the pseudopolar imaging coordinate, the phase of a point target can be expressed as a quadratic phase exponential. It makes that only 1-D FrFT is needed for the azimuth compression of the time-domain backscatter data for the GB-SAR imaging problem. By further research, the optimal transformation order that represents the spatial frequency changes by the FrFT was given subsequently. Taking advantage of this optimal representation, the proposed approach avoids the large calculation that occurs in the time-domain back projection (TDBP). Comparing to the far-field pseudopolar format algorithm (FPFA), the accuracy of the proposed algorithm is much improved. Meanwhile, the proposed approach holds almost the same computational cost and complexity as the FPFA. The proposed approach keeps the advantages of the imaging quality of the TDBP and the computational cost of the FPFA that are two important aspects of the GB-SAR applications. Both the numerical simulation and the field GB-SAR experiment show that the algorithm is more suitable for the high-precision GB-SAR imaging, especially for the near field. [ABSTRACT FROM AUTHOR]