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A new double-moment bulk microphysics parameterization with in-cloud microphysical processes is developed for use in weather forecasting and climate studies. A main ingredient of the scheme utilizes a concept to represent the partial cloudiness effect on the microphysical processes, following the study of Kim and Hong (2018). The underlying assumption is that all the microphysical processes occur in a cloudy part of the grid box. Based on the long-term evaluation of the WRF Single-Moment (WSM) and WRF Double-Moment (WDM) schemes by WRF community, several revisions are made in microphysics terms, along with a newly introduced aerosol effect in ice processes. An aerosol-aware feature with prognostic aerosol emissions of sea salt, dust, anthropogenic and wildfire organic carbon for CCN is also designed.A mass-conserving Semi-Lagrangian sedimentation is re-configured for double-moment physics, which is superior to the conventional Eulerian algorithm in the context of the computational accuracy and numerical accuracy. The scheme is to be evaluated in WRF and UFS models., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

The development of a label-free, non-destructive and safe analytical method such as Raman spectroscopy for assessing cartilage degradation is highly desirable. Compared to non-optical imaging modalities, Raman mapping offers a more sensitive means of directly assessing the chemical composition of cartilage in three-dimensional space and the potential to monitor cartilage degeneration to inform intervention and treatment strategies. Herein, we report the application of Raman spectroscopic methods ex vivo and at arthroscopy to identify molecular alterations in cartilage specimens containing minor focal lesions characteristic of the early disease phase. Our initial ex vivo analysis, obtained by single-point Raman spectroscopy of cartilage samples, supports previous findings based on S-O stretching vibration bands associated with sulphated glycosaminoglycans (sGAGs). We extended the analyses to the high-wavenumber region where we observed that vibrational bands assigned to C-H and O-H stretching modes discriminated early cartilage alterations from healthy cartilage samples. Furthermore, we performed a proof-of-concept in-clinic study using a custom-built optical probe to acquire Raman spectral measurements for the first time in patients undergoing arthroscopy of knee joints. Spectra were obtained with adequate signal-to-noise ratios that similarly discriminated between lesion and adjacent cartilage sites and identified reductions in sGAGs in apparently healthy cartilage. Building on this, we present initial results from Raman mapping to spatially resolve the molecular constituents of cartilage through its depth and across a lesion. Mapping revealed a non-uniform and reduced sGAG distribution within the lesion and peripheral cartilage that was otherwise visually normal, similar to the in-clinic observations, showing that the degradative influence of the lesion extended beyond its border. This was accompanied by a decreased fluorescence signal intensity, which suggests that fluorescence may provide valuable information as an adjunct to the Raman signal in discriminating normal and degenerating cartilage. This work demonstrates the value of Raman mapping over single-point Raman measurements for the analysis of the anisotropy of articular cartilage and highlights the potential of the technology for in vivo articular joint arthroscopy applications.

Rapid developments in Regenerative Medicine and Tissue Engineering has witnessed an increasing drive toward clinical translation of breakthrough technologies. However, the progression of promising preclinical data to achieve successful clinical market authorisation remains a bottleneck. One hurdle for progress to the clinic is the transition from small animal research to advanced preclinical studies in large animals to test safety and efficacy of products. Notwithstanding this, to draw meaningful and reliable conclusions from animal experiments it is critical that the species and disease model of choice is relevant to answer the research question as well as the clinical problem. Selecting the most appropriate animal model requires in-depth knowledge of specific species and breeds to ascertain the adequacy of the model and outcome measures that closely mirror the clinical situation. Traditional reductionist approaches in animal experiments, which often do not sufficiently reflect the studied disease, are still the norm and can result in a disconnect in outcomes observed between animal studies and clinical trials. To address these concerns a reconsideration in approach will be required. This should include a stepwise approach using in vitro and ex vivo experiments as well as in silico modeling to minimize the need for in vivo studies for screening and early development studies, followed by large animal models which more closely resemble human disease. Naturally occurring, or spontaneous diseases in large animals remain a largely untapped resource, and given the similarities in pathophysiology to humans they not only allow for studying new treatment strategies but also disease etiology and prevention. Naturally occurring disease models, particularly for longer lived large animal species, allow for studying disorders at an age when the disease is most prevalent. As these diseases are usually also a concern in the chosen veterinary species they would be beneficiaries of newly developed therapies. Improved awareness of the progress in animal models is mutually beneficial for animals, researchers, human and veterinary patients. In this overview we describe advantages and disadvantages of various animal models including domesticated and companion animals used in regenerative medicine and tissue engineering to provide an informed choice of disease-relevant animal models.

37th European Photovoltaic Solar Energy Conference and Exhibition; 1247-1249, This study presents enhancements of the Weather Research and Forecasting model with solar extensions (WRFSolar) to provide probabilistic forecasts of solar radiation. Our approach builds ensemble WRF-Solar runs by introducing stochastic perturbations of variables that produce the largest uncertainties in predicting surface irradiance and clouds. The key variables are identified using tangent linear sensitivity analysis of six physics packages responsible for all-sky irradiance variability. An optimal strategy to stochastically perturb the selected variables is developed and applied to WRF-Solar to generate ensemble members for day-ahead solar prediction. The National Solar Radiation Database (NSRDB) is used to validate the ensemble forecast at arbitrary locations on the model grid. Preliminary results indicate that the proposed technique can potentially produce WRF-Solar ensembles providing reliable information of solar prediction uncertainty. This study describes the implemented methodology and initial results as well as future research to improve ensemble-based probabilistic forecasts with WRF-Solar.

Domestic cats suffer from a range of inherited genetic diseases, many of which display similarities with equivalent human conditions. Developing cellular models for these inherited diseases would enable drug discovery, benefiting feline health and welfare as well as enhancing the potential of cats as relevant animal models for translation to human medicine. Advances in our understanding of these diseases at the cellular level have come from the use of induced pluripotent stem cells (iPSCs). iPSCs are capable of differentiating into derivatives of all three germ layers, therefore overcoming the limitations of primary differentiated cells and the ethical concerns of using embryonic stem cells. No studies however report the generation of iPSCs from domestic cats (fiPSCs). Feline adipose derived fibroblasts were infected with amphotropic retrovirus containing the coding sequences for human Oct4, Sox2, Klf4, cMyc and Nanog. Isolated iPSC clones were expanded on mouse inactivated embryonic fibroblasts in the presence of feline leukaemia inhibitory factor (LIF). Retroviral delivery of human pluripotent genes gave rise to putative fiPSC colonies within 5-7 days. These iPS-like cells required foetal bovine serum and feline LIF for maintenance. Colonies were domed with refractile edges, similar to mouse iPSCs. Immunocytochemistry demonstrated positive staining for stem cell markers: alkaline phosphatase, Oct4, Sox2, Nanog and SSEA1. Cells were negative for SSEA4. Expression of endogenous feline Nanog was confirmed by qPCR. The cells were able to differentiate in vitro into cells representative of the three germ layers. These results confirm the generation of the first induced pluripotent cells from domestic cats. These cells will provide valuable models to study genetic diseases and explore novel therapeutic strategies.

Cold air outbreaks can bring snow to populated areas and can affect aviation safety. Shortcomings in the representation of these phenomena in global and regional models are thought to be associated with large systematic cloud related radiative flux errors across many models. In this study, nine regional models have been used to simulate a cold air outbreak case at a range of grid spacings (1 km to 16 km) with convection represented explicitly or by a parametrization. Overall, there is more spread between model results for the simulations in which convection is parametrized when compared to simulations in which convection is represented explicitly. The quality of the simulations of both the stratocumulus and the convective regions of the domain are assessed with observational comparisons 24 hours into the simulation. The stratocumulus region is not well reproduced by the models, which tend to predict open cell convection with increasing resolution rather than stratocumulus. For the convective region the model spread reduces with increased resolution and there is some improvement in comparison to observations. Comparing models that have the same physical parametrizations or dynamical core suggest that both are important for accurately reproducing this case.

The spread of a dense gas in the atmosphere is a phenomenon that occurs widely with natural (and anthropogenic) causes and is often associated with high impact and hazardous events. In this study a method of simulating the spread of dense gases in a numerical weather prediction model is presented. This approach has the advantage that dense gases can be simulated in regions of complex terrain using realistic forcings (in terms of both the driving meteorological fields and the representation of surface characteristics). The model formulation is tested against semi-idealized gravity-current-type experiments and similar modelling studies. As an example application, the Lake Nyos disaster of 1986, where a dense CO2 cloud spread through a mountainous region of Cameroon, is simulated. The predicted spread of CO2 agrees (qualitatively) very well with the observations. The method provides a means of determining a potential ‘safe height’ above which simulated concentrations are not hazardous, and thus the height above which refuge should be taken during similar future events. The simulation demonstrates a novel application which can be rapidly applied to other scenarios.