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Objective At present, it is still uncertain whether single screw has the same stability as double screws in the treatment of ulnar coronal process basal fracture (Regan–Morry type III). So, we aimed to compare the pull‐out force and anti‐rotation torque of anterior single/double screw‐cancellous bone fixation (aSSBF, aDSBF) in this fracture, and further study the influencing factors on anatomical and biomechanical stability of smart screw internal fixations. Methods A total of 63 adult volunteers with no history of elbow injury underwent elbow CT scanning with associated three‐dimensional reconstruction that enabled the measurements of bone density and fixed length of the proximal ulna and coronoid. The models of coronal process basal fracture, aSSBF and aDSBF, were developed and validated. Using the finite element model test, the sensitivity analysis of pull‐out force and rotational torque was carried out. Results The pull‐out force of aSSBF model was positively correlated with the density of the cancellous bone and linearly related to the fixed depth of the screw. The load pattern of pull‐out force of aDSBF model was similar to that of aSSBF model. The ultimate torque of aDSBF model was higher than that of aSSBF model, but the load pattern of ultimate torque of both models was similar to each other when the fracture reset was satisfactory, and the screw nut attaches closely to coronoid process. Moreover, with enhancement of initial pre‐tightening force, the increase of ultimate torque of both models was small. Conclusions In addition to three pull‐out stability factors of smart screw fixations, fracture surface fitting degree and nut fitting degree are the other two important anatomical and biomechanical stability factors of smart screw fixations both for rotational stability. When all pull‐out stability and rotational stability factors meet reasonable conditions simultaneously, single or double screw fixation methods are stable for the treatments of ulnar coronoid basal fractures.

This study utilizes satellite-based rainfall CHIRPS to evaluate GCMs-CMIP6 models over Sudan from 1985 to 2014. Overall, the GCMs of BCC-CSM2-MR, CAMS-CSM1-0, CESM2, ECEarth3-Veg, GFDL-ESM4, MIROC-ES2L, and NorESM2-MM are well reproduced in the unimodal pattern of June to September (JJAS), and hence employed to calculate Multi-Model Ensemble (MME). Then, we examine the capability of the GCMs and MME in replicating the precipitation patterns on annual and seasonal scales over Sudan using numerous ranking metrics, including Pearson Correlation Coefficient (CC), Standard Deviation (SD), Taylor Skill Score (TSS), Mean Absolute Error (MAE), absolute bias (BIAS), and, normalized mean root square error (RMSD). The results show that the MME has the lowest bias and slightly overestimates rainfall over most parts of our study domain, whilst, others (ACCESS-CM2, BCC-CSM2-MR, CAMS-CSM1-0, CESM2, CNRM-CM6-1, CNRM-CM6-1-HR, CNRM-ESM2-1, FGOALS-f3-L, FGOALS-g3) consistently overestimate rainfall in referring to CHIRPS data, respectively, but FIO-ESM-2-0 underestimates bias value. Moreover, MIROC-ES2L and NorESM2-MM demonstrate better performance than the other models. Finally, we employed a bias correction (BC) technique, namely Delta BC, to adjust the GCMs model products through the annual and monsoon seasons. The applied bias correction technique revealed remarkable improvement in the GCMs against the observations, with an improvement of 0 - 18% over the original. However, MME and MIROC-ES2L show better performance after correction than other models. [ABSTRACT FROM AUTHOR]

Deep learning artificial intelligence technology, which has the advantages of nonlinear mapping ability, massive information extraction ability, spatial-temporal modeling ability, and so on, provides new ideas and methods for further improving the accuracy of weather and climate extreme event prediction. A transfer learning CNN (Convolutional Neural Networks) classification model is established to classify the circulation patterns, along with the newly reconstructed dataset of regional persistent historical heavy rain events, daily rainfall data of 2474 observational stations, and the NCEP/NCAR global reanalysis data of daily geopotential height field in 1981–2018. Different from previous classifications, usually with one level variable, here, in addition to 500 hPa heights, 200 hPa zonal winds and 850 hPa meridional winds over the key areas are also considered in the model. The results show that the multi-level circulation pattern classification based on the transfer learning CNN network has a higher accuracy in the independent test than the single-level model, with the accuracy reaching 92.5% (while only 85% for the single-level model). The spatial correlation coefficient of precipitation between each typical mode and related patterns obtained by the multi-level transfer learning CNN classification is greater than that obtained by the single-level transfer learning CNN, and the variance of 500 hPa heights between each typical mode and the associated patterns is also greater than that obtained by the single-level transfer learning CNN. These results show that the performance of the classification by the multi-level transfer learning CNN model is better than that by the single-level transfer learning CNN. The study is helpful to develop circulation classifications related to large-scale weather or climate disaster events and then to provide a physical basis for further improving the forecast effect and extending the valid time of the forecast through combining the numerical model products.

This study investigated the capability of the Weather Research and Forecasting (WRF) model to simulate seven different heavy precipitation (PRE) events that occurred across East Africa in the summer of 2020. The WRF model outputs were evaluated against high-resolution satellite-based observations, which were obtained from prior evaluations of several satellite observations with 30 stations’ data. The synoptic conditions accompanying the events were also investigated to determine the conditions that are conducive to heavy PRE. The verification of the WRF output was carried out using the area-related root mean square error (RMSE)-based fuzzy method. This method quantifies the similarity of PRE intensity distribution between forecast and observation at different spatial scales. The results showed that the WRF model reproduced the heavy PRE with PRE magnitudes ranging from 6 to >30 mm/day. The spatial pattern from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification-Climate Data Record (PERSIANN-CCS-CDR) was close to that of the WRF output. The area-related RMSE with respect to observation showed that the error in the model tended to reduce as the spatial scale increased for all the events. The WRF and high-resolution satellite data had an obvious advantage when validating the heavy PRE events in 2020. This study demonstrated that WRF may be used for forecasting heavy PRE events over East Africa when high resolutions and subsequent simulation setups are used.

This study adopts a two-way approach to CORDEX-CORE RegCM4-7 seasonal precipitation simulations’ Added Value (AV) analysis over Africa, which aims to quantify potential improvements introduced by the downscaling approach at high and low resolution, using satellite-based observational products. The results show that RegCM4-7 does add value to its driving Global Climate Models (GCMs) with a positive Added Value Coverage (AVC) ranging between 20 and 60% at high resolution, depending on the season and the boundary conditions. At low resolution, the results indicate an increase in the positive AVC by up to 20% compared to the high-resolution results, with an up to 8% decrease for instances where an increase is not observed. Typical climate zones such as West Africa, Central Africa, and Southern East Africa, where improvements by Regional Climate Models (RCMs) are expected due to strong dependence on mesoscale and fine-scale features, show positive AVC greater than 20%, regardless of the season and the driving GCM. These findings provide more evidence for confirming the hypothesis that the RCMs AV is influenced by their internal physics rather than being the product of a mere disaggregation of large-scale features provided by GCMs. Although the results show some dependencies to the driving GCMs relating to their equilibrium climate sensitivity nature, the findings at low resolutions similar to the native GCM resolutions make the influence of internal physics more important. The findings also feature the CORDEX-CORE RegCM4-7 precipitation simulations’ potential in bridging the quality and resolution gap between coarse GCMs and high-resolution remote sensing datasets. Even if further post-processing activities, such as bias correction, may still be needed to remove persistent biases at high resolution, using upscaled RCMs as an alternative to GCMs for large-scale precipitation studies over Africa can be insightful if the AV and other performance statistics are satisfactory for the intended application.