Forecast Skill Research Articles

Forecast skill assessment of an operational continental heat-cold-health forecasting system: New avenues for health early warning systems.

More than 110,000 Europeans died as a result of the record-breaking temperatures of 2022 and 2023. A new generation of impact-based early warning systems, using epidemiological models to transform weather forecasts into health forecasts for targeted population subgroups, is an essential adaptation strategy to increase resilience against climate change. Here, we assessed the skill of an operational continental heat-cold-health forecasting system. We used state-of-the-art temperature-lag-mortality epidemiological models to transform bias-corrected ensemble weather forecasts into daily temperature-related mortality forecasts. We found that temperature forecasts can be used to issue skillful forecasts of temperature-related mortality. However, the forecast skill varied by season and location, and it was different for temperature and temperature-related mortality due to the use of epidemiological models. Overall, our study demonstrates and quantifies the forecast skill horizon of heat-cold-health forecasting systems, which is a necessary step toward generating trust among public health authorities and end users.

How Skilful Are Cloud Cover Products in Representing Observed Cloudiness in Québec?

ABSTRACT In this study, we performed, for the first time, a detailed analysis of cloudiness in Québec using station based observations and evaluated the trustworthiness of various satellite and reanalysis based cloud cover products. We found only 12 stations with observational time series long enough for providing robust analysis due to various issues related to observing methods and recording of information during the 1990s. Our results showed that Québec can be fairly cloudy throughout the year with annual mean cloud cover fraction ranging between 0.5 and 0.7 with autumn being the cloudiest season at most station locations. We also found a significantly increasing trend in cloudiness in the winter season over Québec during 1982–2012 (∼2–6% per decade depending on the location). Among the cloudiness products evaluated in this study, a satellite based cloudiness product (i.e. based on AVHRR) performed best in representing the observed cloudiness over Québec including the wintertime increasing trend. Two reanalysis based cloudiness products (i.e. ERA5 and NARR) also performed well in representing the observed cloudiness indicated by high correlations, relatively lower mean biases and smaller root mean square errors. However, the reanalysis products did not capture well the observed seasonal trends. All products showed relatively larger biases in the winter season except JRA-25 reanalysis product. During the warmer months, however, JRA-25 showed largest biases followed by MERRA-2 reanalysis product. JRA-25 and MERRA-2 also had lower correlation for annual and winter means and highest root mean square errors for all seasons. Furthermore, based on two skill scores, we found that the ERA5, AVHRR and NARR performed relatively better than JRA-25 and MERRA-2. Based on these results we conclude that the satellite product and the two reanalysis products can be useful resources (as observational proxies) along with station based observations for understanding long term changes in cloudiness of this region and also potentially evaluating regional climate model simulations.

Seasonal forecasts have sufficient skill to inform some agricultural decisions

Abstract Seasonal forecasts, which look several months into the future, are currently underutilized in active decision-making, particularly for agricultural and natural resource management. This underutilization can be attributed to the absence of forecasts for decision-relevant variables at the required spatiotemporal resolution and at the time when the decisions are made and a perception of poor skill by decision-makers. Addressing these constraints, we quantified the skill of seasonal forecasts in informing two agricultural decisions with differing decision timeframes and influencer variables: (a) whether to apply fertilizer in fall or wait until spring based on expected winter temperatures, and (b) drought response, such as whether to lease water based on expectations of drought. We also looked into how early the forecast can be provided without significant degradation in skill. Currently, drought response decisions are typically formulated in April, utilizing drought forecasts issued in the same month, while fall fertilization decisions are generally made between August and September. There is growing interest among stakeholders in the availability of earlier forecasts to inform these critical choices. We utilized the North American Multi-Model Ensemble (NMME) hindcasts for the time period 1982 - 2020 over the Pacific Northwest US to obtain meteorological variables. Runoff was estimated via simulations of the coupled crop-hydrology VIC-CropSyst model. The skill assessment with the Heidke Skill Score (HSS) yielded promising outcomes in both decisions for the entire Pacific Northwest region. Notably, NMME’s positive skill (median HSS of 30%) in predicting warmer winters identifies years when fertilizer application should be avoided to prevent fertilizer loss through mineralization (and associated costs). Similarly, there is skill in forecasting drought conditions in most irrigated watersheds for up to two months in advance of April, the current decision time. In conclusion, our findings affirm that contrary to the perception of low skill and resulting underutilization, current seasonal forecasts hold the potential to inform at least some key agricultural decisions.

Integrating Playful Learning to Enhance Education for Sustainability: Case Study of a Business School in Slovenia

Business schools play a pivotal role in training future leaders equipped to tackle complex challenges in sustainable development. This study investigates the effects of integrating playful learning methodologies into sustainable entrepreneurship education, aligning with the goals of education for sustainable development and Sustainable Development Goals. The research involved a quantitative approach, utilizing pre- and post-intervention surveys targeting 50 business school students to assess changes in their knowledge of sustainable entrepreneurship, entrepreneurial skills, and engagement levels. Results from descriptive statistics and paired t-tests revealed significant enhancements; knowledge scores increased from an average of 3.01 to 3.70 (t(49) = −23.04, p < 0.001), skills scores from 3.19 to 3.90 (t(49) = −20.88, p < 0.001), and engagement scores from 3.40 to 4.00 (t(49) = −16.28, p < 0.001), indicating statistically significant improvements across all measured domains. These findings suggest that playful learning methodologies can profoundly enrich the educational experience, making it more dynamic and impactful. The study underscores the potential of such pedagogical strategies in fostering a comprehensive understanding of sustainability and cultivating essential competencies among future business leaders. This research has significant implications for curriculum design, advocating for the inclusion of interactive and experiential learning methods to improve educational outcomes and better prepare students for the complex challenges of sustainable development.

The Effect of Emergency Training on Improving Knowledge and Skills in Emergency Management Among Students at MAN 2 Palu

Introduction: An emergency is a situation or condition where there is an immediate threat or occurrence of harm that may lead to further damage (loses). In modern times, emergency preparedness has become a critical aspect of daily life, particularly in school environments. Incidents such as accidents, fires, and the need for first aid interventions highlight the increasing importance of emergency training for students. Objective: To analyze the effect of emergency training on improving students' knowledge and skills in managing emergencies. Method: This study employed a pre-experimental design with a one-group pretest-posttest approach. A total of 81 students from MAN 2 Palu participated in the study. The Wilcoxon signed-rank test was used to assess changes in knowledge and skills, with a significance level set at p < 0.05. Result:. The mean pretest knowledge score was 9.16 (SD: 1.94), and the posttest score was 17.54 (SD: 0.67). The Wilcoxon test indicated a significant improvement in knowledge following the training (p = 0.000, p < 0.05). Similarly, the mean pretest score for basic life support skills was 3.44 (SD: 0.63), which increased to 5.93 (SD: 0.26) post-training. The mean score for splint dressing skills improved from 2.37 (SD: 0.64) pretest to 7.93 (SD: 0.30) posttest, and choking aid skills improved from 2.69 (SD: 0.54) to 3.98 (SD: 0.15) posttest. All skill improvements were statistically significant (p = 0.000, p < 0.05), which means providing emergency training has a positive effect on improving emergency skills among students. Conclusion: Emergency training significantly improves both the knowledge and skills of students in managing emergencies. This training is expected to improve students' ability to provide effective first aid in emergencies before professional medical assistance is available.

OGC SO16 - International upper GI robotic fellowship on the CMR Versius platform

Abstract Background The impetus for UGI surgery trainees to attain skills in robotic surgery is likely to increase following the global acceptance and utilisation of robotic platforms. UGI robotic fellowship programs are few in number and highly desirable providing trainees with a unique selling point when embarking on independent consultant practice. We describe the first UGI robotic surgery fellowship on the CMR Versius platform at the Gloucestershire Royal Hospital which has the largest experience of UGI robotic surgery on the CMR Versius platform in the UK, having performed over 200 cases including the first robot assisted minimally invasive oesophagectomy. Method A structured robotic training curriculum was created in collaboration with CMR Versius. It consists of a pre-clinical phase of E-learning (10 modules), simulator training (16 assessed tasks) and dry-laboratory training (a two-day course encompassing robotic system set-up, bed-side assist and assessed dry lab practical tasks). The clinical phase begins with bed-side assisting of robotic cases followed by supervised operating on the console. Operations performed on the console are recorded for review and evaluation. The videos will be scored by two independent surgeons using the modified Global Evaluation Assessment of Robotic Skills (mGEARS) score to track growth and progress. Results The E-learning modules and assessments were completed prior to commencement of the fellowship. The remainder of the pre-clinical phase of the curriculum were completed within 4 weeks of commencement followed by bed-side assisting. Once competent at bed-side assisting and able to demonstrate proficiency in set-up (selecting port site placement, docking, arm spacing and configuration), supervised console training was commenced 6 weeks into the fellowship. Console training began with robotic assisted laparoscopic assisted cholecystectomy to gain familiarity, confidence, and efficiency. After 6 cases as the only console surgeon, the surgical complexity of cases was progressed to hiatal surgery and Heller’s cardiomyotomy Conclusion A structured training curriculum with defined phases provides a feasible and effective framework for robotic fellowship training. Adherence to the pre-clinical phase of training allows safe and efficient progression to hands-on console experience. This is achievable within relatively short timeframe to allow for ample independent console surgeon experience and the development of proficiency within a 12-month fellowship period.

SAL Method Applied in Grid Forecasting Product Verification with Three-Source Fusion Product

Quantitative precipitation forecast (QPF) verification stands out as one of the most formidable endeavors in the realm of forecast verification. Traditional verification methods are not suitable for high-resolution forecasting products in some cases. Therefore, the SAL (structure, amplitude and location) method was proposed as a method of object-based spatial verification that studies precipitation verification in a certain range, which is combined with factors including structure, amplitude and location of the targets. However, the setting of the precipitation threshold would affect the result of the verification. This paper presented an improved method for determining the precipitation threshold using the QPF from ECMWF, which is an ensemble forecast model and three-source fusion product that was used in China from 1 July to 31 August 2020, and then the results obtained with this method were compared with the other two traditional methods. Furthermore, the SAL and the traditional verification methods were carried out for geometric, simulated and real cases, respectively. The results showed the following: (1) The proposed method in this paper for determining the threshold was more accurate at identifying the precipitation objects. (2) The verification area size was critical for SAL calculation. If the area selected was too large, the calculated SAL value had little significance. (3) ME (Mean Error) could not identify the displacement between prediction and observation, while HSS (Heidke Skill Score) was sensitive to the displacement of the prediction field. (4) Compared with the traditional verification methods, the SAL method was more straight forward and simple, and it could give a better representation of prediction ability. Therefore, forecasters can better understand the model prediction effect and what needs to be improved.

Evaluation of Machine Learning Models for Predicting the Hot Deformation Flow Stress of Sintered Al-Zn-Mg Alloy

Abstract In predicting flow stress, machine learning (ML) offers significant advantages by leveraging data-driven approaches, enhancing material design, and accurately forecasting material performance. Thus, the present study employs various supervised ML models, including Linear Regression (Lasso and Ridge), Support Vector Regression (SVR), Ensemble Methods (RF, GB, XGB), and Neural Networks (ANN, MLP), to predict flow stress in the hot deformation of an Al-Zn-Mg alloy. The ML methodology involves sequential steps from data extraction to cross-validation and hyperparameter tuning, which is conducted using the hyperopt library. Model performance is assessed using average absolute relative error (AARE), root mean squared error (RMSE), and mean squared error (MSE). The results show that ensemble methods (RF, GB, XGB) and neural networks outperform traditional regression methods, demonstrating superior predictive accuracy. Visualization using half violin plots reveals the models' error ranges, with XGB consistently exhibiting the best performance. SVR, RF, GB, XGB, ANN, and MLP showed better performance than the Arrhenius model in the context of AARE and MSE metrics. Interestingly, SVR had a somewhat higher AARE of 1.89% and an MSE of 0.251 MPa2, while XGB had the lowest AARE of 0.2% and the lowest MSE of 0.011 MPa2. When ML models were evaluated using the skill score in relation to the Arrhenius model, XGB scored higher than SVM at 0.714, with a score of 0.986. In contrast Lasso and Ridge exhibited negative scores of −0.847 and −0.456, respectively.

Design, Implementation, and Evaluation of a Training Package for Violence Prevention in Adolescents: Its Effect on Self-control and Social Skills

Background: Adolescence is a period marked by significant changes, during which individuals face major emotional, cognitive, and behavioral challenges. Violence remains a significant global concern, and factors such as self-control and social skills play a crucial role in reducing and managing adolescent violence. Objectives: This study aimed to design, implement, and evaluate a training package for violence prevention (TPVP) in adolescents and assess its impact on self-control and social skills among students in Isfahan. Methods: This study was conducted on sixty 13 - 15-year-old female students in Isfahan, Iran, during the 2022 - 2023 academic year. First, the TPVP was developed after reviewing relevant theories and existing training packages, then it was evaluated by a panel of faculty members from the Department of Social Medicine, psychiatrists, and clinical psychologists. The students were randomly divided into two groups: (1) an intervention group, and (2) a control group, with 30 students in each group. The intervention group received the TPVP and attended five training sessions. To assess social skills and self-control, the Inderbitzen and Foster Social Skills Questionnaire and the Tangney Self-control Questionnaire were administered to both groups before and three months after the intervention. Results: Sixty female students aged 13 - 15 participated in this study. The mean self-control score in the intervention group was significantly higher than in the control group after the intervention (46.33 ± 6.488 vs. 40.44 ± 7.307, P = 0.002). Prior to the training, there was no significant difference in social skills scores between the two groups. However, following the intervention, the mean social skills score in the intervention group was significantly higher than in the control group (118.38.726 vs. 102.913.468, P < 0.0001). Conclusion: The violence prevention training package was effective in improving self-control and social skills among students. The study demonstrated that the implementation of a structured TPVP can enhance both self-control and social skills in adolescents.

Subseasonal-to-seasonal (S2S) prediction of atmospheric rivers in the Northern Winter

Atmospheric rivers (ARs) are characterized by intense lower tropospheric plumes of moisture transport that are frequently responsible for midlatitude wind and precipitation extremes. The prediction of ARs at subseasonal-to-seasonal (S2S) timescales is currently at a low level of skill, reflecting a need to improve our understanding of their underlying sources of predictability. Based on 20 year hindcast experiments from the Geophysical Fluid Dynamics Laboratory’s SPEAR S2S forecast system, we evaluate the S2S prediction skill of AR activities in the northern winter. Higher forecast skill is detected for high-frequency AR activities (3–7 days/week) compared to low-frequency AR activities (1–2 days/week), even though the occurrence rate of high-frequency ARs exceeds that of low-frequency ARs. For the first time, we have applied the Average Predictability Time technique to the SPEAR system to identify the three most predictable modes of AR in the North Pacific sector. These modes can be attributed to the influences of the El Niño–Southern Oscillation, the Pacific North American pattern, and the Arctic Oscillation. S2S AR forecast skill in western United States is modulated by various phases of large-scale variability. This study highlights potential windows of opportunity for operational S2S AR forecasting.

Tropospheric links to uncertainty in stratospheric subseasonal predictions

Abstract. Variability in the stratosphere, especially extreme events such as sudden stratospheric warmings (SSWs), can impact surface weather. Understanding stratospheric prediction uncertainty is therefore crucial for skillful surface weather forecasts on weekly to monthly timescales. Using ECMWF subseasonal hindcasts, this study finds that stratospheric uncertainty is most strongly linked to tropospheric uncertainty over the North Pacific and Northern Europe, regions that can modulate but also respond to stratospheric variability, suggesting a two-way propagation of uncertainty. A case study of the 2018 SSW event shows an initial poleward and upward propagation of uncertainty from tropical convection, followed by a downward propagation where ensemble members that accurately predict the SSW are also better at predicting its downward impacts. These findings highlight the locations in the troposphere that are linked to stratospheric uncertainty and suggest that improved model representation of tropospheric mechanisms linked to polar vortex variability could enhance both stratospheric and extratropical surface prediction.

Evaluation of the Predictive Capability of CMA Climate Prediction System Model for Summer Surface Heat Source on the Tibetan Plateau

Surface heat source (SHS) is a crucial factor affecting local weather systems. Particularly SHS on the Tibetan Plateau (TP) significantly influences East Asian atmospheric circulation and global climate. Accurate prediction of summer SHS on the TP is of urgent demand for economic development and local climate change. To evaluate the performance of SHS on the TP, the observed SHS data from the eleven sites on the TP verified against CRA40-land (CRA) is evidenced significantly better than ERA5-land (ERA5), another widely used reanalysis. The predictive capability of the CMA Climate Prediction System Model (CMA-CPS) for SHS on the TP was assessed using multiple scoring methods, including the anomaly correlation coefficient and temporal correlation coefficient, among others. Furthermore, relative variability and trend analysis were conducted. Finally, based on these assessments, the causes of the biases were preliminarily discussed. The CMA-CPS demonstrates a reasonable ability to predict the spatial distribution patterns of SHS, sensible heat (SH), and latent heat (LH) on the TP in summer. Specifically, the prediction results of SHS and LH exhibit an “east-high and west-low” distribution, while the distribution of the predicted SH is opposite. Nevertheless, the predicted values are generally lower than CRA, particularly in interannual variations and trends. Among the predictions, LH exhibits the highest temporal correlation coefficients, consistently above 0.6, followed by SHS, while SH predictions are less accurate. The spatial distribution and skill scores indicate that LH on the TP contributes more significantly to SHS than SH in summer. Furthermore, discrepancies in the predictions of surface temperature gradients, ground wind speed, and humidity on the TP may partly explain the biases in SHS and their components.

Improving subseasonal forecast skill in the Norwegian Climate Prediction Model using soil moisture data assimilation

AbstractThis study shows the importance of soil moisture (SM) in subseasonal-to-seasonal (S2S) predictions at mid-latitudes. We do this through introducing the Norwegian Climate Prediction Model Land (NorCPM-Land), a land reanalysis framework tailored for integration with the Norwegian Climate Prediction Model (NorCPM). NorCPM-Land assimilates blended SM data from the European Space Agency’s Climate Change Initiative into a 30-member offline simulation of the Community Land Model with fluxes from the coupled model. The assimilation of SM data reduces error in SM by 10.5 % when validated against independent SM observations. It also improves latent heat flux estimates, illustrating that the adjustment of underlying SM significantly augments the capacity to model land surface dynamics. We evaluate the added value of land initialisation for subseasonal predictions, by comparing the performance of hindcasts (retrospective prediction) using the standard NorCPM with a version where the land initial condition is taken from NorCPM-Land reanalysis. The hindcast covers the period 2000 to 2019 with four start dates per year. Land initialisation enhances SM predictions, reducing error by up to 2.5-month lead time. Likewise, the error for precipitation and temperature shows improvement up to a lead time of 1.5-month. The largest improvements are observed in regions with significant land-atmospheric coupling, such as the Central United States, the Sahel, and Central India. This method further enhances the prediction of extreme temperature variations, both high and low, with the most notable improvements seen in regions at mid and high latitudes, including parts of Europe, the United States, and Asia. Overall, our study provides further evidence for the significant role of SM content in enhancing the accuracy of subseasonal predictions. This study introduces a technique for improved land initialisation, utilising the same model employed in climate predictions.

Forecasting atmospheric persistence and implications for the predictability of temperature and temperature extremes

AbstractExtreme temperatures can cause severe disruptions to society, from negative health consequences to infrastructure damage. Accurate and timely weather forecasts contribute to minimising these detrimental effects, by supporting early‐warning systems. In this context, information on the expected performance of the forecasts is valuable. Here, we investigate whether there is a relationship between the persistence of atmospheric circulation patterns in the Euro‐Atlantic sector and forecast skill for temperatures and temperature extremes in Europe. We first apply an objective method to compute the persistence of large‐scale atmospheric patterns in European Centre for Medium‐Range Weather Forecasts (ECMWF) subseasonal retrospective forecasts. We find that the forecasts successfully predict atmospheric persistence up to time‐scales of approximately two weeks. We next investigate the relationship between the persistence of an atmospheric state and the practical predictability of temperature in terms of the error in surface temperature forecasts. The relationship between the two varies depending on season and location. Nonetheless, in a number of cases atmospheric persistence provides potentially valuable information on the practical predictability of temperature. We specifically highlight the cases of wintertime temperature forecasts up to three weeks lead time and wintertime cold spells up to roughly two weeks lead time.

Observation Definitions and Their Implications in Machine Learning–Based Predictions of Excessive Rainfall

Abstract The implications of definitions of excessive rainfall observations on machine learning model forecast skill are assessed using the Colorado State University Machine Learning Probabilities (CSU-MLP) forecast system. The CSU-MLP uses historical observations along with reforecasts from a global ensemble to train random forests to probabilistically predict excessive rainfall events. Here, random forest models are trained using two distinct rainfall datasets, one that is composed of fixed-frequency (FF) average recurrence intervals exceedances and flash flood reports and the other a compilation of flooding and rainfall proxies [Unified Flood Verification System (UFVS)]. Both models generate 1–3-day forecasts and are evaluated against a climatological baseline to characterize their overall skill as a function of lead time, season, and region. Model comparisons suggest that regional frequencies in excessive rainfall observations contribute to when and where the ML models issue forecasts and subsequently their skill and reliability. Additionally, the spatiotemporal distribution of observations has implications for ML model training requirements, notably, how long of an observational record is needed to obtain skillful forecasts. Experiments reveal that shorter-trained UFVS-based models can be as skillful as longer-trained FF-based models. In essence, the UFVS dataset exhibits a more robust characterization of excessive rainfall and impacts, and machine learning models trained on more representative datasets of meteorological hazards may not require as extensive training to generate skillful forecasts. Significance Statement Machine learning (ML) models have shown significant promise in recent years when used to predict high-impact weather hazards. Here, we explore two similarly trained ML models tasked with predicting excessive rainfall, but they use datasets that define excessive rainfall differently. We explore how definitions of excessive rainfall, for example, an amount of rainfall that would be expected to fall once per year, contribute to forecast skill through where these observations are reported. Generally, we find that the two models have substantial skill relative to climatology out to 3 days, but skill varies by geographical region and season in part because of the distribution of observations geographically. These results suggest that careful attention should be paid to how ML models are trained to predict meteorological hazards like excessive rainfall.

Ensemble Predictability of Week 3/4 Precipitation and Temperature over the United States via Cluster Analysis of the Large-Scale Circulation

Abstract Forecasting the week 3/4 period presents many challenges, resulting in a need for improvements to forecast skill. At this distance from initial conditions, numerical models struggle to present skillful forecasts of temperature, precipitation, and associated extremes. One approach to address this is to utilize more predictable large-scale circulation regimes to make forecasts of temperature and precipitation anomalies, using the association between the regimes and surface weather obtained from reanalysis products. This study explores the utility of k-means cluster analysis on geopotential heights and their ability to make skillful regime predictions in the week 3/4 period. Using 14-day running means of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) 500-hPa geopotential heights for the wintertime December–February (DJF) period, circulation regimes are identified using k-means clustering. Each period is assigned a cluster number, allowing the compositing of any reanalysis or observation variable to form cluster maps. Maps of 500-hPa height, 2-m temperature, precipitation, and storm-track anomalies are some of the variables composited. The utility of these relationships in a dynamical forecast setting is tested via Global Ensemble Forecast System v12 (GEFSv12) hindcasts and real-time ensemble suite forecasts. Week 3/4 deterministic and probabilistic experimental forecasts are then derived from cluster assignments using several methods. We find, via a conditional skill analysis, forecasts strongly correlated with a cluster exhibit greater skill for both dynamical model and cluster-derived forecasts. Our preliminary results represent a step forward to aid forecasters make more skillful assessments of the circulation regime and its associated surface weather for this challenging forecast time scale. Significance Statement Our paper links the local statistics of 14-day precipitation and temperature within the United States to very broad preferred patterns of winds and pressure over the wide Pacific–North American region. Such patterns, known as “circulation regimes,” provide the background that heavily influences local surface conditions. We find that forecasts for which midatmospheric anomalies are closely aligned with one or more circulation regimes are better at predicting the U.S. weather probabilities than other week 3–4 forecasts. A tool based on this finding identifies forecasts for which confidence is higher. Future work will be designed to further exploit the links between circulation regimes and weather to improve the accuracy of week 3–4 forecasts.

Predictability of Severe Convective Storm Environments in Global Ensemble Forecast System, Version 12, Reforecasts

Abstract Prediction of severe convective storms at time scales of 2–4 weeks is of interest to forecasters and stakeholders due to their impacts on life and property. Prediction of severe convective storms on this time scale is challenging, since the large-scale weather patterns that drive this activity begin to lose dynamic predictability beyond week 1. Previous work related to severe convective storms on the subseasonal time scale has mostly focused on observed relationships with teleconnections. The skill of numerical weather prediction forecasts of convective-related variables has been comparatively less explored. In this study over the United States, a forecast evaluation of variables relevant in the prediction of severe convective storms is conducted using Global Ensemble Forecast System, version 12, reforecasts at lead times of up to 4 weeks. We find that kinematic and thermodynamic fields are predicted with skill out to week 3 in some cases, while composite parameters struggle to achieve meaningful skill into week 2. Additionally, using a novel method of weekly summations of daily maximum composite parameters, we suggest that the aggregation of certain variables may assist in providing additional predictability beyond week 1. These results should serve as a reference for forecast skill for the relevant fields and help inform the development of convective forecasting tools at time scales beyond current operational products. Significance Statement Prediction of severe weather beyond 1 week in advance is of interest to stakeholders given their impacts on life and property. In this study, we evaluate 20 years of forecast data generated by a numerical model ensemble to determine whether variables relevant in severe weather forecasts can be predicted in weeks 2–4. The variables with the best skill measures generally represent large-scale weather patterns that are more predictable on longer time scales, although some manipulation of other severe weather parameters yielded additional results. We suggest that the results found in this study can inform future work that assesses the predictability of severe weather in weeks 2–4 using more complex methods such as machine learning.

Investigating the Influence of First Principles of Instruction on Trauma Management Skills in Nursing Students.

This study aimed to evaluate the effects of first principles of instruction on trauma management skills for undergraduate nursing students. Two hundred twenty-four nursing students from Fujian Medical University were divided into control and experimental groups. Participants in the experimental group applied first principles of instruction and introduced design modifications to acquire trauma management skills. Scores for trauma management skills were significantly higher in the experimental group than in the control group. In addition, 89% of students strongly agreed with using first principles of instruction and indicated satisfaction with the teaching mode. First principles of instruction enhance theoretical understanding of trauma management skills, with significant score differences between experimental and control groups. High student satisfaction underscores the effectiveness of this approach in creating a conducive learning environment. Integrating first principles of instruction holds promise for deeper comprehension and long-term educational success among nursing students. [J Nurs Educ. 2024;63(11):731-736.].

Impact‐Based Skill Evaluation of Seasonal Precipitation Forecasts

AbstractWe introduce an impact‐based framework to evaluate seasonal forecast model skill in capturing extreme weather and climate events over regions prone to natural disasters such as floods and wildfires. Forecasting hydroclimatic extremes holds significant importance in an era of increasing hazards such as wildfires, floods, and droughts. We evaluate the performance of five Copernicus Climate Change Service (C3S) seasonal forecast models (CMCC, DWD, ECCC, UK‐Met, and Météo‐France) in predicting extreme precipitation events from 1993 to 2016 using 14 indices reflecting timing and intensity (using absolute and locally defined thresholds) of precipitation at a seasonal timescale. Performance metrics, including Percent Bias, Kendall Tau Rank Correlation Score, and models' discrimination capacity, are used for skill evaluation. Our findings indicate that the performance of models varies markedly across regions and seasons. While models generally show good skill in the tropical regions, their skill in extra‐tropical regions is markedly lower. Elevated precipitation thresholds (i.e., higher intensity indices) correlate with heightened model biases, indicating deficiencies in modeling severe precipitation events. Our analysis using an impact‐based framework highlights the superior predictive capabilities of the UK‐Met and Météo‐France models in capturing the underlying processes that drive precipitation events, or lack thereof, across many regions and seasons. Other models exhibit strong performance in specific regions and/or seasons, but not globally. These results advance our understanding of an impact‐based framework in capturing a broad spectrum of extreme weather and climatic events, and inform strategic amalgamation of diverse models across different regions and seasons, thereby offering valuable insights for disaster management and risk analysis.

Observing Array Designed for Improving the Short‐Term Prediction of Kuroshio Extension State Transition Processes

AbstractGiven the essential implications of Kuroshio Extension (KE) bimodality on oceanic dynamical environment and climate, the present study investigates the targeted observation schemes, based on the conditional nonlinear optimal perturbation (CNOP) method and a reduced‐gravity shallow‐water model, to improve the forecast skills of transition processes of KE bimodal states. To obtain a suitable observing array, the observation schemes, with different numbers of observation sites and observation distances between two sites, are designed. Furthermore, to demonstrate the superiority of the observing networks in predicting KE transition processes, two existing observation schemes and six random observation schemes are compared with the CNOP‐determined observing array. Based on this, a relatively optimal observing array with three sites and observation distance of 90 km is established, which is mainly located between 31°N and 33°N in the south of Japan. This targeted observing network is universal for two KE transition processes. The removal of initial errors on this array results in the mean prediction improvements of about 9.2% and 22.5% for KE transition processes from the low‐ to the high‐energy state and from the high‐ to the low‐energy state, respectively.