Forecast Skill Research Articles

Основные особенности климатических условий зимнего сезона 2023/2024 гг. по данным мониторинга и прогнозов

The main results of the comprehensive analysis of the Northern Hemisphere large-scale atmospheric circulation features are presented for the 2023/2024 winter season. Skill scores of the consensus forecast for the 2023/2024 Northern Eurasia winter season are discussed in the context of reproducing the temperature and humidity regime. The qualitative analysis of the multimodel forecast from WMO’s Lead Center and the consensus forecast issued by NEACOF for seasonal anomalies in air temperature and precipitation for the winter of 2023/2024 resulted in the conclusion of the superior accuracy of the consensus forecast. This forecast was based on the data of three Russian models SL-AV, MGO, and INM with equal weighting coefficients.

Comparing the impact of different transfusion medicine teaching methods—A mobile application versus task-based learning—On two dimensions of clinical competence among nursing students: A quasi-experimental study

AimTo evaluate and compare the impact of a transfusion medicine training course delivered via two different teaching methods—a mobile application and task-based learning—on nursing students' knowledge and clinical decision-making skill. BackgroundTo advance nursing education and clinical competence, it is crucial to integrate innovative methods, such as mobile applications and task-based learning, that promote active and competency-based learning. Evaluating these methods provides educators with insights to enhance nursing students' knowledge and clinical decision-making skill, ultimately improving patient care and supporting professional development. DesignA quasi-experimental study using a pretest-intervention-posttest design with a control group. MethodsThis study included 82 nursing students from the 6th to 8th semesters, selected through convenience sampling and randomly assigned to three groups—mobile application (n=30), task-based learning (n=30) and control (n=30). The first group received educational content through a mobile application, the second group through task-based learning and the control group received no intervention. Data were collected using a valid and reliable three-part researcher-made tool, including a demographic/educational checklist and questionnaires on knowledge and clinical decision-making skill, administered before and two weeks after the intervention. Analysis was done using paired t-test, analysis of covariance and Bonferroni post hoc test, with a significance level set at p<0.05. ResultsFollowing the intervention, both intervention groups showed a significant increase in knowledge and clinical decision-making skill scores (p<0.05). Although both groups had higher knowledge scores than the control group, the differences were not significant (p>0.05). In contrast, clinical decision-making scores were significantly higher in both intervention groups compared with the control (p<0.05). No significant differences were observed between the intervention groups regarding the mean post-test knowledge and clinical decision-making skill scores (p>0.05). ConclusionBoth teaching methods effectively improved nursing students' knowledge and clinical decision-making skills in transfusion medicine. To leverage these findings effectively, educational authorities should integrate mobile applications and task-based learning into nursing curricula, test these methods in various settings to assess their impact and use the findings to update curricula in line with current educational needs.

Improving weather forecast skill of the Korean Integrated Model by assimilating Soil Moisture Active Passive soil moisture anomalies

Improving weather forecast skill of the Korean Integrated Model by assimilating Soil Moisture Active Passive soil moisture anomalies

Atmospheric Precursors of Skillful SST Prediction in the Northeast Pacific

Abstract Forecasts of sea surface temperature anomalies (SSTAs) provide essential information to stakeholders of marine resources in coastal ecosystems, such as the California Current Large Marine Ecosystem (CCLME), at management-relevant monthly-to-annual time scales. Diagnosing dynamical sources of predictability and the mechanisms differentiating skill among forecasts is required for verification and improvement in operational forecasting systems. Using retrospective forecasts (1982–2020) from a four-member subset of the North American Multi-Model Ensemble (NMME), we evaluate the conditional skill of SSTA forecasts in the CCLME at monthly resolution for lead times up to 10.5 months. Forecasts from ensemble members with relatively small SSTA errors at shorter lead times retain higher skill at longer lead times, with the most substantial and long-lasting increases for forecasts initialized in the fall and early spring. The “best” low-error SSTA forecasts are characterized by increased skill in the prediction of North Pacific atmospheric circulation [sea level pressure (SLP) and 200-hPa geopotential height] the month prior to the evaluation of SSTA errors in the CCLME and exhibit more realistic progressions of anomalous SLP. The Pacific meridional mode (PMM) emerges as a diagnostic of skillful North Pacific atmosphere–ocean coupling, as forecasts that correctly simulate the PMM and its associated SLP variability increase the SSTA prediction skill in the CCLME in the fall through spring. Predictable coupled ocean–atmosphere modes provide a target for enhancing predictability with early detection of the onset of a deterministic progression emerging from stochastic atmospheric variability. Significance Statement Global forecast systems provide near-term climate predictions that inform the management of marine resources, such as those of the California Current Large Marine Ecosystem. In this study, we probe the processes which lead forecasts to succeed or fail at predicting sea surface temperatures in the California Current at seasonal time scales among retrospective forecasts from the North American Multimodel Ensemble. We demonstrate that forecasts which best simulate sea surface temperatures at the earliest lead times sustain advantages in forecast skill and find that correctly simulating extratropical atmospheric circulation increases the predictive skill of sea surface temperatures in the northeast Pacific in the following lead times. Our results offer North Pacific atmospheric circulation as a target for forecast model improvement that would additionally enhance ocean forecasts.

Medium‐range predictability of temperature extremes and biases in Rossby‐wave amplitude

AbstractThis study investigates the medium‐range predictability of warm and cold extremes in the Northern Hemisphere and the role that upper‐tropospheric circulation biases play in this regard. Deterministic ERA5 reforecasts for the period 1979–2019 are evaluated based on the ERA5 reanalysis of the respective period, thus providing a large sample for verification and bias identification. The predictability of temperature extremes at 850 hPa is assessed based on the Gilbert Skill Score and other metrics and is shown to exhibit regional and seasonal variations. Summer is generally characterized by lower forecast skill scores than winter for both warm and cold extremes. Moreover, cold extremes in summer have slightly lower skill scores than warm extremes, while the opposite is true in winter. Biases in the frequency of temperature extremes are, to some extent, consistent with biases in mean temperature and indicate an underestimation in the total amount of extremes for much of the hemisphere in summer. Associated with the latter, biases also emerge in the standard deviation of the daily temperature distribution, with the summer values being largely underestimated over most of the hemisphere. The role of upper‐tropospheric circulation in these biases is then assessed by verifying the representation of Rossby‐wave packet (RWP) properties. It is found that the amplitude of RWPs is systematically underestimated in most of the hemisphere in summer, while it is overestimated in many parts of the midlatitudes in winter. Overall, the results suggest that the underestimation of RWP amplitude in summer hinders the medium‐range predictability of temperature extremes in the explored retrospective and operational forecasts. Although operational European Centre for Medium‐Range Weather Forecasts (ECMWF) forecasts gradually improve between 2013 and 2022 in terms of the 850‐hPa temperature and 300‐hPa RWP amplitude absolute errors, the aforementioned summer biases remain qualitatively similar.

The characterization, mechanism, predictability, and impacts of the unprecedented 2023 Southeast Asia heatwave

In April and May 2023, Southeast Asia (SEA) encountered an exceptional heatwave. The Continental SEA was hardest hit, where all the countries broke their highest temperature records with measurements exceeding 42 °C, and Thailand set the region’s new record of 49 °C. This study provides a comprehensive analysis of this event by investigating its spatiotemporal evolution, physical mechanisms, forecast performance, return period, and extensive impacts. The enhanced high-pressure influenced by tropical waves, moisture deficiency and strong land-atmosphere coupling are considered as the key drivers to this extreme heatwave event. The ECMWF exhibited limited forecast skills for the reduced soil moisture and failed to capture the land-atmosphere coupling, leading to a severe underestimation of the heatwave’s intensity. Although the return period of this heatwave event is 129 years based on the rarity of temperature records, the combination of near-surface drying and soil moisture deficiency that triggered strong positive land-atmosphere feedback and rapid warming was extremely uncommon, with an occurrence probability of just 0.08%. These analyses underscore the exceptional nature of this unparalleled heatwave event and its underlying physical mechanisms, revealing its broad impacts, including significant health repercussions, a marked increase in wildfires, and diminished agricultural yields.

Low‐Level Jet and Its Effect on the Onset of Summertime Nocturnal Rainfall in Beijing

AbstractLow‐level Jets (LLJs) play a critical role in triggering nocturnal rainfall in many areas on our planet. Nevertheless, little is known regarding the vertically resolved evolution of LLJs preceding rainfall. Here, the high‐resolution wind measurements from a radar wind profiler network in Beijing are used to monitor the continuous evolution of boundary‐layer jets (BLJs) and synoptic‐system‐related LLJs (SLLJs) before nocturnal rainfall under the southwesterlies synoptic pattern for the summers of 2020–2023. The results show that inertial oscillations precede LLJ‐induced nocturnal rainfall. The typical northward propagation of LLJ is driven primarily by horizontal advection. Starting 48 min before rainfall, the BLJ nose rapidly strengthens and moves downward from 0.8 km above ground level (AGL), while the SLLJ nose moves upward to 2.4 km AGL. This leads to enhanced moisture transport and convergence, which triggers nocturnal rainfall. The findings are of great significance for improving the forecast skill of nocturnal rainfall.

Relationships between sensory reactivity and occupational performance in children with paediatric acute-onset neuropsychiatric syndrome (PANS).

Paediatric acute-onset neuropsychiatric syndrome (PANS) is a neurodevelopmental disorder affecting children's performance in a fluctuating manner. This study investigates the relationship between sensory reactivity and occupational performance in children with PANS during exacerbation phases. Understanding these relationships is crucial for developing tailored interventions and managing the condition effectively. This study is part of a larger project in which a two-period bidirectional case-crossover design was employed to assess sensory reactivity and occupational performance in children with PANS. The current study used data from exacerbation phases only. Parents of children aged 4.6 to 13.1years with PANS were recruited globally, and data were collected via online surveys. The Vineland Adaptive Behaviour Scale (VABS) evaluated occupational performance, and the Sensory Processing Measure (SPM) assessed sensory reactivity. Analysis utilised Pearson correlation to determine the relationships between sensory reactivity and occupational performance domains. Data from 60 participants who completed the SPM and 21 who completed the VABS showed moderate to strong negative correlations between the SPM total and several domain scores (vision, hearing, body awareness, balance, and motion) and the VABS Communication and Daily Living Skills scores, indicating that increased sensory reactivity is associated with decreased occupational performance during exacerbations. No significant correlation was found between sensory reactivity and socialisation skills. Findings highlight the significant relationship between sensory reactivity and occupational performance during PANS exacerbations, particularly in communication and daily living skills domains. Further research is needed to examine factors influencing socialisation skills and to assess the effectiveness of sensory interventions in improving occupational performance. Although there was no direct consumer and community involvement, two researchers on our team have family members with PANS. Their personal experiences provided vital insights into the challenges faced by children with PANS, deeply influencing our study's design, focus, and interpretation, ensuring it reflects the realities of those affected. We examined the relationship between children's daily activities and their sensory reactivity during PANS symptom flare-ups. Parents of children aged 4.6 to 13.1 years completed surveys measuring sensory reactivity and their children's performance of daily tasks. Our findings showed that during symptom flare-ups, children experienced significant difficulties with communication and daily living skills. These challenges were directly related to heightened sensory reactivity during flare-ups. This highlights the importance of providing targeted occupational therapy during these times to help children manage their symptoms and improve their daily functioning.

Children with Auditory Brainstem Implants: Language Proficiency and Reading Comprehension Process

Introduction: Auditory performance and language proficiency in young children who utilize auditory brainstem implants (ABIs) throughout the first 3 years of life are difficult to predict. ABI users have challenges as a result of delays in language proficiency and the acquisition of reading comprehension, even if ABI technology offers auditory experiences that enhance spoken language development. The aim of this study was to evaluate about the impact of language proficiency on reading comprehension skills in children with ABI. Method: In this study, 20 children with ABI were evaluated for their reading comprehension abilities and language proficiency using an Informal Reading Inventory, Test of Early Language Development-Third Edition (TELD-3), Categories of Auditory Performance-II (CAP-II), and Speech Intelligibility Rating (SIR). Three distinct aspects of reading comprehension were assessed and analyzed to provide a composite score for reading comprehension abilities. TELD-3, which measures receptive and expressive language proficiency, was presented through spoken language. Results: Studies have shown that there was a relationship between language proficiency and reading comprehension in children with ABI. In the present study, it was determined that the total scores of reading comprehension skills of the children who had poor language proficiency and enrolled in the school for the deaf were also low. The children use short, basic sentences, often repeat words and phrases, and have a restricted vocabulary. In addition, the children had difficulty reading characters and detailed paragraphs and could not remember events in a logical order. Conclusion: Children with ABI may potentially have complicated reading comprehension abilities due to lack of access to all the speech formants needed to develop spoken language. In addition, variables affecting the reading levels of children with ABI include factors such as age at implantation, duration of implant use, presence of additional disability, communication model, and access to auditory rehabilitation. The reading comprehension skills of ABI users were evaluated in this study for the first time in the literature and may constitute a starting point for the examination of variables affecting reading comprehension in this area.

The influence of the personality traits of newly graduated nurses on the knowledge, skills and professional self-efficacy in standardized training: a cross-sectional study

BackgroundNew graduate nurses often have certain potential safety hazards for patients due to insufficient knowledge and lack of clinical practice ability. To address these challenges, China has implemented a standardized training strategy. The improvements in the quality of this training involve not only the intervention of teaching methods but also the consideration of personality traits.MethodsThe application software based on the BFI-2 Chinese scale was utilized to administer personality tests to nursing students; nursing students were invited to scan the QR code and voluntarily fill in a questionnaire, including basic information, personality test results, and a professional self-efficacy test scale; offline paper-based theoretical examination results of nursing students were collected before and after training. The data was then analyzed using SPSS software version 26.0, which involved descriptive analysis, one-way between-groups analysis of variance (ANOVA) and Spearman correlation analysis.ResultsBased on the data, there were no observable differences in the theoretical results before and after training across different personality traits. In terms of skill assessment, conscientiousness exhibited the highest score at 78.91 ± 2.98 points, while negative emotionality showed the lowest score at 74.59 ± 2.12 points. These differences between different personality traits are statistically significant (P < 0.001). In terms of professional self-efficacy, conscientiousness scored the highest at 98.48 ± 12.69, while negative emotionality scored the lowest at 85.89 ± 11.71, with significant differences between different personality traits (P < 0.001). Professional self-efficacy was negatively correlated with agreeableness (r = -0.150, P = 0.044) and positively correlated with conscientiousness (r = -0.310, P < 0.001). Skill scores were negatively correlated with negative emotionality (r = -0.257, P < 0.001) and positively correlated with conscientiousness (r = 0.182, P = 0.014).ConclusionsThis study shows that personality traits affect the skills test results and professional self-efficacy of nursing students. Conscientiousness scored the highest in this study, while negative emotionality scored the lowest. Therefore, personalized training plans are recommended to improve the quality of care for such nursing students and to further enhance patient safety.

Evaluation and projection of changes in temperature and precipitation over Northwest China based on CMIP6 models

AbstractNorthwest China is much more sensitive to climate warming, and the climate has varied rapidly from warm and drought to warm and humid conditions. In addition, due to the complex terrain of Northwest China, the methods and parameterization schemes of different CMIP6 models, these models are mostly applied to arid areas in Northwest China or Central Asia, lacking climate data for plateau areas and eastern Lanzhou, specifically in filtering CMIP6 models and evaluating applicable models. In this paper, 34 CMIP6 climate models are used to evaluate and forecast future trends in Northwest China under the SSP126, SSP245 and SSP585 scenarios in the short, medium and long term. CMIP6 models of temperature and precipitation are identified by applying the interannual variability skill score (IVS) between CN05.1 datasets and historical CMIP6 models, which are suitable for Northwest China. Then, we assess the characteristics, warming and wetting deviations, and uncertainties in the prediction of climatic change according to CMIP6 models over Northwest China. The results show that CMIP6 models in precipitation and temperature applicable to Northwest China are AWI‐CM‐1‐1‐MR, BCC‐CSM2‐MR, FGOALS‐g3, INM‐CM4‐8, INM‐CM5‐0 and MRI‐ESM2‐0. The multi‐model ensemble mean (MMEM) has better capability than individual CMIP6 models in precipitation and temperature prediction. Spatiotemporal climatic change over Northwest China shows overall warming and wetting trends. The IVS provides the ability to estimate CMIP6 model simulation performance both temporally and spatially. The temperature simulation is quite good in the Tarim Basin and Hexi Corridor region, and the precipitation simulation is quite good in the plateau region, Altai Mountains, Tianshan Mountains and Hexi Corridor region. Cold and wet deviations occur in Northwest China due to the topography and few stations, which are common reasons. The main sources of uncertainties in temperature prediction during this century are model uncertainty (before the 2090s) and scenario variability (after the 2090s), and model uncertainty in precipitation for CMIP6 becomes the main source of uncertainty.

Experience of observation skill workshop intervention for ophthalmologists in fellowship training.

To gauge the impact of an interventional workshop conducted to measure the observation skills of 34 postgraduates during induction into an ophthalmology fellowship. A seven-hour workshop was conducted with the ophthalmology trainees. Trainees from the 2022 batch of ophthalmology fellowships (21 females and 13 males) were included in the study. The pre-workshop assessment comprised two non-clinical images to spot the difference and five clinical images from various subspecialties of ophthalmology. This was followed by workshop and Post workshop assessment. The pre- and post-observation grades of participants were then compared by masked ophthalmologists. The Wilcoxon signed-rank test was used to compare scores at two time points, with a p-value < 0.05 for statistical significance. Statistical analysis revealed that the observation skill score was significantly higher after the workshop intervention (M d = 4.00, n = 34) compared to the pre-workshop score (M d = 1.85, n = 34), p-value = 0.000. Workshops on specific/selected foundational skills, such as observation skills and communication skills, must be integrated into the curricula of basic medical degree and specialty medicine to equip medical professionals with attentive observation and deep learning.

Case study: problem-based learning model for soccer basic movement skills and learning activity

Background: The education curriculum is developed to prepare students to gain comprehensive experiences and life skills. Learning effectiveness, especially in physical education, depends on the teacher's ability to choose the right learning strategies, models and methods. However, problems often faced in the learning process include lack of student attention, teacher dominance in the learning process, and passive learning tendencies. The Problem-Based Learning (PBL) learning model emerged as a potential approach to address these issues and improve the quality of learning. Study Objectives: This study aims to evaluate the effectiveness of the Problem-Based Learning (PBL) learning model in improving the basic soccer movement skills and learning activities of elementary school students in the context of physical education. Materials and Methods: This study used a pseudo-experimental method with a one-group pretest-posttest design. The research subjects comprised 24 fifth-grade Krakitan State Elementary School, Klaten Regency students. The research instruments included basic soccer skills tests (dribbling and short passes) and student learning activity observation sheets. Data were analyzed using paired samples t-test to determine the effect of learning with the PBL model on primary skills of playing football and student learning activities. Results: The study revealed a substantial enhancement in both the basic soccer skills and student learning activities following the implementation of the PBL model. The average basic soccer skills score surged from 99.0833 in the pretest to 109.0417 in the posttest, marking a notable increase of 9.9584 points. Similarly, student learning activities showed a commendable rise, with the mean score climbing from 3.0833 at the start of the treatment to 4.3750 at the end, indicating a significant increase of 1.2917 points. Statistical analysis using paired samples t-test confirmed the significance of these improvements, with the calculated t values for basic soccer skills (6.89943) and learning activities (7.369) both surpassing the t table value (2.069). Conclusion: In conclusion, this study affirms the effectiveness of the Problem-Based Learning (PBL) model in enhancing students' basic soccer movement skills and learning activities in the context of physical education at the primary school level. The substantial improvements in both the technical ability to play soccer and in students' active engagement in the learning process underscore the potential of PBL as a potent learning strategy for physical education. These findings not only underscore the importance of problem-based learning approaches in enhancing physical skills but also in fostering students' cognitive and social engagement in the learning process, thereby making a significant contribution to the field of physical education. Keywords: Problem Based Learning, Physical Education, Soccer Skills, Learning Activity, Primary School

Social skills programme for adolescents with depression: initial outcomes of a hospital based study in Sri Lanka

ObjectiveThis quasi-experimental study aimed to describe the outcome of a social skills intervention for adolescents with depression followed up at an outpatient Child and Adolescent Psychiatry Unit. Twenty adolescents with depression attending a tertiary care facility in Colombo, Sri Lanka were recruited, who participated in two social skills training programmes (6 months apart), in addition to pharmacological treatment. Outcome of the programme was measured by a self-administered questionnaire that assessed perceived changes in social skills, before and after the intervention. Wilcoxon Signed Rank test was used to determine the statistical significance of improvement in social skills.ResultsThe sample consisted of 80% (n = 16) female adolescents. Median scores (inter-quartile range) for social skills before and after the first social skills programme were 31.5 (21.0–36.0) and 33.0 (28.25–40.25) respectively, indicating a significant improvement in social skills following intervention (p = 0.011, r = 0.57). A similar improvement was observed after the second programme (n = 10), reporting higher median scores for social skills after intervention (29.5 vs. 37.5, p = 0.005, r = 0.89). A significant improvement was also observed in perceived ability to cope with depressive thoughts after intervention (p = 0.032), indicating that social skills training can be a useful adjunct to pharmacological interventions, in the management of depression in adolescents.

Exploring self-directed learning readiness and related factors: the role of time management skills in nursing students

BackgroundPrevious studies have reported varying levels of self-directed learning readiness and time management skills among nursing students, and the relationship between these two skills has not been extensively explored. Consequently, this study aimed to assess self-directed learning readiness and its influencing factors, with a specific emphasis on the role of time management skills among nursing students.MethodsThis cross-sectional study involved the participation of 110 undergraduate nursing students who were conveniently recruited for the research. The data collection tools included a demographic information form, Fisher’s Self-Directed Learning Readiness questionnaire, and the Time Management Inventory developed by Britton and Tesser. The collected data were analyzed using STATA-14 statistical software.ResultsThe findings revealed that 96.4% of the nursing students demonstrated readiness for self-directed learning. The mean overall score for self-directed learning readiness was 162.3 ± 6.1 out of 200, indicating a relatively high level of readiness. The mean score for time management skills was 98.1 ± 5.0 out of 135, suggesting satisfactory proficiency in this area. In terms of the relationship between self-directed learning readiness and its associated factors, time management skills, academic probation history, grade point average, place of residence, and level of interest in the field of study collectively accounted for 9.2% of the variance in self-directed learning readiness. Among these factors, academic probation history, grade point average, and place of residence emerged as statistically significant predictors (P < 0.05).ConclusionsThe study findings indicate that the level of self-directed learning readiness and time management skills among the nursing students were considered acceptable. Academic probation history, grade point average, and place of residence emerged as significant predictors of self-directed learning readiness. These results highlight the importance of considering students’ academic background and living conditions to effectively enhance their level of self-directed learning readiness. Further research is recommended to explore additional factors that may influence self-directed learning readiness among nursing students.

The added value of combining solar irradiance data and forecasts: A probabilistic benchmarking exercise

Despite the growing awareness in academia and industry of the importance of solar probabilistic forecasting for further enhancing the integration of variable photovoltaic power generation into electrical power grids, there is still no benchmark study comparing a wide range of solar probabilistic methods across various local climates. Having identified this research gap, experts involved in the activities of IEA PVPS T1611International Energy Agency - Photovoltaic Power Systems - Solar Resource for High Penetration and Large Scale Applications. agreed to establish a benchmarking exercise to evaluate the quality of intra-hour and intra-day probabilistic irradiance forecasts.The tested forecasting methodologies are based on different input data including ground measurements, satellite-based forecasts and Numerical Weather Predictions (NWP), and different statistical methods are employed to generate probabilistic forecasts from these. The exercise highlights different forecast quality depending on the method used, and more importantly, on the input data fed into the models.In particular, the benchmarking procedure reveals that the association of a point forecast that blends ground, satellite and NWP data with a statistical technique generates high-quality probabilistic forecasts. Therefore, in a subsequent step, an additional investigation was conducted to assess the added value of such a blended point forecast on forecast quality. Three new statistical methods were implemented using the blended point forecast as input.To ensure a fair evaluation of the different methods, we calculate a skill score that measures the performance of the proposed model relative to that of a trivial baseline model. The closer the skill score is to 100%, the more efficient the method is. Overall, skill scores of methods that use the blended point forecast ranges from 42% to 46% for the intra-hour scenario and 27% to 32% for the intra-day scenario. Conversely, methods that do not use the blended point forecast exhibit skill scores ranging from 33% to 43% for intra-hour forecasts and 8% to 16% for intra-day forecasts.These results suggest that using (a) blended point forecasts that optimally combine different sources of input data and (b) a post-processing with a statistical method to produce the quantile forecasts is an effective and consistent way to generate high-quality intra-hour or intra-day probabilistic forecasts.

Evaluating Machine Learning–Based Probabilistic Convective Hazard Forecasts Using The HRRR: Quantifying Hazard Predictability and Sensitivity to Training Choices

Abstract The High-Resolution Rapid Refresh (HRRR) model provides hourly updating forecasts of convective-scale phenomena, which can be used to infer the potential for convective hazards (e.g., tornadoes, hail, and wind gusts), across the United States. We used deterministic 2019–20 HRRR, version 4 (HRRRv4), forecasts to train neural networks (NNs) to generate 4-hourly probabilistic convective hazard forecasts [neural network probability forecasts (NNPFs)] for HRRRv4 initializations in 2021, using storm reports as ground truth. The NNPFs were compared to the skill of a smoothed updraft helicity (UH) baseline to quantify the benefit of the NNs. NNPF skill varied by initialization time and time of day but was all superior to the UH forecast. NNPFs valid at hours between 1800 and 0000 UTC were most skillful in aggregate, significantly exceeding the baseline forecast skill. Overnight NNPFs (i.e., valid 0600–1200 UTC) were least skillful, indicating a diurnal cycle in hazard predictability that was present across all HRRRv4 initializations. We explored the sensitivity of HRRRv4 NNPF skill to NN training choices. Including an additional year of 2021 HRRRv4 forecasts for training slightly improved skill for 2022 HRRRv4 NNPFs, while reducing the training dataset size by 40% using only forecasts with storm reports was not detrimental to forecast skill. Finally, NNs trained with 2018–20 HRRRv3 forecasts led to a reduction in NNPF skill when applied to 2021 HRRRv4 forecasts. In addition to documenting practical predictability challenges with convective hazard prediction, these findings reinforce the need for a consistent model configuration for optimal results when training NNs and provide best practices when constructing a training dataset with operational convection-allowing model forecasts. Significance Statement Convective hazards, such as hail and tornadoes, are often challenging to predict. To improve hazard predictions, we used machine learning (ML) to generate forecasts of convective hazards across the United States leveraging forecasts of prior events. The ML hazard forecasts were consistently better than a non-ML approach and varied in skill based on the time of day, with nighttime forecasts being particularly challenging. ML forecasts of wind gusts and hail were more skillful than tornadoes. Different strategies of constructing the dataset of prior events led to differences in forecast performance; thus, this work provides recommendations for how to assemble these datasets and train ML models to generate improved forecasts of severe weather events.

Description and Evaluation of the CNRM‐Cerfacs Climate Prediction System (C3PS)

AbstractThe CNRM‐Cerfacs Climate Prediction System (C3PS) is a new research modeling tool for performing climate reanalyzes and seasonal‐to‐multiannual predictions for a wide array of Earth system variables. C3PS is based on the CNRM‐ESM2‐1 model including interactive aerosols and stratospheric chemistry schemes as well as terrestrial and marine biogeochemistry enabling a comprehensive representation of the global carbon cycle. C3PS operates through a seamless coupled initialization for the atmosphere, land, ocean, sea ice and biogeochemistry components that allows a continuum of predictions across seasonal to multiannual time‐scales. C3PS has also contributed to the Decadal Climate Prediction Project (DCPP‐A) as part of the sixth Coupled Model Intercomparison Project (CMIP6). Here we describe the main characteristics of this novel Earth system‐based prediction platform, including the methodological steps for obtaining initial states to produce forecasts. We evaluate the entire C3PS initialization procedure with the most up‐to‐date observations and reanalyzes over 1960–2021, and we discuss the overall performance of the system in the light of the lessons learned from previous and actual prediction platforms. Regarding the forecast skill, C3PS exhibits comparable seasonal predictive skill to other systems. At the multiannual scale, C3PS shows significant predictive skill in surface temperature during the first 2 years after initialization in several regions of the world. C3PS also exhibits potential predictive skill in Net primary production (NPP) and carbon fluxes several years in advance. This expands the possibility of applications of forecasting systems, such as the possibility of performing multiannual predictions of marine ecosystems and carbon cycle.

Observing System Simulation Experiments Exploring Potential Spaceborne Deployment Options for a Differential Absorption Radar Measuring Marine Surface Pressures

AbstractA new technology for remote measurements of marine surface pressure has been proposed, employing a V‐band differential absorption radar and a radiometric temperature sounder to calculate the total column atmospheric mass. Observing System Simulation Experiments (OSSEs) are performed to evaluate the potential impact of Spaceborne Marine Surface Pressure (SMSP) on Numerical Weather Prediction. These experiments build on prior efforts (Privé, McLinden, et al., 2023, https://doi.org/10.16993/tellusa.3254), but with an updated version of the OSSE framework and with more sophisticated simulation of the SMSP observations and a longer experiment period. Several different instrument configurations are compared, including both scanning and non‐scanning orbits. SMSP impacts are calculated for analysis quality and forecast skill, and a forecast sensitivity observation impact tool is employed to place SMSP observations in context with the global observing network. The effects of rain contamination on observation quality are explored. Different magnitudes of simulated SMSP observation error are tested in the context of data assimilation to show the range of potential behaviors. Overall, SMSP observations are found to be most beneficial in the southern hemisphere extratropics, with statistically significant forecast improvements for the first 72 hr of the forecast. A constellation of four non‐scanning SMSP satellites is found to outperform a single scanning instrument with a 250 km wide swath.

Precise and Accurate Short-term Forecasting of Solar Energetic Particle Events with Multivariate Time-series Classifiers

Solar energetic particle (SEP) events are one of the most crucial aspects of space weather that require continuous monitoring and forecasting using robust methods. We demonstrate a proof of concept of using a data-driven supervised classification framework on a multivariate time-series data set covering solar cycles 22, 23, and 24. We implement ensemble modeling that merges the results from three proton channels (E ≥ 10 MeV, 50 MeV, and 100 MeV) and the long-band X-ray flux (1–8 Å) channel from the Geostationary Operational Environmental Satellite missions. Our task is binary classification, such that the aim of the model is to distinguish strong SEP events from nonevents. Here, strong SEP events are those crossing the Space Weather Prediction Center’s “S1” threshold of solar radiation storm and proton fluxes below that threshold are weak SEP events. In addition, we consider periods of nonoccurrence of SEPs following a flare with magnitudes ≥C6.0 to maintain a natural imbalance of sample distribution. In our data set, there are 244 strong SEP events comprising the positive class. There are 189 weak events and 2460 “SEP-quiet” periods for the negative class. We experiment with summary statistic, one-nearest neighbor, and supervised time-series forest (STSF) classifiers and compare their performance to validate our methods for prediction windows from 5 minutes up to 60 minutes. We find the STSF model to perform better under all circumstances. For an optimal classification threshold of ≈0.3 and a 60 minutes prediction window, we obtain a true skill statistic TSS = 0.850, Heidke skill score HSS = 0.878, and Gilbert skill score GSS = 0.783.