Forecast Skill Research Articles

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.

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.

Examination of the effect of different techniques (demonstration, telesimulation and game-based training) in teaching the management of shoulder dystocia: A randomized controlled trial

BackgroundThis study evaluated the effect of demonstration, telesimulation and game-based training in teaching SD management for midwifery students. MethodsThis randomized controlled trial was completed with 123 participants. To collect data, the ‘Introductory Information Form’, ‘Self-efficacy Visual Analog Scale (VAS)’, ‘Shoulder Dystocia Skill Checklist’, and ‘Instructional Materials Motivation Survey (IMMS) were used. ResultsAfter the training, mean VAS scores of self-efficacy in diagnosing and intervening SD were higher in the telesimulation and demonstration groups, and mean VAS scores of self-efficacy in evaluating the postpartum mother and infant were higher in all intervention groups than in the control group (p < .001). There is a difference between the control group and all intervention groups in terms of total skill score in SD management (p < .001). Demonstration group's mean total score of MMS of students is higher than kahoot and control groups (p < .005). ConclusionIt was seen that all three methods were more effective than theoretical training in gaining SD management skills, but telesimulation was more effective than other methods.

Updates to NOAA’s Unified Forecast System’s Cumulus Convection Parameterization Scheme between GFSv16 and GFSv17

Abstract This study outlines the updates made to cumulus convection parameterizations between Global Forecast System, version 16 (GFSv16), and the forthcoming GFSv17 which will be the first global forecast application to become operational under the Unified Forecast System infrastructure. The updates, addressing known systematic errors and biases, incorporate innovations such as stochasticity, three-dimensional subgrid organizational effects, and a prognostic closure evolution. The changes are shown to improve tropical temperature/humidity biases, convective available potential energy (CAPE) forecasts, CONUS precipitation, and tropical variability. By examining individual updates’ impact on temperature, humidity profiles, and precipitation power spectra, we find that the new prognostic closure and stricter precipitation evaporation criteria alleviate a dry and cold bias in the tropical boundary layer and enhance precipitation variability in the tropics. Stricter triggering criteria also allow for more CAPE buildup, particularly over the tropics. The cumulus convection updates have a modest impact on precipitation skill scores over CONUS, but overall, there is an improvement when comparing GFSv16 and the latest GFSv17 prototype configurations, in particularly for larger thresholds. The study also highlights the challenges in developing convection parameterizations suitable for both coupled and uncoupled model configurations. Evaluation of the MJO shows varying responses to cumulus convection changes depending on whether the model is coupled with a dynamic ocean model.

Application of a Two-Step Space–Time EOF Statistical Postprocessing Algorithm to Mitigate Subseasonal 200-hPa Geopotential Height Forecast Error

Abstract The time-extended empirical orthogonal function (EEOF) patterns of error developed in a companion paper are applied in this study for model data postprocessing. Projecting forecast anomalies from the model seasonal cycle from GEFSv12 outputs onto these EEOF patterns reconstructs the error principal components (PCs) in a manner that can be applied in real time. When these reconstructed error anomalies are compared with the original GEFSv12 200-hPa geopotential height (Z200) reforecast error anomalies, the projected and validation error signals tend to align temporally. When distributed globally, projection and validation error anomalies have statistically significant positive Pearson correlation coefficients with each other at most grid points outside of the tropics. Categorical Heidke skill score (HSS) analysis reaffirms that by lead day 16 of a forecast, the algorithm skillfully predicts systematic errors for locations outside of the tropics. Hemispheric winter demonstrates that the highest magnitude HSS values are in the high latitudes, illustrating the underlying seasonality of Z200 behavior and predictability. On average, wintertime HSS values hover around 20%, with high-latitude locations exceeding mean values of 40%. Comparatively, summertime values generally exceed 10% improvement outside of the tropics. High-latitude storm-track regions, such as the North Atlantic, North Pacific, and South Pacific, contain the most Z200 variance in the globe and the highest HSS values. This algorithm will be applied to real-time forecasts and additional variables in future work, with the end goal of implementation into the NOAA-CPC subseasonal forecasts.

Application of the asicc learning model in human physiological system to improve students' critical thinking and creative thinking skills

Critical thinking and creative thinking skills are needed to improve the quality and competitive human resources. Therefore, the application of appropriate learning models is expected to stimulate students' critical thinking and creative thinking skills. This research aims to improve critical thinking and creative thinking skill scores using the ASICC learning model. This type of research is classroom action research which is carried out in 2 cycles, with 3 meetings in each cycle. Data on students' critical thinking and creative thinking skills were collected using essay tests on science learning materials. Data analysis was carried out by comparing the N-gain scores in cycle 1 and cycle 2 for students' critical thinking and creative thinking skills. The results of the research revealed that there was an increase that occurred in cycle 2, namely 5.6% in the aspects of moderate critical thinking skills and high creative thinking, as well as in the aspects of critical thinking skills and high creative thinking by 5.6%. Meanwhile, in the previous cycle, these two aspects did not have a high category. Another result found from this research is that students who have low critical thinking skills do not necessarily have low creative thinking skills. This is because students who have low critical thinking skills may have moderate creative thinking skills or vice versa. Thus, the application of the ASICC learning model is effective in improving the scores of critical thinking skills and creative thinking skills of students at Junior High School 5 Kertosono.

Integration of Multiple Intelligences Theory in Project-Based Learning, How It Impacts on Students' Critical Thinking and Mathematical Communication Skills?

In this study, we aim to assess the effectiveness of Project-Based Learning (PjBL) based on Multiple Intelligences (MI) theory on students' critical thinking and mathematical communication skills. This research is a quasi-experiment using non-equivalent (pre-test and post-test) control-group design. The population in this study were VIII grade students in a public junior high school in Yogyakarta and two sample classes were selected based on recommendations from teachers and had mathematical abilities that could be considered equal. The experimental class applied PjBL based on MI theory and the control class applied PjBL learning. Data collection was conducted using a test instrument in the form of description questions. This instrument is valid and reliable. Learning is said to be effective if: (1) the mean test score of critical thinking and communication skills is at least 70 (from a score range of 0-100); (2) the mean of post-test of critical thinking and communication skills is higher than the mean of pre-test; (3) the mean final score of students' critical thinking and mathematical communication skills in the experimental class is higher than the control class. Data analysis used one-sample t-test, paired-sample t-test, and T2 Hotteling test. The results of the study using the significance level α = 5% showed that (1) PjBL learning based on MI theory is effective in terms of students' critical thinking and mathematical communication skills; (2) PjBL based on MI theory learning is more effective than PjBL learning in terms of communication, but not more effective in terms of critical thinking skills. Although the data from the sample showed the acquisition of mean scores and the increase in the mean value of critical thinking learning in PjBL based on MI theory was better than PjBL learning in terms of critical thinking skill, but statistically both did not show a significant difference.

Effect of rooming-in kangaroo mother care on breastfeeding and behavioral status of full-term newborns.

We aimed to assess the effects of rooming-in kangaroo mother care (KMC) on the breastfeeding and behavioral status of full-term newborns. Full-term newborns born from June to December 2021 were divided into Group A (n = 50) and Group B (n = 50) using a random number table. The rooming-in routine nursing intervention and KMC were given to Group A (n = 50) and Group B, respectively. The outcomes were compared. The scores of breastfeeding knowledge, breastfeeding skills, and sense of responsibility for breastfeeding of Group B were higher than those of Group A (p < 0.05). The breastfeeding rate and lactation yield within 3 days of Group B were higher than those of Group A (p < 0.05). Compared with Group A, Group B had higher effective sucking times of newborns and lower behavioral status scores 1, 2, and 3 days after birth, and shorter duration of crying after invasive operation (p < 0.05). The newborns in Group B had better sleep quality, longer sleep time, and lower procedural pain score than those of Group A (p < 0.05). Rooming-in KMC improves the maternal breastfeeding self-efficacy, breastfeeding rate, and lactation yield, ameliorates the behavioral status and sleep quality of newborns, shortens the crying time and relieves the procedural pain.

So Easy to Write a Story With a Mind Map: An Experimental Study

This study aimed to determine the effect of mind mapping on story writing skills of the students. A pre-test, a post-test, and a semi-experimental research design were used in this study. The study group consisted of a total of 86 third-grade students; 33 students from the experimental group, 23 students from the control 1 group, and 30 students from the control 2 groups. Stories were written by the students in the experimental group by using the mind map method during 18 lesson hours. In the control 1 group, story topics were given to the students, and they were asked to write stories about them as homework during one lesson hour for 9 weeks. In the control 2 group, the researcher did not intervene in their lessons during the term. As a result of the research, there was an increase in the post-test writing skill scores of the experimental and control 1 groups. The mind map method and story writing with homework exercises improved students' writing skills.

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.

THE COMMUNICATION SKILLS OF HEALTHCARE PROFESSIONALS AS OPPOSED TO THOSE WORKING OUTSIDE THE HEALTHCARE SECTOR

Communication, a fundamental aspect of human interaction, was the focus of this study. We compared the communication skills of individuals in the healthcare sector with those outside it. While the research did not uncover significant differences based on demographic characteristics, it did reveal noteworthy disparities in communication skill scores. These findings, we believe, will significantly contribute to the existing literature and provide valuable insights for addressing communication challenges at an institutional level. We recommend similar research in different institutions and sectors, with results evaluated separately, to further enhance our understanding of this crucial area.

The Effect of Audio Visual Media on the Skills of Translating Negotiation Text from Dialogue Form to Narrative Form of Phase E Students of UN P Laboratory Development High School

This research aims to elucidate the impact of audiovisual media on the skills of Phase E students at the UNP Laboratory Development High School in converting negotiation texts from dialogue to narrative form. The study adopts a one-group pretest-posttest design, with a sample consisting of 31 students from Phase E1. Data collected include the students' scores in converting negotiation texts from dialogue to narrative form, assessed both before and after the implementation of audiovisual media. The findings indicate the following: First, prior to the use of audiovisual media, the students' average skill score was 70.16, placing it within the 66-75% range, which is classified as more than adequate (LdC). Second, following the use of audiovisual media, the average score increased to 80.38, within the 76-85% range, corresponding to a good qualification (B). Third, the use of audiovisual media was found to have a significant effect on the students' skills in converting negotiation texts, as evidenced by a tvalue that exceeds the critical ttable value (4.19 &gt; 1.70). This research contributes valuable insights into the application of audiovisual media in educational contexts and underscores the importance of technology in enhancing the quality of Indonesian language instruction. The results provide a foundation for educators to develop more effective and interactive pedagogical strategies in the future.

Using Adjoint-Based Forecast Sensitivity to Observation to Evaluate a Wind Profiler Data Assimilation Strategy and the Impact of Data on Short-Term Forecasts

A wind profiler radar detects fine spatiotemporal resolution dynamical information, enabling the capture of meso- and micro-scale systems. Experience gained from observing system experiments (OSEs) studies confirms that reasonable profiler assimilation techniques can achieve improved short-term forecasts. This study further applies the adjoint-based forecast sensitivity to observation (FSO) method to investigate the quantitative impact of a profiler data assimilation strategy on short-term forecasts, and the results are consistent with those obtained from OSEs, further demonstrating that FSO and OSEs can be used to evaluate the effect of data assimilation techniques from different perspectives. Considering the unique advantage that the FSO can quantify the interactions between various observing systems and the impact on improving the model forecasts according to specific needs without costly additional calculations, we further diagnose in detail the observation impacts from multiple perspectives, including the observation platform, observation variables, and spatial distribution. And the results show that dynamical variables are more significant in improving forecasts compared to the other observed variables. Meanwhile, the dense profiler observations resulted in a more significant impact when radiosonde observations were not detected. The upper-level single winds monitored by profiler radars play a more important role in improving forecast skill. The FSO method measures the impact of an individual observing system, which can be used to enrich the evaluation of data assimilation schemes, efficiently calculate the impacts of multisource observations, and contribute to future development in adaptive observation, observation quality control, and observation error optimization.

An attention fused sequence -to-sequence convolutional neural network for accurate solar irradiance forecasting and prediction using sky images

The challenge of accurately forecasting ultra-short-term solar irradiance for photovoltaic systems is complicated by rapidly changing weather, and while ground-based sky images offer potential improvements, effectively extracting spatiotemporal data from these images remains a significant hurdle for current computer vision models. A new hybrid model, "An Attention Fused Sequence-to-Sequence Convolutional Neural Network," is being developed to address this challenge. The model predicts intra-hour GHI, DNI, and DHI with a 10-min lead time by combining a Convolutional Neural Network (for spatial feature extraction from sky images), an attention mechanism (to focus on relevant regions), and a sequence-to-sequence model (for temporal feature extraction from time-series data). The proposed Model is trained using the NREL Solar Radiation Research Laboratory Dataset while evaluating the model with the Mean Bias Error, Mean Absolute Error, Root Mean Squared Error, R Squared, and forecasting skill score. The 10-min and sequence length 2 interval is considered to be the best performing across most of the evaluation metrics with an MBE value is 2.321 W/m2, MAE value of 39.490 W/m2, RMSE value of 62.086 W/m2, R2 value is 0.909 W/m2, FSS value of 23.589 W/m2 and an MBE of 4.876 W/m2, MAE of 56.887 W/m2, RMSE of 85.346 W/m2, R2 of 0.834 and FSS of 24.881 W/m2 respectively. Furthermore, the sensitivity analysis reveals that the proposed model's performance is influenced by both the sequence length and the lead time. The proposed framework outperforms other techniques for ultra-short-term PV generation forecasting, demonstrating its potential for practical deployment in PV systems to improve grid reliability and energy management.