Forecast Skill Research Articles

Circumpolar Transport and Overturning Strength Inferred From Satellite Observables Using Deep Learning in an Eddying Southern Ocean Channel Model

AbstractThe Southern Ocean connects the ocean's major basins via the Antarctic Circumpolar Current (ACC), and closes the global meridional overturning circulation (MOC). Observing these transports is challenging because three‐dimensional mesoscale‐resolving measurements of currents, temperature, and salinity are required to calculate transport in density coordinates. Previous studies have proposed to circumvent these limitations by inferring subsurface transports from satellite measurements using data‐driven methods. However, it is unclear whether these approaches can identify the signatures of subsurface transport in the Southern Ocean, which exhibits an energetic mesoscale eddy field superposed on a highly heterogeneous mean stratification and circulation. This study employs Deep Learning techniques to link the transports of the ACC and the upper and lower branches of the MOC to sea surface height (SSH) and ocean bottom pressure (OBP), using an idealized channel model of the Southern Ocean as a test bed. A key result is that a convolutional neural network produces skillful predictions of the ACC transport and MOC strength (skill score of 0.74 and 0.44, respectively). The skill of these predictions is similar across timescales ranging from daily to decadal but decreases substantially if SSH or OBP is omitted as a predictor. Using a fully connected or linear neural network yields similarly accurate predictions of the ACC transport but substantially less skillful predictions of the MOC strength. Our results suggest that Deep Learning offers a route to linking the Southern Ocean's zonal transport and overturning circulation to remote measurements, even in the presence of pronounced mesoscale variability.

Modelling beach volume changes caused by moderate and weak hydrodynamic conditions

The goals of this study were the calibration of the XBeach model for moderate and weak hydrodynamic conditions in 2D mode and the determination of the volumetric changes induced by them. An evaluation of model performance was made based on the Brier Skill Score (BSS), the visual match of the profile shape (VMS), the absolute volumetric change error (m3/m) and the relative volumetric change error (%). An analysis of accuracy in determining volumetric changes in the dune coast, with XBeach applied, provided an average absolute error in determination of volumetric changes of 4.0 m3/m for a significant storm and 1.5 m3/m for moderate and weak hydrodynamic conditions in a 2D model calibration process. Simulations of morphological changes caused by moderate and weak hydrodynamic conditions for seven timeframes classified into three groups have been run in 2D mode. Within a validation process, the average absolute error in the determination of volumetric changes in the beach was ranging from 0.64 m3/m to 2.42 m3/m depending on the group of hydrodynamic conditions. A high value of the correlation coefficient (R) between the measured and modelled balance of volumetric changes (m3/m) for all timeframes – 0.79 and for the ‘strongest’ group no. 3 – 0.91 were revealed. For the remaining groups, i.e., group no. 1 and no. 2 of hydrodynamic conditions, no such correlation was observed. Furthermore, the temporal analysis of the sum of beach volumes proved that a rising trend was observed over 4 months. Thus, moderate and weak hydrodynamic conditions dominated the accumulation within the area of study. XBeach reflected this trend well, producing a difference between the measured and modelled sum of volumes at the end of the timeframe of 7.27 m3/m, which is close to the volume determination error by the model.

Improving the skill of medium range ensemble rainfall forecasts over India using MoES grand ensemble (MGE)-part-I

Improving the skill of medium range ensemble rainfall forecasts over India using MoES grand ensemble (MGE)-part-I

Three-dimensional microscope skill acquisition: A randomised controlled study comparing two-dimensional laboratory microscope training, video gaming and virtual reality gaming

IntroductionFine microsurgical motor skill acquisition can be challenging. With increasing technological innovation the method of microsurgical skills acquisition may change. Studies show that laboratory-based microsurgical training programs on a 2D microscope significantly improves novice’s microsurgical skill acquisition. However, are these skills transferable to a 3D microscope or is gaming agility more important? We present a randomised control trial of three interventions, namely laboratory tabletop microscope training (LM), high-fidelity video gaming (Sony PlayStation 4 console) (VG) and high-fidelity virtual reality gaming (Sony PlayStation VR console) (VR) versus a control group. MethodsForty novice medical students were block randomised to four groups: control (no intervention) n=10, LM n=10, VG n=10 and VR n=10. Participants performed chicken femoral artery anastomosis using the Aesculap Aeos® 3D microscope platform at baseline and again after intervention. Performance was evaluated using a modified structured assessment of microsurgery skills (mSAMS) score, time taken to complete anastomosis and time for suture placement by two blinded independent assessors. ResultNo statistically significant difference was noted between the groups at baseline. There was a statistically significant improvement in the LM arm between baseline and post-training for mSAMS score and time for suture placement. In the VG, VR and control groups no statistically significant difference was observed. ConclusionOur study demonstrates that during early microsurgical training, an intense laboratory-based microsurgical training program significantly improves novice’s anastomotic performance on a 2D microscope, and these skills are transferable when a 3D anastomosis is carried out. However, focussed gaming had no significant effect, and the results were akin to the non-intervention group.

MEMPSEP‐II. Forecasting the Properties of Solar Energetic Particle Events Using a Multivariate Ensemble Approach

AbstractSolar Energetic Particles (SEPs) form a critical component of Space Weather. The complex, intertwined dynamics of SEP sources, acceleration, and transport make their forecasting very challenging. Yet, information about SEP arrival and their properties (e.g., peak flux) is crucial for space exploration on many fronts. We have recently introduced a novel probabilistic ensemble model called the Multivariate Ensemble of Models for Probabilistic Forecast of Solar Energetic Particles (MEMPSEP). Its primary aim is to forecast the occurrence and physical properties of SEPs. The occurrence forecasting, thoroughly discussed in a preceding paper (MEMPSEP‐I by Chatterjee et al., 2024a, https://doi.org/10.1029/2023sw003568), is complemented by the work presented here, which focuses on forecasting the physical properties of SEPs. The MEMPSEP model relies on an ensemble of Convolutional Neural Networks, which leverage a multi‐variate data set comprising full‐disc magnetogram sequences and numerous derived and in‐situ data from various sources (MEMPSEP‐III by Moreland et al., 2024, https://doi.org/10.1029/2023SW003765). Skill scores demonstrate that MEMPSEP exhibits improved predictions on SEP properties for the test set data with SEP occurrence probability above 50%, compared to those with a probability below 50%. Results present a promising approach to address the challenging task of forecasting SEP physical properties, thus improving our forecasting capabilities and advancing our understanding of the dominant parameters and processes that govern SEP production.

IMPROVING STUDENTS’ WRITING SKILL IN NARRATIVE TEXT BY USING PIE (POINT, ILLUSTRATION,EXPLANATION) STRATEGY AT SMPN 8 MAKASSAR

The purpose of this research was to improve students’ writing skill by using Pie (Point, Illustration, Explanation) Strategy in narrative text for eight grade Students’ of SMPN 8 Makassar in the Academic year 2022/2023. The research used the pre-experiment with pre-test and post-test research.The total population of the research was the eight grade Students of which consist of 36 Students. Than, the data had been collecting by using writing test which was applied in pre-test and post-test. The process of the research was applied four times. The research was conducted by giving treatment and succeeded in improving Students’ writing skill in several aspect, namely content, organization, vocabulary, grammar, and mechanic.The treatment was carried out using PIE Strategy in narrative text. After conducting the research then the data were analyzed using test analysis on SPSS 16. The result of the average writing skill score on pre-test and post- test was 47.61. After the reserarcher gave treatment using Pie Strategy, it turned out that the score of student writing skill in narrative text in the post-test increased to 79.03 . Therefore, the researcher concludes that writing skill in narrative text using PIE strategy can improve the writing narrative text ability of class VIII.9 Students’ at SMPN 8 Makassar.Keywords: Writing, skill, Narrative,text, PIE (Point, Illustration, Explanation)

Fully Telemetric Robotic Microsurgery: Clinical Experience With 23 Cases.

Recently, there is an ongoing trend in plastic surgery with robotic-assisted microsurgery and supermicrosurgery devices being developed. Combining a telemetrically controlled robotic microscope with an also telemetrically controlled microsurgery robot unlocks synergistic effects with complete disconnection of the operating surgeon from the operating field. Here, we report the first clinical free flap reconstructions using this setup. Twenty-three surgeries were performed with the combined remote approach using the Symani Surgical System and the RoboticScope in open microsurgery procedures. Anastomosis time and ischemia time were recorded. The surgical performance for anastomoses was assessed using the modified Structured Assessment of Microsurgical Skills (SAMS) score. Subjective satisfaction was evaluated by the surgeons in comparison with conventional microsurgery. To evaluate the learning curve, the senior authors first four (first group) and last four (last group) procedures were compared. Overall, flap survival was 95.7%. The average arterial anastomosis time was 36.7 ± 10.9 min. Total time of surgery was 277.7 ± 63.8 min, and ischemia time was 100.6 ± 24.9 min. Most SAMS score parameters were significantly higher in the last group of surgical procedures compared with the first operations. Subjective satisfaction was equal or better with the combined robotic-assisted approach in most categories. Our data demonstrates safety and feasibility of the use of a combined remote approach. Robotic systems for microsurgical procedures may hold promising potential for improvement of surgical quality and open up new frontiers in microsurgery.

Ensemble seasonal forecasting of typhoon frequency over the western North Pacific using multiple machine learning algorithms

Abstract This study introduces an ensemble prediction methodology employing multiple machine learning algorithms for forecasting the frequency of typhoons (TYFs) over the western North Pacific (WNP) during June‒November. Potential predictors were initially identified based on the relationships between the year-by-year variation (DY) of the TYFs and preseason (March–May) environmental factors. These predictors were subsequently further refined, resulting in the selection of eight key predictors. Prediction models were constructed using twenty machine learning algorithms, utilizing data from 1965 to 2010. These trained models were then applied to perform hindcasts of TYFs from 2011 to 2023. The forecasted DY was added to the observed TYF of the preceding year to obtain the current year’s TYF. The results indicate that the TYFs predicted by the multi-model ensemble (MME) closely align with the observation during the hindcast period. Compared to individual models, the MME improves the prediction skill for the DY by at least 5.56% and up to 56.92%. Furthermore, the mean bias of the MME for TYF is notably smaller than that of the ECMWF’s most recent seasonal forecasting system (SEAS5) in the years of 2017‒2023. The superior performance of the ensemble prediction approach was also validated through leave-one-out cross-validation. This research underscores the potential of ensemble prediction approach utilizing multiple machine learning algorithms to improve the forecasting skill of TYF over the WNP.

The effect of formative assessment on students' clinical knowledge, skills and self-efficacy levels

AimThis research aims to examine the effect of using formative assessment methods in clinical education on students' knowledge, skills and self-efficacy levels. BackgroundFormative assessment is used as a method designed to identify areas where students fall short and provide feedback for improvement. Formative assessment and feedback represent fundamental characteristics of quality teaching in higher education and play a decisive role in learning in nursing education. Although educators observe students performing practical tasks during clinical education, evaluation is not made by a structured control list. Therefore, just as nursing students are evaluated with “Skill Checklists” in the OSCE exam, there is a need to evaluate nursing skills during patient care in the clinical field. DesignThe study was designed as a pre-test post-test randomized controlled experimental study. MethodBefore the research, both groups filled out the self-efficacy form. The experimental group received formative assessment throughout the course. At the end of the semester, all students were given a skills test and asked to fill out the self-efficacy form again. Finally, a knowledge test was administered to the entire class. ResultsThe average knowledge score of the experimental group is higher than the control group. It was determined that there was a statistical difference of 16.54 points in the average skill scores between the groups. Posttests showed significant differences in skills such as breathing-cough exercise, basic glycemic measurement, subcutaneous injection and blood collection skills. ConclusionIt was determined that the formative assessment method increased nursing students' knowledge, skills and self-efficacy levels regarding basic nursing skills.

Enabling the Assimilation of CrIS Shortwave Infrared Observations in Global NWP at NOAA. Part II: OSEs and Results

Abstract The assimilation of data from hyperspectral infrared sounders in global data assimilation systems has historically been focused on observations in the longwave infrared (LWIR) region of the spectrum (650-1100 cm−1), despite the often concurrent availability of measurements from the shortwave infrared (SWIR) region of the spectrum (2150-2550 cm−1), because issues (like solar effects) have generally prevented the assimilation of SWIR observations. Recent advances in radiative transfer models have worked to address some of the previous challenges in simulating SWIR observations, and the assimilation of SWIR data (e.g. from potential future small-satellites) is now a feasible prospect. Still, a better understanding of how these observations perform in a data assimilation system and impact resulting analyses and NWP forecasts is necessary. In this study, the value of SWIR observations in global NWP is assessed by assimilating SWIR observations from the Cross-track Infrared Sounder (CrIS) in NOAA’s Global Data Assimilation System (GDAS). The methodologies used to enable the assimilation of these observations, including the implementation of a scene-dependent observation error and the enhancement of quality control procedures, are discussed, as are the results of Observing System Experiments (OSEs) conducted to evaluate the impact of assimilating SWIR observations on forecast skill. The overall results show that SWIR assimilation produces similar forecast impacts to LWIR assimilation. The ability to demonstrate that the assimilation or SWIR observations in NWP is a realistic prospect may help to shape future constellations of small-satellites to serve as a beneficial complement to the current constellation if hyperspectral IR sounders.

Be prepared! Impact of structured video-assisted coaching on performance in a simulated bleeding exercise during laparoscopic surgery

BackgroundBleeding during laparoscopic surgery is stressful and requires immediate efficient management. Skills for complication management are rarely trained. This study aims to investigate the impact of video-assisted coaching on laparoscopic skills acquisition and performance in emergency bleeding situations.MethodsParticipants faced simulated emergency scenarios during laparoscopy involving bleeding management in porcine aorta/kidney specimens. Four sequences were conducted over two days, with a structured video-assisted coaching provided between sequences. Performance was assessed using the Global Operative Assessment of Laparoscopic Skills (GOALS) score. The study involved 27 participants attending the advanced colorectal surgery module at the 40th Annual Davos Course in 2023.Results54 video sequences were analyzed. Structured video-assisted coaching improved the GOALS sum score by 0.36 (95%CI: 0.21–0.50, P < 0.001) in contrast to simple repetition (0.05 with 95%CI: −0.43 to 0.53, P = 0.826). This association was observed for depth of perception (P < 0.001), bimanual dexterity (P < 0.001), tissue handling (P < 0.001), overall performance (P < 0.001), and efficiency (P < 0.001). Autonomy did not significantly improve (P = 0.55). Findings were consistent regardless of age, gender, and overall laparoscopic experience of the participants. However, a weaker effect of structured video-assisted coaching was observed in participants with experience in laparoscopic surgery.ConclusionStructured video-assisted coaching improved performance in laparoscopic skills in complex and stress-inducing bleeding scenarios. The findings of this study support the incorporation of video-assisted coaching and complication management exercises into surgical training curricula.

Obvious difference of dominant circulation patterns between dry‐type and humid‐type heatwaves in North China

AbstractBased on the observed maximum temperature (Tmax), relative humidity (RH) and NCEP/NCAR reanalysis data during 1981–2021, basic temporal features and dominant atmospheric circulation patterns of dry‐type and humid‐type heatwaves in North China are investigated and compared. Statistical results indicate the dry heatwaves occur mainly in early summer (from early June to early July), that is, before the rainy season of North China, while the humid heatwaves have a high frequency in mid‐July to mid‐August. During the research period, the increasing trend of dry heatwaves is 0.67 days·decade−1, while the humid heatwaves increase at a greatly higher rate of 1.85 days·decade−1. For the dry heatwave, a high ridge in the subtropical westerlies plays the main role, and the northerly wind in the east of the ridge reduces the air moisture convergence over the region. However, for the humid heatwave, the westward and northward propagations of the western Pacific subtropical high (WPSH) may make the major contribution, and the southerly wind anomalies in the west of the WPSH enlarge the water vapour to the region. The adiabatic heating in subsiding air at all levels and horizontal temperature advection at lower troposphere are stronger for dry heatwaves than for humid heatwaves, which cause a higher Tmax for the former type. These results highlight the diversity of the heatwaves in North China, which suggests that multiple local and large‐scale subseasonal circulations should be considered to improve the subseasonal to seasonal forecast skills for heat extremes.

A novel 3D-printed educational model for the training of laparoscopic bile duct Exploration:a pilot study for beginning trainees

BackgroundLaparoscopic common bile duct exploration (LCBDE) is a minimally invasive procedure for the removal of bile duct stones that is often performed by experienced hepatobiliary surgeons; beginners do not easily master this approach. AimTo investigate the effectiveness and practicality of a three-dimensional printed (3DP) anatomical model based on radiographic images in the training of LCBDE techniques and formulate standardized educational workflows. MethodsColored LCBDE training models were produced using 3DP technology. Twenty standardized training trainees were randomly divided into two groups: a 3DP model training group and a traditional laparoscopic simulation training group. Both groups received the same number of teaching hours. After a 4-weeks training course, the trainees’ subjective and objective progress in basic knowledge and manipulations were evaluated and compared. ResultsCompared with traditional laparoscopic simulation, 3DP model simulation training is of great significance in improving trainers' understanding of surgical procedures and cooperation during the operation. Trainees with 3DP models training demonstrated a significant improvement in their understanding of the key points of surgery (χ2 = 6.139, p = 0.013) and skills scores, especially in core procedural steps operation. More importantly, the trainees showed higher levels of satisfaction and self-confidence while assisting in the surgery. ConclusionWith the development of 3DP models, improvements in the learning effect for theoretical understanding and practical skills were significant. The present study is the initial educational experience with 3DP models to facilitate the operational capabilities of the trainees for LCBDE.

To Identify the Forecast Skill Windows of MJO Based on the S2S Database

AbstractAs a practical reflection of the opportunity window of Madden‐Julian Oscillation (MJO), there are intermittent periods of relatively high forecasting skills, namely the forecast skill windows. Robust forecast skill windows are identified based on the subseasonal‐seasonal reforecast database, during which the majority of models show high forecast skills. A total of 15 MJO forecast skill windows during 1993–2020 have been identified. Most of the forecast skill windows are closely associated with active MJO events with high amplitude. Whether a high‐skill forecast window appears significantly depends on the magnitude of MJO intensity during the same period. The maintenance of active strong MJO events is potentially related with the warmer surface sea temperature anomalies in the western Pacific. Further research into such processes may unveil the MJO development mechanism and improve the MJO forecast skill.

Evaluation of the effect of stoma care training using the pechakucha method on stoma care skills and anxiety in nursing students: A single-blind randomized controlled trial

AimThis study aimed to evaluate the effects of stoma care training delivered using PechaKucha (PK) on the stoma care skills and anxiety of nursing students. BackgroundInnovative learning methods are needed to make the educational content of nursing courses more interesting and comprehensible. The PK presentation technique is used in various business sectors and can be considered as a novel teaching strategy for the development of stoma care skills in nursing education. The PK method is a visual and narrative slide-based presentation technique involving 20 slides (usually containing images only) that are shown for 20 s each, for a total presentation length of 6 min and 40 s. DesignA single-blind randomized controlled study was conducted according to the CONSORT guideline. MethodsThe study sample comprised 96 first-year nursing students (47 in the intervention group and 49 controls). Data were collected during the 2024 spring semester using a student information form, the Stoma Care Skill Rubric and the State-Trait Anxiety Inventory. Stoma care training was delivered using the PK method for the intervention group and a classic lecture presentation for the control group. SPSS version 25.0 was used for statistical analysis. Independent-samples t-test was used to evaluate differences in mean stoma care skill and anxiety scores between the intervention and control groups. ResultsThe mean stoma care skills score two days after training was significantly higher in the intervention group (mean 42.59, SD 4.44) than in the control group (mean 33.87, SD 7.52) (p<0.05). Mean state anxiety scores after training did not differ significantly between the intervention group (mean 41.74, SD 6.51) and the control group (mean 43.08, SD 6.62) (p>0.05; Table 3). ConclusionThe PK method can be used as an alternative to classic slide-based presentations in nursing education. This study offers the first literature data regarding the efficacy of PK-based education for nursing care skills. Concise and image-based PK presentations may make the skill steps easier to understand and remember.

The Effect of Health Education Using Animated Video Media on Knowledge and Skills of Breast Self-Examination in Adolescent Girls

Background: Adolescence lasts from the age of 10-19 years who have experienced the formation of puberty hormones so that they have an increased risk of developing breast cancer. Breast cancer can be detected by breast self-examination (BSE). However, there are still many adolescents who do not know how to do it, so health education is needed, one of which is through animated videos Purpose: To find out the impact of health education using animated video media on the knowledge and skills of BSE in adolescent girls Methods: This study used a Quasi-Experimental design with a pretest-posttest with a control design. To measure SADARI knowledge and skills using the instrument of the SADARI knowledge questionnaire sheet and the SADARI skill observation sheet. The sample was taken using proportional stratified sampling of 19 respondents for each intervention group and control group. Education about BSE was provided to the intervention group through animated videos and control groups through leaflet media. Statistical analysis was performed using a dependent t-test and an independent t-test with an alpha value of 0.05 Result: There was a significant difference in the average score of knowledge and skills before education in the intervention group using animated video media 57.85 and 47.31 to 79.73 and 73.82 (p-value 0.000 &lt; α 0.05), while for the control group using leaflet media before education 47.31 and 19.17 to 62.31 and 66.95 (p-value 0.000). There was a significant difference in average post-education scores between the two groups with a p-value of 0.000 for knowledge and a p-value of 0.003 for skills Implication: There is an effect of health education using animated video media on adolescent girls' knowledge and skills about self-awareness

Application of a Two-Step Space-Time EOF Statistical Postprocessing Algorithm to Mitigate Sub-Seasonal 200hPa 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 realtime. When these reconstructed error anomalies are compared with 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 the highest magnitude HSS values 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 realtime forecasts and additional variables in future work, with the end goal of implementation into the NOAA-CPC subseasonal forecasts.

The effect of social skills-based blended education on social adjustment, self-esteem and social skills among new nursing students: An experimental study

IntroductionSocial adjustment, self-esteem and social skills may influence students' professionalization and academic success. The aim of this study was to determine the effect of a social skills-based blended education on the social adjustment, self-esteem and social skills of new nursing students. MethodsFirst-year undergraduate nursing students in Tehran, Iran, participated in this experimental study in 2023. Participants were selected from two classrooms, one as a control group (26 people) and the other as an intervention group (26 people). The study instruments included the California Personality Test, the Coopersmith Self-Esteem Inventory and the Teenage Inventory of Social Skills. The social skills-based blended education was delivered in 10 sessions of blended education (traditional and virtual education). Data were collected at three stages including baseline, immediately and one month after the intervention. A significance level of 0.05 was used. ResultsAccording to the results, the average score of social adjustment and social skills of the students in the intervention group increased significantly from baseline to one month after the intervention (P < 0.001). In the control group, this variable did not increase significantly. The results of the two-way repeated measures ANOVA (comparing the effect of time and group) showed that social skills-based blended education can improve the overall average score of students' social adjustment and social skills (P < 0.001 and P = 0.01 respectively), but it had no significant effect on their self-esteem. ConclusionThe findings can be used by managers and educational planners to design and implement social skills-based blended education in order to improve the social adjustment and social skills of new nursing students.

Evaluation of WRF-Chem air quality forecasts during the AEROMMA and STAQS 2023 field campaigns

ABSTRACT A real-time air quality forecasting system was developed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to provide support for flight planning activities during the NOAA Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) and NASA Synergistic TEMPO Air Quality Science (STAQS) 2023 field campaigns. The forecasting system operated on two separate domains centered on Chicago, IL, and New York City, NY, and provided 72-hour predictions of atmospheric composition, aerosols, and clouds. This study evaluates the Chicago-centered forecasting system’s 1-, 2-, and 3-day ozone (O3) forecast skill for Chiwaukee Prairie, WI, a rural area downwind of Chicago that often experiences high levels of O3 pollution. Comparisons to vertical O3 profiles collected by a Tropospheric Ozone Lidar Network (TOLNet) instrument revealed that forecast skill decreases as forecast lead time increases. When compared to surface measurements, the forecasting system tended to underestimate O3 concentrations on high O3 days and overestimate on low O3 days at Chiwaukee Prairie regardless of forecast lead time. Using July 25, 2023, as a case study, analyses show that the forecasts underestimated peak O3 levels at Chiwaukee Prairie during this regionwide bad air quality day. Wind speed and direction data indicates that this underestimation can partially be attributed to lake breeze simulation errors. Surface fine particulate matter (PM2.5) measurements, Geostationary Operational Environmental Satellite-16 (GOES-16) aerosol optical depth (AOD) data, and back trajectories from the NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model show that transported Canadian wildfire smoke impacted the Lake Michigan region on this day. Errors in the forecasted chemical composition and transport of the smoke plumes also contributed to underpredictions of O3 levels at Chiwaukee Prairie on July 25, 2023. The results of this work help identify improvements that can be made for future iterations of the WRF-Chem forecasting system. Implications: Air quality forecasting is an important tool that can be used to inform the public about upcoming high pollution days so that individuals may plan accordingly to limit their exposure to health-damaging air pollutants. Forecasting also helps scientists make decisions about where to make observations during air quality field campaigns. A variety of observational datasets were used to evaluate the accuracy of an air quality forecasting system that was developed for NOAA and NASA field campaigns that occurred in the summer of 2023. These evaluations inform areas of improvement for future development of this air quality forecasting system.

Ensemble‐based monthly to seasonal precipitation forecasting for Iran using a regional weather model

AbstractMonthly and seasonal precipitation forecasts can potentially assist disaster risk reduction and water resource management. The aim of this study is to assess the skill of an ensemble framework for monthly and seasonal precipitation forecasts over Iran by focusing on system design and model performance evaluation. The ensemble framework presented in this paper is based on a one‐way double‐nested model that uses Weather Research and Forecasting (WRF) modelling system to downscale the second version of the NCEP Climate Forecast System (CFSv2). The performance is evaluated for October–April period at 1‐, 2‐ and 3‐month lead time. Multiple initial conditions, model parameters and physics are used to construct ensemble members. Using quantile mapping (QM) method, the outputs of the model are bias corrected. This methodology is applied for two periods: (i) climatology from 2000 to 2019 to evaluate the model's ability to precipitation forecast on a monthly and seasonal time scale; (ii) the forecast for 2020 to evaluate the model's performance operationally. The model evaluation is performed using the continuous (e.g., RMSE, r, MBE, NSE) and categorical (e.g., POD, FAR, PC, Heidke skill score) assessment metrics. We conclude that model outputs were improved by the QM bias correction method. According to results, the proposed ensemble framework can accurately predict amount of monthly and seasonal precipitation in Iran with an accuracy of 58 to 45% for lead‐1 to 3. For all three lead times, the averaged NSE, CC, MBE, and RMSE were 0.4, 0.56, −15.5, and 41.6, indicating that the framework has reasonable performance. Our results suggest that precipitation forecast accuracy varies with lead time, so the accuracy for lead‐1 is higher than lead‐2 and lead‐3. Additionally, the model's accuracy differs in various regions of the country and decreases in the spring. Using the approach for an operational case, it was found that the spatial features of precipitation predicted by the framework were close to those observed.