Forecast Skill Research Articles

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.

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.

Reasons for Different Predictability of Tropical Cyclone Tracks in the Western North Pacific and Atlantic Oceans

Abstract Recent several studies have focused on the predictability of tropical cyclone track forecast. As a response to the question issued by Landsea and Cangialosi (2018) about "the approaching limit of predictability for tropical cyclone (TC) track prediction is near or has already been reached", Zhou and Toth (2020) (short for ZT20) and Yu et al. (2022) (short for Y22) have found that the limit of predictability for TC track prediction has not been reached both in Atlantic (ATL) and Western North Pacific (WNP) basins. However, the predictabilities are different in two basins, as ZT20 found that 1 day's improvement can be obtained through 10 years in ATL, while Y22 found that 2 days' improvement can be obtained through 15 years in WNP. To reveal the causes of this difference, the predictability of TC track in WNP is first investigated under the same framework as ZT20. Then important parameters that determined the predictabilities are found and analyzed. Results suggested that the growth rate of true track forecast error in WNP is higher than that in ATL, indicating a lower predictability in WNP. Further explorations suggested that TCs in WNP basin have averagely larger sizes, stronger intensities, lower-latitude locations, and poorer forecast skills of their guided flows. All these factors contribute to the larger track forecast error growth rate. Moreover, it is pointed out that as the improvement of forecast skills over years mainly due to the reduction of initial analysis errors, although a lower predictability is found in WNP, the forecast skill improvement in WNP is faster than that in ATL.

Multiday Soil Moisture Persistence and Atmospheric Predictability Resulting From Sahelian Mesoscale Convective Systems

AbstractSkill in predicting where damaging convective storms will occur is limited, particularly in the tropics. In principle, near‐surface soil moisture (SM) patterns from previous storms provide an important source of skill at the mesoscale, yet these structures are often short‐lived (hours to days), due to both soil drying processes and the impact of new storms. Here, we use satellite observations over the Sahel to examine how the strong, locally negative, SM‐precipitation feedback there impacts rainfall patterns over subsequent days. The memory of an initial storm pattern decays rapidly over the first 3–4 days, but a weak signature is still detected in surface observations 10–20 days later. The wet soil suppresses rainfall over the storm track for the first 2–8 days, depending on aridity regime. Whilst the negative SM feedback initially enhances mesoscale rainfall predictability, the transient nature of SM likely limits forecast skill on sub‐seasonal time scales.

The Role of Visual Performance in Fine Motor Skills

The aim of this study was to analyse the relationship between fine motor skills (FMSs) and visual performance. Thirty young participants with normal binocular vision performed five fine motor tasks: Purdue, Grooved, and O’Connor pegboards, a needle threading task, and a water pouring task, which were characterised by the time taken to complete the task, the number of pegs inserted, the error made in pouring the water, and the volume spilled. To evaluate visual performance, near visual acuity, near contrast sensitivity (CS), and disability glare were assessed. Fine motor skills and visual performance were assessed under monocular and binocular viewing conditions. An overall visual performance score (OVPS) and an overall fine motor skills score (OFMSS) were calculated. All visual functions measured binocularly were better than in monocular conditions, and all FMSs tasks were performed worse monocularly than binocularly (p < 0.001), except for the error made in the water pouring task (p = 0.024). There was a positive correlation between OVPS and OFMSS (rho = 0.329; p = 0.010). The regression model showed that the OFMSS can be predicted by age and CS at 21.3%. Individuals with normal binocular vision and better near visual function exhibit superior fine motor abilities. CS stands out as the visual function that has the greatest bearing on the performance of FMSs.

RUFCO: a deep-learning framework to post-process subseasonal precipitation accumulation forecasts

Abstract Post-processing is a critical step in attaining calibrated and reliable probabilistic forecast output from numerical weather prediction models. A novel deep-learning framework is proposed to post-process 20 years of 7- and 14-day precipitation accumulation reforecasts from the Global Ensemble Forecast System at subseasonal timescales (week 1, week 2, and combined weeks 3-4 forecasts) over the contiguous United States. The network builds upon previous studies and is a combination of three parallel-trained components suitable for subseasonal prediction. The first is a ResUnet architecture which learns non-linear relationships between binned observed precipitation and input images of weather and geographical variables. The second conditions the network to the month-of-year via a Feature-wise Linear Modulation (FiLM) layer. The third helps the network learn when to revert the forecast to that of climatology. The RUFCO (named for its components ResUnet, FiLM, and Climatological-Offramp) forecasts are compared against raw and climatological forecasts as well as those from a state-of-the-art distributional regression post-processing model, “CSGD,” and a simple bias-corrected model. At week 1, every method exhibited a competitive advantage over climatological forecasts. At week 2, RUFCO generated forecasts with statistically significant improvement over climatology at 82-94% of the domain, beating CSGD’s coverage of 76-90% of the grid points. At week 3, RUFCO’s skillful coverage was 65-85% while CSGD’s dropped to only 12-37%. At the longer lead times, RUFCO achieved the highest domain-averaged skill scores across seasons. However, the network tends to “smooth” forecast skill making it less competitive with CSGD in limited areas with strongly spatially-varying biases.

Impact of Directly Assimilating Radar Reflectivity Using a Reflectivity Operator Based on a Double-Moment Microphysics Scheme on the Analysis and Forecast of Typhoon Lekima (1909)

In previous studies, radar reflectivity is often directly assimilated using reflectivity operators based on a single-moment (SM) microphysics scheme, though the forecast model uses a double-moment (DM) microphysics scheme. With the fixed number concentrations, only the mixing ratios of hydrometeors are directly updated during the assimilation, which leads to a mismatch between the analyzed microphysical state variables and the microphysics scheme of the forecast model. In this study, the radar reflectivity is directly assimilated through an observation operator consistent with the DM Thompson microphysics scheme used in numerical integrations, and the impact of reflectivity operators based on SM and DM schemes on the analysis performance of the ensemble Kalman filter for typhoon Lekima on 9 August 2019 is evaluated. Reflectivity observations from a single operational weather radar in Wenzhou City, Zhejiang Province of China are assimilated. In addition, the dual-polarization observations from the same radar are used to evaluate the quality of the analysis. The analyzed reflectivity and dual-polarization characteristics obtained by different reflectivity operators are examined in detail. Compared with the experiments applying the reflectivity operator based on the SM Lin scheme, the use of a reflectivity operator consistent with the DM Thompson scheme adopted in the forecast model results in analyzed reflectivity and polarization characteristics that are more consistent with the observed characteristics in terms of general structure, location, and intensity. Forecasted reflectivity, 3 h accumulated precipitation, and typhoon intensity and track are also evaluated. The application of the reflectivity operator based on the DM scheme makes better forecasts of typhoon intensity, precipitation, and reflectivity, which also improves the forecast skills on typhoon tracks to a certain extent.

Prospective evaluation of extended reality for neonatal intubation education

Purpose360° immersive videos may be useful in procedure-based healthcare settings. We hypothesized that extended reality (XR) neonatal intubation education is a feasible alternative to live demonstration in skill and knowledge transfer.MethodsThis was a prospective single center evaluation of an educational XR intubation video using Premature Anne™ (Laerdal Medical, Stavanger, Norway) comparing to a live demonstration. Participants were randomly allocated to either live demonstration or XR group. Each group received 20 min of teaching from the pediatric respiratory care education specialist, either in person or in video format. Intubation success, knowledge and skill acquisition were measured using a questionnaire and checklist.ResultsWe had a total of 23 participants, XR (n = 11) and live demonstration (n = 12). The groups were heterogeneous in experience level.The XR group was successful at intubating 73% of the time (n = 8), which was not significantly different to the live demonstration group 58% (n = 7) (p = 0.667). For patients who achieved intubation, there was no difference between groups in the mean time to successful intubation; 98 s (SD 73) for the XR group and 65 s (SD 22) for the live demonstration group (p = 0.685).When comparing scores between groups there were no statistically significant differences regarding knowledge scores (p = 0.514), skill scores (p = 0.826) and total scores (p = 0.926).ConclusionThese preliminary findings indicated no significant differences between the XR and live demonstration groups in terms of intubation success, knowledge acquisition, skill performance, total scores or time to successful intubation.

Ramp Rate Metric Suitable for Solar Forecasting

Solar irradiance forecasting plays a crucial role in integrating large quantities of intermittent solar power. Forecasting systems are commonly evaluated using metrics like root‐mean‐ square error (RMSE) and skill scores. However, these metrics aggregated over larger data sets do not adequately assess the prediction of ramp events, which are critical for many applications. This article introduces a novel, simple, and adaptable ramp rate metric that analyzes ramp events between successive lead times within forecasts. A case study on ramp rate mitigation in PV systems benchmarks suitable ramp thresholds for various solar irradiance components. The capabilities and limitations of deterministic and probabilistic forecasts from two all‐sky imager‐based models are evaluated for ramp prediction. A state‐of‐the‐art data‐driven vision transformer End2End model excels in RMSE and skill scores but performs poorly in ramp prediction. Conversely, a novel generative forecasting model combined with a convolutional neural network‐based irradiance model shows superior ramp prediction, achieving an F1 score of ≥0.7 for critical ramp events. This study underscores the importance of suitable ramp rate metrics and highlights the potential of generative models for enhancing ramp forecasting.

Improving a WRF-Based High-Impact Weather Forecast System for a Northern California Power Utility

We describe enhancements to an operational forecast system based on the Weather Research and Forecasting (WRF) model for the prediction of high-impact weather events affecting power utilities, particularly conditions conducive to wildfires. The system was developed for Pacific Gas and Electric Corporation (PG&E) to forecast conditions in Northern and Central California for critical decision-making such as proactively de-energizing selected circuits within the power grid. WRF forecasts are routinely produced on a 2 km grid, and the results are used as input to wildfire fuel moisture, fire probability, wildfire spread, and outage probability models. This forecast system produces skillful real-time forecasts while achieving an optimal blend of model resolution and ensemble size appropriate for today’s computational resources afforded to utilities. Numerous experiments were performed with different model settings, grid spacing, and ensemble configuration to develop an operational forecast system optimized for skill and cost. Dry biases were reduced by leveraging a new irrigation scheme, while wind skill was improved through a novel approach involving the selection of Global Ensemble Forecast System (GEFS) members used to drive WRF. We hope that findings in this study can help other utilities (especially those with similar weather impacts) improve their own forecast system.

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.