Pilot Reports Research Articles

The Prospective Application of Machine Learning in Turbulence Forecasting over China

Abstract This study analyzes the spatiotemporal distribution of turbulence in China from 2020 to 2022 using pilot reports. Results reveal a higher frequency of moderate-to-severe turbulence during spring and winter, particularly in January. Spatially, the primary regions of turbulence occurrence are eastern China, Xinjiang Province, Sichuan Province, and the Qiongzhou Strait, with a focus on altitudes at or above 6000 m. Machine learning models, especially random forest and extreme gradient boosting (XGBoost), demonstrate high accuracy in turbulence prediction, notably for high-altitude events. The random forest model shows optimal performance in winter, achieving an area under the curve of 0.92. The study highlights the importance of thermally related diagnostics, indicating a significant presence of convectively induced turbulence in high-altitude turbulence events. This research not only deepens the understanding of turbulence dynamics in the China region but also underscores the potential of machine learning in enhancing turbulence forecasting.

Analysis of the Relationship between Upper-Level Aircraft Turbulence and the East Asian Westerly Jet Stream

The jet stream is a primary factor contributing to turbulence, especially for upper-level aircraft. This study utilized pilot reports and ERA5 data from 2023 to investigate the relationship between upper-level turbulence and the East Asian westerly jet (EAJ). The results indicate that approximately 45.9% of upper-level aircraft turbulence occurs within the jet stream, with the lowest proportion in August and the highest in January. Additionally, the strongest vertical wind shear (VMS) is found concentrated in the lower part of the jet stream core, particularly in the South–Down part of the jet stream, where upper-level aircraft turbulence occurs most frequently (27.1%). The most turbulent area is located between 30–40° N and 110–120° E, with the main air routes experiencing turbulence being the Henan sections of G212 and B208. From a seasonal perspective, there is less frequent occurrence of upper-level aircraft turbulence in summer and autumn but more in winter and spring. The EAJ volume increases with the strengthening of the jet core wind speed, with the jet core regions being most distinct at altitudes of 200~300 hPa. Meanwhile, the jet stream intensity index peaks at 70.6 m/s in January and reaches its lowest value of 7.1 m/s in August. The jet stream axis shifts southward in winter and northward in summer, reaching the southernmost position in December at 32.2° N and the northernmost position in August at 43.5° N. Furthermore, the VMS at turbulence points within the jet stream is higher than that at the turbulence points outside the jet stream, and the Richardson number (RI) is lower. Moreover, the temporal distribution of upper-level aircraft turbulence is primarily determined by the location and intensity of the jet stream, of which the jet stream intensity index provides guidance and thus serves as a reliable indicator.

Study of low-level wind shear at a Qinghai-Tibetan Plateau airport

The Qinghai-Tibetan Plateau (QTP) is recognized as the world's largest and highest plateau, characterized by intricate topography and underlying surfaces. Within this region, volatile meteorological conditions and severe synoptic systems, including thunderstorms, turbulence, sandstorms, and notably low-level wind shear (LLWS), present safety hazards to aviation operations. Therefore, a comprehensive examination of wind shear at typical QTP airports is essential. Xining Caojiapu International Airport (ZLXN) serves as a crucial transportation hub in the northeastern QTP. The frequent incidence of LLWS events due to the airport's unique geographical and meteorological features makes it an ideal location for investigating LLWS on the plateau. This study analyzed 80 pilot reports collected from 2016 to 2021 to elucidate the spatial and temporal characteristics of LLWS events at this plateau-valley airport. Subsequently, reanalysis data and observations from ground automated weather observing systems (AWOS), a geostationary satellite, a Doppler weather radar (DWR), and a Doppler wind lidar (DWL) were comprehensively employed to investigate the underlying meteorological factors and weather patterns associated with these LLWS events. The findings revealed that LLWS events at ZLXN are predominantly triggered by convective systems, followed by cold fronts and downward momentum transportation, with a smaller proportion being induced by orographic winds and turbulence. Analysis of four representative LLWS events utilizing high-resolution DWL measurements provided insights into the types of LLWS generated and their potential impacts on aircraft operations. Conceptual models, aimed at establishing a foundation for the forecasting and warning of local LLWS, were also proposed based on multiple data sources.

Interpretable semi-supervised clustering enables universal detection and intensity assessment of diverse aviation hazardous winds

The identification of aviation hazardous winds is crucial and challenging in air traffic management for assuring flight safety, particularly during the take-off and landing phases. Existing criteria are typically tailored for special wind types, and whether there exists a universal feature that can effectively detect diverse types of hazardous winds from radar/lidar observations remains as an open question. Here we propose an interpretable semi-supervised clustering paradigm to solve this problem, where the prior knowledge and probabilistic models of winds are integrated to overcome the bottleneck of scarce labels (pilot reports). Based on this paradigm, a set of high-dimensional hazard features is constructed to effectively identify the occurrence of diverse hazardous winds and assess the intensity metrics. Verification of the paradigm across various scenarios has highlighted its high adaptability to diverse input data and good generalizability to diverse geographical and climate zones.

Automatic recognition of airliners wake turbulence using various techniques of machine intelligence

Airplanes generate severe turbulent airflow, known as trailing vortices, which can have an adverse control impact on following airplanes. Aviation authorities worldwide impose separation standards between leading and following airplanes to reduce the capacity of airports to handle large volumes of traffic. Optimizing these separations is highly sought after. These separations are optimized based on several factors such as airplane size, weight, and design in addition to weather conditions. The recognition of trailing vortices is usually done manually through examination of pilot reports and analysis of flight data recorders (FDRs). This process can be subjective and depends greatly on the analyst's experience. Automated processes based on Artificial Intelligence (AI), such as Machine Learning (ML) and Computational Intelligence (CI), have shown a potential to better deal with such problems. Results obtained from these various techniques are presented to show their degree of appropriateness. Flight event identification, such as encounters with airplane vortices, wind shear, turbulence, and uncommanded control, can help establish aircraft warning and monitoring systems to enhance automatic flying, especially during landing and take-off. The developed model can attain great assurance recognition of the aircraft wake turbulence by supporting the air traffic controllers with the decision information in actual work to ensure aircraft safety. The success rates obtained from the investigated CI models compare favorably well to results obtained by either manual and/or other CI-based techniques when applied to vortex identification.

SPE-SHAP: Self-paced ensemble with Shapley additive explanation for the analysis of aviation turbulence triggered by wind shear events

Aviation safety is critically challenged by significant turbulence, often triggered by wind shear events, which jeopardize aircraft controllability and may cause structural damage to the fuselage. Current predictive models, primarily based on Pilot Reports (PIREPs), suffer from data imbalance, undermining their reliability and accuracy. This research addresses the need for a robust approach to accurately estimate and classify aviation turbulence events. We propose a novel framework, the Self-Paced Ensemble (SPE), integrated with Shapley Additive Explanation (SHAP) analysis. This framework utilizes tree-based classifiers such as Gradient Boosting Decision Trees (GBDT), Random Forests (RF), Extreme Gradient Boosting (XGBoost), and Extra Trees (ET) to enhance predictive accuracy and reliability. The SPE model, particularly when combined with XGBoost, demonstrated superior performance with a recall of 70.16 %, specificity of 74.75 %, G-Mean of 73.67 %, Matthews correlation coefficient (MCC) of 0.352, and area under the Receiver Operating Characteristic (ROC) curve of 0.726. Comparatively, the SPE model with RF achieved a recall of 70.05 %, specificity of 73.89 %, G-Mean of 72.57 %, MCC of 0.347, and AU-ROC of 0.721. Post-hoc Wilcoxon signed-rank tests further validated these results. SHAP analysis identified wind shear altitude, magnitude, and causes as the most influential factors affecting significant aviation turbulence prediction. The SPE-SHAP framework not only significantly improves the prediction of aviation turbulence but also enhances model transparency by elucidating key contributing factors. These insights can inform updates to aircraft operation protocols and enhance flight training, thereby increasing safety during wind shear events.

AI-supported estimation of safety critical wind shear-induced aircraft go-around events utilizing pilot reports

The occurrence of wind shear and severe thunderstorms during the final approach phase contributes to nearly half of all aviation accidents. Pilots usually employ the go-around procedure in order to lower the likelihood of an unsafe landing. However, multiple factors influence the go-arounds induced by wind shear. In order to predict the wind shear-induced go-around, this study utilized a cutting-edge AI-based Combined Kernel and Tree Boosting (KTBoost) framework with various data augmentation strategies. First, the KTBoost model was trained, tested, and compared to other Machine Learning models using the data extracted from Hong Kong International Airport (HKIA)-based Pilot Reports for the years 2017–2021. The performance evaluation revealed that the KTBoost model with Synthetic Minority Oversampling Technique - Edited Nearest Neighbor (SMOTE-ENN)- augmented data demonstrated superior performance as measured by the F1-Score (94.37%) and G-Mean (94.87%). Subsequently, the SHapley Additive exPlanations (SHAP) approach was employed to elucidate the interpretation of the KTBoost model using data that had been treated with the SMOTE-ENN technique. According to the findings, flight type, wind shear magnitude, and approach runway contributed the most to the wind shear-induced go-around. Compared to international flights, Hong Kong-based airlines endured the highest number of wind shear-induced go-arounds. Shear due to the tailwind contributed more to the go-around than the headwinds. The runways with the most wind shear-induced Go-arounds were 07C and 07R.

Operational Aviation Icing Forecast Algorithm for the Korea Meteorological Administration

Abstract In this study, we developed and evaluated the Korean Forecast Icing Potential (K-FIP), an in-flight icing forecast system for the Korea Meteorological Administration (KMA) based on the simplified forecast icing potential (SFIP) algorithm. The SFIP is an algorithm used to postprocess numerical weather prediction (NWP) model forecasts for predicting potential areas of icing based on the fuzzy logic formulations of four membership functions: temperature, relative humidity, vertical velocity, and cloud liquid water content. In this study, we optimized the original version of the SFIP for the global NWP model of the KMA through three important updates using 34 months of pilot reports for icing as follows: using total cloud condensates, reconstructing membership functions, and determining the best weight combination for input variables. The use of all cloud condensates and the reconstruction of these membership functions resulted in a significant improvement in the algorithm compared with the original. The weight combinations for the KMA’s global model were determined based on the performance scores. While several sets of weights performed equally well, this process identified the most effective weight combination for the KMA model, which is referred to as the K-FIP. The K-FIP demonstrated the ability to successfully predict icing over the Korean Peninsula using observations made by research aircraft from the National Institute of Meteorological Sciences of the KMA. Eventually, the K-FIP icing forecasts will provide better forecasts of icing potentials for safe and efficient aviation operations in South Korea. Significance Statement In-flight aircraft icing has posed a threat to safe flights for decades. With advances in computing resources and an improvement in the spatiotemporal resolutions of numerical weather prediction (NWP) models, icing algorithms have been developed using NWP model outputs associated with supercooled liquid water. This study evaluated and optimized the simplified forecast icing potential, an NWP model–based icing algorithm, for the global model of the Korean Meteorological Administration (KMA) using a long-term observational dataset to improve its prediction skills. The improvements shown in this study and the SFIP implemented in the KMA will provide more informative predictions for safe and efficient air travel.

Исследование возникновения горных волн над Европейской территорией России и оценка возможности их прогнозирования для авиации с помощью модели COSMO-Ru6.6

The probability of the mountain wave generation over the European part of the Russian Federation in May‒August 2023 is studied. The conditions for the occurrence of strong downdrafts and leeward orographic turbulence are discussed for 4 domains including the mountain systems of the Urals, the North Caucasus, the Kola Peninsula and Crimea. An analysis of circulation and thermodynamic conditions favorable for the generation of mountain waves over European Russia were calculated using the initial and forecast data of the COSMO-Ru6.6 model. The accuracy of forecasting the parameters used in the calculations was assessed. It was found that in some cases, moderate turbulence reported in AIREP Speci aircraft observations near the areas with complex terrain could be of orographic nature. Keywords: mountain waves, pilot reports, COSMO-Ru model, orographic turbulence

Deep Learning–Based Summertime Turbulence Intensity Estimation Using Satellite Observations

Abstract Turbulence is what we want to avoid the most during flight. Numerical weather prediction (NWP) model–based methods for diagnosing turbulence have offered valuable guidance for pilots. NWP-based turbulence diagnostics show high accuracy in detecting turbulence in general. However, there is still room for improvements such as capturing convectively induced turbulence. In such cases, observation data can be beneficial to correctly locate convective regions and help provide corresponding turbulence information. Geostationary satellite data are commonly used for upper-level turbulence detection by utilizing its water vapor band information. The Geostationary Operational Environmental Satellite (GOES)-16 carries the Advanced Baseline Imager (ABI), which enables us to observe further down into the atmosphere with improved spatial, temporal, and spectral resolutions. Its three water vapor bands allow us to observe different vertical parts of the atmosphere, and from its infrared window bands, convective activity can be inferred. Such multispectral information from ABI can be helpful in inferring turbulence intensity at different vertical levels. This study develops U-Net based machine learning models that take ABI imagery as inputs to estimate turbulence intensity at three vertical levels: 10–18, 18–24, and above 24 kft (1 kft ≈ 300 m). Among six different U-Net-based models, U-Net3+ model with a filter size of three showed the best performance against the pilot report (PIREP). Two case studies are presented to show the strengths and weaknesses of the U-Net3+ model. The results tend to be overestimated above 24 kft, but estimates of 10–18 and 18–24 kft agree well with the PIREP, especially near convective regions. Significance Statement Turbulence is directly related to aviation safety as well as cost-effective aircraft operation. To avoid turbulence, turbulence diagnostics are calculated from numerical weather prediction (NWP) model outputs and are provided to pilots. The goal of this study is to develop a satellite data–driven machine learning model that estimates turbulence intensity in three different vertical layers to provide additional information along with the NWP-based turbulence diagnostics. Validation results against pilot reports show that the machine learning model performs comparable to NWP-based turbulence diagnostics. Furthermore, results with different channel selections reveal that using multiple water vapor channels can help extract additional information for estimating turbulence intensity at lower levels.

Explainable Boosting Machine for Predicting Wind Shear-Induced Aircraft Go-around based on Pilot Reports

The go-around is a safety-critical procedure in civil aviation that is rarely executed but is essential to avoid risky landings. Analyzing the factors that trigger go-around events can aid in identifying measures that could lower their frequency. This involves circumstances that could be deemed abnormal and intrinsically harmful. The study employed the Explainable Boosting Machine (EBM), a contemporary transparent model, to predict aircraft go-arounds and interpret different influential factors. The model proposed exhibits comparable accuracy to black-box models. The study utilized pilot reports and applied SMOTE-ENN to address the imbalance problem. The EBM model was trained with treated data in conjunction with Bayesian optimization. The EBM model's performance was evaluated using holdout data and compared to binary logistic regression and decision tree models, as well as black-box models such as adaptive boosting, random forest, and extreme gradient boosting. The EBM model exhibited superior performance compared to other models in terms of precision (83.15%), recall (79.77%), geometric mean (77.29%), and Matthews's correlation coefficient (0.453). The EBM algorithm enables the comprehensive interpretation of individual and pairwise factor interactions in predicting aircraft go-around outcomes from both global and local perspectives. This facilitates the assessment of the impact of different factors on go-around outcomes.

Missed Approach, a Safety-Critical Go-Around Procedure in Aviation: Prediction Based on Machine Learning-Ensemble Imbalance Learning

The final approach phase of an aircraft accounts for nearly half of all aviation incidents worldwide due to low-level wind shear, heavy downpours, runway excursions, and unsteady approaches. Adopting the missed approach (MAP) procedures may prevent a risky landing, which is usually executed in those situations, but it is safety-critical and a rare occurrence. This study employed machine learning-ensemble imbalance learning to predict MAPs under low-level wind shear conditions based on environmental and situational parameters. The models were developed using the 2017–2021 Hong Kong International Airport (HKIA) Pilot Reports (PIREPs). Initially, imbalance data were applied to machine learning models such as the random forest (RF), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost), but these were unable to accurately predict the occurrence of MAPs. Then, these models were used as base estimators for ensemble imbalance learning methods, including the self-paced ensemble (SPE) framework, the balance cascade model, and the easy ensemble model. The SPE framework utilizing XGboost as the base estimator performed better than other frameworks in terms of recall, F1-score, balanced accuracy, and geometric mean. Afterwards, SHAP was utilized to interpret the SPE framework with XGboost as the base estimator. Results showed that low-level wind shear magnitude, runway orientation, and vertical location of low-level wind shear contributed most to MAPs. Runways 07C and 07R had the most MAPs. Most MAPs were initiated when low-level wind shear was within 500 feet of the ground. Strong tailwind triggered MAPs more than headwind. For aviation safety researchers and airport authorities, the framework proposed in this study is a valuable tool.

Characterization of icing conditions using aircraft reports and satellite data

A sample of 115 aircraft icing events in the Western Europe and Northeastern Atlantic sector, identified in aircraft pilot reports (PIREPs), is assessed in terms of spatio-temporal distribution and cloud characteristics, using satellite observations and products. Within our database, most of the reported events occur between October and February, although a few cases were identified during late spring and even summer months. Icing conditions are generally reported at mid-troposphere, with 82.7% and 78.6% of moderate and severe icing, respectively, identified between FL100 and FL250 (≈ 3–7.6 km). Satellite observations allow the identification of icing-prone conditions and also provide an independent means of validating some of the data in the aircraft reports. It is shown that icing occurs mainly within opaque clouds, with ice cloud-tops, or mixed phase (ice and water), noting the severe events, take place mostly under mixed-phase cloud-tops. Accordingly, most events were associated with middle and high opaque clouds, with 10.8μm brightness temperatures (BT10.8) between −40 and − 8 °C. Moreover, a detailed analysis of three events, making use of model, satellite and synoptic observations, illustrates the occurrence of aircraft icing in precipitating clouds. Within those cases, one occurred below the upper layer of a thick nimbostratus associated with an occluded low centred in the Bay of Biscay. The second event, reported as severe, as in the former case, took place within low clouds over southern England in association with northwesterly winds driven by a complex low. Finally, a moderate event happened over southern Portugal in association with a cold front, near the cloud top of nimbostratus. In all cases, the icing index operational at the Portuguese Weather Service, based on the European Centre for Medium-range Weather Forecasts (ECMWF) model, was able to predict prone-icing conditions, with higher severity in the severe cases.

Performance of windshear/microburst detection algorithms using numerical weather prediction model data for selected tropical cyclone cases

AbstractThree windshear/microburst detection algorithms are used operationally at the Hong Kong International Airport. Their performance is studied in the present article by assuming the availability of the complete set of meteorological data from numerical weather prediction model output. Only selected tropical cyclone cases are considered. The performance is evaluated using pilot reports of windshear. It turns out that the glide path scan windshear detection algorithm has the best overall performance by considering the area under curve using the relative operating characteristic curves. This may be related to the better geometry in covering the glide paths of the airport for this particular algorithm.

Statistical Analysis of Aviation Turbulence in the Middle–Upper Troposphere over Japan

Abstract This study examined the statistics of aviation turbulence that occurred in Japan between 2006 and 2018 by analyzing pilot reports (PIREP). In total, 81 639 turbulence events, with moderate or greater intensity, were reported over this period. The monthly number of turbulence cases has an annual periodic variation as observed in different regions by previous studies. The number of turbulence cases is high from March to June and low in July and August. Higher numbers of turbulence cases are experienced along the major flight routes in Japan, especially around Tokyo, for the active period between 0900 and 2000 local time. The number of cases of turbulence peaks when the flight reaches an altitude of 33 000 ft (FL330; 1000 ft ≈ 300 m), whereas it decreases when the flight altitude is above FL380 and below FL280. The statistical features are not largely different among the four seasons; however, there are some exceptions. For instance, the number of turbulence cases is large in high altitudes in summer and small in low altitudes in winter. Considering the number of flights, it is evident that the frequency of turbulence is higher in altitudes between FL200 and FL350, although the number of flights is low in this altitude region. The number of convectively induced turbulence cases is relatively large during the daytime in summer in comparison with the other seasons. A large amount of mountain-wave turbulence is observed around the mountainous region in autumn and winter when the jet stream flows over Japan. Significance Statement This study examines the statistics of aviation turbulence reported over Japan from 2008 to 2018.

Assessing Expertise Using Eye Tracking in a Virtual Reality Flight Simulation

ABSTRACT Objective The aim of this work was to examine the fidelity and validity of an aviation simulation using eye tracking. Background Commercial head-mounted virtual reality (VR) systems offer a convenient and cost-effective alternative to existing aviation simulation (e.g., for refresher exercises). We performed pre-implementation testing of a novel aviation simulation, designed for head-mounted VR, to determine its fidelity and validity as a training device. Method Eighteen airline pilots, with varying levels of flight experience, completed a sequence of training ‘flows.’ Self-reported measures of presence and workload and users’ perceptions of fidelity were taken. Pilots’ eye movements and performance were recorded to determine whether more experienced pilots showed distinct performance and eye gaze profiles in the simulation, as they would in the real-world. Results Real-world expertise correlated with eye gaze patterns characterized by fewer, but longer, fixations and a scan path that was more structured and less random. Multidimensional scaling analyses also indicated differential clustering of strategies in more versus less experienced pilots. Subjective ratings of performance, however, showed little relationship with real-world expertise or eye movements. Conclusion We adopted an evidence-based approach to assessing the fidelity and validity of a VR flight training tool. Pilot reports indicated the simulation was realistic and potentially useful for training, while direct measurement of eye movements was useful for establishing construct validity and psychological fidelity of the simulation.

New Medicine Service by Community Pharmacists: An Opportunity to Enhance Universal Health Coverage at a Primary Health Level in South Africa.

The implementation of universal health coverage (UHC) in South Africa has focused on promoting equitable health care services to all citizens. In this regard, pharmacists are expected to expand their professional capabilities to promote primary healthcare system functionality. The new medicine service (NMS) has proven to be beneficial in medicine optimization and adherence. The aim of the NMS is to assist and advise patients on their newly diagnosed conditions and to promote the safe and rational use of medicines. This study explores the provision of NMS within the UHC primary healthcare service package and the opportunity for enhancing pharmacist practice. This pilot reports on the implementation of NMS in a low-middle income country. Data was obtained using convenience sampling and an interview-based approach. Findings were evaluated, analyzed, and reported using qualitative techniques. This study was conducted at an independent community pharmacy in Durban, South Africa. Fifty-four patients were successfully enrolled into the program based on the eligibility criteria; 19 patients exited the program before completion. From those that completed the program, 65.71% had no problems detected; rather the program served as a platform to provide information and ensure proper adherence practices, 34.29% of patients experienced problems and were referred back to the prescriber, or pharmacist. After the completion of the program, 54.29% where found to be adherent to their medication, however, 45.71% were found to be non-adherent and were counseled accordingly or referred back to the medical practitioner. This paper highlighted that the implementation of a pharmacist's full scope of practice and services such as the NMS is essential in improving therapeutic outcomes, recognize medicine related problems, and avert unnecessary use of medicines.

Prediction of Aircraft Go-Around during Wind Shear Using the Dynamic Ensemble Selection Framework and Pilot Reports

Pilots typically implement the go-around protocol to avoid landings that are hazardous due to wind shear, runway excursions, or unstable approaches. Despite its rarity, it is essential for safety. First, in this study, we present three Dynamic Ensemble Selection (DES) frameworks: Meta-Learning for Dynamic Ensemble Selection (META-DES), Dynamic Ensemble Selection Performance (DES-P), and K-Nearest Oracle Elimination (KNORAE), with homogeneous and heterogeneous pools of machine learning classifiers as base estimators for the prediction of aircraft go-around in wind shear (WS) events. When generating a prediction, the DES approach automatically selects the subset of machine learning classifiers which is most probable to perform well for each new test instance to be classified, thereby making it more effective and adaptable. In terms of Precision (86%), Recall (83%), and F1-Score (84%), the META-DES model employing a pool of Random Forest (RF) classifiers outperforms other models. Environmental and situational factors are subsequently assessed using SHapley Additive exPlanations (SHAP). The wind shear magnitude, corridor, time of day, and WS altitude had the greatest effect on SHAP estimation. When a strong tailwind was present at low altitude, runways 07R and 07C were highly susceptible to go-arounds. The proposed META-DES with a pool of RF classifiers and SHAP for predicting aircraft go-around in WS events may be of interest to researchers in the field of air traffic safety.

Scaling-up digital follow-up care services: collaborative development and implementation of Remote Patient Monitoring pilot initiatives to increase access to follow-up care.

COVID-19 increased the demand for Remote Patient Monitoring (RPM) services as a rapid solution for safe patient follow-up in a lockdown context. Time and resource constraints resulted in unplanned scaled-up RPM pilot initiatives posing risks to the access and quality of care. Scalability and rapid implementation of RPM services require social change and active collaboration between stakeholders. Therefore, a participatory action research (PAR) approach is needed to support the collaborative development of the technological component while simultaneously implementing and evaluating the RPM service through critical action-reflection cycles. This study aims to demonstrate how PAR can be used to guide the scalability design of RPM pilot initiatives and the implementation of RPM-based follow-up services. Using a case study strategy, we described the PAR team's (nurses, physicians, developers, and researchers) activities within and across the four phases of the research process (problem definition, planning, action, and reflection). Team meetings were analyzed through content analysis and descriptive statistics. The PAR team selected ex-ante pilot initiatives to reflect upon features feedback and participatory level assessment. Pilot initiatives were investigated using semi-structured interviews transcribed and coded into themes following the principles of grounded theory and pilot meetings minutes and reports through content analysis. The PAR team used the MoSCoW prioritization method to define the set of features and descriptive statistics to reflect on the performance of the PAR approach. The approach involved two action-reflection cycles. From the 15 features identified, the team classified 11 as must-haves in the scaled-up version. The participation was similar among researchers (52.9%), developers (47.5%), and physicians (46.7%), who focused on suggesting and planning actions. Nurses with the lowest participation (5.8%) focused on knowledge sharing and generation. The top three meeting outcomes were: improved research and development system (35.0%), socio-technical-economic constraints characterization (25.2%), and understanding of end-user technology utilization (22.0%). The scalability and implementation of RPM services must consider contextual factors, such as individuals' and organizations' interests and needs. The PAR approach supports simultaneously designing, developing, testing, and evaluating the RPM technological features, in a real-world context, with the participation of healthcare professionals, developers, and researchers.

A Framework for Satellite-Based 3D Cloud Data: An Overview of the VIIRS Cloud Base Height Retrieval and User Engagement for Aviation Applications

Satellites have provided decades of valuable cloud observations, but the data from conventional passive radiometers are biased toward information from at or near cloud top. Tied with the Joint Polar Satellite System (JPSS) Visible Infrared Imaging Radiometer Suite (VIIRS) Cloud Calibration/Validation research, we developed a statistical Cloud Base Height (CBH) algorithm using the National Aeronautics and Space Administration (NASA) A-Train satellite data. This retrieval, which is currently part of the National Oceanic and Atmospheric Administration (NOAA) Enterprise Cloud Algorithms, provides key information needed to display clouds in a manner that goes beyond the typical top-down plan view. The goal of this study is to provide users with high-quality three-dimensional (3D) cloud structure information which can maximize the benefits and performance of JPSS cloud products. In support of the JPSS Proving Ground Aviation Initiative, we introduced Cloud Vertical Cross-sections (CVCs) along flight routes over Alaska where satellite data are extremely helpful in filling significant observational gaps. Valuable feedback and insights from interactions with aviation users allowed us to explore a new approach to provide satellite-based 3D cloud data. The CVC is obtained from multiple cloud retrieval products with supplementary data such as temperatures, Pilot Reports (PIREPs), and terrain information. We continue to improve the product demonstrations based on user feedback, extending the domain to the contiguous United States with the addition of the Geostationary Operational Environmental Satellite (GOES)-16 Advanced Baseline Imager (ABI). Concurrently, we have refined the underlying science algorithms for improved nighttime and multilayered cloud retrievals by utilizing Day/Night Band (DNB) data and exploring machine learning approaches. The products are evaluated using multiple satellite data sources and surface measurements. This paper presents our accomplishments and continuing efforts in both scientific and user-engagement improvements since the beginning of the VIIRS era.