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Background The ramped position facilitates mask ventilation and endotracheal intubation in patients with obesity. This study aimed to determine whether the ramped position improves supraglottic airway (SGA) insertion in patients with obesity. Methods In this prospective, randomized, single-center trial, 48 obese patients undergoing elective surgery were randomized into either ramped or sniffing position groups. The Ambu® AuraGainTM (Ambu A/S), a second-generation SGA, was used. The primary outcome was the time required for the AuraGain insertion. Secondary outcomes included ease and number of insertion attempts, oropharyngeal leak pressure (OLP), and complications. The number needed to treat (NNT) was calculated to ensure ease of insertion. Results The time required for the AuraGain insertion was significantly shorter in the ramped group than in the sniffing group (13.0 [11.0, 16.0] vs. 24.0 [21.0, 28.0], P < 0.001). The insertion was easier in the ramped group than in the sniffing group (23/24 vs. 13/24, NNT = 2.4 [95% CI, 1.6, 5.0], P = 0.003). The first-attempt success rate was higher in the ramped group than in the sniffing group, although the difference was not statistically significant (22/24 vs. 18/24, P = 0.319). The OLP and postoperative complication rates were not significantly different between the groups. Conclusions The ramped position reduced the time required for the AuraGain insertion in obese patients while providing comparable airway sealing without increasing adverse events. Therefore, a ramped position may be a more suitable option for SGA insertion in this population.

In this study, a machine learning (ML)-based Tropical Cyclones (TCs) Rapid Intensification (RI) prediction model has been developed by using the Net Energy Gain Rate Index (NGR). This index realistically captures the energy exchanges between the ocean and the atmosphere during the intensification of TCs. It does so by incorporating the thermal conditions of the upper ocean and using an accurate parameterization for sea surface roughness. To evaluate the effectiveness of NGR in enhancing prediction accuracy, five distinct ML algorithms were utilized: Decision Tree, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Feed-forward Neural Network. Two sets of experiments were performed for each algorithm. The first set used only traditional predictors, while the second set incorporated NGR. The outcomes revealed that models trained with the inclusion of NGR exhibited superior performance compared to those that only used traditional predictors. Additionally, an ensemble model was developed by utilizing a hard-voting method, combining the predictions of all five individual algorithms. This ensemble approach showed a noteworthy improvement of approximately 10% in the skill score of RI prediction when NGR was included. The findings of this study emphasize the potential of NGR in refining TC intensity prediction and underline the effectiveness of ensemble ML models in RI event detection.

Understanding and quantifying air-sea exchanges of enthalpy and momentum fluxes are crucial for the advanced prediction of tropical cyclone (TC) intensity. Here, we present a new parameterization of air-sea fluxes at extreme wind speeds from 40 m s−1 to 75 m s−1, which covers the range of major TCs. Our approach assumes that the TC can reach its maximum potential intensity (MPI) if there are no influences of external forces such as vertical wind shear or other environmental constraints.This method can estimate the ratio of the enthalpy and momentum exchange coefficient (Ck/Cd) under the most intense TCs without direct flux measurements. The estimation showed that Ck/Cd increases with wind speed at extreme winds above 40 m s−1. Two types of surface layer schemes of the Hurricane Weather and Research Forecast (HWRF) were designed based on the wind speed dependency of the Ck/Cd found at high winds: (i) an increase of Ck/Cd based on decreasing Cd (Cd_DC) and (ii) an increase of Ck/Cd based on increasing Ck (Ck_IC). The modified surface layer schemes were compared to the original HWRF scheme (using nearly fixed Cd and Ck at extreme winds; CTRL) through idealized experiments and real-case predictions. The idealized experiments showed that Cd_DC reduced frictional dissipation in the air-sea interface as well as significantly reduced sea surface cooling, making the TC stronger than other schemes. As a result, Cd_DC reduced the mean absolute error and negative bias by 15.0% (21.0%) and 19.1% (32.0%), respectively, for all lead times of Hurricane Irma in 2017 (Typhoon Mangkhut in 2018) compared to CTRL. This result suggests that new parameterization of Ck/Cd with decreasing Cd at high winds can help improve TC intensity prediction, which currently suffers from underestimating the intensity of the strongest TCs.

This review summarizes experiences at operational centers to forecast tropical cyclone (TC) intensity change as presented to the International Workshop on Tropical Cyclones (IWTC-9) in Hawaii in 2018. Some operational forecast centers have been able to leverage advances in intensity guidance to increase forecast skill, albeit incrementally, while others have struggled to make any significant improvements. Rapid intensity changes continue to present major challenges to operational centers and individual difficult cases illustrate the forecasting challenges. It is noteworthy that the realization of a recommendation from IWTC-8 in 2014, to adapt guidance initially developed for the North Atlantic and North-East Pacific to other basins, has led to improved forecast skill of some agencies. Recent worldwide difficult cases are presented so that the research community can further investigate, potentially leading to improved intensity forecasts when similar cases are observed in the future. Keywords: tropical cyclone, intensity, change, rapid intensification

This review summarizes techniques used by operational centers to forecast tropical cyclone intensity change as presented to the International Workshop on Tropical Cyclones (IWTC-9) in Hawaii in 2018. Recent advances and major changes over the past four years are presented, with a special focus on forecasting rapid intensity changes. Although intensity change remains one of the most difficult aspects of tropical cyclone forecasting, objective guidance has shown some improvement. The greatest improvements are realized when consensus methods are utilized, especially those that blend statistical-dynamical based guidance with dynamical ocean-coupled regional models. These models become even more skillful when initialized with inner core observational data. Continued improvement and availability of intensity guidance along with associated forecaster training are expected to deliver forecasting improvements in the future. Keywords: tropical cyclone, intensity, change, rapid intensification

Abstract A revised predictor called the net energy gain rate (NGR) is suggested by considering wind‐dependent drag coefficient based on the existing maximum potential intensity theory. A series of wind speed‐dependent NGR, known as NGR‐w, is calculated based on pretropical cyclone (TC) averaged ocean temperatures from the surface down to 120 m (at 10‐m intervals) to include the TC‐induced vertical mixing for 13 years (2004–2016) in the western North Pacific. It turns out that NGR50‐w (NGR‐w based on temperature averaged over top 50 m) has the highest correlation with 24‐hr TC intensity change compared with the commonly used sea surface temperature‐based intensification potential (POT), depth‐averaged temperature‐based POT (POTDAT), and constant drag coefficient in the NGR. To demonstrate the effectiveness of NGR50‐w, we designed and conducted experiments for training (2004–2014) and testing (2015–2016). The model with NGR50‐w shows greater skill than does the model with POTDAT or POT by reducing prediction errors by about 16%.

ABSTRACT: In this study, a tropical cyclogenesis detection system, Tropical Cyclone Analysis & Forecast (TCAF), was evaluated with an operational numerical model of the Korea Meteorological Administration (KMA). The tracking performance was compared with the result with the ECMWF model input field (TCAF-ECMWF). In order to improve the performance, different tracking time at an interval of 6 hours were investigated. The lowest false alarm rate and the highest hit rate (correct detection) were achieved at 06 hour after the initial tracking time. The tracking performance was also tested on two typhoons in 2013, LEEPI (1304) and DANAS (1324). The results showed that the TCAF-ECMWF detected tropical depressions 72 hours before the formation of the typhoon DANAS, which is a 12-hour earlier detection compared with the current performance with the use of KMA's numerical weather prediction (NWP) model data. So, it is expected that TC genesis detection could be improved by determining an optimal tracking time and by using more accurate NWP model data. Keywords: tropical cyclone, genesis detection, numerical weather prediction

ABSTRACT: This paper summarizes results of a survey of the operational status of tropical cyclone (TC) forecast verification in the western North Pacific region, covering the Members of the ESCAP/WMO Typhoon Committee, RSMC Tokyo - Typhoon Center, and JTWC. Results of the survey indicate that significant efforts have been focused on the verification of TC forecast guidance by operational forecast agencies in the region, either individually or through international cooperation. A variety of TC forecast verification products are available to the forecasters in either real time or in post-season through multiple means, with the main focus on the deterministic track and intensity forecasts. Only a few verification products are available for probabilistic forecasts. Verification of TC precipitation and high wind forecasts are also lacking sufficient attention in the region. It would be helpful in the future to focus more attention toward these aspects and incorporate methods that will provide a greater depth of information regarding the TC forecast performance. Keywords: Tropical cyclone, forecast, verification

This study introduces an unequally weighted technique for Multi-model Ensemble (MME) forecasting for western North Pacific Tropical Cyclone (TC) tracks. Weights are calculated by partial least square regression, and members are selected by paired t-test. The performances for shorter forecast time ranges, such as 24, 48 and 72 h, are examined in order to improve the MME model, in which the weights for members are equally assigned. For longer forecast time ranges, such as 96 and 120 h, weights for MME members are thought to be less reliable, since the modeling is more likely to be influenced by the climate variability in the data period. A combination of both techniques for the shorter and the longer forecast time ranges is suggested as an alternative MME forecast procedure in operational meteorological agencies.