Icing detection from Communication, Ocean and Meteorological Satellite and Himawari-8 data using machine learning approaches

Aircraft icing is a hazardous phenomenon which has potential to cause fatalities and socioeconomic losses. It is caused by super-cooled droplets (SCDs) colliding on the surface of aircraft frame when an aircraft flies through SCD rich clouds. When icing occurs, the aerodynamic balance of the aircraft is disturbed, resulting in a potential problem in aircraft operation. Thus, identification of potential icing clouds is crucial for aviation. Satellite remote sensing data such as Geostationary Operational Environmental Satellite (GOES) series have been widely used to detect potential icing clouds. An icing detection algorithm, operationally used in the US, consists of several thresholds of cloud optical depth, effective radius, and liquid water path based on the physical properties of icing. On the other hand, there is no operational icing detection algorithm in Asia, although there are several geostationary meteorological satellite sensors. In this study, we proposed machine learning-based models to detect icing over East Asia focusing on the Korean Peninsula using two geostationary satellite sensors—Meteorological Imager (MI) onboard Communication, Ocean and Meteorological Satellite (COMS) and Advanced Himawari Imager (AHI) onboard Himawari-8. While COMS MI provides data at 5 channels, Himawari-8 AHI has advanced capability of data collection, providing data at 16 channels. Instead of simple thresholding approaches used in the literature, we adopted two machine learning algorithms—decision trees (DT) and random forest (RF) to develop icing detection models based on Pilot REPorts (PIREPs) as reference data. Results show that the COMS icing detection model by RF produced a detection rate of 88.67% and a false alarm rate of 14.42%, which were improved when compared with the result of the direct application of the GOES algorithm to the COMS MI data (a detection rate of 20.83% and a false alarm rate of 25.44%). Although much higher accuracy (a detection rate > 95%) was achieved when Himawari AHI data were used, the model was not robust due to the very limited number of training data. Incorporation of MODIS-derived icing reference data may improve the reliability of the machine learning models for Himawari AHI data

Detection of centers of tropical cyclones through the synergistic use of geostationary and polar-orbiting satellite data

Detecting tropical cyclone centers is crucial for better understanding the behavior and characteristics of tropical cyclones, and their development. In particular, early detection of centers of cyclone disturbances are important for tropical countries to prepare and respond to damages by tropical cyclones. There are several approaches for detecting the centers of tropical cyclones . Among them, the best track provided by Joint Typhoon Warning Center (JTWC) has been widely used as reference data of tropical cyclone center locations. However, JTWC uses multiple resources including geostationary satellite data and in situ measurements to determine the best track in a subjective way and makes it available to the public 6 months later after an event occurred. Thus, the best track data cannot be operationally used to identify the centers of tropical cyclones in real time. In this study, we proposed an automated approach for identifying the centers of tropical cyclones using both geostationary (i.e., Meteorological Imager of Communication, Ocean, and Meteorological Satellite by South Korea) and polar-orbiting (i.e., Windsat of Coriolis by Naval Research Laboratory and Air Force Research Laboratory of US) satellite data. Brightness temperatures and wind directions of sea surface wind field were extracted using COMS MI and Windsat data over the western North Pacific between June and August 2011, respectively. We adopted a spatial metric called circular variance to identify the centers of tropical cyclones. Circular variances were calculated from the surface data of brightness temperature gradients and wind field direction. The locations of the maximum circular variance were identified as the centers of tropical cyclones. Results were compared with the best track data, showing the distance between the detected center and the best track center up to 2 degrees. The distance between the centers became smaller as the tropical cyclones developed, which implies that the wind vorticity-based center and the cloud-based center tend to agree when a cyclone becomes tropical depression

A remote-controlled automatic chest compression device capable of moving compression position during CPR: A pilot study in a mannequin and a swine model of cardiac arrest.

BackgroundRecently, we developed a chest compression device that can move the chest compression position without interruption during CPR and be remotely controlled to minimize rescuer exposure to infectious diseases. The purpose of this study was to compare its performance with conventional mechanical CPR device in a mannequin and a swine model of cardiac arrest.Materials and methodsA prototype of a remote-controlled automatic chest compression device (ROSCER) that can change the chest compression position without interruption during CPR was developed, and its performance was compared with LUCAS 3 in a mannequin and a swine model of cardiac arrest. In a swine model of cardiac arrest, 16 male pigs were randomly assigned into the two groups, ROSCER CPR (n = 8) and LUCAS 3 CPR (n = 8), respectively. During 5 minutes of CPR, hemodynamic parameters including aortic pressure, right atrial pressure, coronary perfusion pressure, common carotid blood flow, and end-tidal carbon dioxide partial pressure were measured.ResultsIn the compression performance test using a mannequin, compression depth, compression time, decompression time, and plateau time were almost equal between ROSCER and LUCAS 3. In a swine model of cardiac arrest, coronary perfusion pressure showed no difference between the two groups (p = 0.409). Systolic aortic pressure and carotid blood flow were higher in the LUCAS 3 group than in the ROSCER group during 5 minutes of CPR (p ConclusionThe prototype of a remote-controlled automated chest compression device can move the chest compression position without interruption during CPR. In a mannequin and a swine model of cardiac arrest, the device showed no inferior performance to a conventional mechanical CPR device