Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches

In this research, climate classification maps over the Korean Peninsula at 1 km resolution were generated using the satellite-based climatic variables of monthly temperature and precipitation based on machine learning approaches. Random forest (RF), artificial neural networks (ANN), k-nearest neighbor (KNN), logistic regression (LR), and support vector machines (SVM) were used to develop models. Training and validation of these models were conducted using in-situ observations from the Korea Meteorological Administration (KMA) from 2001 to 2016. The rule of the traditional Koppen-Geiger (K-G) climate classification was used to classify climate regions. The input variables were land surface temperature (LST) of the Moderate Resolution Imaging Spectroradiometer (MODIS), monthly precipitation data from the Tropical Rainfall Measuring Mission (TRMM) 3B43 product, and the Digital Elevation Map (DEM) from the Shuttle Radar Topography Mission (SRTM). The overall accuracy (OA) based on validation data from 2001 to 2016 for all models was high over 95%. DEM and minimum winter temperature were two distinct variables over the study area with particularly high relative importance. ANN produced more realistic spatial distribution of the classified climates despite having a slightly lower OA than the others. The accuracy of the models using high altitudinal in-situ data of the Mountain Meteorology Observation System (MMOS) was also assessed. Although the data length of the MMOS data was relatively short (2013 to 2017), it proved that the snowy, dry and cold winter and cool summer class (Dwc) is widely located in the eastern coastal region of South Korea. Temporal shifting of climate was examined through a comparison of climate maps produced by period: from 1950 to 2000, from 1983 to 2000, and from 2001 to 2013. A shrinking trend of snow classes (D) over the Korean Peninsula was clearly observed from the ANN-based climate classification results. Shifting trends of climate with the decrease/increase of snow (D)/temperate (C) classes were clearly shown in the maps produced using the proposed approaches, consistent with the results from the reanalysis data of the Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC)

Estimation of Spatially Continuous Near-Surface Relative Humidity Over Japan and South Korea

Near-surface relative humidity (RHns) is an essential meteorological parameter for water, carbon, and climate studies. However, spatially continuous RHns estimation is difficult due to the spatial discontinuity of in situ observations and the cloud contamination of satellite-based data. This article proposed machine learning-based models to estimate spatially continuous daily RHns at 1 km resolution over Japan and South Korea under all sky conditions and examined the spatiotemporal patterns of RHns. All sky estimation of RHns using machine learning has been rarely conducted, and it can be an alternative to the currently available RHns data mostly from numerical models, which have relatively low spatial resolution. We combined two schemes for clear sky conditions (scheme A, which uses satellite and reanalysis data) and cloudy sky conditions (scheme B, which uses reanalysis data solely). The relatively small numbers of data in extremely low and high RHns conditions (i.e., <30% or >70%, respectively) were augmented by applying an oversampling method to avoid biased training. The machine learning models based on random forest (RF) and XGBoost were trained and validated using 94 in situ observation sites from meteorological administrations of both countries from 2012 to 2017. The results showed that XGBoost produced slightly better performance than RF, and the spatially continuous RHns model combined based on XGBoost yielded the coefficient of determination of 0.72 and a root-mean-square error of 10.61%. Spatiotemporal patterns of the estimated RHns agreed with in situ observations, reflecting the effect of topography on RHns. We expect that the proposed RHns model could be used in various environmental studies that require RHns under all sky conditions as input data

Icing detection over East Asia from geostationary satellite data using machine learning approaches

Even though deicing or airframe coating technologies continue to develop, aircraft icing is still one of the critical threats to aviation. While the detection of potential icing clouds has been conducted using geostationary satellite data in the US and Europe, there is not yet a robust model that detects potential icing areas in East Asia. In this study, we proposed machine-learning-based icing detection models using data from two geostationary satellites—the Communication, Ocean, and Meteorological Satellite (COMS) Meteorological Imager (MI) and the Himawari-8 Advanced Himawari Imager (AHI)—over Northeast Asia. Two machine learning techniques—random forest (RF) and multinomial log-linear (MLL) models—were evaluated with quality-controlled pilot reports (PIREPs) as the reference data. The machine-learning-based models were compared to the existing models through five-fold cross-validation. The RF model for COMS MI produced the best performance, resulting in a mean probability of detection (POD) of 81.8%, a mean overall accuracy (OA) of 82.1%, and mean true skill statistics (TSS) of 64.0%. One of the existing models, flight icing threat (FIT), produced relatively poor performance, providing a mean POD of 36.4%, a mean OA of 61.0, and a mean TSS of 9.7%. The Himawari-8 based models also produced performance comparable to the COMS models. However, it should be noted that very limited PIREP reference data were available especially for the Himawari-8 models, which requires further evaluation in the future with more reference data. The spatio-temporal patterns of the icing areas detected using the developed models were also visually examined using time-series satellite data

Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data

For a long time, researchers have tried to find a way to analyze tropical cyclone (TC) intensity in real-time. Since there is no standardized method for estimating TC intensity and the most widely used method is a manual algorithm using satellite-based cloud images, there is a bias that varies depending on the TC center and shape. In this study, we adopted convolutional neural networks (CNNs) which are part of a state-of-art approach that analyzes image patterns to estimate TC intensity by mimicking human cloud pattern recognition. Both two dimensional-CNN (2D-CNN) and three-dimensional-CNN (3D-CNN) were used to analyze the relationship between multi-spectral geostationary satellite images and TC intensity. Our best-optimized model produced a root mean squared error (RMSE) of 8.32 kts, resulting in better performance (~35%) than the existing model using the CNN-based approach with a single channel image. Moreover, we analyzed the characteristics of multi-spectral satellite-based TC images according to intensity using a heat map, which is one of the visualization means of CNNs. It shows that the stronger the intensity of the TC, the greater the influence of the TC center in the lower atmosphere. This is consistent with the results from the existing TC initialization method with numerical simulations based on dynamical TC models. Our study suggests the possibility that a deep learning approach can be used to interpret the behavior characteristics of TCs

Anti-allergic and anti-inflammatory effects of butanol extract from Arctium Lappa L

Background: Atopic dermatitis is a chronic, allergic inflammatory skin disease that is accompanied by markedly increased levels of inflammatory cells, including eosinophils, mast cells, and T cells. Arctium lappa L. is a traditional medicine in Asia. This study examined whether a butanol extract of A. lappa (ALBE) had previously unreported anti-allergic or anti-inflammatory effects.Methods: This study examined the effect of ALBE on the release of ??-hexosaminidase in antigen-stimulated-RBL-2H3 cells. We also evaluated the ConA-induced expression of IL-4, IL-5, mitogen-activated protein kinases (MAPKs), and nuclear factor (NF)-??B using RT-PCR, Western blotting, and ELISA in mouse splenocytes after ALBE treatment.Results: We observed significant inhibition of ??-hexosaminidase release in RBL-2H3 cells and suppressed mRNA expression and protein secretion of IL-4 and IL-5 induced by ConA-treated primary murine splenocytes after ALBE treatment. Additionally, ALBE (100 ??g/mL) suppressed not only the transcriptional activation of NF-??B, but also the phosphorylation of MAPKs in ConA-treated primary splenocytes.Conclusions: These results suggest that ALBE inhibits the expression of IL-4 and IL-5 by downregulating MAPKs and NF-??B activation in ConA-treated splenocytes and supports the hypothesis that ALBE may have beneficial effects in the treatment of allergic diseases, including atopic dermatitis. ?? 2011 Sohn et al; licensee BioMed Central Ltd

Detection of tropical overshooting cloud tops using himawari-8 imagery

Overshooting convective cloud Top (OT)-accompanied clouds can cause severe weather conditions, such as lightning, strong winds, and heavy rainfall. The distribution and behavior of OTs can affect regional and global climate systems. In this paper, we propose a new approach for OT detection by using machine learning methods with multiple infrared images and their derived features. Himawari-8 satellite images were used as the main input data, and binary detection (OT or nonOT) with class probability was the output of the machine learning models. Three machine learning techniques-random forest (RF), extremely randomized trees (ERT), and logistic regression (LR)-were used to develop OT classification models to distinguish OT from non-OT. The hindcast validation over the Southeast Asia andWest Pacific regions showed that RF performed best, resulting in a mean probabilities of detection (POD) of 77.06% and a mean false alarm ratio (FAR) of 36.13%. Brightness temperature at 11.2 ??m (Tb11) and its standard deviation (STD) in a 3 ?? 3 window size were identified as the most contributing variables for discriminating OT and nonOT classes. The proposed machine learning-based OT detection algorithms produced promising results comparable to or even better than the existing approaches, which are the infrared window (IRW)-texture and water vapor (WV) minus IRW brightness temperature difference (BTD) methods

Case Report: Intellectual disability and borderline intellectual functioning in two sisters with a 12p11.22 loss

Multiple genome sequencing studies have identified genetic abnormalities as major causes of severe intellectual disability (ID). However, many children affected by mild ID and borderline intellectual functioning (BIF) lack a genetic diagnosis because known causative ID genetic mutations have not been identified or the role of genetic variants in mild cases is less understood. Genetic variant testing in mild cases is necessary to provide information on prognosis and risk of occurrence. In this study, we report two sibling patients who were 5 years 9 months old and 3 years 3 months old and presented to the hospital due to developmental delay. Clinical assessment and chromosomal microarray analysis were performed. The patients were diagnosed with mild intellectual disability (ID) and borderline intellectual functioning (BIF). Genetic analysis identified a loss of 12p11.22, including the OVCH1-AS1, OVCH1, and TMTC1 genes, which was the only variant that occurred in both sisters. Identical variants were found in their father with probable BIF. Neither patient presented any brain structural abnormalities or dysmorphism, and no exogenous factors or parenting problems were reported. Thus, loss of 12p11.22 may be associated with our patients’ cognitive impairment. The OVCH1, OVCH1-AS1 and TMTC1 variants identified in this study are the most likely disease-causing genes in the sisters. Our findings may expand as yet limited knowledge on mild ID and BIF causative variants, which would further support the diagnosis even if the severity is mild

Organizing home welfare services for the dependent elderly : a comparative study on the work of "second line" professionals in gerontology in France and Korea

Avec l'instauration, en 2002 en France, de l'Allocation Personnalisée à l'Autonomie (APA), et en 2008 en Corée, du dispositif Long Term Care (LTC), la prise en charge de la dépendance de la population âgée s'affirme comme un enjeu majeur de la politique sociale contemporaine de ces deux pays confrontés au phénomène du vieillissement démographique. En dépit de contextes démographiques, historiques, culturels, politiques, économiques et sociaux contrastés, ces deux pays ont en commun de mettre en place, pour faire face aux besoins des personnes âgées, des dispositifs dont les logiques d'action paraissent proches ou sensiblement similaires. En adoptant une optique comparative entre la France et la Corée, cette thèse cherche à mettre en regard le travail des professionnels socio-gérontologiques chargés de la mise en œuvre de ces dispositifs en faveur de la population âgée dite dépendante à domicile. Pour cela les procédures mises en place dans le cadre de l'APA en France et du LTC en Corée et la manière dont l'accompagnement socio-médical des personnes âgées dépendantes à domicile s'y organise ont été étudiées. Tant en France qu'en Corée, les professionnels qui interviennent pour assurer le maintien à domicile des personnes âgées dépendantes sont multiples ; les uns travaillent en "première ligne" (aides à domicile, auxiliaires de vie, aides soignants, infirmiers) et les autres, chargés de la coordination et de l'encadrement des précédents, en "seconde ligne". Elle traite également de la nécessaire - et difficile - coordination entre ces professionnels et des logiques, parfois divergentes et conflictuelles, qui sous-tendent leurs actions dans des réalités organisationnelles complexes. L'observation a été effectuée sur un territoire circonscrit dans chacun des deux pays : Lille-Hellemmes pour la France et Cheongju-Cheongwon pour la Corée.With the establishment of the Individual Public Allowance for Autonomy (APA) in France in 2002 and the Long Term Care (LTC) insurance in Korea in 2008, the management and the support for the dependence of the elderly population became a major issue of contemporary social policy in these two nations faced with the phenomenon of aging. Despite demographic, historical, cultural, political, economical and social contrasting contexts, they have installed devices in common with modes that appear close or substantially similar to cope with the increasing needs of the elderly. By adopting a comparative approach between France and Korea, this thesis sought to analyze the work of social gerontological professionals in charge of these devices for the elderly dependent who stay at home. For this, the application procedure of the APA in France and the LTC in Korea and the organization of the socio-medical asistance for the dependent elderly at home were studied. In both France and Korea, the professionals who are involved with the home care services are multiple. Some are working in the "front line" (home helpers, care assistance, home nurses) and others are occupied with the coordination, the supervision/organization of the foregoing (front line workers) in the "second line". This research focuses on the impact of devices which are placed on the work of professionals, especially those who are concerned with various tasks in the second line and their necessary - and difficul t- coordination. How the different policies and the strategic relationship between these various participating professionals sometimes diverge and how the conflicts intrinsic to their actions and practice within complex organizational realities are settled were investigated and explored. The observation was carried out in one area within each country : Hellemmes-Lille in France and Cheongju-Cheongwon in Korea

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