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Department of Urban and Environmental Engineering (Environmental Science and Engineering)This thesis seek to 1) better understand the characteristics of drought factors in different climate regions (arid and humid regions), 2) monitor drought effectively using improved soil moisture to high resolution data (~1 km) by developing drought index, 3) predict drought by identifying the relationship between drought and climate variability. In this dissertation, there are five chapters. Chapter 1 summarizes the background of research and overviews of the thesis research. In Chapter 2, the characteristics of drought factors in different climate regions were understood based on relative variable importance which is provided from machine learning approaches. Three machine learning approaches (Random forest, Boosted regression trees, and Cubist) were used to target Standardized Precipitation Index (SPI) and crop yield, and Random forest outperformed the other approaches. The variable importance targeting SPI and crop yield was applied as weight factors to produce drought maps. Those drought maps well monitored drought comparing U.S. Drought Monitor (USDM). In Chapter 3, Soil moisture with coarse spatial resolution (25 km) was downscaled to high resolution (1 km), and drought index (High resolution Soil Moisture Drought Index; HSMDI) was developed using high resolution soil moisture. HSMDI was evaluated for three types of droughts (meteorological drought, agricultural drought, and hydrological drought). The index well monitored meteorological and agricultural drought, but there is limitation in monitoring hydrological drought. In Chapter 4, short-term (one pentad) prediction of drought conducted over East Asia by integrating remote sensing data and climate variability. The real-time multivariate (RMM) Madden???Julian oscillation (MJO) indices were used because the MJO is intraseasonal climate variability. MJO improved the performance of prediction model. Chapter 5 provides a brief summary of this study. Chapter 6 discusses future work of this study.clos

Machine learning approaches for detecting tropical cyclone formation using satellite data

This study compared detection skill for tropical cyclone (TC) formation using models based on three different machine learning (ML) algorithms-decision trees (DT), random forest (RF), and support vector machines (SVM)-and a model based on Linear Discriminant Analysis (LDA). Eight predictors were derived from WindSat satellite measurements of ocean surface wind and precipitation over the western North Pacific for 2005-2009. All of the ML approaches performed better with significantly higher hit rates ranging from 94 to 96% compared with LDA performance (~77%), although false alarm rate by MLs is slightly higher (21-28%) than that by LDA (~13%). Besides, MLs could detect TC formation at the time as early as 26-30 h before the first time diagnosed as tropical depression by the JTWC best track, which was also 5 to 9 h earlier than that by LDA. The skill differences across MLs were relatively smaller than difference between MLs and LDA. Large yearly variation in forecast lead time was common in all models due to the limitation in sampling from orbiting satellite. This study highlights that ML approaches provide an improved skill for detecting TC formation compared with conventional linear approaches

Classification and mapping of paddy rice by combining Landsat and SAR time series data

Rice is an important food resource, and the demand for rice has increased as population has expanded. Therefore, accurate paddy rice classification and monitoring are necessary to identify and forecast rice production. Satellite data have been often used to produce paddy rice maps with more frequent update cycle (e.g., every year) than field surveys. Many satellite data, including both optical and SAR sensor data (e.g., Landsat, MODIS, and ALOS PALSAR), have been employed to classify paddy rice. In the present study, time series data from Landsat, RADARSAT-1, and ALOS PALSAR satellite sensors were synergistically used to classify paddy rice through machine learning approaches over two different climate regions (sites A and B). Six schemes considering the composition of various combinations of input data by sensor and collection date were evaluated. Scheme 6 that fused optical and SAR sensor time series data at the decision level yielded the highest accuracy (98.67% for site A and 93.87% for site B). Performance of paddy rice classification was better in site A than site B, which consists of heterogeneous land cover and has low data availability due to a high cloud cover rate. This study also proposed Paddy Rice Mapping Index (PMI) considering spectral and phenological characteristics of paddy rice. PMI represented well the spatial distribution of paddy rice in both regions. Google Earth Engine was adopted to produce paddy rice maps over larger areas using the proposed PMI-based approach

Downscaling GLDAS Soil Moisture Data in East Asia through Fusion of Multi-Sensors by Optimizing Modified Regression Trees

Soilmoisture is a key part of Earth's climate systems, including agricultural and hydrological cycles. Soil moisture data from satellite and numerical models is typically provided at a global scale with coarse spatial resolution, which is not enough for local and regional applications. In this study, a soil moisture downscaling model was developed using satellite-derived variables targeting Global Land Data Assimilation System (GLDAS) soil moisture as a reference dataset in East Asia based on the optimization of a modified regression tree. A total of six variables, Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced SCATterometer (ASCAT) soil moisture products, Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and MODerate resolution Imaging Spectroradiometer (MODIS) products, including Land Surface Temperature, Normalized Difference Vegetation Index, and land cover, were used as input variables. The optimization was conducted through a pruning approach for operational use, and finally 59 rules were extracted based on root mean square errors (RMSEs) and correlation coefficients (r). The developed downscaling model showed a good modeling performance (r = 0.79, RMSE = 0.056 m(3)center dot m(3), and slope = 0.74). The 1 km downscaled soil moisture showed similar time series patterns with both GLDAS and ground soil moisture and good correlation with ground soil moisture (average r = 0.47, average RMSD = 0.038 m(3)center dot m(3)) at 14 ground stations. The spatial distribution of 1 km downscaled soil moisture reflected seasonal and regional characteristics well, although the model did not result in good performance over a few areas such as Southern China due to very high cloud cover rates. The results of this study are expected to be helpful in operational use to monitor soil moisture throughout East Asia since the downscaling model produces daily high resolution (1 km) real time soil moisture with a low computational demand. This study yielded a promising result to operationally produce daily high resolution soil moisture data from multiple satellite sources, although there are yet several limitations. In future research, more variables including Global Precipitation Measurement (GPM) precipitation, Soil Moisture Active Passive (SMAP) soil moisture, and other vegetation indices will be integrated to improve the performance of the proposed soil moisture downscaling model.ope

Spatial mapping of short-term solar radiation prediction incorporating geostationary satellite images coupled with deep convolutional LSTM networks for South Korea

A practical approach to continuously monitor and provide real-time solar energy prediction can help support reliable renewable energy supply and relevant energy security systems. In this study on the Korean Peninsula, contemporaneous solar radiation images obtained from the Communication, Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) system, were used to design a convolutional neural network and a long short-term memory network predictive model, ConvLSTM. This model was applied to predict one-hour ahead solar radiation and spatially map solar energy potential. The newly designed ConvLSTM model enabled reliable prediction of solar radiation, incorporating spatial changes in atmospheric conditions and capturing the temporal sequence-to-sequence variations that are likely to influence solar driven power supply and its overall stability. Results showed that the proposed ConvLSTM model successfully captured cloud-induced variations in ground level solar radiation when compared with reference images from a physical model. A comparison with ground pyranometer measurements indicated that the short-term prediction of global solar radiation by the proposed ConvLSTM had the highest accuracy [root mean square error (RMSE) = 83.458 Wcenterdotm−2, mean bias error (MBE) = 4.466 Wcenterdotm−2, coefficient of determination (R2) = 0.874] when compared with results of conventional artificial neural network (ANN) [RMSE = 94.085 Wcenterdotm−2, MBE = −6.039 Wcenterdotm−2, R2 = 0.821] and random forest (RF) [RMSE = 95.262 Wcenterdotm−2, MBE = −11.576 Wcenterdotm−2, R2 = 0.839] models. In addition, ConvLSTM better captured the temporal variations in predicted solar radiation, mainly due to cloud attenuation effects when compared with two selected ground stations. The study showed that contemporaneous satellite images over short-term or near real-time intervals can successfully support solar energy exploration in areas without continuous environmental monitoring systems, where satellite footprints are available to model and monitor solar energy management systems supporting real-life power grid systems

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

Multivalent electrostatic pi–cation interaction between synaptophysin and synapsin is responsible for the coacervation

We recently showed that synaptophysin (Syph) and synapsin (Syn) can induce liquid–liquid phase separation (LLPS) to cluster small synaptic-like microvesicles in living cells which are highly reminiscent of SV cluster. However, as there is no physical interaction between them, the underlying mechanism for their coacervation remains unknown. Here, we showed that the coacervation between Syph and Syn is primarily governed by multivalent pi–cation electrostatic interactions among tyrosine residues of Syph C-terminal (Ct) and positively charged Syn. We found that Syph Ct is intrinsically disordered and it alone can form liquid droplets by interactions among themselves at high concentration in a crowding environment in vitro or when assisted by additional interactions by tagging with light-sensitive CRY2PHR or subunits of a multimeric protein in living cells. Syph Ct contains 10 repeated sequences, 9 of them start with tyrosine, and mutating 9 tyrosine to serine (9YS) completely abolished the phase separating property of Syph Ct, indicating tyrosine-mediated pi-interactions are critical. We further found that 9YS mutation failed to coacervate with Syn, and since 9YS retains Syphs negative charge, the results indicate that pi–cation interactions rather than simple charge interactions are responsible for their coacervation. In addition to revealing the underlying mechanism of Syph and Syn coacervation, our results also raise the possibility that physiological regulation of pi–cation interactions between Syph and Syn during synaptic activity may contribute to the dynamics of synaptic vesicle clustering.This work was supported by grants from the National Research Foundation of Korea (Grants 2019R1A2C2089182 to S.C.). This work was also supported by the Education and Research Encouragement Fund of Seoul National University Hospital