Impact of Sea Surface Temperature and Surface Air Temperature on Maximizing Typhoon Rainfall: Focusing on Typhoon Maemi in Korea

In this study, the effects of surface air temperature (SAT) and sea surface temperature (SST) changes on typhoon rainfall maximization are analysed. Based on the numerically reproduced Typhoon Maemi, this study tried to maximize the typhoon-induced rainfall by increasing the amount of saturated water vapour in the atmosphere and the amount of water vapour entering the typhoon. Using the Weather Research and Forecasting (WRF) model, which is one of the regional climate models (RCMs), the rainfall simulated by WRF while increasing the SAT and SST to various sizes at initial conditions and boundary conditions of the model was analysed. As a result of the simulated typhoon rainfall, the spatial distribution of total rainfall depth on the land due to the increase combination of SAT and SST showed a wide variety without showing a certain pattern. This is attributed to the geographical location of the Korean peninsula, which is a peninsula between the continent and the ocean. In other words, under certain conditions, typhoons may drop most of the rainfall on the southern sea of the peninsula before landing on the peninsula. For instance, the 6-hour duration maximum precipitation (MP) in Busan Metropolitan City was 472.1 mm when the SST increased by 2.0°C. However, when the SST increased by 4.0°C, the MP was found to be 395.3 mm, despite the further increase in SST. This indicates that the MP at a particular area and the increase in temperature do not have a linear relationship. Therefore, in order to maximize typhoon rainfall, it is necessary to repeat the numerical experiment on various conditions and search for the combination of SAT and SST increase which is most suitable for the target typhoon

Quantum advantage through the magic pentagram problem

Through the two specific problems, the 2D hidden linear function problem and the 1D magic square problem, Bravyi et al. have recently shown that there exists a separation between $\mathbf{QNC^0}$ and $\mathbf{NC^0}$, where $\mathbf{QNC^0}$ and $\mathbf{NC^0}$ are the classes of polynomial-size and constant-depth quantum and classical circuits with bounded fan-in gates, respectively. In this paper, we present another problem with the same property, the magic pentagram problem based on the magic pentagram game, which is a nonlocal game. In other words, we show that the problem can be solved with certainty by a $\mathbf{QNC^0}$ circuit but not by any $\mathbf{NC^0}$ circuits.Comment: 10 pages, 5 figure

Influence of /\u27 lattice misfit on hydrogen embrittlement mechanism of single-crystal nickel-based superalloy CMSX-4

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Indirect Band Gap in Scrolled MoS₂ Monolayers

MoS2 nanoscrolls that have inner core radii of similar to 250 nm are generated from MoS2 monolayers, and the optical and transport band gaps of the nanoscrolls are investigated. Photoluminescence spectroscopy reveals that a MoS2 monolayer, originally a direct gap semiconductor (similar to 1.85 eV (optical)), changes into an indirect gap semiconductor (similar to 1.6 eV) upon scrolling. The size of the indirect gap for the MoS2 nanoscroll is larger than that of a MoS2 bilayer (similar to 1.54 eV), implying a weaker interlayer interaction between concentric layers of the MoS2 nanoscroll compared to Bernal-stacked MoS2 few-layers. Transport measurements on MoS2 nanoscrolls incorporated into ambipolar ionic-liquid-gated transistors yielded a band gap of similar to 1.9 eV. The difference between the transport and optical gaps indicates an exciton binding energy of 0.3 eV for the MoS2 nanoscrolls. The rolling up of 2D atomic layers into nanoscrolls introduces a new type of quasi-1D nanostructure and provides another way to modify the band gap of 2D materials.11Nsciescopu

Spin configuration of top quark pair production with large extra dimensions at photon-photon colliders

Top quark pair production at photon-photon colliders is studied in low scale quantum gravity scenario. From the dependence of the cross sections on the spin configuration of the top quark and anti-quark, we introduce a new observable, top spin asymmetry. It is shown that there exists a special top spin basis where with the polarized parent electron beams the top spin asymmetry vanishes in the standard model but retains substantial values with the large extra dimension effects. We also present lower bounds of the quantum gravity scale $M_S$ from total cross sections with various combinations of the laser, electron beam, and top quark pair polarizations. The measurements of the top spin state $(t_\uparrow\bar{t}_\downarrow)$ with unpolarized initial beams are shown to be most effective, enhancing by about 5% the $M_S$ bounds with respect to totally unpolarized case.Comment: 18 pages, 4 figures, ReVTe

Four Light Neutrinos in Singular Seesaw Mechanism with Abelian Flavor Symmetry

The four light neutrino scenario, which explains the atmosphere, solar and LSND neutrino experiments, is studied in the framework of the seesaw mechanism. By taking both the Dirac and Majorana mass matrix of neutrinos to be singular, the four neutrino mass spectrum consisting of two almost degenerate pairs separated by a mass gap $\sim 1$ eV is naturally generated. Moreover the right-handed neutrino Majorana mass can be at $\sim 10^{14}$ GeV scale unlike in the usual singular seesaw mechanism. Abelian flavor symmetry is used to produce the required neutrino mass pattern. A specific example of the flavor charge assignment is provided to show that maximal mixings between the $\nu_\mu-\nu_\tau$ and $\nu_e-\nu_s$ are respectively attributed to the atmosphere and solar neutrino anomalies while small mixing between two pairs to the LSND results. The implication in the other fermion masses is also discussed.Comment: Firnal version to appear in PR

A Phenomenological Study on Lepton Mass Matrix Textures

The three active light neutrinos are used to explain the neutrino oscillations. The inherently bi-large mixing neutrino mass matrix and the Fritzsch type, bi-small mixing charged lepton mass matrix are assumed. By requiring the maximal \nu_\mu-\nu_\tau mixing for the atmospheric neutrino problem and the mass-squared difference approperiate for the almost maximal mixing solution to the solar neutrino problem, the following quantities are predicted: the \nu_e-\nu_\mu mixing, V_{e3}, CP violation in neutrino oscillations, and the effective electron-neutrino mass relevant to neutrinoless double beta decays.Comment: 6 pages, revtex, no figures, confusing points corrected, clarification and refernces adde

A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation

A new method was developed for classifying aerosol types involving a machine-learning approach to the use of satellite data. An Aerosol Robotic NETwork (AERONET)-based aerosol-type dataset was used as a target variable in a random forest (RF) model. The contributions of satellite input variables to the RF-based model were quantified to determine an optimal set of input variables. The new method, based on inputs of satellite variables, allows the classification of seven aerosol types: pure dust, dust-dominant mixed, pollution-dominant mixed aerosols, and pollution aerosols (strongly, moderately, weakly, and non-absorbing). The performance of the model was statistically evaluated using AERONET data excluded from the model training dataset. Model accuracy for classifying the seven aerosol types was 59%, improving to 72% for four types (pure dust, dust-dominant mixed, strongly absorbing, and non-absorbing). The performance of the model was evaluated against an earlier aerosol classification method based on the wavelength dependence of single-scattering albedo (SSA) and fine-mode-fraction values from AERONET. Typical wavelength dependences of SSA for individual aerosol types are consistent with those obtained for aerosol types by the new method. This study demonstrates that an RF-based model is capable of satellite aerosol classification with sensitivity to the contribution of non-spherical particles

Usefulness of Global Root Zone Soil Moisture Product for Streamflow Prediction of Ungauged Basins

Using modelling approaches to predict stream flow from ungauged basins requires new model calibration strategies and evaluation methods that are different from the existing ones. Soil moisture information plays an important role in hydrological applications in basins. Increased availability of remote sensing data presents a significant opportunity to obtain the predictive performance of hydrological models (especially in ungauged basins), but there is still a limit to applying remote sensing soil moisture data directly to models. The Soil Moisture Active Passive (SMAP) satellite mission provides global soil moisture data estimated by assimilating remotely sensed brightness temperature to a land surface model. This study investigates the potential of a hydrological model calibrated using only global root zone soil moisture based on satellite observation when attempting to predict stream flow in ungauged basins. This approach’s advantage is that it is particularly useful for stream flow prediction in ungauged basins since it does not require observed stream flow data to calibrate a model. The modelling experiments were carried out on upstream watersheds of two dams in South Korea with high-quality stream flow data. The resulting model outputs when calibrated using soil moisture data without observed stream flow data are particularly impressive when simulating monthly stream flows upstream of the dams, and daily stream flows also showed a satisfactory level of predictive performance. In particular, the model calibrated using soil moisture data for dry years showed better predictive performance than for wet years. The performance of the model calibrated using soil moisture data was significantly improved under low flow conditions compared to the traditional regionalization approach. Additionally, the overall stream flow was also predicted better. In addition, the uncertainty of the model calibrated using soil moisture was not much different from that of the model calibrated using observed stream flow data, and showed more robust outputs when compared to the traditional regionalization approach. These results prove that the application of the global soil moisture product for predicting stream flows in ungauged basins is promising

Learning Enhancement Method of Long Short-Term Memory Network and Its Applicability in Hydrological Time Series Prediction

Many studies have applied the Long Short-Term Memory (LSTM), one of the Recurrent Neural Networks (RNNs), to rainfall-runoff modeling. These data-driven modeling approaches learn the patterns observed from input and output data. It is widely known that the LSTM networks are sensitive to the length and quality of observations used for learning. However, the discussion on a better composition of input data for rainfall-runoff modeling has not yet been sufficiently conducted. This study focuses on whether the composition of input data could help improve the performance of LSTM networks. Therefore, we first examined changes in streamflow prediction performance by various compositions of meteorological variables which are used for LSTM learning. Second, we evaluated whether learning by integrating data from all available basins can improve the streamflow prediction performance of a specific basin. As a result, using all available meteorological data strengthened the model performance. The LSTM generalized by the multi-basin integrated learning showed similar performance to the LSTMs separately learned for each basin but had more minor errors in predicting low flow. Furthermore, we confirmed that it is necessary to group by selecting basins with similar characteristics to increase the usefulness of the integrally learned LSTM