Lagrangian Floer potential of orbifold spheres

For each sphere with three orbifold points, we construct an algorithm to compute the open Gromov–Witten potential, which serves as the quantum-corrected Landau–Ginzburg mirror and is an infinite series in general. This gives the first class of general-type geometries whose full potentials can be computed. As a consequence we obtain an enumerative meaning of mirror maps for elliptic curve quotients. Furthermore, we prove that the open Gromov–Witten potential is convergent, even in the general-type cases, and has an isolated singularity at the origin, which is an important ingredient of proving homological mirror symmetry.National Research Foundation of Korea; 2010-0019516; 2012R1A1A2003117; 2013R1A1A1058646 - National Research Foundation of Kore

Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks

Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due to global warming. In this study, a novel 1-month sea ice concentration (SIC) prediction model is proposed, with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). This monthly SIC prediction model based on CNNs is shown to perform better predictions (mean absolute error - MAE - of 2.28 %, anomaly correlation coefficient - ACC - of 0.98, root-mean-square error - RMSE - of 5.76 %, normalized RMSE - nRMSE - of 16.15 %, and NSE - Nash-Sutcliffe efficiency - of 0.97) than a random-forest-based (RF-based) model (MAE of 2.45 %, ACC of 0.98, RMSE of 6.61 %, nRMSE of 18.64 %, and NSE of 0.96) and the persistence model based on the monthly trend (MAE of 4.31 %, ACC of 0.95, RMSE of 10.54 %, nRMSE of 29.17 %, and NSE of 0.89) through hindcast validations. The spatio-temporal analysis also confirmed the superiority of the CNN model. The CNN model showed good SIC prediction results in extreme cases that recorded unforeseen sea ice plummets in 2007 and 2012 with RMSEs of less than 5.0 %. This study also examined the importance of the input variables through a sensitivity analysis. In both the CNN and RF models, the variables of past SICs were identified as the most sensitive factor in predicting SICs. For both models, the SIC-related variables generally contributed more to predict SICs over ice-covered areas, while other meteorological and oceanographic variables were more sensitive to the prediction of SICs in marginal ice zones. The proposed 1-month SIC prediction model provides valuable information which can be used in various applications, such as Arctic shipping-route planning, management of the fishing industry, and long-term sea ice forecasting and dynamics

The Effect of Deep Convection on Temperatures in the Tropical Tropopause Layer and Its Implications to the Regulation of Tropical Lower Stratospheric Humidity

This dissertation focuses on the impact of deep convection on the thermal structure in the Tropical Tropopause Layer (TTL). Temperatures in this region play an important role in the regulation of water vapor, which in turn affects radiation, chemistry, and dynamics in the lower stratosphere. This dissertation includes two important conclusions concerning the regulation of temperature in the TTL. First, significant cooling near the tropical tropopause is observed during the time when active convection is occurring. A composite technique is used to relate the local temperature anomalies to the evolution of local convection. Temperature profiles are measured by the Atmospheric Infrared Sounder (AIRS) onboard the Aqua satellite, and the time evolution of local convections are determined by the National Centers for Environmental Protection / Aviation Weather Center (NCEP/AWS) half-hourly infrared global geostationary composite. The observations demonstrate that the TTL is cooled by convection, in agreement with previous observations and model simulations. By using a global data set, the variations in this convective cooling are investigated by season and region. The estimated cooling rate during active convection is - 7 K/day. This exceeds the likely contribution from cloud-top radiative cooling, suggesting turbulent mixing of deep convection plays a role in cooling the TTL. Second, height and thermal structure of the overshooting deep convection in the TTL are investigated using visible and infrared observations from the Visible and Infrared Scanner (VIRS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The heights of overshooting clouds are estimated from the sizes of the visible shadows that these clouds cast. The temperature information is obtained from the mid-infrared channel. From these, the lapse rate in the cloud is estimated. The result shows that the measured lapse rate of these clouds is significantly below adiabatic. Mixing between these clouds and the near-tropopause environment is the most likely explanation. As a result, these clouds will likely settle at a final altitude above the convections' initial level of neutral buoyancy

Arctic lead detection using a waveform mixture algorithm from CryoSat-2 data

We propose a waveform mixture algorithm to detect leads from CryoSat-2 data, which is novel and different from the existing threshold-based lead detection methods. The waveform mixture algorithm adopts the concept of spectral mixture analysis, which is widely used in the field of hyperspectral image analysis. This lead detection method was evaluated with high-resolution (250 m) MODIS images and showed comparable and promising performance in detecting leads when compared to the previous methods. The robustness of the proposed approach also lies in the fact that it does not require the rescaling of parameters (i.e., stack standard deviation, stack skewness, stack kurtosis, pulse peakiness, and backscatter sigma(0)), as it directly uses L1B waveform data, unlike the existing threshold-based methods. Monthly lead fraction maps were produced by the waveform mixture algorithm, which shows interannual variability of recent sea ice cover during 2011-2016, excluding the summer season (i.e., June to September). We also compared the lead fraction maps to other lead fraction maps generated from previously published data sets, resulting in similar spatiotemporal patterns