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Event(s) on April/ 2015


  • Thursday, 9th April, 2015

    Title: ICTS Data Assimilation Program: Introduction to Numerical Weather Prediction and Data Assimilation (Lecture 1)
    Speaker: Dr. Chian-Yi Liu, Center for Space and Remote Sensing Research, National Central University, Taiwan
    Time/Place: 10:00  -  11:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: Numerical weather prediction (NWP) is an initial and boundary conductions problem. Provided an estimate of the atmospheric state, in terms of the variables of the NWP model, the model simulates the atmospheric state at later times. It also calculates precipitation and other important properties used by weather forecasters. With the increased observation not only from ground based instruments but also remote sensed sensors (e.g., radar and satellite) ranging from regional to global scales, these data improve the quality of the current state significantly and hence are supposed to advance the forecast performance. In this one-hour talk, a quick summary of NWP model and introduction to data assimilation techniques will be given. Audiences may have an idea about the production of the public weather forecasts.


  • Thursday, 9th April, 2015

    Title: ICTS Data Assimilation Program: The Impact of Advanced Infrared and Microwave Soundings on Short-term Weather Forecast (Lecture 2)
    Speaker: Dr. Chian-Yi Liu, Center for Space and Remote Sensing Research, National Central University, Taiwan
    Time/Place: 11:30  -  12:30
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: Earth atmosphere could be considered as a three dimensional non-uniform fluid. It is critical to understand the current state in order to forecast future through the assist from numerical weather prediction (NWP) model. It is also challenge to get the current state of atmosphere by using conventional surface observation data especially over the ocean, high mountains and polar region. On the other hand, the satellite observation could help to condition the three-dimensional atmospheric state globally in all-weather skies. Therefore, we would like to evaluate the impact from the use of satellite retrieved soundings in the NWP model. Due to the nature of retrieval uncertainties are varies, and microwave sounder (MW) have bigger field-of-view (FOV) than infrared sounder (IR). We propose a series of experiments to quantify the optimal use of both MW and IR soundings in regional Weather and Research Forecasting (WRF) model through Three-Dimensional Variation (3D-var) data assimilation scheme. Two hurricanes cases were chosen to simulate the forecast. The results showed an improvement in the hurricane track due to the assimilation of satellite retrieved temperature profiles in the hurricane environment.


  • Tuesday, 14th April, 2015

    Title: The immersed boundary method: its extensions and applications
    Speaker: Dr. Yongsam Kim, The department of Mathematics, Chung-Ang University, Korea
    Time/Place: 11:30  -  12:30
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: The immersed boundary (IB) method is a generally useful computational method for problems in which elastic materials interact with a viscous incompressible fluid. In this talk, I introduce two extensions of the IB method. The first one, which is called the penalty IB method, is introduced to take into account both the inertial and gravitational effects of the elastic materials with mass. The example problems include vortex induced vibration, 3D parachute, Rayleigh Taylor instability and its dynamic stabilization. The second extension is to deal with the case in which the immersed boundary is a porous material though which the surrounding fluid passes. As the application examples of the present method, we will show the simulation results on 2D parachute, 2D and 3D dry foam dynamics.


  • Thursday, 16th April, 2015

    Title: Discrete Conformality with Uniformization: Theory and Algorithms
    Speaker: Dr. SUN Jian, Mathematical Sciences Center, Tsinghua University, China
    Time/Place: 16:00  -  17:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: In this talk, I present a definition of discrete conformality for triangle meshes and a discrete uniformization theorem based on this definition. This discrete uniformization theorem leads to an algorithm for solving the problem of conformal parameterization. I will also present the numerical evidence showing the convergence of our discrete conformality and compare to the state of the art.


  • Friday, 17th April, 2015

    Title: 甚麼是大數據
    Speaker: Mr. Herbert Chia, Alibaba Group, China
    Time/Place: 16:00  -  17:00 (Preceded by Reception at 3:30pm)
    SWT501, Council Chamber, Shaw Tower, Shaw Campus, Hong Kong Baptist University
    Abstract: A Hong Kong native, Chia has spent his life living between the United States, Britain, Australia and Mainland China. Achieving academic success at the University of New South Wales, INSEAD and Tsinghua University, Mr. Chia's international background has influenced and informed his career enabling a uniquely multicultural eye for ecommerce innovation. Now, as a pioneer on big data and ecommerce industry.


  • Monday, 20th April, 2015

    Title: ICTS Data Assimilation Program: Numerical Weather Prediction: Chaos, Predictability, and Data Assimilation (Lecture 1)
    Speaker: Prof. Takemasa Miyoshi, Data Assimilation Research Team, RIKEN Advanced Institute for Computational Science, Japan
    Time/Place: 10:00  -  11:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: This lecture provides an introduction to numerical weather prediction with more emphasis on data assimilation. The weather system is chaotic, and data assimilation plays a central role in synchronizing a numerical simulation to the chaotic nature. Data assimilation integrates simulations (i.e., virtual world on computers) and real-world data based on statistical mathematics and brings synergy.


  • Monday, 20th April, 2015

    Title: ICTS Data Assimilation Program: Data Assimilation Toward Big Data and Post-Peta-Scale Supercomputing: A Personal Perspective (Lecture 2)
    Speaker: Prof. Takemasa Miyoshi, Data Assimilation Research Team, RIKEN Advanced Institute for Computational Science, Japan
    Time/Place: 11:30  -  12:30
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: This lecture discusses my personal perspective on the next 10-20 years of data assimilation with the future-generation sensors and post-peta-scale supercomputers. New sensors produce orders of magnitude more data than the current sensors, and faster computers enable orders of magnitude more precise simulations, namely, "Big Simulations". Data assimilation deals with the "Big Data" from both new sensors and Big Simulations. We started a "Big Data Assimilation" project, aiming to develop a revolutionary weather forecasting system to refresh 30-minute forecasts at a 100-m resolution every 30 seconds, 120 times more rapid than the current hourly-updated systems. We also investigate ensemble data assimilation using 10240 ensemble members, largest ever ensemble for the global atmosphere. Based on our experience using the Japanese 10-petaflops "K computer", we will discuss the future of data assimilation in the Big Data and Big Simulation era.


  • Monday, 20th April, 2015

    Title: 概率破玄機,統計解迷離
    Speaker: 嚴加安, 中科院數學與系統科學研究院, China
    Time/Place: 16:45  -  17:45 ( Preceded by Reception at 4:30pm)
    RRS905, Sir Run Run Shaw Building, HSH Campus, Hong Kong Baptist University
    Abstract: 報告人長期從事概率論研究,對概率統計學科的本質有些領悟,曾寫過下面 這首「悟道詩」:隨機非隨意,概率破玄機。無序隱有序,統計解迷離。本 報告通過日常生活中的一些例子,展示概率如何破玄機和統計如何解迷 離。這些例子包括「生日問題」、評估疾病診斷的確診率、設計對敏感性問 題的社會調查、辛普森悖論、統計平均的陷阱、體育競賽規則隱藏的玄機、 血標本檢驗的最優設計、如何確定抽樣調查的樣本量等。


  • Tuesday, 21st April, 2015

    Title: Principal Component Analysis: Regularization, Supervision, Application, Asymptotic
    Speaker: Prof. Haipeng SHEN, Department of Statistics & Operations Research, University of North Carolina at Chapel Hill, USA
    Time/Place: 11:00  -  12:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: multivariate data. Regularization of PCA becomes essential for high dimensionality, for example, in techniques such as functional PCA and sparse PCA. Maximizing variance of a standardized linear


  • Tuesday, 28th April, 2015

    Title: The Institute of Computational and Theoretical Studies Data Assimilation Program - Data Assimilation: Challenge for Big Data through Numerical Simulation (Lecture 1)
    Speaker: Professor Tomoyuki Higuchi, The Institute of Statistical Mathematics, Japan
    Time/Place: 11:30  -  12:30
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: Data Assimilation (DA) is a technique for a synthesis of information from a dynamic (numerical) model and observation data. It is an emerging area in earth sciences, particularly oceanography, stimulated by recent improvements in computational and modeling capabilities and the increase in the amount of available observations. Its research motivation is easily understood simply if we notice that there are too many uncertainties in the model such as the boundary condition, initial condition, unknown parameters, and unknown dynamics. DA yields an accommodation ability to make a simulation real, and the better initial and boundary conditions can be automatically obtained. In statistical methodology, DA can be formulated in the generalized state space model that draws much interest of the researchers in various domains such as the time series analysis, signal processing, and control theory? We are studying the ensemble-based sequential DA (EnSDA) methods such as the ensemble Kalman filter (EnKF) and particle filter (PF), and conducting the DA experiments in several areas?In this talk, We will give a brief explanation for the sequential DA and demonstrate a part of applications carried out by our DA research group.


  • Wednesday, 29th April, 2015

    Title: The Institute of Computational and Theoretical Studies Data Assimilation Program - Big Data and Personalization Technology: Augmentation and Reduction (Lecture 2)
    Speaker: Professor Tomoyuki Higuchi, The Institute of Statistical Mathematics, Japan
    Time/Place: 11:30  -  12:30
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: The volume of data has obviously grown at a remarkable rate. In this talk, we first give an overview on what is big data, and describe major elemental technologies for big data analysis. Next we address three key points in analytics from a viewpoint of statistics: "Curse of dimensionality," "Association and Causality," and "Interpolation and Extrapolation." We discuss an important concept in statistical methodology for big data, and explain four stages in big data usage. In addition, we demonstrate several examples of big data application carried out by Japanese industry.


  • Thursday, 30th April, 2015

    Title: Extending the Empirical Likelihood by Domain Expansion
    Speaker: Prof. TSAO Min, Mathematics Department, University of Victoria, Canada
    Time/Place: 11:30  -  12:30
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: The method of empirical likelihood is a powerful and versatile nonparametric method of inference which combines modern computing power with the classical asymptotic approach. Empirical likelihood confidence regions are known to suffer from an under-coverage problem in that the observed coverage probabilities tend to be lower than the nominal levels. In the first half of this talk, I give a brief review of the empirical likelihood and the under-coverage problem. I note that the boundedness of the domain of the empirical likelihood is a contributing factor to the under-coverage problem. I also review two high-order methods, the Bartlett correction and the adjusted empirical likelihood for dealing with the under-coverage. In the second half, I discuss a new extended empirical likelihood derived through a geometric expansion of the original empirical likelihood domain. With a larger domain, the extended empirical likelihood gives larger confidence regions with higher coverage probabilities, alleviating the under-coverage problem. The extended empirical likelihood also retains all important geometric characteristics of the original empirical likelihood.