課程名稱：數據的物理分析
地 點：西康路1号伟德国际1916备用网址電氣館507
授課時間：5月28日—6月1日；6月4日—6月8日（周一到周五，為期兩周）
下午2:00—5:00（每天授課3小時）
授課對象：青年科研人員和研究生
課程簡介：
數據分析是任何科學和工程學科研究的基礎。科學研究中的數據分析最終目的是為了解析隐藏在數據中的物理系統的演變特征，但大多數的數據分析方法更側重于數學方面而不是物理方面。這種數據分析方法給理解物理系統帶來了許多副作用，這主要是因為，分析方法作為黑箱，盲目地應用于數據，不考慮在某一具體問題上的适用性而作機械的應用。本門課程将介紹數據分析中物理觀點，通過對傳統分析方法，特别是對氣候和海洋研究中的常用方法仔細分析，揭示其優缺點和可适用性。由此進一步讨論數據分析的物理觀念和約束數據分析的一些基本物理定律，着重介紹滿足這些物理約束的黃變換（希爾伯特-黃變換）的一系列方法，實現從數據的數學分析向物理分析的跨越。
授課專家：
吳召華副教授于1988年在南京大學大氣科學系獲得學士學位，1988-1991年在中科院大氣物理研究所獲得碩士學位，1998年在美國華盛頓大學大氣科學系獲得博士學位，2000-2001年在美國馬裡蘭大學海洋-陸地-大氣研究中心做博士後研究，2001-2005年在美國華盛頓東南大學計算機科學系擔任講師，2002-2008年在美國馬裡蘭大學海洋-陸地-大氣研究中心工作，2009至今在美國弗羅裡達州立大學氣象學系以及弗羅裡達州立大學海洋-大氣預測研究中心擔任副教授。吳召華副教授的主要研究領域為大氣和氣候動力學，尤其擅長研究地球氣候系統變化的基礎理論。他對氣象和氣候資料的分析方法研究也有一定的貢獻，他提出了一個改進的數據分析方法：經驗模式分解（EMD）和噪聲數據分析方法（NADA），為人們加深對氣候變率的理解和全面認識全球氣候變化提供了依據。
Objective:
The course discusses advantages and drawbacks of various types of the data analysis methods used to interpret data sets in the Earth, ocean, and atmospheric science. This is a tools class: the objective is to provide a working knowledge of the analysis tools. Emphasis is placed on the application of the tools discussed in class to the analysis of climate data.
Course Content Summary:
The data analysis has been used in every scientific or engineering field. Although the ultimate goal of data analysis in scientific research is to understand physical systems hidden in data, most of data analysis methods emphasize more on the mathematical aspects than on the physical aspects. This approach has brought many side-effects to understanding physical systems through data analysis, e.g., an analyzer taking an analysis method as a black box and applying it blindly to data without considering the deficiencies of the method and questioning its applicability.
In this course, I will present physical perspectives of data analysis. By introducing and disseminating the traditional data analysis methods, especially those widely used in climate sciences, I will introduce the popular methods based on well-established mathematical rules that are used widely in climate literatures; discuss the applicability of these methods to climate data; and expose their limitations. From there, I will introduce the physical perspective of data analysis in which a few most fundamental physical principles serve as the constraints of data analysis. In this part, we will take advantages of the recently developed Huang Transform (HT) (formerly Hilbert-Huang Transform, HHT), a novel and rapidly spreading adaptive and local data analysis method that has already been widely used with great success in many scientific and engineering fields, to illustrate the physical aspects of data analysis, especially the temporal locality of analysis and the "physicapability" of an analysis method: the capability of the method of isolating physically meaningful signals in data.
The course will cover both fundamental and advance concepts and techniques of data analysis, at a level comprehensible to graduate students. Through the class, home works and projects will be assigned to help to understand class content as well as to facilitate research in one’s own interests. For this reason, it is welcome that students bring in their own research project into the class.
Course Contents (tentative):
1.Data, data processing, and data analysis: An overview
Part I: Global domain analysis
2.Statistical descriptions of data
3.Methods with a priori determined basis (Fourier transform and wavelets)
Part II: Adaptive but global domain data analysis
4.Adaptive basis
5.Traditional analysis of temporal-spatial structures of data
Part III: Adaptive and local domain data analysis: Huang transform
6.Nonlinearity and non-stationarity of data
7.Adaptive and local analysis of data
8.Empirical Mode Decomposition
9.Ensemble Empirical Mode Decomposition
10.Multidimensional Ensemble Empirical Mode Decomposition
11.Analysis the evolution of temporal-spatial structures
12.Holo-spectral Analysis
Part IV: Optional topics
13.Trend and detrending
14.Reference frames for climate anomaly
