The weather is getting warm

大家好 今天带来一篇关于全球气候的report 由于全球气候变化，许多地区的海平面多年来一直在上升。从之前构建的访谈中，我们可以发现全球变暖是造成这一现象的关键因素。然而，如何挖掘这一现象背后的日期对我们来说似乎很重要，因为许多有意义的发现可以从日期中获得。因此，有必要对如何用有效的方法挖掘数据进行研究。 下面一起来看看

In this part, we mainly talks about the research method which is applied to obtain the relevant data and prediction of the sea level in Shanghai region. 
As is well known to all, the sea level in many regions has been rising for many years due to the global climate change. From the interview constructed before, we could find global warming is the key factor contributing to this phenomenon. However, how to dig the date behind the phenomenon seems important for us because many meaningful findings can be obtained from the date. So it is necessary to do some researches on how to dig the date with some efficient methods. 
Before applying detailed research methods to analyze the date, the collection of the relevant data is necessary in the first place. As for the sea level in Shanghai region, in order to obtain more accurate findings, some types of data are needed such as altimetric data, submarine topography data, tide station data, ground subsidence data and so on. The collection can be finished by means of consulting the relevant database which is shared on the website. After getting these data, the next step is to pretreat them with some analysis softwares such as SPSS, Matlab, Vensim, Arcgis and so on. In that case, some unauthentic data may be removed from the initial sources. Then, some research methods can be applied in digging the information behind the treated data. 

Many research methods are proposed in dealing with the analysis about sea level. The first on is Wavelet Transform Method which gains much attention in recent years. It originates from functional analysis, Fourier analysis, spline analysis and harmonic analysis. Wang (2013) used Wavelet Transform Method to analyze the sea-level change in Japanese waters. Based on his study in his paper, Wavelet Transform Method was mainly applied to get rid of some the negative effects brought by the local loss of the relevant data. In that case, the analysis about the sea-level change in Japanese waters could be more accurate. The second method is Winters’ exponential smoothing method in time series. In this method, the estimation for the data is nonlinear, and its goal is to minimum the mean square error between the predicted value and accurate one. Besides, this method is useful to treat those date which differ with the seasonal factors in time series. Wang (2013) applied Winters’ exponential smoothing method to obtain the prediction of the sea-level change in Shanghai region from 2007 to 2015. In his study, he found that the sea level would have 140-150 mm rise than that in 2006. Figure 1 depicts the prediction with Winters’ exponential smoothing method (Wang, 2013).
 
Figure 1. The fitting curve for the sea level in China East Coast

The third method is Classic Spectral Estimation Method which belongs to spectral analysis. It can be divided into direct method and indirect method, respectively. In direct method, apply Fourier transformation to the samples to obtain the power spectrum of the data. While, in indirect method, execute the estimation of autocorrelation among the samples first. Then, get the power spectrum of the data by means of Fourier transformation. Duan et al. (2014) applied Spectral Estimation Method to predict the sea-level change with the data from Tanggu tide gauge. Besides, he mixed Wavelet Transform Method with Spectral Estimation Method in order to remedy the drawbacks existing in both methods in order to lift the prediction accuracy. Apart from the methods listed above, Specific Volume Height Calculation Method is often adopted to calculate the specific volume of the sea water. Tabata et al. (1986 first established the model used to calculate the specific volume. Chen (2009) applied this method to calculate the thermosteric component in North Pacific Ocean. See figure 2 in detail. 

 
Figure 2. Thermosteric component in North Pacific Ocean (Chen, 2009)
Based on the introduction of the relevant research methods, we can find many different methods can be used to predict the sea-level change. It is obvious that some inherent drawbacks and limitations exist among these methods. So mixture of different methods are often adopted such as the proposed method in Duan et al. (2014) above. 
As for the research method used in this report, we may apply Wavelet Transform Method to predict the sea-level change in Shanghai region due to its practical convenience and high computational efficiency. The data are collected through some shared database including China Oceanic Administration, Shanghai Water Conservancy Bureau, and some global climate shared database. After obtaining the expected data, the following section of this report is about the result of the data treatment.


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