Python-Practical-Application-on-Climate-Variability-Studies 项目推荐

Python-Practical-Application-on-Climate-Variability-Studies 项目推荐

Python-Practical-Application-on-Climate-Variability-Studies This tutorial is a companion volume of Matlab versionm but add more. Main objective is the transference of know-how in practical applications and management of statistical tools commonly used to explore meteorological time series, focusing on applications to study issues related with the climate variability and climate change. This tutorial starts with some basic statistic for time series analysis as estimation of means, anomalies, standard deviation, correlations, arriving the estimation of particular climate indexes (Niño 3), detrending single time series and decomposition of time series, filtering, interpolation of climate variables on regular or irregular grids, leading modes of climate variability (EOF or HHT), signal processing in the climate system (spectral and wavelet analysis). In addition, this tutorial also deals with different data formats such as CSV, NetCDF, Binary, and matlab'mat, etc. It is assumed that you have basic knowledge and understanding of statistics and Python. 项目地址: https://gitcode.com/gh_mirrors/py/Python-Practical-Application-on-Climate-Variability-Studies

1. 项目的基础介绍和主要的编程语言

Python-Practical-Application-on-Climate-Variability-Studies 是一个专注于气候变化和气候变异性研究的实用教程项目。该项目的主要编程语言是 Python，旨在通过实际应用和统计工具的管理，帮助用户深入理解气象时间序列的分析方法。项目的目标是传授如何在气候变异性和气候变化相关问题上应用这些工具的技能。

2. 项目的核心功能

该项目涵盖了多个核心功能，主要包括：

• 时间序列分析：包括均值估计、异常值计算、标准差、相关性分析等。
• 气候指数估计：如Niño 3指数的估计。
• 时间序列分解与去趋势：对单个时间序列进行去趋势和分解。
• 滤波与插值：对气候变量在规则或不规则网格上的插值处理。
• 气候变异性的主导模式分析：如EOF（经验正交函数）或HHT（希尔伯特-黄变换）。
• 信号处理：包括频谱分析和连续小波变换。
• 数据格式处理：支持CSV、NetCDF、二进制和Matlab的.mat文件等多种数据格式。

3. 项目最近更新的功能

最近更新的功能包括：

• 新增了EOF分析：对全球SST（海表温度）和hgt500（500百帕高度场）的EOF分析。
• 连续小波变换：增加了对NINO3 SST的连续小波变换示例。
• 气候变量插值：改进了对2D场在规则和不规则网格上的插值方法。
• 数据可视化：新增了全球洪水风险图的可视化功能。
• 时间序列分解：引入了基于集合经验模态分解（EEMD）的东北太平洋海表温度分析。

通过这些更新，项目进一步增强了其在气候变异性和气候变化研究中的实用性和教学价值。

Python-Practical-Application-on-Climate-Variability-Studies This tutorial is a companion volume of Matlab versionm but add more. Main objective is the transference of know-how in practical applications and management of statistical tools commonly used to explore meteorological time series, focusing on applications to study issues related with the climate variability and climate change. This tutorial starts with some basic statistic for time series analysis as estimation of means, anomalies, standard deviation, correlations, arriving the estimation of particular climate indexes (Niño 3), detrending single time series and decomposition of time series, filtering, interpolation of climate variables on regular or irregular grids, leading modes of climate variability (EOF or HHT), signal processing in the climate system (spectral and wavelet analysis). In addition, this tutorial also deals with different data formats such as CSV, NetCDF, Binary, and matlab'mat, etc. It is assumed that you have basic knowledge and understanding of statistics and Python. 项目地址: https://gitcode.com/gh_mirrors/py/Python-Practical-Application-on-Climate-Variability-Studies