Xarray provides pandas-level convenience for working with ultidimensional data. Xarray has two fundamental data structure: a DataArray, which holds a single multi-dimensional variable and its coordinates;
a Dataset, which holds multiple variables that potentially share the same coordinates.

Moreover, a DataArray has four attributes:

• values: a numpy.ndarray holding the array’s values (矩阵数值，例如地表温度具体数值)
• dims: dimension names for each axis (e.g., (‘x’, ‘y’, ‘z’)) (维度名称，如经度、纬度、垂直分层、时间等)
• coords: a dict-like container of arrays (coordinates) that label each * point (e.g., 1-dimensional arrays of numbers, datetime objects or strings) (各维度坐标体系，如时间序列)
• attrs: an OrderedDict to hold arbitrary metadata (attributes) (对各属性的描述)

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提要：定义嵌套网格是运行WRF模型的重要工作。本文基于Python 3.7，在Salem工具包基础上加以改进，结合Cartopy对中国国界线进行替换，同时实现修改默认底图。

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部分互联网资料仅提供在线预览，无法直接下载。若预览系统设计欠佳，或限于网速内容加载过慢，都会对阅读体验造成严重影响。在此分别以国家自然基金研究报告和学位论文为例，介绍两种不同的下载合成方法，供有需要的老师同学们参考。欢迎大家提出宝贵意见。

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When you’ve got the all the files from WRF simulations, you might want to merge them without the spin-up frames (to reach a balanced state with the boundary conditions, i.e., 12 hours for a 5-day simulation). Meanwhile, the variables/grids which are not focused on can be ignore. Therefore, a general workflow in pythonic way is presented. I will also rewritten this function as my first Python Package. Please note the updates on my website.

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I’m writing today about downloading, handling, and plotting satellite derived air pollution maps with cartopy and fiona using Python. One key task in this post is to clip a raster-like (2-d array) dataset with a polygon in pure Python environment (i.e., no need for ArcGIS or QGIS GUI-based software).

The satellite sensor can offer critical supplementary data of several atmospheric species, e.g., SO₂, NO₂, PM_2.5. Comparaing to ground-based monitoring which might be sparse in some areas (e.g., Africa, South America, oceans), the satellite observation offers a full picture for better understanding the spatiotemporal patterns of some air pollutants.

Below is an excerpt of a NO₂ column maps within Chengyu urabn agglomeration in China.

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In this post, I present some simple programs written in Python for post-processing the flexpart-wrf output.

It mainly contains several aspects, data merging, data processing and data visualization. I will also show some tips tp creat self-defined colormaps for nice plots.

PS: All th codes are also uploaded in my GitHub respority PyFlex

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以前处理WRF等气象模型的输出文件，总是下载到本地电脑做后处理分析。由于计算量不断增加，模拟生成的文件往往会很大。因而，我考虑直接在服务器中处理数据。本来是很容易的事情，却因为课题组服务器的系统版本较旧，在安装有关工具时耗去了不少时间。在此记录我的探索过程。

气象模拟文件的后处理PartA: 曲折的安装

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FLEXPART is a Lagrangian particle dispersion model (LPDM) developed by Norwegian Institute of Air Research, Norway. It allows researchers to simulate the long-range transportation, diffusion, dry/wet deposition processes of atmospheric spcecies from their sources. It also can be utilized for backward calculation based on the observation of receptor to anaysis source-receptor relationships.

This model is coded following the Fortran 95 standard, and can be freely download from the page here. Flexpart 8.x/9.x is easy for compilation following the offical reference. I noticed that netCDF-format output (which would make the post-processing easier compared to the original binary output files) has been merged in the newer veision. Therefore, I tried to compile FLEXPART 10.0 beta version in the Linux system, while lots of issues appeared. My installing steps are listed as follows

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全国空气质量历史数据是大气环境研究的重要基础资料，而我国官方平台仅提供实时监测信息，而且还只支持IE浏览器(╮(╯_╰)╭)。

我曾尝试采用爬虫工具获取并存储逐时信息，但限于权限，未能在实验室服务器上连续长时间运行。互联网上直接提供数据或API端口的网站有很多，如环境云，PM25.in, 青悦开放环境数据中心等。其中，beijingair最具分享精神,提供了2013年至今的详尽历史数据，且完全免费。

由衷感谢@王_晓磊的出色工作和无私分享，极大地推动了我国环境数据的公开与透明。其数据格式为每日一份csv文件存储当日所有站点/城市的逐时信息。在长时间尺度的数据分析时，需逐一阅读各原始文件。此处，我考虑将全年数据文件整合为多维度数据存储格式(HDF5)文件，便于调用和处理。

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提要：此文介绍利用Python语言处理NASA MODIS火点数据（Global Monthly Fire Location Product，MCD14ML），可实现的基本功能包括：（1）特定时期的火点信息提取；（2）特定区域内的火点信息提取；（3）火点密度空间分布的计算与可视化表达

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