我一直想对茶叶中复杂的化学成分充满兴趣。实验室有同事需要测量生物气溶胶样品（如孢子、真菌等），借助这次协助其调试维护质谱仪器的机会，开展了一项课外探索：使用高分辨率质谱方法，探索不同类型茶叶在分子层面是否具有可辨识的特征。
本次尝试使用非靶向直接注入分析，结合Orbitrap与TOF两台质谱，评估我构建的分析流程在实际复杂样品中的表现。
我一直想对茶叶中复杂的化学成分充满兴趣。实验室有同事需要测量生物气溶胶样品（如孢子、真菌等），借助这次协助其调试维护质谱仪器的机会，开展了一项课外探索：使用高分辨率质谱方法，探索不同类型茶叶在分子层面是否具有可辨识的特征。
本次尝试使用非靶向直接注入分析，结合Orbitrap与TOF两台质谱，评估我构建的分析流程在实际复杂样品中的表现。
本文持续更新个人网络搜集的各类数据集，包括中国/世界行政区划、自然地理、气象水文、空气质量及社会经济数据等。
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:
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。
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.
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, PM2.5. 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.
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
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。
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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