#WRF

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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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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