使用salem处理wrfout数据，进行切片、并插值到等压面
最近，有学习到通过salem处理wrfout数据，非常的简单快捷，读取的变量也比较方面。也可以快速出图，下面简单对比一下xarray和salem读取wrfout文件的区别：
import xarray as xr from salem.utils import get_demo_file ds = xr.open_dataset(get_demo_file('wrfout_d01.nc'))
可以发现，通过xarray 读取wrfout文件的内容非常的混乱，让人摸不着头脑，无从下手
Out[4]: ds <xarray.Dataset> Dimensions: (Time: 3, south_north: 150, west_east: 150, bottom_top: 27, west_east_stag: 151, south_north_stag: 151, bottom_top_stag: 28) Coordinates: XLONG (Time, south_north, west_east) float32 ... XLONG_U (Time, south_north, west_east_stag) float32 ... XLAT_U (Time, south_north, west_east_stag) float32 ... XLAT_V (Time, south_north_stag, west_east) float32 ... XLONG_V (Time, south_north_stag, west_east) float32 ... XLAT (Time, south_north, west_east) float32 ... Dimensions without coordinates: Time, south_north, west_east, bottom_top, west_east_stag, south_north_stag, bottom_top_stag Data variables: Times (Time) |S19 ... T2 (Time, south_north, west_east) float32 ... RAINC (Time, south_north, west_east) float32 ... RAINNC (Time, south_north, west_east) float32 ... U (Time, bottom_top, south_north, west_east_stag) float32 ... V (Time, bottom_top, south_north_stag, west_east) float32 ... PH (Time, bottom_top_stag, south_north, west_east) float32 ... PHB (Time, bottom_top_stag, south_north, west_east) float32 ... Attributes: note: Global attrs removed.
而如果使用salem的话，则大大简化：
import salem ds = salem.open_wrf_dataset(get_demo_file('wrfout_d01.nc'))
Out[7]: ds <xarray.Dataset> Dimensions: (time: 3, south_north: 150, west_east: 150, bottom_top: 27) Coordinates: lon (south_north, west_east) float32 70.72 70.97 ... 117.5 117.8 lat (south_north, west_east) float32 7.789 7.829 ... 46.52 46.46 * time (time) datetime64[ns] 2008-10-26T12:00:00 ... 2008-10-26T18... * west_east (west_east) float64 -2.235e+06 -2.205e+06 ... 2.235e+06 * south_north (south_north) float64 -2.235e+06 -2.205e+06 ... 2.235e+06 Dimensions without coordinates: bottom_top Data variables: (12/14) T2 (time, south_north, west_east) float32 ... RAINC (time, south_north, west_east) float32 ... RAINNC (time, south_north, west_east) float32 ... U (time, bottom_top, south_north, west_east) float32 ... V (time, bottom_top, south_north, west_east) float32 ... PH (time, bottom_top, south_north, west_east) float32 ... ... ... PRCP (time, south_north, west_east) float32 ... WS (time, bottom_top, south_north, west_east) float32 ... GEOPOTENTIAL (time, bottom_top, south_north, west_east) float32 ... Z (time, bottom_top, south_north, west_east) float32 ... PRCP_NC (time, south_north, west_east) float32 ... PRCP_C (time, south_north, west_east) float32 ... Attributes: note: Global attrs removed. pyproj_srs: +proj=lcc +lat_0=29.0399971008301 +lon_0=89.8000030517578 +l...
可以发现，已经重命名了一些维度/坐标，定义了新的变量。
同时，salem也可以实现快速出图的功能:
ds.PRCP.isel(time=-1).salem.quick_map(cmap='Purples', vmax=5)
功能还是非常强大的，但是，注意的是：对于诊断量的计算并不完善，期待后续的跟进。
下面根据我的需求，使用的函数进行简单记录：
切片
首先是实现对于纬度切片的功能， 假设我只需要纬度：0-15°N范围的数据，可以这样实现：
ds = ds.salem.subset(corners=((100, 0.0), (210, 15)))
其中，corners=((100, 0.0), (210, 15))分布表示，提取矩形区域内的左下角的点以及右上角的点的经纬度坐标，如下图所示
经过我的测试，提取还是比较成功的，但是可能提取的结果并没有那么令人满意，总的来说问题不大。
插值到等压面
一般来说，wrf模式跑出的数据是： eta-levels，对于分析和作图来说是非常不方便的，因此salem也提供了 wrf_zlevel()、wrf_plevel() 两个函数，分布实现对于等高面和等压面的插值，这里仅示范插值到等压面的用法，使用如下所示：
DatasetAccessor.wrf_plevel(varname, levels=None, fill_value=nan, use_multiprocessing=True) ds.isel(time=0).salem.wrf_plevel('V', levels=[850.])
“U”：你要插值的变量
levels：你要插值的气压层
通过上述两个方法，就能实现切片和插值的需求啦~
官网链接：salem
