WRF

WRF | WRF散度计算步骤及Python可视化实现

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环境设置

依赖

wrf-python metpy

数据处理

import numpy as np
import xarray as xr
import metpy.calc as mpcalc
from metpy.interpolate import cross_section
from wrf import getvar, interplevel, to_np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
from netCDF4 import Dataset
from metpy.units import units
# 读取WRF输出文件
file_path = "/home/mw/input/typhoon9537/wrfout_d01_2019-08-08_19_00_00"
ncfile = Dataset(file_path)

# 获取需要的变量
u = getvar(ncfile, "ua")  # 纬向风 (m/s)
v = getvar(ncfile, "va")  # 经向风 (m/s)
p = getvar(ncfile, "pressure")  # 气压 (hPa)
z = getvar(ncfile, "z")
lat = getvar(ncfile, "lat")
lon = getvar(ncfile, "lon")

# 计算网格间距
dx, dy = mpcalc.lat_lon_grid_deltas(lon.values, lat.values)

# 初始化散度数组
div = np.zeros_like(u.values)

# 逐层计算散度
for k in range(u.shape[0]):
    div[k, :, :] = mpcalc.divergence(
        u[k, :, :] * units('m/s'), 
        v[k, :, :] * units('m/s'),
        dx=dx, 
        dy=dy
    )

# 将散度数组转为xarray.DataArray
div_da = xr.DataArray(
    div,
    dims=u.dims,
    coords=u.coords,
    attrs={'units': 's^-1', 'description': 'divergence'}
)
div_da

取剖面 from wrf import (getvar, to_np, get_cartopy, latlon_coords, vertcross, cartopy_xlim, cartopy_ylim, interpline, CoordPair) cross_start = CoordPair(lat=32, lon=110) cross_end = CoordPair(lat=28, lon=115) # 计算垂直剖面 div_cross = vertcross(div_da, p, wrfin=ncfile, start_point=cross_start, end_point=cross_end, latlon=True, meta=True) div_cross

绘图

# 绘制剖面图
fig = plt.figure(figsize=(12, 8))
ax = plt.axes()

# 绘制填色图
contour = ax.contourf(div_cross['cross_line_idx'], div_cross['vertical'], 
                     div_cross, levels=20, cmap='coolwarm')

# 添加色标
plt.colorbar(contour, ax=ax, label='Divergence (s$^{-1}$)')

# 设置坐标轴
ax.set_yscale('log')
ax.set_ylim(1000, 100)
ax.set_xlabel('nx')
ax.set_ylabel('Pressure (hPa)')
ax.set_title('Divergence Vertical Cross-Section')

plt.show()

细化绘图

小结

本来想基于metpy的插值函数做个两种方法做个对比，但是其函数对于数据的要求比较苛刻，不太适合wrf数据 还是放弃了