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我的目标是使用 mpcalc.frontogenesis 方法计算给定压力水平下的锋面发生。

我已阅读 NOMADS 的相关数据：

t7 = data['temp'].sel(lev=700.0,lat=lats,lon=lons)
u7 = data['u'].sel(lev=700.0,lat=lats,lon=lons).squeeze()*1.94384449
v7 = data['v'].sel(lev=700.0,lat=lats,lon=lons).squeeze()*1.94384449
h7 = data['gph'].sel(lev=700.0,lat=lats,lon=lons).squeeze()

计算相关坐标/网格间距值：

x, y = t7.metpy.coordinates('x', 'y')
dx, dy = mpcalc.lat_lon_grid_deltas(lons, lats)
lat, lon = xr.broadcast(y, x)

用单位标记每个数组：

t7u = t7.values * units.degC
u7u = u7.values * units.knots
v7u = v7.values * units.knots

然后我去计算FGEN：

h7_fgen = mpcalc.frontogenesis(t7u,u7u,v7u,dx,dy,dim_order='xy')

这会产生以下错误：

ValueError: operands could not be broadcast together with shapes (359,219) (358,220)

温度、u、v数组的形状分别为：

(220, 360)
(220, 360)
(220, 360)

因此，虽然 dx 和 dy 的长度可以理解为 n-1，但我不确定为什么这不起作用。

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1 回答 1

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如果直接从 NOMADS GFS 源中提取数据，看起来您的数据应该是 yx 维度顺序（这也是 MetPylat_lon_grid_deltas函数假定的顺序）。因此，我认为脚本中关于维度排序的内容有些混乱。解决这个问题，并额外使用 MetPy 的一些 xarray 集成功能来清理实现，我们有：

预 MetPy v1.0

# Imports
import metpy.calc as mpcalc
from metpy.units import units
import xarray as xr

# Open data and parse for CRS
data = xr.open_dataset('http://nomads.ncep.noaa.gov/dods/gfs_0p25_1hr/gfs20201230/gfs_0p25_1hr_12z').metpy.parse_cf()

# Subset out the desired data
# Note that this leaves in time, but that can also be selected out if only
# one time is needed and you want to have smaller data arrays to compute on
t7 = data['tmpprs'].sel(lev=700.0,lat=slice(15, 70),lon=slice(220, 310))
u7 = data['ugrdprs'].sel(lev=700.0,lat=slice(15, 70),lon=slice(220, 310)).squeeze()
v7 = data['vgrdprs'].sel(lev=700.0,lat=slice(15, 70),lon=slice(220, 310)).squeeze()
h7 = data['hgtprs'].sel(lev=700.0,lat=slice(15, 70),lon=slice(220, 310)).squeeze()

# Clean up the xarray metadata for MetPy's use, since provided data is missing
# the units attribute
t7.attrs['units'] = 'K'
u7.attrs['units'] = v7.attrs['units'] = 'm/s'
h7.attrs['units'] = 'm'

# Get grid deltas
dx, dy = mpcalc.grid_deltas_from_dataarray(t7)

# Compute frontogenesis
h7_fgen = mpcalc.frontogenesis(t7, u7, v7, dx, dy)

发布 MetPy v1.0

在 MetPy v1.0 中，网格参数自动从 DataArray 输入中提取，因此，这可以简化为

# Imports
import metpy.calc as mpcalc
from metpy.units import units
import xarray as xr

# Open data and parse for CRS
data = xr.open_dataset('http://nomads.ncep.noaa.gov/dods/gfs_0p25_1hr/gfs20201230/gfs_0p25_1hr_12z').metpy.parse_cf()

# Subset out the desired data
t7 = data['tmpprs'].sel(lev=700.0,lat=slice(15, 70),lon=slice(220, 310))
u7 = data['ugrdprs'].sel(lev=700.0,lat=slice(15, 70),lon=slice(220, 310)).squeeze()
v7 = data['vgrdprs'].sel(lev=700.0,lat=slice(15, 70),lon=slice(220, 310)).squeeze()
h7 = data['hgtprs'].sel(lev=700.0,lat=slice(15, 70),lon=slice(220, 310)).squeeze()

# Clean up the xarray metadata for MetPy's use, since provided data is missing
# the units attribute
t7.attrs['units'] = 'K'
u7.attrs['units'] = v7.attrs['units'] = 'm/s'
h7.attrs['units'] = 'm'

# Compute frontogenesis
h7_fgen = mpcalc.frontogenesis(t7, u7, v7)
于 2020-12-30T18:32:25.813 回答