格点数据可视化(美国站点的日降雨数据)

python 复制代码

from datetime import datetime, timedelta
from urllib.request import urlopen

import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.colors as mcolors
import matplotlib.pyplot as plt
from metpy.units import masked_array, units
from netCDF4 import Dataset

python 复制代码

nc = Dataset('20200309_conus.nc')
prcpvar = nc.variables['observation']
data = masked_array(prcpvar[:], units(prcpvar.units.lower())).to('mm')
x = nc.variables['x'][:]
y = nc.variables['y'][:]
proj_var = nc.variables[prcpvar.grid_mapping]

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globe = ccrs.Globe(semimajor_axis=proj_var.earth_radius)
proj = ccrs.Stereographic(central_latitude=90.0,
                          central_longitude=proj_var.straight_vertical_longitude_from_pole,
                          true_scale_latitude=proj_var.standard_parallel, globe=globe)

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fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(1, 1, 1, projection=proj)

# 绘制海岸线、国界线、州界线
ax.coastlines()
ax.add_feature(cfeature.BORDERS)
ax.add_feature(cfeature.STATES)

# 设置降雨量等级间隔
clevs = [0, 1, 2.5, 5, 7.5, 10, 15, 20, 30, 40,
         50, 70, 100, 150, 200, 250, 300, 400, 500, 600, 750]
# In future MetPy
# norm, cmap = ctables.registry.get_with_boundaries('precipitation', clevs)
# 单独设置cmap
cmap_data = [(1.0, 1.0, 1.0),
             (0.3137255012989044, 0.8156862854957581, 0.8156862854957581),
             (0.0, 1.0, 1.0),
             (0.0, 0.8784313797950745, 0.501960813999176),
             (0.0, 0.7529411911964417, 0.0),
             (0.501960813999176, 0.8784313797950745, 0.0),
             (1.0, 1.0, 0.0),
             (1.0, 0.6274510025978088, 0.0),
             (1.0, 0.0, 0.0),
             (1.0, 0.125490203499794, 0.501960813999176),
             (0.9411764740943909, 0.250980406999588, 1.0),
             (0.501960813999176, 0.125490203499794, 1.0),
             (0.250980406999588, 0.250980406999588, 1.0),
             (0.125490203499794, 0.125490203499794, 0.501960813999176),
             (0.125490203499794, 0.125490203499794, 0.125490203499794),
             (0.501960813999176, 0.501960813999176, 0.501960813999176),
             (0.8784313797950745, 0.8784313797950745, 0.8784313797950745),
             (0.9333333373069763, 0.8313725590705872, 0.7372549176216125),
             (0.8549019694328308, 0.6509804129600525, 0.47058823704719543),
             (0.6274510025978088, 0.42352941632270813, 0.23529411852359772),
             (0.4000000059604645, 0.20000000298023224, 0.0)]
            
cmap = mcolors.ListedColormap(cmap_data, 'precipitation')
norm = mcolors.BoundaryNorm(clevs, cmap.N)

cs = ax.contourf(x, y, data, clevs, cmap=cmap, norm=norm)

# 添加colorbar
cbar = plt.colorbar(cs, orientation='horizontal')
cbar.set_label(data.units)
# 设置标题
ax.set_title(prcpvar.long_name + ' for period ending ' + nc.creation_time)
plt.show()

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dt = datetime.utcnow() - timedelta(days=1)  # 获取过去1天的时间
url = ('http://water.weather.gov/precip/downloads/{dt:%Y/%m/%d}/nws_precip_1day_'
       '{dt:%Y%m%d}_conus.nc'.format(dt=dt))
data = urlopen(url).read()
nc = Dataset('data', memory=data)

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import xarray as xr
from xarray.backends import NetCDF4DataStore
data = xr.open_dataset(NetCDF4DataStore(nc))
data

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data.to_netcdf('{dt:%Y%m%d}_conus.nc'.format(dt=dt),'w')