读取远程数据集（NBM）时出现xarray MissingDimensionsError

debugcn 发表于 Dev

雷·贝尔

当读取NBM的远程数据集（https://vlab.ncep.noaa.gov/web/mdl/nbm）时，我得到一个xarray.core.variable.MissingDimensionsError。我确定我在中缺少了一些arg设置open_dataset。

您可以在此处查看数据的结构：https : //thredds-jumbo.unidata.ucar.edu/thredds/dodsC/grib/NCEP/NBM/CONUS/TwoD.html。完整的结构显示在这里使用ncdump -h https://thredds-jumbo.unidata.ucar.edu/thredds/dodsC/grib/NCEP/NBM/CONUS/TwoD

使用的var time1：

Precipitation_type_surface_probability_between_1p0_and_2

import xarray as xr
url = "https://thredds-jumbo.unidata.ucar.edu/thredds/dodsC/grib/NCEP/NBM/CONUS/TwoD"
ds = xr.open_dataset(url)

如果删除此变量，则转到下一次暗淡

ds = xr.open_dataset(url, drop_variables="time1")
xarray.core.variable.MissingDimensionsError: 'time2' has more than 1-dimension and the same name as one of its dimensions ('reftime4', 'time2'). xarray disallows such variables because they conflict with the coordinates used to label dimensions.

完整回溯

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/ray.bell/miniconda/envs/test_env/lib/python3.8/site-packages/xarray/backends/api.py", line 575, in open_dataset
    ds = maybe_decode_store(store, chunks)
  File "/Users/ray.bell/miniconda/envs/test_env/lib/python3.8/site-packages/xarray/backends/api.py", line 471, in maybe_decode_store
    ds = conventions.decode_cf(
  File "/Users/ray.bell/miniconda/envs/test_env/lib/python3.8/site-packages/xarray/conventions.py", line 600, in decode_cf
    ds = Dataset(vars, attrs=attrs)
  File "/Users/ray.bell/miniconda/envs/test_env/lib/python3.8/site-packages/xarray/core/dataset.py", line 630, in __init__
    variables, coord_names, dims, indexes, _ = merge_data_and_coords(
  File "/Users/ray.bell/miniconda/envs/test_env/lib/python3.8/site-packages/xarray/core/merge.py", line 467, in merge_data_and_coords
    return merge_core(
  File "/Users/ray.bell/miniconda/envs/test_env/lib/python3.8/site-packages/xarray/core/merge.py", line 594, in merge_core
    collected = collect_variables_and_indexes(aligned)
  File "/Users/ray.bell/miniconda/envs/test_env/lib/python3.8/site-packages/xarray/core/merge.py", line 278, in collect_variables_and_indexes
    variable = as_variable(variable, name=name)
  File "/Users/ray.bell/miniconda/envs/test_env/lib/python3.8/site-packages/xarray/core/variable.py", line 154, in as_variable
    raise MissingDimensionsError(
xarray.core.variable.MissingDimensionsError: 'time1' has more than 1-dimension and the same name as one of its dimensions ('reftime', 'time1'). xarray disallows such variables because they conflict with the coordinates used to label dimensions.

您可以在本地测试

wget https://ftp.ncep.noaa.gov/data/nccf/com/blend/prod/blend.20210214/00/core/blend.t00z.core.f001.co.grib2

里奇·西德尔（Rich Signell）

如果要从Xarray中的THREDDS预测模型运行集合（FRMC）虚拟数据集中访问这些“ TwoD ”数据集，则可以首先使用NetCDF库对其进行切片，然后将所切片的变量传递给Xarray。而且，如果用Dask包装NetCDF变量，则可以保持懒惰。

这是为HRRR的后60个值提取“最佳时间序列”的示例，但是使用1小时的预测数据（而不是使用FMRC最佳时间序列的默认“分析” 0小时预测）：

import netCDF4
import xarray as xr
from dask import array as da
import hvplot.xarray

url = 'https://thredds.unidata.ucar.edu/thredds/dodsC/grib/NCEP/HRRR/CONUS_2p5km/TwoD'
nc = netCDF4.Dataset(url)
arr = da.from_array(nc['Temperature_height_above_ground'])
tau = 1
da = xr.DataArray(arr[-60:,tau,0,:,:], dims=['time','y','x'], name='temp')

这是证明它起作用的时间序列图：