从多维NETCDF创建[时间，LON，LAT]维度

发布于 2025-01-25 10:32:09 字数 1157 浏览 3 评论 0原文

我有来自Jason具有不寻常维度的卫星高度学数据（点，未握住）：时间和suse_index。我想拥有一个[时间，LON，LAT]维度，而不会影响数据集。目前，LAT和LON是坐标，我尝试了 this ，但是我们有不同的数据集。

import xarray as xr
import pandas as pd

all_lat = xr.open_dataset('dataset.nc')

xarray.Dataset
Dimensions: time: 3031 meas_ind: 20
Coordinates:
**time** (time) datetime64[ns] 2002-01-23T02:26:13.761423824 .....
**meas_ind** (meas_ind) int8 0 1 2 3 4 5 6 ... 14 15 16 17 18 19
lat (time) float64 ...
lon (time) float64 ...
lon_20hz (time, meas_ind) float64 ...
lat_20hz (time, meas_ind) float64 ...
Data variables: (141)
Attributes: (25)

此外，我只需要SSHA变量，该变量在LAT和LON是坐标时会索引到时间。

xarray.DataArray'ssha' time: 3031
array([ 0.011, -0.019,  0.026, ...,    nan,    nan,    nan], dtype=float32)
Coordinates:
time (time) datetime64[ns] 2002-01-23T02:26:13.761423824 .....
lat (time) float64 -66.15 -66.15 ... 66.15 66.15
lon (time) float64 34.11 34.24 34.37 ... 199.0 199.2

我在这里上传了数据集：

原文

I have this satellite altimetry data (points, not gridded) from Jason that has unusual dimensions: time and meas_index. I would like to have a [time, lon, lat] dimension without affecting the dataset. Currently, the lat and lon are coordinates and I have tried this but we have different datasets.

import xarray as xr
import pandas as pd

all_lat = xr.open_dataset('dataset.nc')

xarray.Dataset
Dimensions: time: 3031 meas_ind: 20
Coordinates:
**time** (time) datetime64[ns] 2002-01-23T02:26:13.761423824 .....
**meas_ind** (meas_ind) int8 0 1 2 3 4 5 6 ... 14 15 16 17 18 19
lat (time) float64 ...
lon (time) float64 ...
lon_20hz (time, meas_ind) float64 ...
lat_20hz (time, meas_ind) float64 ...
Data variables: (141)
Attributes: (25)

In addition, I only need the ssha variable, which is indexed to time while the lat and lon are coordinates.

xarray.DataArray'ssha' time: 3031
array([ 0.011, -0.019,  0.026, ...,    nan,    nan,    nan], dtype=float32)
Coordinates:
time (time) datetime64[ns] 2002-01-23T02:26:13.761423824 .....
lat (time) float64 -66.15 -66.15 ... 66.15 66.15
lon (time) float64 34.11 34.24 34.37 ... 199.0 199.2

I uploaded the dataset here: link

分享到QQ

分享到微博