在Python xarray中将10年以上的季节性数据升级为每日数据 [英] Upsample seasonal data to daily data over 10 years in Python xarray

问题描述

我有一个netCDF文件来获取季节性数据.加载到数据集后，它包含season，latitude和longitude维度.

I have a netCDF file for seasonal data. When loaded into Dataset, it contains season, latitude and longitude dimensions.

print(dataset_seasonal_nc)


<xarray.Dataset>
Dimensions:               (latitude: 106, longitude: 193, season: 4)
Coordinates:
  * latitude              (latitude) float32 -39.2 -39.149525 ... -33.9
  * longitude             (longitude) float32 140.8 140.84792 ... 150.0
  * season                (season) object 'DJF' 'JJA' 'MAM' 'SON'
Data variables:
    FFDI 95TH PERCENTILE  (season, latitude, longitude) float64 dask.array<shape=(4, 106, 193), chunksize=(4, 106, 193)>

我需要将seasnonal数据上采样为10年的每日数据(例如，从1972年到1981年，总共3653天).这意味着上采样的数据集对象应为:

I need to upsample the seasnonal data to daily data for 10 years (for example from 1972 to 1981, 3653 days in total). This means the upsampled Dataset object should be:

<xarray.Dataset>
Dimensions:    (latitude: 106, longitude: 193, time: 3653)
Coordinates:
  * latitude   (latitude) float32 -39.2 -39.149525 ... -33.950478 -33.9
  * longitude  (longitude) float32 140.8 140.84792 140.89584 ... 149.95209 150.0
  * time       (time) datetime64[ns] 1972-01-01T00:00:00 1972-01-02T00:00:00 1972-01-03T00:00:00 ... 1981-12-30T00:00:00 1981-12-31T00:00:00
Data variables:
    FFDI 95TH PERCENTILE  (time, latitude, longitude) float64 dask.array<shape=(3653, 106, 193), chunksize=(3653, 106, 193)>

一天的变量应与该天的季节的变量相同.这意味着1972-01-01、1972-02-02和1972-02-28的值应与DJF的季节；和1972-04-01、1972-05-02和1972-05-31应该具有与MAM季节相同的值.

The variable for a day should be the same as the variable for the season that the day falls in. This means, 1972-01-01, 1972-02-02 and 1972-02-28 should have the same value as the season DJF has; and 1972-04-01, 1972-05-02 and 1972-05-31 should have the same value as the season MAM has.

我试图使用数据集的resample函数:

I was trying to use the Dataset's resample function:

upsampled = dataset_seasonal_nc.resample(time='D').ffill()

但这给了我以下错误:

...\venv\lib\site-packages\xarray\core\dataset.py", line 896, in _construct_dataarray
    variable = self._variables[name]
KeyError: 'time'

推荐答案

对于

This seems like a good candidate for xarray's advanced label-based indexing. I think something like the following should work:

import pandas as pd

times = pd.date_range('1972', '1982', freq='D', closed='left')
time = xr.DataArray(times, [('time', times)])
upsampled = dataset_seasonal_nc.sel(season=time.dt.season)

此处time.dt.season是一个DataArray，表示与上采样的数据集中的每次相关的季节标签:

Here time.dt.season is a DataArray representing the season labels associated with each time in your upsampled Dataset:

In [16]: time.dt.season
Out[16]:
<xarray.DataArray 'season' (time: 3653)>
array(['DJF', 'DJF', 'DJF', ..., 'DJF', 'DJF', 'DJF'],
      dtype='|S3')
Coordinates:
  * time     (time) datetime64[ns] 1972-01-01 1972-01-02 1972-01-03 ...

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