Contents

Dependencies
Imports
Retrieving sample data from GeoCAT-datafiles
Parsing the data with xarray
Using climatology_average
- Related NCL Functions
Adjusting Parameters

A More Complicated Example: Calculating Climatological Averages¶

The previous example showed one of the most basic GeoCAT-comp functions. This example showcases how some functions in GeoCAT-comp have a variety of options that can be selected using keyword arguments. The particular function we’ll be looking at is climatology_average.

The datafile we’re using contains surface temperature data at one location generated by numerous weather models in a model ensemble. The temporal resolution is 6 hours from January 1st 1990 to January 1st 1996. We want to take the data from each model and summarize it. We can do this in a few different ways with climatology_average. We can find the daily, monthly, or seasonal climatological averages.

Dependencies¶

geocat-comp
geocat-datafiles
xarray
nc-time-axis

Imports¶

[1]:

from geocat.comp import climatology_average

import geocat.datafiles as gdf
import xarray as xr
import nc_time_axis

Downloading file 'registry.txt' from 'https://github.com/NCAR/GeoCAT-datafiles/raw/main/registry.txt' to '/home/docs/.cache/geocat'.

Retrieving sample data from GeoCAT-datafiles¶

GeoCAT-datafiles is a software package that makes it simple to download datafiles from the pacakge’s GitHub repository. The repository holds data files of different types such as NetCDF and GRIB. They are used for GeoCAT tutorials, demonstrations, and test cases. You won’t need to use GeoCAT-datafiles when working with your own data.

[2]:

nc_file = gdf.get('netcdf_files/atm.20C.hourly6-1990-1995-TS.nc')
nc_file

Downloading file 'netcdf_files/atm.20C.hourly6-1990-1995-TS.nc' from 'https://github.com/NCAR/GeoCAT-datafiles/raw/main/netcdf_files/atm.20C.hourly6-1990-1995-TS.nc' to '/home/docs/.cache/geocat'.

[2]:

'/home/docs/.cache/geocat/netcdf_files/atm.20C.hourly6-1990-1995-TS.nc'

Parsing the data with xarray¶

GeoCAT-comp is designed to work with `xarray <https://xarray.dev/>`__ and `numpy <https://numpy.org/>`__ data structures. Here we will be using xarray to import the netCDF data.

[3]:

ds = xr.open_dataset(nc_file)
ds

[3]:

<xarray.Dataset>
Dimensions:    (member_id: 40, time: 8761, nbnd: 2)
Coordinates:
    lat        float64 ...
    lon        float64 ...
  * member_id  (member_id) int64 1 2 3 4 5 6 7 8 ... 34 35 101 102 103 104 105
  * time       (time) object 1990-01-01 00:00:00 ... 1996-01-01 00:00:00
    time_bnds  (time, nbnd) object ...
Dimensions without coordinates: nbnd
Data variables:
    TS         (member_id, time) float32 ...
Attributes: (12/13)
    Conventions:               CF-1.0
    NCO:                       4.3.4
    Version:                   $Name$
    important_note:            This data is part of the project 'Blind Evalua...
    initial_file:              b.e11.B20TRC5CNBDRD.f09_g16.001.cam.i.1920-01-...
    logname:                   mudryk
    ...                        ...
    revision_Id:               $Id$
    source:                    CAM
    title:                     UNSET
    topography_file:           /scratch/p/pjk/mudryk/cesm1_1_2_LENS/inputdata...
    intake_esm_varname:        TS
    intake_esm_dataset_key:    atm.20C.hourly6-1990-2005

xarray.Dataset

Dimensions:
- member_id: 40
- time: 8761
- nbnd: 2

Coordinates: (5)

lat
()
float64
...
axis :
Y
bounds :
lat_bnds
long_name :
latitude
standard_name :
latitude
units :
degrees_north
```
[1 values with dtype=float64]
```
lon
()
float64
...
long_name :
longitude
units :
degrees_east
```
[1 values with dtype=float64]
```

member_id

(member_id)

int64

1 2 3 4 5 6 ... 101 102 103 104 105

array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,
        15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,
        29,  30,  31,  32,  33,  34,  35, 101, 102, 103, 104, 105])

time

(time)

object

1990-01-01 00:00:00 ... 1996-01-...

bounds :: time_bnds
long_name :: time

array([cftime.DatetimeNoLeap(1990, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1990, 1, 1, 6, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1990, 1, 1, 12, 0, 0, 0, has_year_zero=True), ...,
       cftime.DatetimeNoLeap(1995, 12, 31, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1995, 12, 31, 18, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1996, 1, 1, 0, 0, 0, 0, has_year_zero=True)],
      dtype=object)

time_bnds
(time, nbnd)
object
...
long_name :
time interval endpoints
```
[17522 values with dtype=object]
```

Data variables: (1)
- TS
  (member_id, time)
  float32
  ...
  long_name :
  Surface temperature (radiative)
  units :
  K
```
[350440 values with dtype=float32]
```

Indexes: (2)

member_id

PandasIndex

PandasIndex(Int64Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
             14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,
             27,  28,  29,  30,  31,  32,  33,  34,  35, 101, 102, 103, 104,
            105],
           dtype='int64', name='member_id'))

time

PandasIndex

PandasIndex(CFTimeIndex([1990-01-01 00:00:00, 1990-01-01 06:00:00, 1990-01-01 12:00:00,
             1990-01-01 18:00:00, 1990-01-02 00:00:00, 1990-01-02 06:00:00,
             1990-01-02 12:00:00, 1990-01-02 18:00:00, 1990-01-03 00:00:00,
             1990-01-03 06:00:00,
             ...
             1995-12-29 18:00:00, 1995-12-30 00:00:00, 1995-12-30 06:00:00,
             1995-12-30 12:00:00, 1995-12-30 18:00:00, 1995-12-31 00:00:00,
             1995-12-31 06:00:00, 1995-12-31 12:00:00, 1995-12-31 18:00:00,
             1996-01-01 00:00:00],
            dtype='object', length=8761, calendar='noleap', freq='6H'))

Attributes: (13)
Conventions :
CF-1.0
NCO :
4.3.4
Version :
$Name$
important_note :
This data is part of the project 'Blind Evaluation of Lossy Data-Compression in LENS'. Please exercise caution before using this data for other purposes.
initial_file :
b.e11.B20TRC5CNBDRD.f09_g16.001.cam.i.1920-01-01-00000.nc
logname :
mudryk
nco_openmp_thread_number :
1
revision_Id :
$Id$
source :
CAM
title :
UNSET
topography_file :
/scratch/p/pjk/mudryk/cesm1_1_2_LENS/inputdata/atm/cam/topo/USGS-gtopo30_0.9x1.25_remap_c051027.nc
intake_esm_varname :
TS
intake_esm_dataset_key :
atm.20C.hourly6-1990-2005

We can see here that the datafile has surface temperature data from 40 model ensemble members. Let’s simplify things and just look at one model for now. We use xarray’s DataArray.isel method to pick out the data for the first member_id at index 0.

[4]:

ds = ds.isel(member_id=0)  # select one model from the ensemble
temp = ds.TS  # surface temperature data
temp

[4]:

<xarray.DataArray 'TS' (time: 8761)>
[8761 values with dtype=float32]
Coordinates:
    lat        float64 ...
    lon        float64 ...
    member_id  int64 1
  * time       (time) object 1990-01-01 00:00:00 ... 1996-01-01 00:00:00
Attributes:
    long_name:  Surface temperature (radiative)
    units:      K

xarray.DataArray

'TS'

time: 8761

...
```
[8761 values with dtype=float32]
```

Coordinates: (4)

lat
()
float64
...
axis :
Y
bounds :
lat_bnds
long_name :
latitude
standard_name :
latitude
units :
degrees_north
```
[1 values with dtype=float64]
```
lon
()
float64
...
long_name :
longitude
units :
degrees_east
```
[1 values with dtype=float64]
```
member_id
()
int64
1
```
array(1)
```

time

(time)

object

1990-01-01 00:00:00 ... 1996-01-...

bounds :: time_bnds
long_name :: time

array([cftime.DatetimeNoLeap(1990, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1990, 1, 1, 6, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1990, 1, 1, 12, 0, 0, 0, has_year_zero=True), ...,
       cftime.DatetimeNoLeap(1995, 12, 31, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1995, 12, 31, 18, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1996, 1, 1, 0, 0, 0, 0, has_year_zero=True)],
      dtype=object)

Indexes: (1)

time

PandasIndex

PandasIndex(CFTimeIndex([1990-01-01 00:00:00, 1990-01-01 06:00:00, 1990-01-01 12:00:00,
             1990-01-01 18:00:00, 1990-01-02 00:00:00, 1990-01-02 06:00:00,
             1990-01-02 12:00:00, 1990-01-02 18:00:00, 1990-01-03 00:00:00,
             1990-01-03 06:00:00,
             ...
             1995-12-29 18:00:00, 1995-12-30 00:00:00, 1995-12-30 06:00:00,
             1995-12-30 12:00:00, 1995-12-30 18:00:00, 1995-12-31 00:00:00,
             1995-12-31 06:00:00, 1995-12-31 12:00:00, 1995-12-31 18:00:00,
             1996-01-01 00:00:00],
            dtype='object', length=8761, calendar='noleap', freq='6H'))

Attributes: (2)
long_name :
Surface temperature (radiative)
units :
K

Now that we have the temperature data for the first ensemble member, let’s plot it. xarray has built in methods to quickly plot data. The generated plot isn’t publication quality, but it’s a quick and dirty way to see what your dataset contains.

[5]:

temp.plot()

[5]:

[<matplotlib.lines.Line2D at 0x7f04d29ffe80>]

../../_images/notebooks_geocat-comp_02-more_advanced_functionality_11_1.png

This plot shows that the temperature doesn’t change much throughout the year. This makes sense since the data is for the location 0.4712N 180E, which is in the Pacific Ocean just north of the equator. There is some variation though, and it’d be interesting to know what the average temperature is in each month. We can calculate this using climatology_average.

Using climatology_average¶

We can see in the documentation that climatology_average calculates long term hourly, daily, monthly, or seasonal averages across all years in a given dataset. This is a good function to quickly summarize our data, so let’s use it.

Related NCL Functions¶

Notice at the bottom of the documentation page there is a box with links to related NCL and GeoCAT functions. This indicates all of the NCL functions that climatology_average is replicating through it’s keyword arguments. We want something similar to the clmMon family of functions to summarize our data using monthly climate averages. We can see that our chosen GeoCAT function replicates that NCL capability.

A note on the climatology_average documentation page about related NCL functions clmDayHourTLL, clmDauHourTLLL, clmDayTLL, clmDayTLLL, clmMonLLLT, clmMonLLT, clmMonTLL, clmMonTLLL, month_to_season

Let’s summarize our data using monthly climate averages. Using the keyword argument freq, we can specify the time span for the average. The accepted options are hour, day, month, and season.

[6]:

monthly_temp = climatology_average(temp, freq='month')
monthly_temp

[6]:

<xarray.DataArray 'TS' (time: 12)>
array([299.70563, 299.382  , 299.45645, 299.87674, 300.33078, 300.91946,
       301.19077, 300.82486, 300.08514, 299.94785, 300.152  , 300.01614],
      dtype=float32)
Coordinates:
    lat        float64 ...
    lon        float64 ...
    member_id  int64 1
  * time       (time) object 1993-01-16 12:00:00 ... 1993-12-16 12:00:00
Attributes:
    long_name:  Surface temperature (radiative)
    units:      K

xarray.DataArray

'TS'

time: 12

299.7 299.4 299.5 299.9 300.3 300.9 ... 300.8 300.1 299.9 300.2 300.0

array([299.70563, 299.382  , 299.45645, 299.87674, 300.33078, 300.91946,
       301.19077, 300.82486, 300.08514, 299.94785, 300.152  , 300.01614],
      dtype=float32)

Coordinates: (4)

lat
()
float64
...
axis :
Y
bounds :
lat_bnds
long_name :
latitude
standard_name :
latitude
units :
degrees_north
```
[1 values with dtype=float64]
```
lon
()
float64
...
long_name :
longitude
units :
degrees_east
```
[1 values with dtype=float64]
```
member_id
()
int64
1
```
array(1)
```

time

(time)

object

1993-01-16 12:00:00 ... 1993-12-...

array([cftime.DatetimeNoLeap(1993, 1, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1993, 2, 15, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1993, 3, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1993, 4, 16, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1993, 5, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1993, 6, 16, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1993, 7, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1993, 8, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1993, 9, 16, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1993, 10, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1993, 11, 16, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1993, 12, 16, 12, 0, 0, 0, has_year_zero=True)],
      dtype=object)

Indexes: (1)

time

PandasIndex

PandasIndex(CFTimeIndex([1993-01-16 12:00:00, 1993-02-15 00:00:00, 1993-03-16 12:00:00,
             1993-04-16 00:00:00, 1993-05-16 12:00:00, 1993-06-16 00:00:00,
             1993-07-16 12:00:00, 1993-08-16 12:00:00, 1993-09-16 00:00:00,
             1993-10-16 12:00:00, 1993-11-16 00:00:00, 1993-12-16 12:00:00],
            dtype='object', length=12, calendar='noleap', freq='None'))

Attributes: (2)
long_name :
Surface temperature (radiative)
units :
K

Notice how much smaller the time dimension is. Before, the time dimension had a length of 8761 points, one for every 6 hour interval over 5 years. Now, the time dimension has a length of 12, one for every month of the year. Notice that the value of the time coordinate is for the middle of each month in 1993. Why 1993? It’s approximatly the middle year in the dataset.

[7]:

monthly_temp.plot()

[7]:

[<matplotlib.lines.Line2D at 0x7f04d28e08e0>]

../../_images/notebooks_geocat-comp_02-more_advanced_functionality_19_1.png

When we plot this, the data is more granular and has even less variation in temperature from month to month. But we can now see which months are coldest and warmest on average for this location.

Adjusting Parameters¶

Monthly climate averages are great, but what else can climatology_average do? We can also calculate hourly, daily, and seasonal climate averages. For this dataset, we don’t have a fine enough temporal resolution to compute an hourly average, but we can compute daily averages. The only change to the function call is passing in day instead of month.

[8]:

daily_temp = climatology_average(temp, freq='day')
daily_temp

[8]:

<xarray.DataArray 'TS' (time: 365)>
array([299.90625, 299.9027 , 299.9038 , 299.89267, 299.89645, 299.90247,
       299.88416, 299.86523, 299.8602 , 299.84595, 299.8206 , 299.79782,
       299.7791 , 299.7557 , 299.72598, 299.70053, 299.66797, 299.6409 ,
       299.63803, 299.62735, 299.60635, 299.598  , 299.58813, 299.57175,
       299.55392, 299.5314 , 299.5034 , 299.48175, 299.47153, 299.47998,
       299.4739 , 299.4499 , 299.42944, 299.4248 , 299.43323, 299.44882,
       299.45566, 299.45822, 299.47192, 299.4838 , 299.48218, 299.4673 ,
       299.44363, 299.43234, 299.41302, 299.386  , 299.36398, 299.34192,
       299.32126, 299.32187, 299.33716, 299.321  , 299.29294, 299.27682,
       299.26843, 299.27237, 299.28223, 299.2999 , 299.31555, 299.32324,
       299.32336, 299.29977, 299.27838, 299.28983, 299.305  , 299.32205,
       299.34653, 299.3856 , 299.42453, 299.4302 , 299.41373, 299.40256,
       299.4077 , 299.4224 , 299.4483 , 299.48758, 299.52597, 299.54443,
       299.54855, 299.55234, 299.53928, 299.5068 , 299.4921 , 299.5035 ,
       299.52048, 299.54846, 299.5812 , 299.60837, 299.65668, 299.71072,
       299.75012, 299.77316, 299.77704, 299.77573, 299.76965, 299.76822,
       299.7783 , 299.79297, 299.81876, 299.8422 , 299.8571 , 299.8822 ,
       299.89114, 299.8904 , 299.88116, 299.87808, 299.89078, 299.90726,
       299.89944, 299.88193, 299.86453, 299.84775, 299.86725, 299.9086 ,
       299.92896, 299.94403, 299.97934, 300.02603, 300.08334, 300.14673,
...
       300.21384, 300.14963, 300.09665, 300.0553 , 300.02277, 299.99667,
       299.9679 , 299.9452 , 299.91772, 299.90115, 299.9091 , 299.92722,
       299.9534 , 299.9951 , 300.02258, 300.00687, 299.9744 , 299.9299 ,
       299.8682 , 299.8135 , 299.78452, 299.7746 , 299.7549 , 299.7144 ,
       299.68152, 299.6678 , 299.66446, 299.66776, 299.6966 , 299.7579 ,
       299.82526, 299.87814, 299.91672, 299.94186, 299.9529 , 299.973  ,
       300.01187, 300.03363, 300.0538 , 300.08664, 300.11258, 300.12372,
       300.13623, 300.14963, 300.1493 , 300.14633, 300.13452, 300.11118,
       300.09152, 300.06946, 300.05896, 300.04553, 300.0262 , 300.0137 ,
       300.00223, 300.00314, 300.02576, 300.0657 , 300.12503, 300.19614,
       300.25937, 300.2955 , 300.2975 , 300.2842 , 300.26498, 300.23477,
       300.20242, 300.202  , 300.23282, 300.25613, 300.2426 , 300.20947,
       300.17593, 300.1535 , 300.14764, 300.15042, 300.14536, 300.12527,
       300.1005 , 300.0703 , 300.0401 , 300.01175, 299.9951 , 299.99915,
       300.005  , 300.01852, 300.03638, 300.03754, 300.02664, 300.02902,
       300.04208, 300.05743, 300.0775 , 300.10675, 300.13696, 300.14713,
       300.14026, 300.12463, 300.10367, 300.08554, 300.05856, 300.02856,
       300.01175, 300.0094 , 299.98972, 299.941  , 299.89905, 299.87585,
       299.86655, 299.87582, 299.89938, 299.92636, 299.94885],
      dtype=float32)
Coordinates:
    lat        float64 ...
    lon        float64 ...
    member_id  int64 1
  * time       (time) object 1993-01-01 12:00:00 ... 1993-12-31 12:00:00
Attributes:
    long_name:  Surface temperature (radiative)
    units:      K

xarray.DataArray

'TS'

time: 365

299.9 299.9 299.9 299.9 299.9 299.9 ... 299.9 299.9 299.9 299.9 299.9

array([299.90625, 299.9027 , 299.9038 , 299.89267, 299.89645, 299.90247,
       299.88416, 299.86523, 299.8602 , 299.84595, 299.8206 , 299.79782,
       299.7791 , 299.7557 , 299.72598, 299.70053, 299.66797, 299.6409 ,
       299.63803, 299.62735, 299.60635, 299.598  , 299.58813, 299.57175,
       299.55392, 299.5314 , 299.5034 , 299.48175, 299.47153, 299.47998,
       299.4739 , 299.4499 , 299.42944, 299.4248 , 299.43323, 299.44882,
       299.45566, 299.45822, 299.47192, 299.4838 , 299.48218, 299.4673 ,
       299.44363, 299.43234, 299.41302, 299.386  , 299.36398, 299.34192,
       299.32126, 299.32187, 299.33716, 299.321  , 299.29294, 299.27682,
       299.26843, 299.27237, 299.28223, 299.2999 , 299.31555, 299.32324,
       299.32336, 299.29977, 299.27838, 299.28983, 299.305  , 299.32205,
       299.34653, 299.3856 , 299.42453, 299.4302 , 299.41373, 299.40256,
       299.4077 , 299.4224 , 299.4483 , 299.48758, 299.52597, 299.54443,
       299.54855, 299.55234, 299.53928, 299.5068 , 299.4921 , 299.5035 ,
       299.52048, 299.54846, 299.5812 , 299.60837, 299.65668, 299.71072,
       299.75012, 299.77316, 299.77704, 299.77573, 299.76965, 299.76822,
       299.7783 , 299.79297, 299.81876, 299.8422 , 299.8571 , 299.8822 ,
       299.89114, 299.8904 , 299.88116, 299.87808, 299.89078, 299.90726,
       299.89944, 299.88193, 299.86453, 299.84775, 299.86725, 299.9086 ,
       299.92896, 299.94403, 299.97934, 300.02603, 300.08334, 300.14673,
...
       300.21384, 300.14963, 300.09665, 300.0553 , 300.02277, 299.99667,
       299.9679 , 299.9452 , 299.91772, 299.90115, 299.9091 , 299.92722,
       299.9534 , 299.9951 , 300.02258, 300.00687, 299.9744 , 299.9299 ,
       299.8682 , 299.8135 , 299.78452, 299.7746 , 299.7549 , 299.7144 ,
       299.68152, 299.6678 , 299.66446, 299.66776, 299.6966 , 299.7579 ,
       299.82526, 299.87814, 299.91672, 299.94186, 299.9529 , 299.973  ,
       300.01187, 300.03363, 300.0538 , 300.08664, 300.11258, 300.12372,
       300.13623, 300.14963, 300.1493 , 300.14633, 300.13452, 300.11118,
       300.09152, 300.06946, 300.05896, 300.04553, 300.0262 , 300.0137 ,
       300.00223, 300.00314, 300.02576, 300.0657 , 300.12503, 300.19614,
       300.25937, 300.2955 , 300.2975 , 300.2842 , 300.26498, 300.23477,
       300.20242, 300.202  , 300.23282, 300.25613, 300.2426 , 300.20947,
       300.17593, 300.1535 , 300.14764, 300.15042, 300.14536, 300.12527,
       300.1005 , 300.0703 , 300.0401 , 300.01175, 299.9951 , 299.99915,
       300.005  , 300.01852, 300.03638, 300.03754, 300.02664, 300.02902,
       300.04208, 300.05743, 300.0775 , 300.10675, 300.13696, 300.14713,
       300.14026, 300.12463, 300.10367, 300.08554, 300.05856, 300.02856,
       300.01175, 300.0094 , 299.98972, 299.941  , 299.89905, 299.87585,
       299.86655, 299.87582, 299.89938, 299.92636, 299.94885],
      dtype=float32)

Coordinates: (4)

lat
()
float64
...
axis :
Y
bounds :
lat_bnds
long_name :
latitude
standard_name :
latitude
units :
degrees_north
```
[1 values with dtype=float64]
```
lon
()
float64
...
long_name :
longitude
units :
degrees_east
```
[1 values with dtype=float64]
```
member_id
()
int64
1
```
array(1)
```

time

(time)

object

1993-01-01 12:00:00 ... 1993-12-...

array([cftime.DatetimeNoLeap(1993, 1, 1, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1993, 1, 2, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1993, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,
       cftime.DatetimeNoLeap(1993, 12, 29, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1993, 12, 30, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(1993, 12, 31, 12, 0, 0, 0, has_year_zero=True)],
      dtype=object)

Indexes: (1)

time

PandasIndex

PandasIndex(CFTimeIndex([1993-01-01 12:00:00, 1993-01-02 12:00:00, 1993-01-03 12:00:00,
             1993-01-04 12:00:00, 1993-01-05 12:00:00, 1993-01-06 12:00:00,
             1993-01-07 12:00:00, 1993-01-08 12:00:00, 1993-01-09 12:00:00,
             1993-01-10 12:00:00,
             ...
             1993-12-22 12:00:00, 1993-12-23 12:00:00, 1993-12-24 12:00:00,
             1993-12-25 12:00:00, 1993-12-26 12:00:00, 1993-12-27 12:00:00,
             1993-12-28 12:00:00, 1993-12-29 12:00:00, 1993-12-30 12:00:00,
             1993-12-31 12:00:00],
            dtype='object', length=365, calendar='noleap', freq='D'))

Attributes: (2)
long_name :
Surface temperature (radiative)
units :
K

[9]:

daily_temp.plot()

[9]:

[<matplotlib.lines.Line2D at 0x7f04ca6ae020>]

../../_images/notebooks_geocat-comp_02-more_advanced_functionality_22_1.png

A Soft Starter: Max Daylight Intro to UXarray

By GeoCAT
© Copyright 2022, University Corporation for Atmospheric Research.

The National Center for Atmospheric Research is sponsored by the National Science Foundation. Any opinions, findings and conclusions or recommendations expressed in this material do not necessarily reflect the views of the National Science Foundation.