Processing CMIP6 data in Zarr format with Dask & AWS Fargate¶
This notebook demonstrates how to work with the CMIP6 Zarr data available as part of the AWS Open Data Program (https://registry.opendata.aws/cmip6/).
This notebook utilizies Amazon SageMaker & AWS Fargate for providing an environment with a Jupyter notebook and Dask cluster. There is an example AWS CloudFormation template available at https://github.com/awslabs/amazon-asdi/tree/main/examples/dask for quickly creating this environment in your own AWS account to run this notebook.
Python Imports¶
In [1]:
%matplotlib inline
import boto3
import botocore
import datetime
import matplotlib.pyplot as plt
import matplotlib
import xarray as xr
import numpy as np
import s3fs
import fsspec
import dask
from dask.distributed import performance_report
from dask.distributed import Client
font = {'family' : 'sans-serif',
'weight' : 'normal',
'size' : 18}
matplotlib.rc('font', **font)
Scale out Dask Workers¶
In [2]:
ecs = boto3.client('ecs')
resp = ecs.list_clusters()
clusters = resp['clusterArns']
if len(clusters) > 1:
print("Please manually select your cluster")
cluster = clusters[0]
cluster
Out[2]:
In [3]:
# Scale up the Fargate cluster
numWorkers=70
ecs.update_service(cluster=cluster, service='Dask-Worker', desiredCount=numWorkers)
ecs.get_waiter('services_stable').wait(cluster=cluster, services=['Dask-Worker'])
Set up the Dask Client to talk to our Fargate Dask Distributed Cluster¶
In [4]:
client = Client('Dask-Scheduler.local-dask:8786')
client
Out[4]:
Open 2-m air temperature for a single ensemble member from the GFDL-ESM4¶
In [5]:
@dask.delayed
def s3open(path):
fs = s3fs.S3FileSystem(anon=True, default_fill_cache=False,
config_kwargs = {'max_pool_connections': 50})
return s3fs.S3Map(path, s3=fs)
In [16]:
%%time
files_mapper = [s3open('s3://cmip6-pds/ScenarioMIP/NOAA-GFDL/GFDL-ESM4/ssp119/r1i1p1f1/day/tas/gr1/')]
ds = xr.open_mfdataset(files_mapper, engine='zarr', parallel=True)
In [17]:
print('ds size in GB {:0.2f}\n'.format(ds.nbytes / 1e9))
ds.info
Out[17]:
The ds.info output above shows us that there are four dimensions to the data: lat, lon, and time0; and two data variables: air_temperature_at_2_metres, and air_pressure_at_mean_sea_level.
Convert units to F from K¶
In [18]:
ds['tas'] = (ds.tas - 273.15) * 9.0 / 5.0 + 32.0
ds.tas.attrs['units'] = 'F'
Calculate the mean 2-m air temperature for all times¶
In [19]:
# calculates the mean along the time dimension
temp_mean = ds['tas'].mean(dim='time')
The expressions above didn’t actually compute anything. They just build the dask task graph. To do the computations, we call the compute method:
In [20]:
%%time
with performance_report(filename="dask-report.html"):
temp_mean = temp_mean.compute()
Plot Average Surface Temperature¶
In [21]:
temp_mean.plot(figsize=(20, 10))
plt.title('2015-2100 Mean 2-m Air Temperature')
Out[21]:
Repeat for standard deviation¶
In [22]:
temp_std = ds['tas'].std(dim='time')
In [23]:
%time temp_std = temp_std.compute()
In [24]:
temp_std.plot(figsize=(20, 10))
plt.title('2015-2100 Standard Deviation 2-m Air Temperature')
Out[24]:
Plot temperature time series for points¶
In [25]:
# location coordinates
locs = [
{'name': 'Santa Barbara', 'lon': -119.70, 'lat': 34.42},
{'name': 'Colorado Springs', 'lon': -104.82, 'lat': 38.83},
{'name': 'Honolulu', 'lon': -157.84, 'lat': 21.29},
{'name': 'Seattle', 'lon': -122.33, 'lat': 47.61},
]
# convert westward longitudes to degrees east
for l in locs:
if l['lon'] < 0:
l['lon'] = 360 + l['lon']
locs
Out[25]:
In [30]:
ds_locs = xr.Dataset()
air_temp_ds = ds
# interate through the locations and create a dataset
# containing the temperature values for each location
for l in locs:
name = l['name']
lon = l['lon']
lat = l['lat']
var_name = name
ds2 = air_temp_ds.sel(lon=lon, lat=lat, method='nearest')
lon_attr = '%s_lon' % name
lat_attr = '%s_lat' % name
ds2.attrs[lon_attr] = ds2.lon.values.tolist()
ds2.attrs[lat_attr] = ds2.lat.values.tolist()
ds2 = ds2.rename({'tas' : var_name}).drop(('lat', 'lon', 'height', 'lat_bnds', 'lon_bnds'))
ds_locs = xr.merge([ds_locs, ds2], compat='override')
ds_locs.data_vars
Out[30]:
Convert to dataframe¶
In [31]:
df_f = ds_locs.to_dataframe()
df_f.describe()
Out[31]:
Plot the entire time series of temperatures¶
In [32]:
ax = df_f.plot(figsize=(20, 10), title="GFDL-ESM4/ssp119/r1i1p1f1", grid=1)
ax.set(xlabel='Date', ylabel='2-m Air Temperature (deg F)')
plt.show()
Cluster scale down¶
When we are temporarily done with the cluster we can scale it down to save on costs
In [33]:
numWorkers=0
ecs.update_service(cluster=cluster, service='Dask-Worker', desiredCount=numWorkers)
ecs.get_waiter('services_stable').wait(cluster=cluster, services=['Dask-Worker'])
In [ ]: