Processing ERA5 data in NetCDF Format¶
This notebook demonstrates how to work with the ECMWF ERA5 reanalysis available as part of the AWS Public Dataset Program (https://registry.opendata.aws/ecmwf-era5/).
This notebook utilizes 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, Client, progress
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
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You will need to update the --cluster option in the comamnd below to make your cluster name from above
In [3]:
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
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Open an Example File and Check the Native Chunking¶
We want to chunk in a similar way for maximum performance
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url = 's3://era5-pds/2010/01/data/air_temperature_at_2_metres.nc'
ncfile = fsspec.open(url)
ds = xr.open_dataset(ncfile.open())
ds.air_temperature_at_2_metres.encoding
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Open 2-m air temperature as a single dataset¶
In [6]:
start_year = 1979
end_year = 2020
years = list(np.arange(start_year, end_year+1, 1))
months = ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"]
file_pattern = 's3://era5-pds/{year}/{month}/data/air_temperature_at_2_metres.nc'
@dask.delayed
def s3open(path):
fs = s3fs.S3FileSystem(anon=True, default_fill_cache=False)
return fs.open(path)
files_mapper = [s3open(file_pattern.format(year=year,month=month)) for year in years for month in months]
In [7]:
%%time
ds = xr.open_mfdataset(files_mapper, engine='h5netcdf', chunks={'lon':200,'lat':200,'time0':720}, concat_dim='time0', combine='nested', coords='minimal', compat='override', parallel=True)
In [8]:
print('ds size in GB {:0.2f}\n'.format(ds.nbytes / 1e9))
ds.info
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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 [9]:
ds['air_temperature_at_2_metres'] = (ds.air_temperature_at_2_metres - 273.15) * 9.0 / 5.0 + 32.0
ds.air_temperature_at_2_metres.attrs['units'] = 'F'
Calculate the mean 2-m air temperature for all times¶
In [10]:
# calculates the mean along the time dimension
temp_mean = ds['air_temperature_at_2_metres'].mean(dim='time0')
The expressions above didn’t actually compute anything. They just build the dask task graph. To do the computations, we call the persist method:
In [11]:
temp_mean = temp_mean.persist()
progress(temp_mean)
Plot Average Surface Temperature¶
In [12]:
temp_mean.compute()
temp_mean.plot(figsize=(20, 10))
plt.title('1979-2020 Mean 2-m Air Temperature')
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Repeat for standard deviation¶
In [13]:
temp_std = ds['air_temperature_at_2_metres'].std(dim='time0')
In [14]:
temp_std = temp_std.persist()
progress(temp_std)
In [15]:
temp_std.compute()
temp_std.plot(figsize=(20, 10))
plt.title('1979-2020 Standard Deviation 2-m Air Temperature')
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Plot temperature time series for points¶
In [16]:
# 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[16]:
In [17]:
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({'air_temperature_at_2_metres' : var_name}).drop(('lat', 'lon'))
ds_locs = xr.merge([ds_locs, ds2])
ds_locs.data_vars
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Convert to dataframe¶
In [18]:
df_f = ds_locs.to_dataframe()
df_f.describe()
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Plot temperature timeseries¶
In [19]:
ax = df_f.plot(figsize=(20, 10), title="ERA5", 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
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numWorkers=0
ecs.update_service(cluster=cluster, service='Dask-Worker', desiredCount=numWorkers)
ecs.get_waiter('services_stable').wait(cluster=cluster, services=['Dask-Worker'])
In [ ]: