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.
%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)
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
'arn:aws:ecs:us-east-1:816257832715:cluster/daska-Fargate-Dask-Cluster'
# 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'])
client = Client('Dask-Scheduler.local-dask:8786')
client
Client
|
Cluster
|
def fix_accum_var_dims(ds, var):
# Some varibles like precip have extra time bounds varibles, we drop them here to allow merging with other variables
# Select variable of interest (drops dims that are not linked to current variable)
ds = ds[[var]]
if var in ['air_temperature_at_2_metres',
'dew_point_temperature_at_2_metres',
'air_pressure_at_mean_sea_level',
'northward_wind_at_10_metres',
'eastward_wind_at_10_metres']:
ds = ds.rename({'time0':'valid_time_end_utc'})
elif var in ['precipitation_amount_1hour_Accumulation',
'integral_wrt_time_of_surface_direct_downwelling_shortwave_flux_in_air_1hour_Accumulation']:
ds = ds.rename({'time1':'valid_time_end_utc'})
else:
print("Warning, Haven't seen {var} varible yet! Time renaming might not work.".format(var=var))
return ds
@dask.delayed
def s3open(path):
fs = s3fs.S3FileSystem(anon=True, default_fill_cache=False,
config_kwargs = {'max_pool_connections': 20})
return s3fs.S3Map(path, s3=fs)
def open_era5_range(start_year, end_year, variables):
''' Opens ERA5 monthly Zarr files in S3, given a start and end year (all months loaded) and a list of variables'''
file_pattern = 'era5-pds/zarr/{year}/{month}/data/{var}.zarr/'
years = list(np.arange(start_year, end_year+1, 1))
months = ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"]
l = []
for var in variables:
print(var)
# Get files
files_mapper = [s3open(file_pattern.format(year=year, month=month, var=var)) for year in years for month in months]
# Look up correct time dimension by variable name
if var in ['precipitation_amount_1hour_Accumulation']:
concat_dim='time1'
else:
concat_dim='time0'
# Lazy load
ds = xr.open_mfdataset(files_mapper, engine='zarr',
concat_dim=concat_dim, combine='nested',
coords='minimal', compat='override', parallel=True)
# Fix dimension names
ds = fix_accum_var_dims(ds, var)
l.append(ds)
ds_out = xr.merge(l)
return ds_out
%%time
ds = open_era5_range(1979, 2020, ["air_temperature_at_2_metres"])
air_temperature_at_2_metres CPU times: user 6.48 s, sys: 190 ms, total: 6.67 s Wall time: 49.3 s
print('ds size in GB {:0.2f}\n'.format(ds.nbytes / 1e9))
ds.info
ds size in GB 1529.06
<bound method Dataset.info of <xarray.Dataset> Dimensions: (lat: 721, lon: 1440, valid_time_end_utc: 368184) Coordinates: * lat (lat) float32 90.0 89.75 89.5 ... -89.75 -90.0 * lon (lon) float32 0.0 0.25 0.5 ... 359.5 359.8 * valid_time_end_utc (valid_time_end_utc) datetime64[ns] 1979-01-... Data variables: air_temperature_at_2_metres (valid_time_end_utc, lat, lon) float32 dask.array<chunksize=(372, 150, 150), meta=np.ndarray>>
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.
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'
# calculates the mean along the time dimension
temp_mean = ds['air_temperature_at_2_metres'].mean(dim='valid_time_end_utc')
The expressions above didn’t actually compute anything. They just build the dask task graph. To do the computations, we call the persist
method:
temp_mean = temp_mean.persist()
progress(temp_mean)
VBox()
temp_mean.compute()
temp_mean.plot(figsize=(20, 10))
plt.title('1979-2020 Mean 2-m Air Temperature')
Text(0.5, 1.0, '1979-2020 Mean 2-m Air Temperature')
temp_std = ds['air_temperature_at_2_metres'].std(dim='valid_time_end_utc')
temp_std = temp_std.persist()
progress(temp_std)
VBox()
temp_std.compute()
temp_std.plot(figsize=(20, 10))
plt.title('1979-2020 Standard Deviation 2-m Air Temperature')
Text(0.5, 1.0, '1979-2020 Standard Deviation 2-m Air Temperature')
# 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
[{'name': 'Santa Barbara', 'lon': 240.3, 'lat': 34.42}, {'name': 'Colorado Springs', 'lon': 255.18, 'lat': 38.83}, {'name': 'Honolulu', 'lon': 202.16, 'lat': 21.29}, {'name': 'Seattle', 'lon': 237.67000000000002, 'lat': 47.61}]
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
Data variables: Santa Barbara (valid_time_end_utc) float32 dask.array<chunksize=(372,), meta=np.ndarray> Colorado Springs (valid_time_end_utc) float32 dask.array<chunksize=(372,), meta=np.ndarray> Honolulu (valid_time_end_utc) float32 dask.array<chunksize=(372,), meta=np.ndarray> Seattle (valid_time_end_utc) float32 dask.array<chunksize=(372,), meta=np.ndarray>
df_f = ds_locs.to_dataframe()
df_f.describe()
Santa Barbara | Colorado Springs | Honolulu | Seattle | |
---|---|---|---|---|
count | 368184.000000 | 368184.000000 | 368184.000000 | 368184.000000 |
mean | 60.281300 | 46.359184 | 75.154930 | 52.309616 |
std | 9.913921 | 19.551779 | 2.568616 | 10.984550 |
min | 25.767511 | -25.982491 | 61.992512 | 5.742512 |
25% | 52.655010 | 31.167511 | 73.355011 | 44.330009 |
50% | 60.080009 | 46.355011 | 75.380013 | 51.417511 |
75% | 67.505013 | 60.755013 | 77.067513 | 59.742512 |
max | 97.092514 | 98.330009 | 83.817513 | 98.330009 |
ax = df_f.plot(figsize=(20, 10), title="ERA5", grid=1)
ax.set(xlabel='Date', ylabel='2-m Air Temperature (deg F)')
plt.show()
When we are temporarily done with the cluster we can scale it down to save on costs
numWorkers=0
ecs.update_service(cluster=cluster, service='Dask-Worker', desiredCount=numWorkers)
ecs.get_waiter('services_stable').wait(cluster=cluster, services=['Dask-Worker'])