Source code for dea_tools.climate

# dea_climate.py
'''
Retrieving and manipulating gridded climate data.

Adapted from scripts by Andrew Cherry and Brian Killough.

License: The code in this notebook is licensed under the Apache License,
Version 2.0 (https://www.apache.org/licenses/LICENSE-2.0). Digital Earth 
Australia data is licensed under the Creative Commons by Attribution 4.0 
license (https://creativecommons.org/licenses/by/4.0/).

Contact: If you need assistance, please post a question on the Open Data 
Cube Slack channel (http://slack.opendatacube.org/) or on the GIS Stack 
Exchange (https://gis.stackexchange.com/questions/ask?tags=open-data-cube) 
using the `open-data-cube` tag (you can view previously asked questions 
here: https://gis.stackexchange.com/questions/tagged/open-data-cube). 

If you would like to report an issue with this script, you can file one on 
Github https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/issues

Last modified: October 2020
'''

import os
import datetime
import numpy as np
from dateutil.parser import parse
import boto3
import botocore
import xarray as xr
import warnings

ERA5_VARS = [
    "air_pressure_at_mean_sea_level",
    "air_temperature_at_2_metres",
    "air_temperature_at_2_metres_1hour_Maximum",
    "air_temperature_at_2_metres_1hour_Minimum",
    "dew_point_temperature_at_2_metres",
    "eastward_wind_at_100_metres",
    "eastward_wind_at_10_metres",
    "integral_wrt_time_of_surface_direct_downwelling_shortwave_flux_in_air_1hour_Accumulation",
    "lwe_thickness_of_surface_snow_amount",
    "northward_wind_at_100_metres",
    "northward_wind_at_10_metres",
    "precipitation_amount_1hour_Accumulation",
    "sea_surface_temperature",
    "sea_surface_wave_from_direction",
    "sea_surface_wave_mean_period",
    "significant_height_of_wind_and_swell_waves",
    "snow_density",
    "surface_air_pressure",
]


[docs] def get_era5_daily(var, date_from_arg, date_to_arg=None, reduce_func=None, cache_dir='era5', resample='1D'): """ Download and return an variable from the European Centre for Medium Range Weather Forecasts (ECMWF) global climate reanalysis product (ERA5) for a defined time window. Parameters ---------- var : string Name of the ERA5 climate variable to download, e.g "air_temperature_at_2_metres" date_from_arg: string or datetime object Starting date of the time window. date_to_arg: string or datetime object End date of the time window. If not supplied, set to be the same as starting date. reduce_func: numpy function lets you specify a function to apply to each day's worth of data. The default is np.mean, which computes daily average. To get a sum, use np.sum. cache_dir: sting Path to save downloaded ERA5 data. The path will be created if not already exists. The default is 'era5'. resample: string Temporal resampling frequency to be used for xarray's resample function. The default is '1D', which is daily. Since ERA5 data is provided as one file per month, maximum resampling period is '1M'. Returns ------- A lazy-loaded xarray dataset containing an ERA5 variable for the selected time window. """ # Massage input data assert var in ERA5_VARS, "var must be one of [{}] (got {})".format( ','.join(ERA5_VARS), var) if not os.path.exists(cache_dir): os.mkdir(cache_dir) if reduce_func is None: reduce_func = np.mean if type(date_from_arg) == str: date_from_arg = parse(date_from_arg) if type(date_to_arg) == str: date_to_arg = parse(date_to_arg) if date_to_arg is None: date_to_arg = date_from_arg # Make sure our dates are in the correct order from_date = min(date_from_arg, date_to_arg) to_date = max(date_from_arg, date_to_arg) # Download ERA5 files to local cache if they don't already exist client = None # Boto client (if needed) local_files = [] # Will hold list of local filenames Y, M = from_date.year, from_date.month # Loop vars loop_end = to_date.year * 12 + to_date.month # Loop sentinel while Y * 12 + M <= loop_end: local_file = os.path.join( cache_dir, "{Y:04}_{M:02}_{var}.nc".format(Y=Y, M=M, var=var)) data_key = "{Y:04}/{M:02}/data/{var}.nc".format(Y=Y, M=M, var=var) if not os.path.isfile( local_file ): # check if file already exists (TODO: move to temp, catch failed download) if client is None: client = boto3.client('s3', config=botocore.client.Config( signature_version=botocore.UNSIGNED)) client.download_file('era5-pds', data_key, local_file) local_files.append(local_file) if M == 12: Y += 1 M = 1 else: M += 1 # Load and merge the locally-cached ERA5 data from the list of filenames date_slice = slice(str(from_date.date()), str(to_date.date( ))) # I do this to INCLUDE the whole end date, not just 00:00 def prepro(ds): if 'time0' in ds.dims: ds = ds.rename({"time0": "time"}) if 'time1' in ds.dims: ds = ds.rename({ "time1": "time" }) # This should INTENTIONALLY error if both times are defined ds = ds[[var]] output = ds.sel(time=date_slice).resample( time=resample).reduce(reduce_func) output.attrs = ds.attrs for v in output.data_vars: output[v].attrs = ds[v].attrs return output return xr.open_mfdataset(local_files, combine='by_coords', compat='equals', preprocess=prepro, parallel=True)
[docs] def era5_area_crop(ds, lat, lon): """ Crop a dataset containing European Centre for Medium Range Weather Forecasts (ECMWF) global climate reanalysis product (ERA5) variables to a location. The output spatial grid will either include input grid points within lat/lon boundaries or the nearest point if none is within the search location. Parameters ---------- ds : xarray dataset A dataset containing ERA5 variables of interest. lat: tuple or list Latitude range for query. lon: tuple or list Longitude range for query. Returns ------- An xarray dataset containing ERA5 variables for the selected location. """ # Handle single value lat/lon args by wrapping them in lists try: min(lat) except TypeError: lat = [lat] try: min(lon) except TypeError: lon = [lon] if min(lon) < 0: # re-order along longitude to go from -180 to 180 ds = ds.assign_coords({"lon": (((ds.lon + 180) % 360) - 180)}) ds = ds.reindex({ "lon": np.sort(ds.lon)}) # Issue warnings if args outside range. if min(lat) < ds.lat.min() or max(lat) > ds.lat.max(): warnings.warn("Lats must be in range {} .. {}. Got: {}".format( ds.lat.min().values, ds.lat.max().values, lat)) if min(lon) < ds.lon.min() or max(lon) > ds.lon.max(): warnings.warn("Lons must be in range {} .. {}. Got: {}".format( ds.lon.min().values, ds.lon.max().values, lon)) # Find existing coords between min&max lats = ds.lat[np.logical_and( ds.lat >= min(lat), ds.lat <= max(lat))].values # If there was nothing between, just plan to grab closest if len(lats) == 0: lats = np.unique(ds.lat.sel(lat=np.array(lat), method="nearest")) lons = ds.lon[np.logical_and( ds.lon >= min(lon), ds.lon <= max(lon))].values if len(lons) == 0: lons = np.unique(ds.lon.sel(lon=np.array(lon), method="nearest")) # crop and keep attrs output = ds.sel(lat=lats, lon=lons) output.attrs = ds.attrs for var in output.data_vars: output[var].attrs = ds[var].attrs return output
[docs] def era5_area_nearest(ds, lat, lon): """ Crop a dataset containing European Centre for Medium Range Weather Forecasts (ECMWF) global climate reanalysis product (ERA5) variables to a location. The output spatial grid is snapped to the nearest input grid points. Parameters ---------- ds : xarray dataset A dataset containing ERA5 variables of interest. lat: tuple or list Latitude range for query. lon: tuple or list Longitude range for query. Returns ------- An xarray dataset containing ERA5 variables for the selected location. """ if min(lon) < 0: # re-order along longitude to go from -180 to 180 ds = ds.assign_coords({"lon": (((ds.lon + 180) % 360) - 180)}) ds = ds.reindex({ "lon": np.sort(ds.lon)}) # find the nearest lat lon boundary points test = ds.sel(lat=list(lat), lon=list(lon), method='nearest') # define the lat/lon grid lat_range = slice(test.lat.max().values, test.lat.min().values) lon_range = slice(test.lon.min().values, test.lon.max().values) # crop and keep attrs output = ds.sel(lat=lat_range, lon=lon_range) output.attrs = ds.attrs for var in output.data_vars: output[var].attrs = ds[var].attrs return output
[docs] def load_era5(var, lat, lon, time, grid='nearest', **kwargs): """ Returns a European Centre for Medium Range Weather Forecasts (ECMWF) global climate reanalysis product (ERA5) variable for a selected location and time window. Parameters ---------- var : string Name of the ERA5 climate variable to download, e.g "air_temperature_at_2_metres" lat: tuple or list Latitude range for query. lon: tuple or list Longitude range for query. time: tuple or list Time range for query. grid: string Option for output spatial gridding. The default is 'nearest', for which output spatial grid is snapped to the nearest ERA5 input grid points. Alternatively, output spatial grid will either include input grid points within lat/lon boundaries or the nearest point if none is within the search location. Returns ------- An xarray dataset containing the variable for the selected location and time window. """ ds = get_era5_daily(var, time[0], time[1], **kwargs) if grid == 'nearest': return era5_area_nearest(ds, lat, lon).compute() else: return era5_area_crop(ds, lat, lon).compute()