From f089fcbbfd01eab542e7d9d53f35912282f89afd Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Tue, 29 Jun 2021 10:14:03 +0200
Subject: [PATCH] Implemented download and preprocessing of ERA5 single level
variables.
---
climax/main/preprocess/download_ERA5.py | 21 +++++++++++++--------
1 file changed, 13 insertions(+), 8 deletions(-)
diff --git a/climax/main/preprocess/download_ERA5.py b/climax/main/preprocess/download_ERA5.py
index 2dfd77b..cd78f2a 100644
--- a/climax/main/preprocess/download_ERA5.py
+++ b/climax/main/preprocess/download_ERA5.py
@@ -12,13 +12,9 @@ import cdsapi
import numpy as np
# locals
-from climax.core.constants import ERA5_VARIABLES
+from climax.core.constants import ERA5_P_VARIABLES, ERA5_VARIABLES
from climax.main.config import ERA5_PATH
-# ERA-5 product
-product = 'reanalysis-era5-pressure-levels'
-product_type = 'reanalysis'
-
# pressure levels
pressure_levels = ['850', '500']
@@ -26,15 +22,14 @@ pressure_levels = ['850', '500']
years = [str(y) for y in np.arange(1981, 2011)]
month = [str(m) for m in np.arange(1, 13)]
days = [str(d) for d in np.arange(1, 32)]
-time = ["{:02d}:00".format(t) for t in np.arange(0,24)]
+time = ["{:02d}:00".format(t) for t in np.arange(0, 24)]
# area of interest (Alps): North, West, South, East
area = [52, 2, 40, 20]
# ERA5 download configuration dictionary
CONFIG = {
- 'product_type': product_type,
- 'pressure_level': pressure_levels,
+ 'product_type': 'reanalysis',
'month': month,
'day': days,
'time': time,
@@ -59,6 +54,16 @@ if __name__ == '__main__':
files = [output.joinpath('_'.join(['ERA5', var, year]) + '.nc') for
year in years]
+ # check whether to download variable on pressure levels or single level
+ if var in ERA5_P_VARIABLES:
+ # download configuration: ERA5 variable on pressure levels
+ product = 'reanalysis-era5-pressure-levels'
+ CONFIG['pressure_level'] = pressure_levels # pressure levels
+ else:
+ # download configuration: ERA5 variable on single level
+ product = 'reanalysis-era5-single-levels'
+ CONFIG.pop('pressure_level') # remove pressure levels
+
# split the download to the different years: CDS API cannot handle
# requests over the whole time period
Parallel(n_jobs=min(len(years), os.cpu_count()), verbose=51)(
--
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