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process_cams.py
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from pathlib import Path
import numpy as np
import datetime as dt
from osgeo import gdal
from pyproj import Proj
import cdsapi
from meteo_utils import ecmwf_utils as eu
from meteo_utils import solar_irradiance as sun
from meteo_utils.ecmwf_utils import GRIB_KWARGS
import xarray as xr
import rasterio
METEO_DATA_FIELDS = ["TA", "EA", "WS", "PA", "AOT", "TCWV", "SW-IN", "LW-IN"]
ADS_VARIABLES = ['10m_u_component_of_wind', '10m_v_component_of_wind',
'2m_dewpoint_temperature', '2m_temperature',
'surface_pressure',
'surface_solar_radiation_downward_clear_sky',
'surface_solar_radiation_downwards',
'surface_thermal_radiation_downwards',
'total_column_water_vapour',
"surface_geopotential",
"total_aerosol_optical_depth_550nm",
"forecast_surface_roughness"]
DAILY_VARS = ["SW-IN-DD"]
def process_single_date(elev_input_file,
slope_input_file,
aspect_input_file,
date_int,
acq_time,
dst_folder=None,
svf_input_file=None,
blending_height=100):
"""
Parameters
----------
elev_input_file : str
Path to a GDAL compatible Digital Elevation Model
slope_input_file : str
Path to a GDAL compatible slope image (degrees)
aspect_input_file : str
Path to a GDAL compatible aspect image (0 for flat surfaces)
date_int : int or str
Acquisition date (YYYYMMDD)
acq_time : float
Acquistion time in decimal hour
dst_folder : str, optional
Path to the destination folder in which meteo products will be stored.
svf_input_file : str, optional
Path to a GDAL compatible Sky View Fraction image (0-1)
blending_height : float, optional
Elevation above ground level at which meteo products will be generated, default=100 magl
Returns
-------
output : dict
Dictionary of arrays with the output meteo products
"""
dst_folder = Path(dst_folder)
fid = gdal.Open(elev_input_file, gdal.GA_ReadOnly)
gt = fid.GetGeoTransform()
proj = fid.GetProjection()
p = Proj(proj)
minx = gt[0]
maxy = gt[3]
maxx = minx + gt[1] * fid.RasterXSize
miny = maxy + gt[5] * fid.RasterYSize
del fid
date_obj = dt.datetime.strptime(str(date_int), "%Y%m%d")
date_ini = (date_obj - dt.timedelta(1))
date_end = date_obj + dt.timedelta(1)
date_str = f"{date_ini.strftime('%Y-%m-%d')} / {date_end.strftime('%Y-%m-%d')}"
# Area is North, West, South, East
extent_geo = p(minx, maxy, inverse=True), p(maxx, miny, inverse=True)
area = [extent_geo[0][1] + 1, extent_geo[0][0] - 1,
extent_geo[1][1] - 1, extent_geo[1][0] + 1]
print(f"Querying products for extent {area}\n"
f"..and dates {date_obj - dt.timedelta(1)} to {date_obj + dt.timedelta(1)}")
print(f"Downloading \"{', '.join(ADS_VARIABLES)}\" from the Copernicus Atmospheric Store")
ads_target = dst_folder / f"{date_int}_LEVEL2_ECMWF_CAMS.grib"
eu.download_ADS_data("cams-global-atmospheric-composition-forecasts",
date_obj - dt.timedelta(1),
date_obj + dt.timedelta(1),
ADS_VARIABLES,
ads_target,
overwrite=False,
area=area)
print(f"Saved to file {ads_target}")
date_obj = date_obj + dt.timedelta(hours=acq_time)
time_zone = sun.angle_average(extent_geo[0][0], extent_geo[1][0]) / 15.
print(f"Processing ECMWF data for UTC time {date_obj}\n"
"This may take some time...")
meteo_data_fields = METEO_DATA_FIELDS + DAILY_VARS
output = eu.get_ECMWF_data(ads_target,
date_obj,
meteo_data_fields,
elev_input_file,
blending_height,
slope_input_file,
aspect_input_file,
svf_file=svf_input_file,
time_zone=0,
is_forecast=True)
if dst_folder:
for param, array in output.items():
if param not in DAILY_VARS:
hour = int(np.floor(acq_time))
minute = int(60 * (acq_time - hour))
acq_time_str = f"{hour:02}{minute:02}"
if param == "SW-IN":
for i, var1 in enumerate(["DIR", "DIF"]):
for j, var2 in enumerate(["PAR", "NIR"]):
param = f"{var2}-{var1}"
filename = f"{date_int}T{acq_time_str}_{param.upper()}.tif"
dst_file = str(dst_folder / filename)
print(f"Saving {param} to {dst_file}")
driver = gdal.GetDriverByName("MEM")
values = np.maximum(array[i][j], 0)
dims = values.shape
ds = driver.Create("MEM", dims[1], dims[0], 1, gdal.GDT_Float32)
ds.SetProjection(proj)
ds.SetGeoTransform(gt)
ds.GetRasterBand(1).WriteArray(values)
driver_opt = ['COMPRESS=DEFLATE', 'PREDICTOR=1', 'BIGTIFF=IF_SAFER']
gdal.Translate(dst_file, ds, format="GTiff",
creationOptions=driver_opt, stats=True)
else:
filename = f"{date_int}T{acq_time_str}_{param.upper()}.tif"
dst_file = str(dst_folder / filename)
print(f"Saving {param} to {dst_file}")
driver = gdal.GetDriverByName("MEM")
dims = array.shape
ds = driver.Create("MEM", dims[1], dims[0], 1, gdal.GDT_Float32)
ds.SetProjection(proj)
ds.SetGeoTransform(gt)
ds.GetRasterBand(1).WriteArray(array)
driver_opt = ['COMPRESS=DEFLATE', 'PREDICTOR=1', 'BIGTIFF=IF_SAFER']
gdal.Translate(dst_file, ds, format="GTiff",
creationOptions=driver_opt, stats=True)
else:
filename = f"{date_int}_{param.upper()}.tif"
dst_file = str(dst_folder / filename)
print(f"Saving {param} to {dst_file}")
driver = gdal.GetDriverByName("MEM")
dims = array.shape
ds = driver.Create("MEM", dims[1], dims[0], 1, gdal.GDT_Float32)
ds.SetProjection(proj)
ds.SetGeoTransform(gt)
ds.GetRasterBand(1).WriteArray(array)
driver_opt = ['COMPRESS=DEFLATE', 'PREDICTOR=1', 'BIGTIFF=IF_SAFER']
gdal.Translate(dst_file, ds, format="GTiff",
creationOptions=driver_opt, stats=True)
del ds
return output
if __name__ == "__main__":
workdir = Path()
dst_folder = workdir / "test" / "test_cams"
dem_dir = workdir / "test"
elev_input_file = dem_dir / f"dem.tif"
slope_input_file = dem_dir / f"slope.tif"
aspect_input_file = dem_dir / f"aspect.tif"
svf_input_file = dem_dir / f"svf.tif"
date_int = 20200620 # YYYYMMDD
acq_time = 11.75 # Decimal hour
blending_height = 100 # m above ground level at which meteo will be produced
process_single_date(elev_input_file,
slope_input_file,
aspect_input_file,
date_int,
acq_time,
dst_folder,
svf_input_file=svf_input_file,
blending_height=100)
out_flat = dst_folder / f"{str(date_int)}_flat.tif"
with rasterio.open(elev_input_file) as fp:
profile = fp.profile
xds = xr.open_dataset(dst_folder / f"{date_int}_LEVEL2_ECMWF_CAMS.grib",
**GRIB_KWARGS)
xds.rio.write_crs(4326, inplace=True).rio.set_spatial_dims(
x_dim="longitude",
y_dim="latitude",
inplace=True).rio.write_coordinate_system(inplace=True)
midnight = dt.datetime.strptime(str(date_int), "%Y%m%d")
sdn_flat, gt, proj = eu._get_cummulative_data(xds, "ssrd", midnight,
elev_input_file, 24, True)
# Convert to mean daily flux in W m-2
sdn_flat = sdn_flat / 24 / 3600
with rasterio.open(out_flat, "w", **profile) as fp:
fp.write(sdn_flat, 1)