def load_mask_modis(catchment, mask_folder, modis_dem):
'''
load mask and trimmed mask of catchment for modis clutha domain
'''
if modis_dem == 'clutha_dem_250m':
_, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
None,
extent_w=1.2e6,
extent_e=1.4e6,
extent_n=5.13e6,
extent_s=4.82e6,
resolution=250,
origin='bottomleft')
if modis_dem == 'si_dem_250m':
_, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
None,
extent_w=1.08e6,
extent_e=1.72e6,
extent_n=5.52e6,
extent_s=4.82e6,
resolution=250,
origin='bottomleft')
if modis_dem == 'modis_si_dem_250m':
_, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
None,
extent_w=1.085e6,
extent_e=1.72e6,
extent_n=5.52e6,
extent_s=4.82e6,
resolution=250,
origin='bottomleft')
if modis_dem == 'modis_nz_dem_250m':
_, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
None,
extent_w=1.085e6,
extent_e=2.10e6,
extent_n=6.20e6,
extent_s=4.70e6,
resolution=250,
origin='bottomleft')
# # Get the masks for the individual regions of interest
mask = np.load(mask_folder + '/{}_{}.npy'.format(catchment, modis_dem))
_, _, trimmed_mask, _, _ = trim_lat_lon_bounds(mask, lat_array, lon_array,
mask.copy(), y_centres,
x_centres)
return mask, trimmed_mask
def load_mask_modis(catchment, output_dem, mask_folder, dem_folder, modis_dem):
'''
load mask and trimmed mask of catchment for modis clutha domain
'''
if modis_dem == 'clutha_dem_250m':
# dem_file = dem_folder + modis_dem + '.tif'
_, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(None)
if modis_dem == 'si_dem_250m':
# dem_file = dem_folder + modis_dem + '.tif'
_, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
None,
extent_w=1.08e6,
extent_e=1.72e6,
extent_n=5.52e6,
extent_s=4.82e6,
resolution=250)
if modis_dem == 'modis_si_dem_250m':
# dem_file = dem_folder + modis_dem + '.tif'
_, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
None,
extent_w=1.085e6,
extent_e=1.72e6,
extent_n=5.52e6,
extent_s=4.82e6,
resolution=250)
# # Get the masks for the individual regions of interest
mask = np.load(mask_folder + '/{}_{}.npy'.format(catchment, modis_dem))
_, _, trimmed_mask, _, _ = trim_lat_lon_bounds(mask, lat_array, lon_array,
mask.copy(), y_centres,
x_centres)
return mask, trimmed_mask
years_to_take = range(2000, 2016 + 1) # range(2001, 2013 + 1)
ta_version = 'jobst_ucc'
# input met data
nc_file_rain = 'Z:/JOBST_met_data/PRECIP_WITH_UCC/CLUTHA_PRECIP_250m_' # provide only partial filename up to 01-Jan-2000to31-dec-2000
nc_file_tmax = 'Z:/JOBST_met_data/Tmax/CLUTHA_Tmax_250m_'
nc_file_tmin = 'Z:/JOBST_met_data/Tmin/CLUTHA_Tmin_250m_'
nc_file_srad = 'Z:/newVCSN/srad_vclim_clidb_1972010100_2017102000_south-island_p05_daily.nc'
# output met data
met_out_folder = 'T:/DSC-Snow/input_data_hourly'
####
# set up input and output DEM for processing
# output DEM
nztm_dem, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
dem_file, origin=origin)
data_id = '{}_{}'.format(catchment,
output_dem) # name to identify the output data
if mask_dem == True:
# Get the masks for the individual regions of interest
if mask_created == True: # load precalculated mask
mask = np.load(mask_folder +
'/{}_{}.npy'.format(catchment, output_dem))
if origin == 'topleft':
mask = np.flipud(mask)
else: # create mask and save to npy file
# masks = get_masks() #TODO set up for multiple masks
mask = create_mask_from_shpfile(lat_array, lon_array, mask_shpfile)
if origin == 'topleft': # flip to save as bottom left
mask = np.flipud(mask)
np.save(mask_folder + '/{}_{}.npy'.format(catchment, output_dem),
catchment = 'SI'
output_dem = 'si_dem_250m' # identifier for output dem
years_to_take = np.arange(2001,
2020 + 1) # [2013 + 1] # range(2001, 2013 + 1)
model_swe_sc_threshold = 20 # threshold for treating a grid cell as snow covered (mm w.e)
model_output_folder = '/nesi/nobackup/niwa00004/jonoconway/snow_sims_nz/vcsn'
plot_folder = '/nesi/nobackup/niwa00004/jonoconway/snow_sims_nz'
dem_folder = '/nesi/project/niwa00004/jonoconway' # dem used for output #'C:/Users/conwayjp/OneDrive - NIWA/Data/GIS_DATA/Topography/DEM_NZSOS'#
print('loading dem')
# load si dem and trim to size. (modis has smaller extent to west (1.085e6)
si_dem_file = dem_folder + '/si_dem_250m' + '.tif'
nztm_dem, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
si_dem_file,
extent_w=1.08e6,
extent_e=1.72e6,
extent_n=5.52e6,
extent_s=4.82e6,
resolution=250)
nztm_dem = nztm_dem[:, 20:]
x_centres = x_centres[20:]
lat_array = lat_array[:, 20:]
lon_array = lon_array[:, 20:]
plt.rcParams.update({'font.size': 6})
plt.rcParams.update({'axes.titlesize': 6})
bin_edges = [-0.001, 30, 60, 90, 120, 180, 270, 360
] # use small negative number to include 0 in the interpolation
for i, year_to_take in enumerate(years_to_take):
1) # [2013 + 1] # range(2001, 2013 + 1)
plot_folder = 'C:/Users/conwayjp/OneDrive - NIWA/projects/CARH2101/snow reanalysis/NZ/august2021' #TODO
# plot_folder = '/nesi/nobackup/niwa00004/jonoconway/snow_sims_nz'
# model_analysis_area = 145378 # sq km.
catchment = 'NZ' # string identifying catchment modelled #TODO
mask_folder = 'C:/Users/conwayjp/OneDrive - NIWA/projects/CARH2101/snow reanalysis'
dem_folder = 'C:/Users/conwayjp/OneDrive - NIWA/Data/GIS_DATA/Topography/DEM_NZSOS'
modis_dem = 'modis_nz_dem_250m' #TODO
if modis_dem == 'modis_si_dem_250m':
si_dem_file = dem_folder + '/si_dem_250m' + '.tif'
nztm_dem, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
si_dem_file,
extent_w=1.08e6,
extent_e=1.72e6,
extent_n=5.52e6,
extent_s=4.82e6,
resolution=250)
nztm_dem = nztm_dem[:, 20:]
x_centres = x_centres[20:]
lat_array = lat_array[:, 20:]
lon_array = lon_array[:, 20:]
modis_output_dem = 'si_dem_250m'
mask = np.load(mask_folder + '/{}_{}.npy'.format(catchment, modis_dem))
elif modis_dem == 'modis_nz_dem_250m':
si_dem_file = dem_folder + '/nz_dem_250m' + '.tif'
_, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
None,
extent_w=1.085e6,
in_elev = np.ones((260, 243))
# code to get output lat/lons
# dem_folder = '/mnt/data/GIS_DATA/Topography/DEM_NZSOS/'
#catchment = 'Clutha'
dem = 'clutha_dem_250m'
#subcatchment = 'qldc_ta_area'
catchment = 'Clutha'
mask_folder = r'C:\Users\conwayjp\OneDrive - NIWA\Temp\Masks'
# dem_file = dem_folder + dem + '.tif'
# elev, easting, northing, lat_array, lon_array = setup_nztm_dem(dem_file)
# create new outlons and out_lats using trim for the qldc mask here
nztm_dem, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
dem_file=None)
mask = np.load(mask_folder + '/{}_{}.npy'.format(catchment, dem))
# qldc_mask = np.load(mask_folder + '/{}_{}.npy'.format(subcatchment, dem))
out_lats, out_lons, trimmed_mask, _, _ = trim_lat_lon_bounds(
mask, lat_array, lon_array, mask.copy(), y_centres, x_centres)
# returns: lats, lons, elev, northings, eastings
mask = trimmed_mask
#out_lons = lon_array
out_lats = np.flipud(out_lats) #(lat_array)
# northing = np.flipud(northing)
# out_elev = elev
out_elev = trimmed_mask * 0.0
"""
create masks for NZ and SI wide domains for simulations
Jono Conway
"""
import matplotlib.pylab as plt
import numpy as np
from nz_snow_tools.util.utils import setup_nztm_dem
# # set up modis 250m nz grid
nztm_dem2, x_centres2, y_centres2, lat_array2, lon_array2 = setup_nztm_dem(
None,
extent_w=1.085e6,
extent_e=2.10e6,
extent_n=6.20e6,
extent_s=4.70e6,
resolution=250,
origin='bottomleft')
# read in 250m grid topography from
nztm_dem, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
r"C:\Users\conwayjp\OneDrive - NIWA\Data\GIS_DATA\Topography\DEM_NZSOS\nz_dem_250m.tif",
extent_w=1.05e6,
extent_e=2.10e6,
extent_n=6.275e6,
extent_s=4.70e6,
resolution=250,
origin='bottomleft')
# mask for nz modis domain on nz 250m dem domain
modis_nz_ew_extent = np.logical_and(x_centres > 1.085e6, x_centres < 2.10e6)
import netCDF4 as nc
import numpy as np
import matplotlib.pylab as plt
from nz_snow_tools.util.utils import setup_nztm_dem
origin = 'topleft' # origin of new DEM surface
out_file = 'C:/Users/conwayjp/OneDrive - NIWA/projects/DSC Snow/Projects-DSC-Snow/runs/idealised/met_off_origin{}.nc'.format(origin)
inp_t = -2.0
inp_p = 20.0
# create new dem surface
_, eastings, northings, lats, lons = setup_nztm_dem(dem_file=None, extent_w=1.235e6, extent_e=1.26e6, extent_n=5.05e6, extent_s=5.025e6, resolution=250,
origin=origin)
elev = np.zeros((100, 100)) # set up dummy elevation at 0
# put onto new grid
t_grid = inp_t * np.ones((100, 100))
p_grid = inp_p * np.ones((100, 100))
file_out = nc.Dataset(out_file, 'w')
# start with georeferencing
file_out.createDimension('rows', len(northings))
file_out.createDimension('columns', len(eastings))
file_out.createDimension('latitude', len(northings))
file_out.createDimension('longitude', len(eastings))
# import argparse
import netCDF4 as nc
import numpy as np
import matplotlib.pylab as plt
from nz_snow_tools.util.utils import setup_nztm_dem
from nz_snow_tools.util.write_fsca_to_netcdf import setup_nztm_grid_netcdf
lapse = -0.005
climate_file = nc.Dataset(
r"T:\DSC-Snow\input_data_hourly\met_inp_Clutha_nztm250m_2016_norton_topleft.nc"
)
origin = 'topleft' # origin of new DEM surface
# create dem surface
clutha_dem, inp_eastings, inp_northings, _, _ = setup_nztm_dem(
'Z:/GIS_DATA/Topography/DEM_NZSOS/clutha_dem_250m.tif')
y_point = np.where(
climate_file.variables['northing'][:] == inp_northings[703])[0][0]
x_point = np.where(
climate_file.variables['easting'][:] == inp_eastings[133])[0][0]
inp_t = climate_file.variables['air_temperature'][:, y_point, x_point]
inp_p = climate_file.variables['precipitation_amount'][:, y_point, x_point]
inp_sw = climate_file.variables['surface_downwelling_shortwave_flux'][:,
y_point,
x_point]
inp_elev = climate_file.variables['elevation'][y_point, x_point]
hourly_dt = nc.num2date(climate_file.variables['time'][:],
climate_file.variables['time'].units)
# create new dem surface
import pickle
import netCDF4 as nc
import numpy as np
import matplotlib.pylab as plt
from nz_snow_tools.util.utils import setup_nztm_dem
origin = 'topleft' # origin of new DEM surface
out_file = '/met_offset_origin{}_trimmed2_withnan.nc'.format(origin)
working_folder = 'C:/Users/conwayjp/OneDrive - NIWA/projects/DSC Snow/for_Christian_June2021'
# load dem surface
dem_file = 'C:/Users/conwayjp/OneDrive - NIWA/Data/GIS_DATA/Topography/DEM_NZSOS/si_dem_250m.tif'
elev, eastings, northings, lats, lons = setup_nztm_dem(dem_file,
extent_w=1.08e6,
extent_e=1.72e6,
extent_n=5.52e6,
extent_s=4.82e6,
resolution=250)
# load t offset file - Trimmed2 has areas wiht poor model performance removed.
t_grid = pickle.load(open(
working_folder +
'/t_bias_optim_t_NS_SouthIsland_swe20_norton_5_t-2_p0_topleft_rs4_smooth10_trimmed2_fullres_withnan.pkl',
'rb'),
encoding='latin1')
# set precip offset to 0
# p_grid = np.zeros(t_grid.shape)
file_out = nc.Dataset(working_folder + out_file, 'w')
# start with georeferencing
skip_header=1,
usecols=2)
hs_sites['elevation'] = np.genfromtxt(store_folder +
'/SIN snow depth sites.csv',
delimiter=',',
skip_header=1,
usecols=3)
dem_folder = 'C:/Users/conwayjp/OneDrive - NIWA/Data/GIS_DATA/Topography/DEM_NZSOS/'
dem = 'nz_dem_500m'
dem_file = dem_folder + dem + '.tif'
nztm_dem, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
dem_file,
extent_w=1.05e6,
extent_e=2.10e6,
extent_n=6.275e6,
extent_s=4.70e6,
resolution=500)
# point = wgs84_to_nztm(-45,176)
map_crs = ccrs.TransverseMercator(central_longitude=173.0,
central_latitude=0.0,
false_easting=1600000,
false_northing=10000000,
scale_factor=0.9996,
globe=ccrs.Globe(ellipse='GRS80'))
data_crs = ccrs.PlateCarree() # data is in lat/lon coordinates
plt.figure(figsize=(4.5, 5))
catchment = 'SI' # identifier for catchment mask and output netcdf
mask_dem = False # boolean to set whether or not to mask the output dem
output_dem = 'nztm250m' # identifier for output dem
out_file = 'C:/Users/conwayjp/OneDrive - NIWA/projects/DSC Snow/Projects-DSC-Snow/runs/input_DEM/{}_{}_topo_no_ice_origin{}.nc'.format(
catchment, output_dem, origin)
sky_view_file = 'C:/Users/conwayjp/OneDrive - NIWA/Data/GIS_DATA/Topography/DEM_NZSOS/nzdem_SI_125v1_Vsky_DS.tif'
# parameters needed only for mask
mask_shpfile = (
'Z:/GIS_DATA/Hydrology/Catchments/{} full WGS84.shp'.format(catchment))
mask_created = True # boolean to set whether or not the mask has already been created
#input_dem = 'clutha_dem_250m'
input_dem = dem_file.split('/')[-1].split('.')[0]
if input_dem == 'clutha_dem_250m':
nztm_dem, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
dem_file=dem_file, origin=origin)
elif input_dem == 'si_dem_250m':
nztm_dem, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
dem_file=dem_file,
origin=origin,
extent_w=1.08e6,
extent_e=1.72e6,
extent_n=5.52e6,
extent_s=4.82e6,
resolution=250)
else:
print('cannot interpret dem file specified')
if sky_view_file is not None:
nztm_skyview, _, _, _, _ = setup_nztm_dem(dem_file=sky_view_file,
