def __init__(self):
from utils import log_netCDF_dataset_metadata
logger.info(
'MeanDynamicTopographyDataReader initializing. Loading MDT dataset: {}'
.format(self.MDT_file_path))
self.MDT_dataset = netCDF4.Dataset(self.MDT_file_path)
logger.info('Successfully loaded MDT dataset: {}'.format(
self.MDT_file_path))
log_netCDF_dataset_metadata(self.MDT_dataset)
self.lats = np.array(self.MDT_dataset.variables['lat'])
self.lons = np.array(self.MDT_dataset.variables['lon'])
self.mdt = np.array(self.MDT_dataset.variables['mdt'][0])
self.u_geo = np.array(self.MDT_dataset.variables['u'][0])
self.v_geo = np.array(self.MDT_dataset.variables['v'][0])
self.latgrid_interp = None
self.longrid_interp = None
# Not even using the MDT dataset anymore.
self.mdt_interp = None
# self.interpolate_mdt_field()
self.ugeo_interp = None
self.vgeo_interp = None
self.interpolate_u_geo_field()
def load_surface_wind_dataset(self):
from utils import log_netCDF_dataset_metadata
uwind_dataset_filepath, vwind_dataset_filepath = self.date_to_dataset_filepath(
self.date)
logger.info(
'Loading NCEP u_wind dataset: {}'.format(uwind_dataset_filepath))
self.u_wind_dataset = netCDF4.Dataset(uwind_dataset_filepath)
logger.info('Successfully loaded NCEP u_wind dataset: {}'.format(
uwind_dataset_filepath))
log_netCDF_dataset_metadata(self.u_wind_dataset)
logger.info(
'Loading NCEP v_wind dataset: {}'.format(vwind_dataset_filepath))
self.v_wind_dataset = netCDF4.Dataset(vwind_dataset_filepath)
logger.info('Successfully loaded NCEP v_wind dataset: {}'.format(
vwind_dataset_filepath))
log_netCDF_dataset_metadata(self.v_wind_dataset)
self.lats = np.array(self.u_wind_dataset.variables['lat'])
self.lons = np.array(self.u_wind_dataset.variables['lon'])
self.lons = np.append(self.lons, 360.0)
# Numbering starts from 0 so we minus 1 to get the right index.
self.u_wind = np.array(
self.u_wind_dataset.variables['uwnd'][self.day_of_year - 1])
self.v_wind = np.array(
self.v_wind_dataset.variables['vwnd'][self.day_of_year - 1])
self.u_wind = np.c_[self.u_wind, self.u_wind[:, 0]]
self.v_wind = np.c_[self.v_wind, self.v_wind[:, 0]]
def load_u_geo_dataset(self):
from utils import log_netCDF_dataset_metadata
dataset_filepath = self.date_to_u_geo_dataset_filepath(self.date)
logger.info('Loading geostrophic velocity dataset: {}'.format(
dataset_filepath))
self.u_geo_dataset = netCDF4.Dataset(dataset_filepath)
logger.info(
'Successfully loaded geostrophic velocity dataset: {}'.format(
dataset_filepath))
log_netCDF_dataset_metadata(self.u_geo_dataset)
self.lats = np.array(self.u_geo_dataset.variables['latitude'])
self.lons = np.array(self.u_geo_dataset.variables['longitude'])
self.u_geo = np.array(self.u_geo_dataset.variables['ugos'][0])
self.v_geo = np.array(self.u_geo_dataset.variables['vgos'][0])
def load_temperature_dataset(self):
from utils import log_netCDF_dataset_metadata
dataset_filepath = self.temperature_dataset_filepath()
logger.info('Loading temperature dataset: {}'.format(dataset_filepath))
self.temperature_dataset = netCDF4.Dataset(dataset_filepath)
logger.info('Successfully loaded temperature dataset: {}'.format(dataset_filepath))
log_netCDF_dataset_metadata(self.temperature_dataset)
self.lats = np.array(self.temperature_dataset.variables['lat'])
self.lons = np.array(self.temperature_dataset.variables['lon'])
self.depths = np.array(self.temperature_dataset.variables['depth'])
field_var = 't_' + self.field_type
self.temperature_data = np.array(self.temperature_dataset.variables[field_var])
def load_alpha_dataset(self):
from utils import log_netCDF_dataset_metadata
dataset_filepath = self.date_to_alpha_dataset_filepath(self.date)
logger.info('Loading sea ice concentration dataset: {}'.format(
dataset_filepath))
self.alpha_dataset = netCDF4.Dataset(dataset_filepath)
logger.info(
'Successfully loaded sea ice concentration dataset: {}'.format(
dataset_filepath))
log_netCDF_dataset_metadata(self.alpha_dataset)
self.lats = np.array(self.alpha_dataset.variables['latitude'])
self.lons = np.array(self.alpha_dataset.variables['longitude'])
self.xgrid = np.array(self.alpha_dataset.variables['xgrid'])
self.ygrid = np.array(self.alpha_dataset.variables['ygrid'])
self.alpha = np.array(
self.alpha_dataset.variables['goddard_nt_seaice_conc'][0])
def __init__(self, time_span, avg_period, grid_size, field_type, depth_levels):
"""
:param time_span: Choose from '5564', '6574', '7584', '8594', '95A4', 'A5B2', 'decav', and 'all'.
:param avg_period: Choose from annual ('00'), monthly ('01'-'12'), and seasonal ('13' for JFM, '14' for AMJ,
'15' for JAS, and '16' for OND).
:param grid_size: Choose from '04', '01', and '5d'.
:param field_type: Choose from 'an', 'mn', 'dd', 'ma', 'sd', 'se', 'oa', and 'gp'.
:param depth_level:
"""
self.neutral_density_dataset = None
self.time_span = time_span
self.avg_period = avg_period
self.grid_size = grid_size
self.field_type = field_type
self.depth_levels = depth_levels
self.grid_size_dir = None
if grid_size == '04':
self.grid_size_dir = '0.25'
elif grid_size == '01':
self.grid_size_dir = '1.00'
elif grid_size == '5d':
self.grid_size_dir = '5deg'
self.lats = None
self.lons = None
logger.info('NeutralDensityDataset object initializing for time span {} and averaging period {}...'
.format(self.time_span, self.avg_period))
self.lats = np.linspace(lat_min, lat_max, n_lat)
self.lons = np.linspace(lon_min, lon_max, n_lon)
self.neutral_density_field = np.zeros((len(self.depth_levels), len(self.lats), len(self.lons)))
self.salinity_field = np.zeros((len(self.depth_levels), len(self.lats), len(self.lons)))
self.temperature_field = np.zeros((len(self.depth_levels), len(self.lats), len(self.lons)))
self.salinity_dataset = SalinityDataset(time_span, avg_period, grid_size, field_type)
self.temperature_dataset = TemperatureDataset(time_span, avg_period, grid_size, field_type)
# If dataset already exists and is stored, load it up.
for i in range(len(self.depth_levels)):
neutral_density_dataset_filepath = self.neutral_density_dataset_filepath(self.depth_levels[i])
try:
self.neutral_density_dataset = netCDF4.Dataset(neutral_density_dataset_filepath)
log_netCDF_dataset_metadata(self.neutral_density_dataset)
self.salinity_field[i] = np.array(self.neutral_density_dataset.variables['salinity'])
self.temperature_field[i] = np.array(self.neutral_density_dataset.variables['temperature'])
self.neutral_density_field[i] = np.array(self.neutral_density_dataset.variables['neutral_density'])
except Exception as e:
logger.error('{}'.format(e))
logger.warning('{:s} not found. Neutral density field will now be computed...'
.format(neutral_density_dataset_filepath))
return
self.neutral_density_dataset = None
self.salinity_field = np.zeros((len(self.lats), len(self.lons)))
self.temperature_field = np.zeros((len(self.lats), len(self.lons)))
self.neutral_density_field = np.zeros((len(self.lats), len(self.lons)))
self.calculate_neutral_density_field()
self.save_neutral_density_dataset()
def retroactively_compute_melting_freezing_rate():
from constants import D_e, kappa
# import constants
# constants.output_dir_path = 'D:\\output\\'
start_date = datetime.date(2011, 1, 1)
end_date = datetime.date(2016, 12, 31)
dates = date_range(start_date, end_date)
# dates = []
# for year in range(2011, 2016 + 1):
# start_date = datetime.date(year, 7, 1)
# end_date = datetime.date(year, 9, 30)
#
# dates = dates + date_range(start_date, end_date)
h_ice_dataset = SeaIceThicknessDataset(start_date)
# Load mixed layer salinity maps from Pellichero et al. (2017, 2018).
gamma_filepath = os.path.join(data_dir_path, 'Climatology_MLD_v2017.nc')
logger.info('Loading gamma dataset: {}'.format(gamma_filepath))
gamma_dataset = netCDF4.Dataset(gamma_filepath)
lats_ml = np.array(gamma_dataset.variables['lat'])
lons_ml = np.array(gamma_dataset.variables['lon'])
mixed_layer_data = np.array(gamma_dataset.variables['ML_SA'])
lats_ml_max = len(lats_ml) - 1
lons_ml_max = len(lons_ml) - 1
salinity_monthly_climo = [None] * 12
for month in range(12):
salinity_monthly_climo[month] = mixed_layer_data[month]
try:
tau_filepath = get_netCDF_filepath(field_type='daily', date=start_date)
tau_dataset = netCDF4.Dataset(tau_filepath)
lats = np.array(tau_dataset.variables['lat'])
lons = np.array(tau_dataset.variables['lon'])
except OSError as e:
logger.error('{}'.format(e))
logger.error('{:s} not found. Could not load lat, lon arrays.'.format(
tau_filepath))
alpha_avg_field = np.zeros((len(lats), len(lons)))
alpha_day_field = np.zeros((len(lats), len(lons)))
u_ice_avg_field = np.zeros((len(lats), len(lons)))
u_ice_day_field = np.zeros((len(lats), len(lons)))
v_ice_avg_field = np.zeros((len(lats), len(lons)))
v_ice_day_field = np.zeros((len(lats), len(lons)))
h_ice_avg_field = np.zeros((len(lats), len(lons)))
h_ice_day_field = np.zeros((len(lats), len(lons)))
zonal_div_avg_field = np.zeros((len(lats), len(lons)))
zonal_div_day_field = np.zeros((len(lats), len(lons)))
merid_div_avg_field = np.zeros((len(lats), len(lons)))
merid_div_day_field = np.zeros((len(lats), len(lons)))
div_avg_field = np.zeros((len(lats), len(lons)))
div_day_field = np.zeros((len(lats), len(lons)))
Ekman_term_avg_field = np.zeros((len(lats), len(lons)))
Ekman_term_day_field = np.zeros((len(lats), len(lons)))
geo_term_avg_field = np.zeros((len(lats), len(lons)))
geo_term_day_field = np.zeros((len(lats), len(lons)))
diffusion_term_avg_field = np.zeros((len(lats), len(lons)))
diffusion_term_day_field = np.zeros((len(lats), len(lons)))
salinity_avg_field = np.zeros((len(lats), len(lons)))
salinity_day_field = np.zeros((len(lats), len(lons)))
for date in dates:
tau_filepath = get_netCDF_filepath(field_type='daily', date=date)
logger.info('Averaging {:%b %d, %Y} ({:s})...'.format(
date, tau_filepath))
try:
current_tau_dataset = netCDF4.Dataset(tau_filepath)
log_netCDF_dataset_metadata(current_tau_dataset)
except OSError as e:
logger.error('{}'.format(e))
logger.warning('{:s} not found. Proceeding without it...'.format(
tau_filepath))
continue
alpha_daily_field = np.array(current_tau_dataset.variables['alpha'])
u_ice_daily_field = np.array(current_tau_dataset.variables['ice_u'])
v_ice_daily_field = np.array(current_tau_dataset.variables['ice_v'])
u_geo_daily_field = np.array(current_tau_dataset.variables['geo_u'])
v_geo_daily_field = np.array(current_tau_dataset.variables['geo_v'])
# U_Ekman_daily_field = np.array(current_tau_dataset.variables['Ekman_U'])
# V_Ekman_daily_field = np.array(current_tau_dataset.variables['Ekman_V'])
U_Ekman_daily_field = D_e * np.array(
current_tau_dataset.variables['Ekman_u'])
V_Ekman_daily_field = D_e * np.array(
current_tau_dataset.variables['Ekman_v'])
# salinity_daily_field = np.array(current_tau_dataset.variables['salinity'])
salinity_daily_field = salinity_monthly_climo[date.month - 1]
# logger.info('salinity_daily_field: min = {:}, max = {:}, mean = {:}'
# .format(np.nanmin(salinity_daily_field), np.nanmax(salinity_daily_field),
# np.nanmean(salinity_daily_field)))
h_ice_daily_field = np.zeros((len(lats), len(lons)))
zonal_div_daily_field = np.zeros((len(lats), len(lons)))
merid_div_daily_field = np.zeros((len(lats), len(lons)))
div_daily_field = np.zeros((len(lats), len(lons)))
Ekman_term_daily_field = np.zeros((len(lats), len(lons)))
geo_term_daily_field = np.zeros((len(lats), len(lons)))
diffusion_term_daily_field = np.zeros((len(lats), len(lons)))
salinity_interp_daily_field = np.zeros((len(lats), len(lons)))
salinity_interp_daily_field[:] = np.nan
# Load h_ice field for the day (i.e. the correct seasonal field).
for i in range(len(lats)):
for j in range(len(lons)):
h_ice_daily_field[i][j] = h_ice_dataset.sea_ice_thickness(
i, j, date)
i_max = len(lats) - 1
j_max = len(lons) - 1
for i in range(1, len(lats) - 1):
lat = lats[i]
# progress_percent = 100 * i / (len(lats) - 2)
# logger.info('({:}, ice_div) lat = {:.2f}/{:.2f} ({:.1f}%)'.format(date, lat, -40, progress_percent))
dx = distance(lats[i - 1], lons[0], lats[i + 1], lons[0])
dy = distance(lats[i], lons[0], lats[i], lons[2])
for j in range(len(lons)):
lon = lons[j]
if lon < 0:
lon_ml = lon + 360
else:
lon_ml = lon
# Taking modulus of j-1 and j+1 to get the correct index in the special cases of
# * j=0 (180 W) and need to use the value from j=j_max (180 E)
# * j=j_max (180 E) and need to use the value from j=0 (180 W)
jm1 = (j - 1) % j_max
jp1 = (j + 1) % j_max
u_ice_i_jp1 = u_ice_daily_field[i][jp1]
u_ice_i_jm1 = u_ice_daily_field[i][jm1]
v_ice_ip1_j = v_ice_daily_field[i + 1][j]
v_ice_im1_j = v_ice_daily_field[i - 1][j]
alpha_i_jp1 = alpha_daily_field[i][jp1]
alpha_i_jm1 = alpha_daily_field[i][jm1]
alpha_ip1_j = alpha_daily_field[i + 1][j]
alpha_im1_j = alpha_daily_field[i - 1][j]
h_ice_i_jp1 = h_ice_daily_field[i][jp1]
h_ice_i_jm1 = h_ice_daily_field[i][jm1]
h_ice_ip1_j = h_ice_daily_field[i + 1][j]
h_ice_im1_j = h_ice_daily_field[i - 1][j]
idx_lat = np.abs(lats_ml - lat).argmin()
idx_lon = np.abs(lons_ml - lon_ml).argmin()
idx_lonp1 = (idx_lon + 1) % lons_ml_max
idx_lonm1 = (idx_lon - 1) % lons_ml_max
S_ij = salinity_daily_field[idx_lat][idx_lon]
S_i_jp1 = salinity_daily_field[idx_lat][idx_lonp1]
S_i_jm1 = salinity_daily_field[idx_lat][idx_lonm1]
S_ip1_j = salinity_daily_field[idx_lat + 1][idx_lon]
S_im1_j = salinity_daily_field[idx_lat - 1][idx_lon]
salinity_interp_daily_field[i][j] = S_ij
# S_ij = salinity_daily_field[i][j]
# S_i_jp1 = salinity_daily_field[i][jp1]
# S_i_jm1 = salinity_daily_field[i][jm1]
# S_ip1_j = salinity_daily_field[i+1][j]
# S_im1_j = salinity_daily_field[i-1][j]
u_geo_ij = u_geo_daily_field[i][j]
v_geo_ij = v_geo_daily_field[i][j]
U_Ekman_ij = U_Ekman_daily_field[i][j]
V_Ekman_ij = V_Ekman_daily_field[i][j]
if not np.isnan(u_ice_i_jm1) and not np.isnan(u_ice_i_jp1):
div_x = (alpha_i_jp1 * h_ice_i_jp1 * u_ice_i_jp1 -
alpha_i_jm1 * h_ice_i_jm1 * u_ice_i_jm1) / dx
zonal_div_daily_field[i][j] = div_x
else:
zonal_div_daily_field[i][j] = np.nan
if not np.isnan(v_ice_im1_j) and not np.isnan(v_ice_ip1_j):
div_y = (alpha_ip1_j * h_ice_ip1_j * v_ice_ip1_j -
alpha_im1_j * h_ice_im1_j * v_ice_im1_j) / dy
merid_div_daily_field[i][j] = div_y
else:
merid_div_daily_field[i][j] = np.nan
if not np.isnan(zonal_div_daily_field[i][j]) and not np.isnan(
merid_div_daily_field[i][j]):
div_daily_field[i][j] = div_x + div_y
else:
div_daily_field[i][j] = np.nan
if not np.isnan(S_ij) and not np.isnan(S_i_jm1) and not np.isnan(S_i_jp1) and not np.isnan(S_ip1_j) \
and not np.isnan(S_im1_j):
dSdx = (S_i_jp1 - S_i_jm1) / dx
dSdy = (S_ip1_j - S_im1_j) / dy
del2_S = ((S_i_jp1 - 2 * S_ij + S_i_jm1) / dx**2) + (
(S_ip1_j - 2 * S_ij + S_im1_j) / dy**2)
Ekman_term_daily_field[i][j] = (U_Ekman_ij * dSdx +
V_Ekman_ij * dSdy) / S_ij
geo_term_daily_field[i][j] = (D_e / S_ij) * (
u_geo_ij * dSdx + v_geo_ij * dSdy)
diffusion_term_daily_field[i][j] = (kappa * D_e /
S_ij) * del2_S
else:
Ekman_term_daily_field[i][j] = np.nan
geo_term_daily_field[i][j] = np.nan
diffusion_term_daily_field[i][j] = np.nan
if np.isnan(alpha_daily_field[i]
[j]) or alpha_daily_field[i][j] < 0.15:
Ekman_term_daily_field[i][j] = np.nan
geo_term_daily_field[i][j] = np.nan
diffusion_term_daily_field[i][j] = np.nan
alpha_avg_field = alpha_avg_field + np.nan_to_num(alpha_daily_field)
alpha_daily_field[~np.isnan(alpha_daily_field)] = 1
alpha_daily_field[np.isnan(alpha_daily_field)] = 0
alpha_day_field = alpha_day_field + alpha_daily_field
u_ice_avg_field = u_ice_avg_field + np.nan_to_num(u_ice_daily_field)
u_ice_daily_field[~np.isnan(u_ice_daily_field)] = 1
u_ice_daily_field[np.isnan(u_ice_daily_field)] = 0
u_ice_day_field = u_ice_day_field + u_ice_daily_field
v_ice_avg_field = v_ice_avg_field + np.nan_to_num(v_ice_daily_field)
v_ice_daily_field[~np.isnan(v_ice_daily_field)] = 1
v_ice_daily_field[np.isnan(v_ice_daily_field)] = 0
v_ice_day_field = v_ice_day_field + v_ice_daily_field
h_ice_avg_field = h_ice_avg_field + np.nan_to_num(h_ice_daily_field)
h_ice_daily_field[~np.isnan(h_ice_daily_field)] = 1
h_ice_daily_field[np.isnan(h_ice_daily_field)] = 0
h_ice_day_field = h_ice_day_field + h_ice_daily_field
zonal_div_avg_field = zonal_div_avg_field + np.nan_to_num(
zonal_div_daily_field)
zonal_div_daily_field[~np.isnan(zonal_div_daily_field)] = 1
zonal_div_daily_field[np.isnan(zonal_div_daily_field)] = 0
zonal_div_day_field = zonal_div_day_field + zonal_div_daily_field
merid_div_avg_field = merid_div_avg_field + np.nan_to_num(
merid_div_daily_field)
merid_div_daily_field[~np.isnan(merid_div_daily_field)] = 1
merid_div_daily_field[np.isnan(merid_div_daily_field)] = 0
merid_div_day_field = merid_div_day_field + merid_div_daily_field
div_avg_field = div_avg_field + np.nan_to_num(div_daily_field)
div_daily_field[~np.isnan(div_daily_field)] = 1
div_daily_field[np.isnan(div_daily_field)] = 0
div_day_field = div_day_field + div_daily_field
Ekman_term_avg_field = Ekman_term_avg_field + np.nan_to_num(
Ekman_term_daily_field)
Ekman_term_daily_field[~np.isnan(Ekman_term_daily_field)] = 1
Ekman_term_daily_field[np.isnan(Ekman_term_daily_field)] = 0
Ekman_term_day_field = Ekman_term_day_field + Ekman_term_daily_field
geo_term_avg_field = geo_term_avg_field + np.nan_to_num(
geo_term_daily_field)
geo_term_daily_field[~np.isnan(geo_term_daily_field)] = 1
geo_term_daily_field[np.isnan(geo_term_daily_field)] = 0
geo_term_day_field = geo_term_day_field + geo_term_daily_field
diffusion_term_avg_field = diffusion_term_avg_field + np.nan_to_num(
diffusion_term_daily_field)
diffusion_term_daily_field[~np.isnan(diffusion_term_daily_field)] = 1
diffusion_term_daily_field[np.isnan(diffusion_term_daily_field)] = 0
diffusion_term_day_field = diffusion_term_day_field + diffusion_term_daily_field
# logger.info('salinity_interp_daily_field: min = {:}, max = {:}, mean = {:}'
# .format(np.nanmin(salinity_interp_daily_field), np.nanmax(salinity_interp_daily_field),
# np.nanmean(salinity_interp_daily_field)))
salinity_avg_field = salinity_avg_field + np.nan_to_num(
salinity_interp_daily_field)
salinity_interp_daily_field[~np.isnan(salinity_interp_daily_field)] = 1
salinity_interp_daily_field[np.isnan(salinity_interp_daily_field)] = 0
salinity_day_field = salinity_day_field + salinity_interp_daily_field
# logger.info('salinity_avg_field: min = {:}, max = {:}, mean = {:}'
# .format(np.nanmin(salinity_avg_field), np.nanmax(salinity_avg_field),
# np.nanmean(salinity_avg_field)))
# logger.info('salinity_day_field: min = {:}, max = {:}, mean = {:}'
# .format(np.nanmin(salinity_day_field), np.nanmax(salinity_day_field),
# np.nanmean(salinity_day_field)))
alpha_avg_field = np.divide(alpha_avg_field, alpha_day_field)
u_ice_avg_field = np.divide(u_ice_avg_field, u_ice_day_field)
v_ice_avg_field = np.divide(v_ice_avg_field, v_ice_day_field)
h_ice_avg_field = np.divide(h_ice_avg_field, h_ice_day_field)
zonal_div_avg_field = np.divide(zonal_div_avg_field, zonal_div_day_field)
merid_div_avg_field = np.divide(merid_div_avg_field, merid_div_day_field)
div_avg_field = 3600 * 24 * 365 * np.divide(div_avg_field, div_day_field)
Ekman_term_avg_field = 3600 * 24 * 365 * np.divide(Ekman_term_avg_field,
Ekman_term_day_field)
geo_term_avg_field = 3600 * 24 * 365 * np.divide(geo_term_avg_field,
geo_term_day_field)
diffusion_term_avg_field = 3600 * 24 * 365 * np.divide(
diffusion_term_avg_field, diffusion_term_day_field)
salinity_avg_field = np.divide(salinity_avg_field, salinity_day_field)
logger.info('salinity_avg_field: min = {:}, max = {:}, mean = {:}'.format(
np.nanmin(salinity_avg_field), np.nanmax(salinity_avg_field),
np.nanmean(salinity_avg_field)))
logger.info('salinity_day_field: min = {:}, max = {:}, mean = {:}'.format(
np.nanmin(salinity_day_field), np.nanmax(salinity_day_field),
np.nanmean(salinity_day_field)))
nc_dir = os.path.dirname(output_dir_path)
nc_filepath = os.path.join(
nc_dir,
'melting_freezing_rate_{:}_{:}.nc'.format(start_date, end_date))
if not os.path.exists(nc_dir):
logger.info('Creating directory: {:s}'.format(nc_dir))
os.makedirs(nc_dir)
logger.info('Saving fields to netCDF file: {:s}'.format(nc_filepath))
tau_dataset = netCDF4.Dataset(nc_filepath, 'w')
tau_dataset.title = 'Melting and freezing rates in the Antarctic sea ice zone'
tau_dataset.institution = 'Department of Earth, Atmospheric, and Planetary Science, ' \
'Massachusetts Institute of Technology'
tau_dataset.createDimension('time', None)
tau_dataset.createDimension('lat', len(lats))
tau_dataset.createDimension('lon', len(lons))
# TODO: Actually store a date.
time_var = tau_dataset.createVariable('time', np.float64, ('time', ))
time_var.units = 'hours since 0001-01-01 00:00:00'
time_var.calendar = 'gregorian'
lat_var = tau_dataset.createVariable('lat', np.float32, ('lat', ))
lat_var.units = 'degrees south'
lat_var[:] = lats
lon_var = tau_dataset.createVariable('lon', np.float32, ('lon', ))
lat_var.units = 'degrees west/east'
lon_var[:] = lons
var_fields = {
'alpha': alpha_avg_field,
'u_ice': u_ice_avg_field,
'v_ice': v_ice_avg_field,
'h_ice': h_ice_avg_field,
'zonal_div': zonal_div_avg_field,
'merid_div': merid_div_avg_field,
'div': div_avg_field,
'Ekman_term': Ekman_term_avg_field,
'geo_term': geo_term_avg_field,
'diffusion_term': diffusion_term_avg_field,
'salinity': salinity_avg_field
}
for var_name in var_fields.keys():
field_var = tau_dataset.createVariable(var_name,
float, ('lat', 'lon'),
zlib=True)
field_var[:] = var_fields[var_name]
tau_dataset.close()
def retroactively_compute_sea_ice_advection():
start_date = datetime.date(2005, 1, 1)
end_date = datetime.date(2005, 12, 31)
dates = date_range(start_date, end_date)
h_ice_dataset = SeaIceThicknessDataset(start_date)
import constants
constants.output_dir_path = 'D:\\output\\'
try:
tau_filepath = get_netCDF_filepath(field_type='daily', date=start_date)
tau_dataset = netCDF4.Dataset(tau_filepath)
lats = np.array(tau_dataset.variables['lat'])
lons = np.array(tau_dataset.variables['lon'])
except OSError as e:
logger.error('{}'.format(e))
logger.error('{:s} not found. Could not load lat, lon arrays.'.format(
tau_filepath))
alpha_avg_field = np.zeros((len(lats), len(lons)))
alpha_day_field = np.zeros((len(lats), len(lons)))
u_ice_avg_field = np.zeros((len(lats), len(lons)))
u_ice_day_field = np.zeros((len(lats), len(lons)))
v_ice_avg_field = np.zeros((len(lats), len(lons)))
v_ice_day_field = np.zeros((len(lats), len(lons)))
h_ice_avg_field = np.zeros((len(lats), len(lons)))
h_ice_day_field = np.zeros((len(lats), len(lons)))
zonal_div_avg_field = np.zeros((len(lats), len(lons)))
zonal_div_day_field = np.zeros((len(lats), len(lons)))
merid_div_avg_field = np.zeros((len(lats), len(lons)))
merid_div_day_field = np.zeros((len(lats), len(lons)))
div_avg_field = np.zeros((len(lats), len(lons)))
div_day_field = np.zeros((len(lats), len(lons)))
hu_dadx_avg_field = np.zeros((len(lats), len(lons)))
au_dhdx_avg_field = np.zeros((len(lats), len(lons)))
ah_dudx_avg_field = np.zeros((len(lats), len(lons)))
hv_dady_avg_field = np.zeros((len(lats), len(lons)))
av_dhdy_avg_field = np.zeros((len(lats), len(lons)))
ah_dvdy_avg_field = np.zeros((len(lats), len(lons)))
hu_dadx_day_field = np.zeros((len(lats), len(lons)))
au_dhdx_day_field = np.zeros((len(lats), len(lons)))
ah_dudx_day_field = np.zeros((len(lats), len(lons)))
hv_dady_day_field = np.zeros((len(lats), len(lons)))
av_dhdy_day_field = np.zeros((len(lats), len(lons)))
ah_dvdy_day_field = np.zeros((len(lats), len(lons)))
div2_avg_field = np.zeros((len(lats), len(lons)))
div2_day_field = np.zeros((len(lats), len(lons)))
for date in dates:
tau_filepath = get_netCDF_filepath(field_type='daily', date=date)
logger.info('Averaging {:%b %d, %Y} ({:s})...'.format(
date, tau_filepath))
try:
current_tau_dataset = netCDF4.Dataset(tau_filepath)
log_netCDF_dataset_metadata(current_tau_dataset)
except OSError as e:
logger.error('{}'.format(e))
logger.warning('{:s} not found. Proceeding without it...'.format(
tau_filepath))
continue
alpha_daily_field = np.array(current_tau_dataset.variables['alpha'])
u_ice_daily_field = np.array(current_tau_dataset.variables['ice_u'])
v_ice_daily_field = np.array(current_tau_dataset.variables['ice_v'])
h_ice_daily_field = np.zeros((len(lats), len(lons)))
zonal_div_daily_field = np.zeros((len(lats), len(lons)))
merid_div_daily_field = np.zeros((len(lats), len(lons)))
div_daily_field = np.zeros((len(lats), len(lons)))
hu_dadx_daily_field = np.zeros((len(lats), len(lons)))
au_dhdx_daily_field = np.zeros((len(lats), len(lons)))
ah_dudx_daily_field = np.zeros((len(lats), len(lons)))
hv_dady_daily_field = np.zeros((len(lats), len(lons)))
av_dhdy_daily_field = np.zeros((len(lats), len(lons)))
ah_dvdy_daily_field = np.zeros((len(lats), len(lons)))
div2_daily_field = np.zeros((len(lats), len(lons)))
# Load h_ice field for the day (i.e. the correct seasonal field).
for i in range(len(lats)):
for j in range(len(lons)):
h_ice_daily_field[i][j] = h_ice_dataset.sea_ice_thickness(
i, j, date)
# import astropy.convolution
# kernel = astropy.convolution.Box2DKernel(10)
# alpha_daily_field = astropy.convolution.convolve(alpha_daily_field, kernel, boundary='wrap')
# h_ice_daily_field = astropy.convolution.convolve(h_ice_daily_field, kernel, boundary='wrap')
# u_ice_daily_field = astropy.convolution.convolve(u_ice_daily_field, kernel, boundary='wrap')
# v_ice_daily_field = astropy.convolution.convolve(v_ice_daily_field, kernel, boundary='wrap')
i_max = len(lats) - 1
j_max = len(lons) - 1
for i in range(1, len(lats) - 1):
# lat = lats[i]
# progress_percent = 100 * i / (len(lats) - 2)
# logger.info('({:}, ice_div) lat = {:.2f}/{:.2f} ({:.1f}%)'.format(date, lat, -40, progress_percent))
dx = distance(lats[i - 1], lons[0], lats[i + 1], lons[0])
dy = distance(lats[i], lons[0], lats[i], lons[2])
for j in range(1, len(lons) - 1):
# Taking modulus of j-1 and j+1 to get the correct index in the special cases of
# * j=0 (180 W) and need to use the value from j=j_max (180 E)
# * j=j_max (180 E) and need to use the value from j=0 (180 W)
jm1 = (j - 1) % j_max
jp1 = (j + 1) % j_max
u_ice_ij = u_ice_daily_field[i][j]
u_ice_i_jp1 = u_ice_daily_field[i][jp1]
u_ice_i_jm1 = u_ice_daily_field[i][jm1]
u_ice_ip1_j = u_ice_daily_field[i + 1][j]
u_ice_im1_j = u_ice_daily_field[i - 1][j]
v_ice_ij = v_ice_daily_field[i][j]
v_ice_ip1_j = v_ice_daily_field[i + 1][j]
v_ice_im1_j = v_ice_daily_field[i - 1][j]
v_ice_i_jp1 = v_ice_daily_field[i][jp1]
v_ice_i_jm1 = v_ice_daily_field[i][jm1]
alpha_ij = alpha_daily_field[i][j]
alpha_i_jp1 = alpha_daily_field[i][jp1]
alpha_i_jm1 = alpha_daily_field[i][jm1]
alpha_ip1_j = alpha_daily_field[i + 1][j]
alpha_im1_j = alpha_daily_field[i - 1][j]
h_ice_ij = h_ice_daily_field[i][j]
h_ice_i_jp1 = h_ice_daily_field[i][jp1]
h_ice_i_jm1 = h_ice_daily_field[i][jm1]
h_ice_ip1_j = h_ice_daily_field[i + 1][j]
h_ice_im1_j = h_ice_daily_field[i - 1][j]
if not np.isnan(u_ice_i_jm1) and not np.isnan(u_ice_i_jp1):
div_x = (alpha_i_jp1 * h_ice_i_jp1 * u_ice_i_jp1 -
alpha_i_jm1 * h_ice_i_jm1 * u_ice_i_jm1) / dx
zonal_div_daily_field[i][j] = div_x
else:
zonal_div_daily_field[i][j] = np.nan
if not np.isnan(v_ice_im1_j) and not np.isnan(v_ice_ip1_j):
div_y = (alpha_ip1_j * h_ice_ip1_j * v_ice_ip1_j -
alpha_im1_j * h_ice_im1_j * v_ice_im1_j) / dy
merid_div_daily_field[i][j] = div_y
else:
merid_div_daily_field[i][j] = np.nan
if not np.isnan(zonal_div_daily_field[i][j]) and not np.isnan(
merid_div_daily_field[i][j]):
div_daily_field[i][j] = C_fw * (div_x + div_y)
else:
div_daily_field[i][j] = np.nan
if not np.isnan(alpha_i_jm1) and not np.isnan(alpha_i_jp1):
hu_dadx_daily_field[i][j] = h_ice_ij * u_ice_ij * (
alpha_i_jp1 - alpha_i_jm1) / dx
else:
hu_dadx_daily_field[i][j] = np.nan
if not np.isnan(h_ice_i_jm1) and not np.isnan(h_ice_i_jp1):
au_dhdx_daily_field[i][j] = alpha_ij * u_ice_ij * (
h_ice_i_jp1 - h_ice_i_jm1) / dx
else:
au_dhdx_daily_field[i][j] = np.nan
if not np.isnan(u_ice_i_jm1) and not np.isnan(u_ice_i_jp1):
ah_dudx_daily_field[i][j] = alpha_ij * h_ice_ij * (
u_ice_i_jp1 - u_ice_i_jm1) / dx
else:
ah_dudx_daily_field[i][j] = np.nan
if not np.isnan(alpha_im1_j) and not np.isnan(alpha_ip1_j):
hv_dady_daily_field[i][j] = h_ice_ij * v_ice_ij * (
alpha_ip1_j - alpha_im1_j) / dy
else:
hv_dady_daily_field[i][j] = np.nan
if not np.isnan(h_ice_im1_j) and not np.isnan(h_ice_ip1_j):
av_dhdy_daily_field[i][j] = alpha_ij * v_ice_ij * (
h_ice_ip1_j - h_ice_im1_j) / dy
else:
av_dhdy_daily_field[i][j] = np.nan
if not np.isnan(v_ice_im1_j) and not np.isnan(v_ice_ip1_j):
ah_dvdy_daily_field[i][j] = alpha_ij * h_ice_ij * (
v_ice_ip1_j - v_ice_im1_j) / dy
else:
ah_dvdy_daily_field[i][j] = np.nan
div2_daily_field[i][j] = hu_dadx_daily_field[i][j] + au_dhdx_daily_field[i][j] \
+ ah_dudx_daily_field[i][j] + hv_dady_daily_field[i][j] \
+ av_dhdy_daily_field[i][j] + ah_dvdy_daily_field[i][j]
# import astropy.convolution
# kernel = astropy.convolution.Box2DKernel(10)
# div_daily_field = astropy.convolution.convolve(div_daily_field, kernel, boundary='wrap')
alpha_avg_field = alpha_avg_field + np.nan_to_num(alpha_daily_field)
alpha_daily_field[~np.isnan(alpha_daily_field)] = 1
alpha_daily_field[np.isnan(alpha_daily_field)] = 0
alpha_day_field = alpha_day_field + alpha_daily_field
u_ice_avg_field = u_ice_avg_field + np.nan_to_num(u_ice_daily_field)
u_ice_daily_field[~np.isnan(u_ice_daily_field)] = 1
u_ice_daily_field[np.isnan(u_ice_daily_field)] = 0
u_ice_day_field = u_ice_day_field + u_ice_daily_field
v_ice_avg_field = v_ice_avg_field + np.nan_to_num(v_ice_daily_field)
v_ice_daily_field[~np.isnan(v_ice_daily_field)] = 1
v_ice_daily_field[np.isnan(v_ice_daily_field)] = 0
v_ice_day_field = v_ice_day_field + v_ice_daily_field
h_ice_avg_field = h_ice_avg_field + np.nan_to_num(h_ice_daily_field)
h_ice_daily_field[~np.isnan(h_ice_daily_field)] = 1
h_ice_daily_field[np.isnan(h_ice_daily_field)] = 0
h_ice_day_field = h_ice_day_field + h_ice_daily_field
zonal_div_avg_field = zonal_div_avg_field + np.nan_to_num(
zonal_div_daily_field)
zonal_div_daily_field[~np.isnan(zonal_div_daily_field)] = 1
zonal_div_daily_field[np.isnan(zonal_div_daily_field)] = 0
zonal_div_day_field = zonal_div_day_field + zonal_div_daily_field
merid_div_avg_field = merid_div_avg_field + np.nan_to_num(
merid_div_daily_field)
merid_div_daily_field[~np.isnan(merid_div_daily_field)] = 1
merid_div_daily_field[np.isnan(merid_div_daily_field)] = 0
merid_div_day_field = merid_div_day_field + merid_div_daily_field
div_avg_field = div_avg_field + np.nan_to_num(div_daily_field)
div_daily_field[~np.isnan(div_daily_field)] = 1
div_daily_field[np.isnan(div_daily_field)] = 0
div_day_field = div_day_field + div_daily_field
hu_dadx_avg_field = hu_dadx_avg_field + np.nan_to_num(
hu_dadx_daily_field)
hu_dadx_daily_field[~np.isnan(hu_dadx_daily_field)] = 1
hu_dadx_daily_field[np.isnan(hu_dadx_daily_field)] = 0
hu_dadx_day_field = hu_dadx_day_field + hu_dadx_daily_field
au_dhdx_avg_field = au_dhdx_avg_field + np.nan_to_num(
au_dhdx_daily_field)
au_dhdx_daily_field[~np.isnan(au_dhdx_daily_field)] = 1
au_dhdx_daily_field[np.isnan(au_dhdx_daily_field)] = 0
au_dhdx_day_field = au_dhdx_day_field + au_dhdx_daily_field
ah_dudx_avg_field = ah_dudx_avg_field + np.nan_to_num(
ah_dudx_daily_field)
ah_dudx_daily_field[~np.isnan(ah_dudx_daily_field)] = 1
ah_dudx_daily_field[np.isnan(ah_dudx_daily_field)] = 0
ah_dudx_day_field = ah_dudx_day_field + ah_dudx_daily_field
hv_dady_avg_field = hv_dady_avg_field + np.nan_to_num(
hv_dady_daily_field)
hv_dady_daily_field[~np.isnan(hv_dady_daily_field)] = 1
hv_dady_daily_field[np.isnan(hv_dady_daily_field)] = 0
hv_dady_day_field = hv_dady_day_field + hv_dady_daily_field
av_dhdy_avg_field = av_dhdy_avg_field + np.nan_to_num(
av_dhdy_daily_field)
av_dhdy_daily_field[~np.isnan(av_dhdy_daily_field)] = 1
av_dhdy_daily_field[np.isnan(av_dhdy_daily_field)] = 0
av_dhdy_day_field = av_dhdy_day_field + av_dhdy_daily_field
ah_dvdy_avg_field = ah_dvdy_avg_field + np.nan_to_num(
ah_dvdy_daily_field)
ah_dvdy_daily_field[~np.isnan(ah_dvdy_daily_field)] = 1
ah_dvdy_daily_field[np.isnan(ah_dvdy_daily_field)] = 0
ah_dvdy_day_field = ah_dvdy_day_field + ah_dvdy_daily_field
div2_avg_field = div2_avg_field + np.nan_to_num(div2_daily_field)
div2_daily_field[~np.isnan(div2_daily_field)] = 1
div2_daily_field[np.isnan(div2_daily_field)] = 0
div2_day_field = div2_day_field + div2_daily_field
alpha_avg_field = np.divide(alpha_avg_field, alpha_day_field)
u_ice_avg_field = np.divide(u_ice_avg_field, u_ice_day_field)
v_ice_avg_field = np.divide(v_ice_avg_field, v_ice_day_field)
h_ice_avg_field = np.divide(h_ice_avg_field, h_ice_day_field)
zonal_div_avg_field = np.divide(zonal_div_avg_field, zonal_div_day_field)
merid_div_avg_field = np.divide(merid_div_avg_field, merid_div_day_field)
div_avg_field = 3600 * 24 * 365 * np.divide(div_avg_field, div_day_field)
hu_dadx_avg_field = 3600 * 24 * 365 * np.divide(hu_dadx_avg_field,
hu_dadx_day_field)
au_dhdx_avg_field = 3600 * 24 * 365 * np.divide(au_dhdx_avg_field,
au_dhdx_day_field)
ah_dudx_avg_field = 3600 * 24 * 365 * np.divide(ah_dudx_avg_field,
ah_dudx_day_field)
hv_dady_avg_field = 3600 * 24 * 365 * np.divide(hv_dady_avg_field,
hv_dady_day_field)
av_dhdy_avg_field = 3600 * 24 * 365 * np.divide(av_dhdy_avg_field,
av_dhdy_day_field)
ah_dvdy_avg_field = 3600 * 24 * 365 * np.divide(ah_dvdy_avg_field,
ah_dvdy_day_field)
div2_avg_field = 3600 * 24 * 365 * np.divide(div2_avg_field,
div2_day_field)
nc_dir = os.path.dirname(output_dir_path)
nc_filepath = os.path.join(
nc_dir, 'ice_flux_div_{:}_{:}.nc'.format(start_date, end_date))
if not os.path.exists(nc_dir):
logger.info('Creating directory: {:s}'.format(nc_dir))
os.makedirs(nc_dir)
logger.info('Saving fields to netCDF file: {:s}'.format(nc_filepath))
tau_dataset = netCDF4.Dataset(nc_filepath, 'w')
tau_dataset.title = 'Ice flux divergence in the Antarctic sea ice zone'
tau_dataset.institution = 'Department of Earth, Atmospheric, and Planetary Science, ' \
'Massachusetts Institute of Technology'
tau_dataset.createDimension('time', None)
tau_dataset.createDimension('lat', len(lats))
tau_dataset.createDimension('lon', len(lons))
# TODO: Actually store a date.
time_var = tau_dataset.createVariable('time', np.float64, ('time', ))
time_var.units = 'hours since 0001-01-01 00:00:00'
time_var.calendar = 'gregorian'
lat_var = tau_dataset.createVariable('lat', np.float32, ('lat', ))
lat_var.units = 'degrees south'
lat_var[:] = lats
lon_var = tau_dataset.createVariable('lon', np.float32, ('lon', ))
lat_var.units = 'degrees west/east'
lon_var[:] = lons
var_fields = {
'alpha': alpha_avg_field,
'u_ice': u_ice_avg_field,
'v_ice': v_ice_avg_field,
'h_ice': h_ice_avg_field,
'zonal_div': zonal_div_avg_field,
'merid_div': merid_div_avg_field,
'div': div_avg_field,
'hu_dadx': hu_dadx_avg_field,
'au_dhdx': au_dhdx_avg_field,
'ah_dudx': ah_dudx_avg_field,
'hv_dady': hv_dady_avg_field,
'av_dhdy': av_dhdy_avg_field,
'ah_dvdy': ah_dvdy_avg_field,
'div2': div2_avg_field
}
for var_name in var_fields.keys():
field_var = tau_dataset.createVariable(var_name,
float, ('lat', 'lon'),
zlib=True)
field_var[:] = var_fields[var_name]
tau_dataset.close()