def validate_areaavg_annual_max(nemo_configs:dict, obs_manager:CisNicIceManager, start_year=-np.Inf, end_year=np.Inf,
season_month_start=11, season_month_count=5, mask_shape_file=""):
"""
the year of the start of the season corresonds to the aggregated value for the season, i.e. if season starts in Oct 2009 and ends in March 2010, then the maximum value
for the season would correspond to 2009
:param nemo_configs:
:param obs_manager:
:param start_year:
:param end_year:
"""
lake_mask_obs = get_mask(obs_manager.lons, obs_manager.lats, shp_path=mask_shape_file) > 0.5
icefr_obs = obs_manager.get_area_avg_ts(lake_mask_obs, start_year=start_year, end_year=end_year)
plot_utils.apply_plot_params(width_cm=10, height_cm=8, font_size=8)
fig = plt.figure()
icefr_obs_ann_max = icefr_obs.groupby(lambda d: __map_date_to_seasonyear(d, season_month_start, season_month_count)).max().drop(-1)
ax = icefr_obs_ann_max.plot(label="Obs.", marker="o", markersize=0.5, linewidth=0.5)
label_to_nemo_ts = OrderedDict()
for label, nemo_config in nemo_configs.items():
assert isinstance(nemo_config, NemoYearlyFilesManager)
lake_mask_mod = get_mask(nemo_config.lons, nemo_config.lats, shp_path=mask_shape_file) > 0.5
label_to_nemo_ts[label] = nemo_config.get_area_avg_ts(lake_mask_mod, start_year=start_year, end_year=end_year, )
annual_max = label_to_nemo_ts[label].groupby(lambda d: __map_date_to_seasonyear(d, season_month_start, season_month_count)).max().drop(-1)
assert isinstance(annual_max, pd.Series)
annual_max.plot(
ax=ax, label=label + " (R = {:.2f})".format(annual_max.corr(icefr_obs_ann_max)), marker="o", markersize=0.5, linewidth=0.5)
ax.legend()
ax.grid(True, linewidth=0.2, linestyle="dashed")
ax.set_ylim([0, 1])
img_file = img_folder.joinpath("icefr_area_avg_max_{}-{}.png".format(start_year, end_year))
fig.savefig(str(img_file), bbox_inches="tight", dpi=300)
plt.close(fig)
def __get_lons_lats_basemap_from_rpn(path=DEFAULT_PATH_FOR_GEO_DATA,
vname="STBM", region_of_interest_shp=None, **bmp_kwargs):
"""
:param path:
:param vname:
:return: get basemap object for the variable in the given file
"""
with RPN(str(path)) as r:
_ = r.variables[vname][:]
proj_params = r.get_proj_parameters_for_the_last_read_rec()
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
rll = RotatedLatLon(**proj_params)
if region_of_interest_shp is not None:
mask = get_mask(lons, lats, region_of_interest_shp)
delta_points = 10
i_arr, j_arr = np.where(mask >= 0.5)
i_min, i_max = i_arr.min() - delta_points, i_arr.max() + delta_points
j_min, j_max = j_arr.min() - delta_points, j_arr.max() + delta_points
slices = (slice(i_min,i_max + 1), slice(j_min,j_max + 1))
bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons[slices], lats2d=lats[slices], **bmp_kwargs)
else:
bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons, lats2d=lats, **bmp_kwargs)
return lons, lats, bmp
def __get_lons_lats_basemap_from_rpn(path=DEFAULT_PATH_FOR_GEO_DATA,
vname="STBM",
region_of_interest_shp=None,
**bmp_kwargs):
"""
:param path:
:param vname:
:return: get basemap object for the variable in the given file
"""
with RPN(str(path)) as r:
_ = r.variables[vname][:]
proj_params = r.get_proj_parameters_for_the_last_read_rec()
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
rll = RotatedLatLon(**proj_params)
if region_of_interest_shp is not None:
mask = get_mask(lons, lats, region_of_interest_shp)
delta_points = 10
i_arr, j_arr = np.where(mask >= 0.5)
i_min, i_max = i_arr.min() - delta_points, i_arr.max() + delta_points
j_min, j_max = j_arr.min() - delta_points, j_arr.max() + delta_points
slices = (slice(i_min, i_max + 1), slice(j_min, j_max + 1))
bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons[slices],
lats2d=lats[slices],
**bmp_kwargs)
else:
bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons,
lats2d=lats,
**bmp_kwargs)
return lons, lats, bmp
def get_lake_masks(lons2d, lats2d):
"""
Get a mask for each lake in the LAKE_IDS list
:param lons2d:
:param lats2d:
"""
res = {}
for lid, lid_shp_name in LAKE_ID_TO_SHP_POLYGON_NAME.items():
the_mask = mask_from_shp.get_mask(lons2d=lons2d, lats2d=lats2d, shp_path=GL_COASTLINES_SHP, polygon_name=lid_shp_name)
res[lid] = the_mask
return res
def plot_accumulation_area_and_glaciers_for_selected_basin(
basin_shp="/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/fraizer/fraizer.shp",
polygon_name=None,
hints=None):
route_data_path = "/RESCUE/skynet3_rech1/huziy/NEI_geophysics/WC_0.11_deg/geophys_CORDEX_NA_0.11deg_695x680_filled_grDes_barBor_Crop2Gras_peat_with_directions"
lons, lats, basemap = get_lons_lats_basemap(route_data_path,
varname="FACC")
basin_mask = get_mask(lons2d=lons,
lats2d=lats,
shp_path=basin_shp,
polygon_name=polygon_name,
hints=hints)
i_arr, j_arr = np.where(basin_mask)
assert basin_mask.sum() > 0
plt.figure()
plt.pcolormesh(basin_mask.T)
plt.show()
i_min, i_max = i_arr.min() - 25, i_arr.max() + 5
j_min, j_max = j_arr.min() - 5, j_arr.max() + 5
i_min = max(0, i_min)
i_max = min(i_max, lons.shape[0] - 1)
j_min = max(0, j_min)
j_max = min(j_max, lons.shape[1] - 1)
lons_target, lats_target = lons[i_min:i_max + 1,
j_min:j_max + 1], lats[i_min:i_max + 1,
j_min:j_max + 1]
plot_acc_area_with_glaciers(
gmask_vname="VF",
gmask_level=2,
gmask_path=
"/RESCUE/skynet3_rech1/huziy/NEI_geophysics/WC_0.11_deg/fields_from_Caio/WC011_VF2.rpn",
route_data_path=
"/RESCUE/skynet3_rech1/huziy/NEI_geophysics/WC_0.11_deg/geophys_CORDEX_NA_0.11deg_695x680_filled_grDes_barBor_Crop2Gras_peat_with_directions",
lons_target=lons_target,
lats_target=lats_target,
basin_shape_files=[
basin_shp,
])
def get_basin_outlet_indices(lons2d, lats2d, accumulation_area, shp_path=""):
the_mask = get_mask(lons2d=lons2d, lats2d=lats2d, shp_path=shp_path)
basin_ids = np.unique(the_mask[the_mask > 0])
print("basin_ids = ", basin_ids)
i_out_list = []
j_out_list = []
for the_id in list(basin_ids):
vmax = np.max(accumulation_area[the_mask == the_id])
i1, j1 = np.where(accumulation_area == vmax)
i_out_list.append(i1)
j_out_list.append(j1)
i_out_list = np.array(i_out_list)
j_out_list = np.array(j_out_list)
return i_out_list, j_out_list
def get_basin_outlet_indices(lons2d, lats2d, accumulation_area, shp_path=""):
the_mask = get_mask(lons2d=lons2d, lats2d=lats2d, shp_path=shp_path)
basin_ids = np.unique(the_mask[the_mask > 0])
print("basin_ids = ", basin_ids)
i_out_list = []
j_out_list = []
for the_id in list(basin_ids):
vmax = np.max(accumulation_area[the_mask == the_id])
i1, j1 = np.where(accumulation_area == vmax)
i_out_list.append(i1)
j_out_list.append(j1)
i_out_list = np.array(i_out_list)
j_out_list = np.array(j_out_list)
return i_out_list, j_out_list
def get_basemap_using_shape_with_polygons_of_interest(self, lons, lats, shp_path=None, mask_margin=5, **kwargs):
if shp_path is None:
return self.get_basemap(lons=lons, lats=lats, **kwargs)
reg_of_interest = get_mask(lons, lats, shp_path=shp_path) > 0
i_list, j_list = np.where(reg_of_interest)
i_min = min(i_list) - mask_margin
i_max = max(i_list) + mask_margin
j_min = min(j_list) - mask_margin
j_max = max(j_list) + mask_margin
bsmap = self.get_basemap(lons=lons[i_min:i_max + 1, j_min:j_max + 1],
lats=lats[i_min:i_max + 1, j_min:j_max + 1])
return bsmap, reg_of_interest
def get_basemap_using_shape_with_polygons_of_interest(
self, lons, lats, shp_path=None, mask_margin=5, **kwargs):
if shp_path is None:
return self.get_basemap(lons=lons, lats=lats, **kwargs)
reg_of_interest = get_mask(lons, lats, shp_path=shp_path) > 0
i_list, j_list = np.where(reg_of_interest)
i_min = min(i_list) - mask_margin
i_max = max(i_list) + mask_margin
j_min = min(j_list) - mask_margin
j_max = max(j_list) + mask_margin
bsmap = self.get_basemap(lons=lons[i_min:i_max + 1, j_min:j_max + 1],
lats=lats[i_min:i_max + 1, j_min:j_max + 1],
**kwargs)
return bsmap, reg_of_interest
def get_gl_mask(path: Path):
"""
:param path:
:return:
"""
sel_file = None
if not path.is_dir():
sel_file = path
else:
for f in path.iterdir():
sel_file = f
break
with xarray.open_dataset(sel_file) as ds:
lons, lats = [ds[k].values for k in ["lon", "lat"]]
lons[lons > 180] -= 360
return get_mask(lons2d=lons, lats2d=lats, shp_path="data/shp/Great_lakes_coast_shape/gl_cst.shp") > 0.5
def plot_accumulation_area_and_glaciers_for_selected_basin(basin_shp="/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/fraizer/fraizer.shp",
polygon_name=None,
hints=None):
route_data_path = "/RESCUE/skynet3_rech1/huziy/NEI_geophysics/WC_0.11_deg/geophys_CORDEX_NA_0.11deg_695x680_filled_grDes_barBor_Crop2Gras_peat_with_directions"
lons, lats, basemap = get_lons_lats_basemap(route_data_path, varname="FACC")
basin_mask = get_mask(lons2d=lons, lats2d=lats, shp_path=basin_shp,
polygon_name=polygon_name,
hints=hints)
i_arr, j_arr = np.where(basin_mask)
assert basin_mask.sum() > 0
plt.figure()
plt.pcolormesh(basin_mask.T)
plt.show()
i_min, i_max = i_arr.min() - 25, i_arr.max() + 5
j_min, j_max = j_arr.min() - 5, j_arr.max() + 5
i_min = max(0, i_min)
i_max = min(i_max, lons.shape[0] - 1)
j_min = max(0, j_min)
j_max = min(j_max, lons.shape[1] - 1)
lons_target, lats_target = lons[i_min: i_max + 1, j_min: j_max + 1], lats[i_min: i_max + 1, j_min: j_max + 1]
plot_acc_area_with_glaciers(gmask_vname="VF", gmask_level=2,
gmask_path="/RESCUE/skynet3_rech1/huziy/NEI_geophysics/WC_0.11_deg/fields_from_Caio/WC011_VF2.rpn",
route_data_path="/RESCUE/skynet3_rech1/huziy/NEI_geophysics/WC_0.11_deg/geophys_CORDEX_NA_0.11deg_695x680_filled_grDes_barBor_Crop2Gras_peat_with_directions",
lons_target=lons_target, lats_target=lats_target,
basin_shape_files=[basin_shp, ])
def get_gl_mask(path: Path):
"""
:param path:
:return:
"""
sel_file = None
if not path.is_dir():
sel_file = path
else:
for f in path.iterdir():
sel_file = f
break
with xarray.open_dataset(sel_file) as ds:
lons, lats = [ds[k].values for k in ["lon", "lat"]]
lons[lons > 180] -= 360
#return get_mask(lons2d=lons, lats2d=lats, shp_path="data/shp/Great_lakes_coast_shape/gl_cst.shp") > 0.5
return get_mask(lons2d=lons,
lats2d=lats,
shp_path="data/shp/Great_Lakes/Great_Lakes.shp") > 0.5
def plot_directions(nc_path_to_directions="", grid_config=gc, margin=20, shape_path_to_focus_polygons=None):
"""
:param margin: margin=20 corresponds to the usual free zone
:param grid_config:
:param nc_path_to_directions:
:param shape_path_to_focus_polygons: if Not None, the path to the polygons
Everything outside the specified polygons is masked
"""
fig = plt.figure(figsize=(15, 15))
ds = Dataset(nc_path_to_directions)
var_name = "accumulation_area"
data = ds.variables[var_name][margin:-margin, margin:-margin]
data = np.ma.masked_where(data <= 0, data)
# flow directions
fldr = ds.variables["flow_direction_value"][margin:-margin, margin:-margin]
i_shifts, j_shifts = direction_and_value.flowdir_values_to_shift(fldr)
lons, lats = [ds.variables[key][margin:-margin, margin:-margin] for key in ["lon", "lat"]]
reg_of_interest = None
if shape_path_to_focus_polygons is not None:
reg_of_interest = get_mask(lons, lats, shp_path=shape_path_to_focus_polygons) > 0
i_list, j_list = np.where(reg_of_interest)
mask_margin = 5 # margin of the mask in grid points
i_min = min(i_list) - mask_margin
i_max = max(i_list) + mask_margin
j_min = min(j_list) - mask_margin
j_max = max(j_list) + mask_margin
bsmap = grid_config.get_basemap(
lons=lons[i_min : i_max + 1, j_min : j_max + 1], lats=lats[i_min : i_max + 1, j_min : j_max + 1]
)
assert isinstance(bsmap, Basemap)
bsmap.readshapefile(shapefile=shape_path_to_focus_polygons[:-4], name="basin", linewidth=1, color="m")
else:
bsmap = grid_config.get_basemap(lons=lons, lats=lats)
x, y = bsmap(lons, lats)
nx, ny = x.shape
inds_j, inds_i = np.meshgrid(range(ny), range(nx))
inds_i_next = inds_i + i_shifts
inds_j_next = inds_j + j_shifts
inds_i_next = np.ma.masked_where((inds_i_next == nx) | (inds_i_next == -1), inds_i_next)
inds_j_next = np.ma.masked_where((inds_j_next == ny) | (inds_j_next == -1), inds_j_next)
u = np.ma.masked_all_like(x)
v = np.ma.masked_all_like(x)
good = (~inds_i_next.mask) & (~inds_j_next.mask)
u[good] = x[inds_i_next[good], inds_j_next[good]] - x[inds_i[good], inds_j[good]]
v[good] = y[inds_i_next[good], inds_j_next[good]] - y[inds_i[good], inds_j[good]]
bsmap.fillcontinents(color="0.2", lake_color="aqua")
bsmap.drawmapboundary(fill_color="aqua")
bsmap.quiver(
x, y, u, v, pivot="tail", width=0.0005, scale_units="xy", headlength=20, headwidth=15, scale=1, zorder=5
)
bsmap.drawcoastlines(linewidth=0.5)
bsmap.drawrivers(color="b")
plt.savefig(nc_path_to_directions[:-3] + ".png", bbox_inches="tight")
def main(path="", reg_of_interest=None):
out_folder = Path(path).parent
clevs = common_params.clevs_lkeff_snowfall
ds = xr.open_dataset(path)
snfl = ds["snow_fall"].squeeze()
lons, lats = snfl.coords["lon"].values, snfl.coords["lat"].values
near_lake_100km_zone_mask = None
if reg_of_interest is None:
reg_of_interest = common_params.great_lakes_limits.get_mask_for_coords(lons, lats)
# temporary
lake_mask = get_mask(lons, lats, shp_path=common_params.GL_COAST_SHP_PATH) > 0.1
print("lake_mask shape", lake_mask.shape)
# mask lake points
reg_of_interest &= ~lake_mask
# get the KDTree for interpolation purposes
ktree = KDTree(data=list(zip(*lat_lon.lon_lat_to_cartesian(lon=lons.flatten(), lat=lats.flatten()))))
# define the 100km near lake zone
near_lake_100km_zone_mask = get_zone_around_lakes_mask(lons=lons, lats=lats, lake_mask=lake_mask,
ktree=ktree, dist_km=100)
reg_of_interest &= near_lake_100km_zone_mask
# snfl.plot()
# plt.show()
b = Basemap(lon_0=180,
llcrnrlon=common_params.great_lakes_limits.lon_min,
llcrnrlat=common_params.great_lakes_limits.lat_min,
urcrnrlon=common_params.great_lakes_limits.lon_max,
urcrnrlat=common_params.great_lakes_limits.lat_max,
resolution="i")
xx, yy = b(lons, lats)
# print("Basemap corners: ", lons[i_min, j_min] - 360, lons[i_max, j_max] - 360)
plot_utils.apply_plot_params(font_size=20)
fig = plt.figure()
nrows = 1
ncols = 1
gs = GridSpec(ncols=ncols, nrows=nrows)
# bn = BoundaryNorm(clevs, len(clevs) - 1)
# cmap = cm.get_cmap("nipy_spectral")
cmap, bn = colors.from_levels_and_colors(clevs, ["white", "indigo", "blue", "dodgerblue", "aqua", "lime", "yellow", "gold",
"orange", "red"])
def main(path="", reg_of_interest=None):
out_folder = Path(path).parent
clevs = common_params.clevs_lkeff_snowfall
ds = xr.open_dataset(path)
snfl = ds["snow_fall"].squeeze()
lons, lats = snfl.coords["lon"].values, snfl.coords["lat"].values
near_lake_100km_zone_mask = None
if reg_of_interest is None:
reg_of_interest = common_params.great_lakes_limits.get_mask_for_coords(lons, lats)
# temporary
lake_mask = get_mask(lons, lats, shp_path=common_params.GL_COAST_SHP_PATH) > 0.1
print("lake_mask shape", lake_mask.shape)
# mask lake points
reg_of_interest &= ~lake_mask
# get the KDTree for interpolation purposes
ktree = KDTree(data=list(zip(*lat_lon.lon_lat_to_cartesian(lon=lons.flatten(), lat=lats.flatten()))))
# define the 100km near lake zone
near_lake_100km_zone_mask = get_zone_around_lakes_mask(lons=lons, lats=lats, lake_mask=lake_mask,
ktree=ktree, dist_km=200)
reg_of_interest &= near_lake_100km_zone_mask
# snfl.plot()
# plt.show()
b = Basemap(lon_0=180,
llcrnrlon=common_params.great_lakes_limits.lon_min,
llcrnrlat=common_params.great_lakes_limits.lat_min,
urcrnrlon=common_params.great_lakes_limits.lon_max,
urcrnrlat=common_params.great_lakes_limits.lat_max,
resolution="i")
xx, yy = b(lons, lats)
# print("Basemap corners: ", lons[i_min, j_min] - 360, lons[i_max, j_max] - 360)
plot_utils.apply_plot_params(font_size=20)
fig = plt.figure()
nrows = 1
ncols = 1
gs = GridSpec(ncols=ncols, nrows=nrows)
# bn = BoundaryNorm(clevs, len(clevs) - 1)
# cmap = cm.get_cmap("nipy_spectral")
cmap, bn = colors.from_levels_and_colors(clevs, ["white", "indigo", "blue", "dodgerblue", "aqua", "lime", "yellow", "gold",
"orange", "red"])
area_avg_lkeff_snowfall = []
col = 0
row = 0
ax = fig.add_subplot(gs[row, col])
to_plot = np.ma.masked_where(~reg_of_interest, snfl.values)
print(xx.shape, to_plot.shape)
to_plot *= 100 # convert to cm
im = b.contourf(xx, yy, to_plot, norm=bn, cmap=cmap, levels=clevs)
area_avg_lkeff_snowfall.append(to_plot[(~to_plot.mask) & (to_plot > 0)].mean())
cb = b.colorbar(im, ax=ax)
cb.ax.set_title("cm")
b.drawcoastlines()
b.drawparallels(np.arange(-90, 90, 10), labels=[1, 0, 0, 1])
b.drawmeridians(np.arange(-180, 180, 10), labels=[1, 0, 0, 1])
# ax.set_title("{}".format(y))
fig.tight_layout()
img_file = "{}_processed.png".format(Path(path).name[:-3])
img_file = str(out_folder.joinpath(img_file))
plt.savefig(img_file, bbox_inches="tight")
# plt.show()
plt.close(fig)
return reg_of_interest
def calculate_enh_lakeffect_snowfall_for_a_datasource(data_mngr, label="", period=None, out_folder="."):
months_of_interest = period.months_of_interest
if not isinstance(out_folder, Path):
out_folder_p = Path(out_folder)
else:
out_folder_p = out_folder
# Try to create the output folder if it does not exist
if not out_folder_p.exists():
out_folder_p.mkdir()
out_file = "{}_lkeff_snfl_{}-{}_m{}-{}.nc".format(
label, period.start.year, period.end.year, months_of_interest[0], months_of_interest[-1], out_folder
)
out_file = str(out_folder_p.joinpath(out_file))
# for each period
# 1. get daily snowfall
# 2. get sum of daily snowfalls
lkeff_snow_falls = []
lkeff_snow_fall_days = []
years_index = []
reg_of_interest = None
lons = None
lats = None
ktree = None
lake_mask = None
near_lake_100km_zone_mask = None
secs_per_day = timedelta(days=1).total_seconds()
for start in period.range("years"):
p = Period(start, start.add(months=len(months_of_interest)).subtract(seconds=1))
print("Processing {} ... {} period".format(p.start, p.end))
try:
air_temp = data_mngr.read_data_for_period(p, default_varname_mappings.T_AIR_2M)
except IOError as e:
print(e)
continue
day_dates = [datetime(d.year, d.month, d.day) for d in pd.to_datetime(air_temp.coords["t"].values)]
day_dates = DataArray(day_dates, name="time", dims="t")
air_temp = air_temp.groupby(day_dates).mean(dim="t")
# try to read snowfall if not available, try to calculate from total precip
try:
snfl = data_mngr.read_data_for_period(p, default_varname_mappings.SNOWFALL_RATE)
snfl = snfl.groupby(day_dates).mean(dim="t")
rhosn = base_utils.get_snow_density_kg_per_m3(tair_deg_c=air_temp.values)
# convert from water depth to snow depth
snfl *= base_utils.WATER_DENSITY_KG_PER_M3 / rhosn
except (IOError, KeyError):
print("Could not find snowfall rate in {}".format(data_mngr.base_folder))
print("Calculating from 2-m air temperature and total precipitation.")
# use daily mean precip (to be consistent with the 2-meter air temperature)
precip_m_s = data_mngr.read_data_for_period(p, default_varname_mappings.TOTAL_PREC)
precip_m_s = precip_m_s.groupby(day_dates).mean(dim="t")
# Calculate snowfall from the total precipitation and 2-meter air temperature
snfl = precip_m_s.copy()
snfl.name = default_varname_mappings.SNOWFALL_RATE
snfl.values = base_utils.get_snow_fall_m_per_s(precip_m_per_s=precip_m_s.values, tair_deg_c=air_temp.values)
print("===========air temp ranges=======")
print(air_temp.min(), " .. ", air_temp.max())
print("Snowfall values ranges: ")
print(snfl.min(), snfl.max(), common_params.lower_limit_of_daily_snowfall)
# set to 0 snowfall lower than 1 cm/day
snfl.values[snfl.values <= common_params.lower_limit_of_daily_snowfall] = 0
snfl *= timedelta(days=1).total_seconds()
assert isinstance(snfl, DataArray)
years_index.append(start.year)
if reg_of_interest is None:
lons, lats = snfl.coords["lon"].values, snfl.coords["lat"].values
reg_of_interest = common_params.great_lakes_limits.get_mask_for_coords(lons, lats)
# temporary
lake_mask = get_mask(lons, lats, shp_path=common_params.GL_COAST_SHP_PATH) > 0.1
print("lake_mask shape", lake_mask.shape)
# mask lake points
reg_of_interest &= ~lake_mask
# get the KDTree for interpolation purposes
ktree = KDTree(data=list(zip(*lat_lon.lon_lat_to_cartesian(lon=lons.flatten(), lat=lats.flatten()))))
# define the 100km near lake zone
# near_lake_100km_zone_mask = get_zone_around_lakes_mask(lons=lons, lats=lats, lake_mask=lake_mask,
# ktree=ktree, dist_km=200)
# check the winds
print("Reading the winds into memory")
u_we = data_mngr.read_data_for_period(p, default_varname_mappings.U_WE)
u_we = u_we.groupby(day_dates).mean(dim="t")
v_sn = data_mngr.read_data_for_period(p, default_varname_mappings.V_SN)
v_sn = v_sn.groupby(day_dates).mean(dim="t")
print("Successfully imported wind components")
wind_blows_from_lakes = winds.get_wind_blows_from_lakes_mask(
lons,
lats,
u_we.values,
v_sn.values,
lake_mask,
ktree=ktree,
region_of_interest=reg_of_interest,
dt_secs=secs_per_day,
nneighbours=4,
)
snfl = wind_blows_from_lakes * snfl
# count the number of days with lake effect snowfall
lkeff_snow_fall_days.append((snfl > 0).sum(dim="time"))
# Get the accumulation of the lake effect snowfall
snfl_acc = snfl.sum(dim="time")
# takes into account the 100km zone near lakes
# snfl_acc.values = np.ma.masked_where((~reg_of_interest) | (~near_lake_100km_zone_mask), snfl_acc)
snfl_acc.values = np.ma.masked_where((~reg_of_interest), snfl_acc)
lkeff_snow_falls.append(snfl_acc)
if len(years_index) == 0:
print("Nothing to plot, exiting.")
return
# concatenate the yearly accumulated snowfall and save the result to a netcdf file
# select the region of interest before saving calculated fields to the file
years_index = DataArray(years_index, name="year", dims="year")
i_arr, j_arr = np.where(reg_of_interest)
i_min, i_max = i_arr.min(), i_arr.max()
j_min, j_max = j_arr.min(), j_arr.max()
snfl_yearly = xarray.concat(
[arr.loc[i_min : i_max + 1, j_min : j_max + 1] for arr in lkeff_snow_falls], dim=years_index
)
snfl_yearly.attrs["units"] = "m"
snfl_days_yearly = xarray.concat(
[arr.loc[i_min : i_max + 1, j_min : j_max + 1] for arr in lkeff_snow_fall_days], dim=years_index
)
snfl_days_yearly.attrs["units"] = "days"
ds = snfl_yearly.to_dataset()
assert isinstance(ds, xarray.Dataset)
ds["lkeff_snowfall_days"] = (("year", "x", "y"), snfl_days_yearly)
ds.to_netcdf(out_file)
# Plot snowfall maps for each year
clevs_total_snowfall = [0, 10, 50, 90, 130, 170, 210, 250, 400, 500]
clevs_lkeff_snowfall = [0, 1, 2, 10, 15, 20, 40, 80, 120, 160]
clevs = clevs_lkeff_snowfall
b = Basemap(
lon_0=180,
llcrnrlon=common_params.great_lakes_limits.lon_min,
llcrnrlat=common_params.great_lakes_limits.lat_min,
urcrnrlon=common_params.great_lakes_limits.lon_max,
urcrnrlat=common_params.great_lakes_limits.lat_max,
resolution="i",
)
xx, yy = b(lons, lats)
print("Basemap corners: ", lons[i_min, j_min] - 360, lons[i_max, j_max] - 360)
plot_utils.apply_plot_params(font_size=10)
fig = plt.figure()
ncols = 3
nrows = len(years_index) // ncols + 1
gs = GridSpec(ncols=ncols, nrows=nrows)
# bn = BoundaryNorm(clevs, len(clevs) - 1)
# cmap = cm.get_cmap("nipy_spectral")
cmap, bn = colors.from_levels_and_colors(
clevs, ["indigo", "blue", "dodgerblue", "aqua", "lime", "yellow", "gold", "orange", "red"]
)
area_avg_lkeff_snowfall = []
for i, y in enumerate(years_index.values):
col = i % ncols
row = i // ncols
ax = fig.add_subplot(gs[row, col])
to_plot = np.ma.masked_where(~reg_of_interest, lkeff_snow_falls[i])
print(xx.shape, to_plot.shape)
to_plot *= 100 # convert to cm
im = b.contourf(xx, yy, to_plot, norm=bn, cmap=cmap, levels=clevs)
area_avg_lkeff_snowfall.append(to_plot[(~to_plot.mask) & (to_plot > 0)].mean())
cb = b.colorbar(im, ax=ax)
cb.ax.set_title("cm")
b.drawcoastlines()
b.drawparallels(np.arange(-90, 90, 10), labels=[1, 0, 0, 1])
b.drawmeridians(np.arange(-180, 180, 10), labels=[1, 0, 0, 1])
ax.set_title("{}".format(y))
fig.tight_layout()
img_file = "{}_acc_lakeff_snow_{}-{}.png".format(label, period.start.year, period.end.year - 1)
img_file = str(out_folder_p.joinpath(img_file))
plt.savefig(img_file, bbox_inches="tight")
# plt.show()
plt.close(fig)
# plot area-averaged lake-effect snowfall
fig = plt.figure()
ax = plt.gca()
ax.plot(years_index.values.astype(int), area_avg_lkeff_snowfall, "r", lw=2)
ax.set_title("Area averaged annual lake-effect snowfall")
sf = ScalarFormatter(useOffset=False)
ax.xaxis.set_major_formatter(sf)
ax.grid()
fig.tight_layout()
img_file = "{}_acc_lakeff_snow_area_avg_{}-{}.png".format(label, period.start.year, period.end.year - 1)
img_file = str(out_folder_p.joinpath(img_file))
plt.savefig(img_file, bbox_inches="tight")
plt.close(fig)
def main():
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
season_to_months = OrderedDict([
("Winter (DJF)", (1, 2, 12)),
("Spring (MAM)", range(3, 6)),
("Summer (JJA)", range(6, 9)),
("Fall (SON)", range(9, 12)),
])
varnames = ["TT", "PR"]
plot_utils.apply_plot_params(font_size=10, width_pt=None, width_cm=20, height_cm=17)
# reanalysis_driven_config = RunConfig(data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
# start_year=1980, end_year=2010, label="ERAI-CRCM5-L")
#
reanalysis_driven_config = RunConfig(data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.4_crcm5-hcd-rl.hdf5",
start_year=1980, end_year=2010, label="ERAI-CRCM5-L(0.4)")
nx_agg_model = 1
ny_agg_model = 1
nx_agg_anusplin = 4
ny_agg_anusplin = 4
gcm_driven_config = RunConfig(
data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5",
start_year=1980, end_year=2010, label="CanESM2-CRCM5-L")
bmp_info = analysis.get_basemap_info(r_config=reanalysis_driven_config)
xx, yy = bmp_info.get_proj_xy()
field_cmap = cm.get_cmap("jet", 10)
vname_to_clevels = {
"TT": np.arange(-30, 32, 2), "PR": np.arange(0, 6.5, 0.5)
}
vname_to_anusplin_path = {
"TT": "/home/huziy/skynet3_rech1/anusplin_links",
"PR": "/home/huziy/skynet3_rech1/anusplin_links"
}
vname_to_cru_path = {
"TT": "/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc",
"PR": "/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc"
}
for vname in varnames:
fig = plt.figure()
ncols = len(season_to_months)
gs = GridSpec(4, ncols + 1, width_ratios=ncols * [1., ] + [0.09, ])
clevels = vname_to_clevels[vname]
# get anusplin obs climatology
season_to_obs_anusplin = plot_performance_err_with_anusplin.get_seasonal_clim_obs_data(
rconfig=reanalysis_driven_config,
vname=vname, season_to_months=season_to_months, bmp_info=bmp_info,
n_agg_x=nx_agg_anusplin, n_agg_y=ny_agg_anusplin)
row = 0
# Plot CRU values-------------------------
bmp_info_agg, season_to_obs_cru = plot_performance_err_with_cru.get_seasonal_clim_obs_data(
rconfig=reanalysis_driven_config, bmp_info=bmp_info, season_to_months=season_to_months,
obs_path=vname_to_cru_path[vname], vname=vname
)
# Mask out the Great Lakes
cru_mask = get_mask(bmp_info_agg.lons, bmp_info_agg.lats, shp_path=os.path.join(GL_SHP_FOLDER, "gl_cst.shp"))
for season in season_to_obs_cru:
season_to_obs_cru[season] = np.ma.masked_where(cru_mask > 0.5, season_to_obs_cru[season])
ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
cs = None
xx_agg, yy_agg = bmp_info_agg.get_proj_xy()
for j, (season, obs_field) in enumerate(season_to_obs_cru.items()):
ax = ax_list[j]
cs = bmp_info_agg.basemap.contourf(xx_agg, yy_agg, obs_field.copy(), levels=clevels, ax=ax)
bmp_info.basemap.drawcoastlines(ax=ax)
bmp_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax)
ax.set_title(season)
ax_list[0].set_ylabel("CRU")
# plt.colorbar(cs, caax=ax_list[-1])
row += 1
# Plot ANUSPLIN values-------------------------
ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
cs = None
for j, (season, obs_field) in enumerate(season_to_obs_anusplin.items()):
ax = ax_list[j]
cs = bmp_info.basemap.contourf(xx, yy, obs_field, levels=clevels, ax=ax)
bmp_info.basemap.drawcoastlines(ax=ax)
bmp_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax)
ax.set_title(season)
ax_list[0].set_ylabel("Hopkinson")
cb = plt.colorbar(cs, cax=fig.add_subplot(gs[:2, -1]))
cb.ax.set_xlabel(infovar.get_units(vname))
_format_axes(ax_list, vname=vname)
row += 1
# Plot model (CRCM) values-------------------------
# ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
# cs = None
#
# season_to_field_crcm = analysis.get_seasonal_climatology_for_runconfig(run_config=reanalysis_driven_config,
# varname=vname, level=0,
# season_to_months=season_to_months)
#
# for j, (season, crcm_field) in enumerate(season_to_field_crcm.items()):
# ax = ax_list[j]
# cs = bmp_info.basemap.contourf(xx, yy, crcm_field * 1000 * 24 * 3600, levels=clevels, ax=ax)
# bmp_info.basemap.drawcoastlines(ax=ax)
# bmp_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax)
# ax.set_title(season)
#
# ax_list[0].set_ylabel(reanalysis_driven_config.label)
# cb = plt.colorbar(cs, cax=fig.add_subplot(gs[:2, -1]))
# cb.ax.set_xlabel(infovar.get_units(vname))
# _format_axes(ax_list, vname=vname)
# row += 1
# Plot (Model - CRU) Performance biases-------------------------
ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
cs = plot_performance_err_with_cru.compare_vars(vname_model=vname, vname_obs=None,
r_config=reanalysis_driven_config,
season_to_months=season_to_months,
obs_path=vname_to_cru_path[vname],
bmp_info_agg=bmp_info_agg, diff_axes_list=ax_list,
mask_shape_file=os.path.join(GL_SHP_FOLDER, "gl_cst.shp"),
nx_agg_model=nx_agg_model, ny_agg_model=ny_agg_model)
ax_list[0].set_ylabel("{label}\n--\nCRU".format(label=reanalysis_driven_config.label))
_format_axes(ax_list, vname=vname)
row += 1
# Plot performance+BFE errors with respect to CRU (Model - CRU)-------------------------
# ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
# plot_performance_err_with_cru.compare_vars(vname, vname_obs=None, obs_path=vname_to_cru_path[vname],
# r_config=gcm_driven_config,
# bmp_info_agg=bmp_info_agg, season_to_months=season_to_months,
# axes_list=ax_list)
# _format_axes(ax_list, vname=vname)
# ax_list[0].set_ylabel("{label}\nvs\nCRU".format(label=gcm_driven_config.label))
# row += 1
# Plot performance errors with respect to ANUSPLIN (Model - ANUSPLIN)-------------------------
ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
plot_performance_err_with_anusplin.compare_vars(vname, {vname: season_to_obs_anusplin},
r_config=reanalysis_driven_config,
bmp_info_agg=bmp_info, season_to_months=season_to_months,
axes_list=ax_list)
_format_axes(ax_list, vname=vname)
ax_list[0].set_ylabel("{label}\n--\nHopkinson".format(label=reanalysis_driven_config.label))
row += 1
# Plot performance+BFE errors with respect to ANUSPLIN (Model - ANUSPLIN)-------------------------
# ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
# plot_performance_err_with_anusplin.compare_vars(vname, {vname: season_to_obs_anusplin},
# r_config=gcm_driven_config,
# bmp_info_agg=bmp_info, season_to_months=season_to_months,
# axes_list=ax_list)
# _format_axes(ax_list, vname=vname)
# ax_list[0].set_ylabel("{label}\nvs\nHopkinson".format(label=gcm_driven_config.label))
cb = plt.colorbar(cs, cax=fig.add_subplot(gs[-2:, -1]))
cb.ax.set_xlabel(infovar.get_units(vname))
# Save the plot
img_file = "{vname}_{sy}-{ey}_{sim_label}.png".format(
vname=vname, sy=reanalysis_driven_config.start_year, ey=reanalysis_driven_config.end_year,
sim_label=reanalysis_driven_config.label)
img_file = img_folder.joinpath(img_file)
with img_file.open("wb") as f:
fig.savefig(f, bbox_inches="tight")
plt.close(fig)
def plot_directions(nc_path_to_directions="", grid_config=gc, margin=20, shape_path_to_focus_polygons=None):
"""
:param margin: margin=20 corresponds to the usual free zone
:param grid_config:
:param nc_path_to_directions:
:param shape_path_to_focus_polygons: if Not None, the path to the polygons
Everything outside the specified polygons is masked
"""
fig = plt.figure(figsize=(15, 15))
ds = Dataset(nc_path_to_directions)
var_name = "accumulation_area"
data = ds.variables[var_name][margin:-margin, margin:-margin]
data = np.ma.masked_where(data <= 0, data)
# flow directions
fldr = ds.variables["flow_direction_value"][margin:-margin, margin:-margin]
i_shifts, j_shifts = direction_and_value.flowdir_values_to_shift(fldr)
lons, lats = [ds.variables[key][margin:-margin, margin:-margin] for key in ["lon", "lat"]]
reg_of_interest = None
if shape_path_to_focus_polygons is not None:
reg_of_interest = get_mask(lons, lats, shp_path=shape_path_to_focus_polygons) > 0
i_list, j_list = np.where(reg_of_interest)
mask_margin = 5 # margin of the mask in grid points
i_min = min(i_list) - mask_margin
i_max = max(i_list) + mask_margin
j_min = min(j_list) - mask_margin
j_max = max(j_list) + mask_margin
bsmap = grid_config.get_basemap(lons=lons[i_min:i_max + 1, j_min:j_max + 1], lats=lats[i_min:i_max + 1, j_min:j_max + 1])
assert isinstance(bsmap, Basemap)
bsmap.readshapefile(shapefile=shape_path_to_focus_polygons[:-4], name="basin", linewidth=1, color="m")
else:
bsmap = grid_config.get_basemap(lons=lons, lats=lats)
x, y = bsmap(lons, lats)
nx, ny = x.shape
inds_j, inds_i = np.meshgrid(range(ny), range(nx))
inds_i_next = inds_i + i_shifts
inds_j_next = inds_j + j_shifts
inds_i_next = np.ma.masked_where((inds_i_next == nx) | (inds_i_next == -1), inds_i_next)
inds_j_next = np.ma.masked_where((inds_j_next == ny) | (inds_j_next == -1), inds_j_next)
u = np.ma.masked_all_like(x)
v = np.ma.masked_all_like(x)
good = (~inds_i_next.mask) & (~inds_j_next.mask)
u[good] = x[inds_i_next[good], inds_j_next[good]] - x[inds_i[good], inds_j[good]]
v[good] = y[inds_i_next[good], inds_j_next[good]] - y[inds_i[good], inds_j[good]]
bsmap.fillcontinents(color='0.2', lake_color='aqua')
bsmap.drawmapboundary(fill_color="aqua")
bsmap.quiver(x, y, u, v,
pivot="tail", width=0.0005, scale_units="xy", headlength=20, headwidth=15, scale=1, zorder=5)
bsmap.drawcoastlines(linewidth=0.5)
bsmap.drawrivers(color="b")
plt.savefig(nc_path_to_directions[:-3] + ".png", bbox_inches="tight")
def calculate_enh_lakeffect_snowfall_for_a_datasource(data_mngr, label="", period=None, out_folder: Path = Path(".")):
months_of_interest = period.months_of_interest
out_file = "{}_lkeff_snfl_{}-{}_m{:02d}-{:02d}.nc".format(label, period.start.year, period.end.year,
months_of_interest[0], months_of_interest[-1], out_folder)
lake_effect_zone_radius = DEFAULT_LAKE_EFFECT_ZONE_RADIUS_KM
out_file = out_folder.joinpath(out_file)
if out_file.exists():
print("{} already exists, won't redo!".format(out_file))
# plot_acc_snowfall_map(data_path=out_file, label=label, period=period, out_folder=out_folder,
# months_of_interest=months_of_interest)
return
# for each period
# 1. get daily snowfall
# 2. get sum of daily snowfalls
lkeff_snow_falls = []
lkeff_snow_fall_days = []
lkeff_snow_fall_eventcount = []
years_index = []
reg_of_interest = None
lons = None
lats = None
lons_rad = None
lats_rad = None
ktree = None
lake_mask = None
near_lake_x_km_zone_mask = None
ktree_for_nonlocal_snowfall_calculations = None
secs_per_day = timedelta(days=1).total_seconds()
for start in period.range("years"):
end_date = start.add(months=len(months_of_interest))
end_date = min(period.end, end_date)
end_date = end_date.subtract(seconds=1)
print("start_date={}, end_date={}, period.end={}, months_of_interest={}".format(start, end_date, period.end, months_of_interest))
p = Period(start, end_date)
# build the name of the daily output file
out_file_daily = "{}_lkeff_snfl_{}-{}_m{:02d}-{:02d}_daily.nc".format(label, p.start.year, p.end.year,
months_of_interest[0],
months_of_interest[-1],
out_folder)
out_file_daily = out_folder.joinpath(out_file_daily)
print(f"Processing {p.start} ... {p.end} period")
# try to read snowfall if not available, try to calculate from total precip
try:
snfl = data_mngr.read_data_for_period(p, default_varname_mappings.SNOWFALL_RATE)
snfl = snfl.resample("1D", dim="t", how="mean")
rhosn = base_utils.get_snow_density_kg_per_m3(tair_deg_c=air_temp.values)
# convert from water depth to snow depth
snfl *= base_utils.WATER_DENSITY_KG_PER_M3 / rhosn
except (IOError, KeyError, Exception):
print(f"Could not find snowfall rate in {data_mngr.base_folder}")
print("Calculating from 2-m air temperature and total precipitation.")
try:
air_temp = data_mngr.read_data_for_period(p, default_varname_mappings.T_AIR_2M)
except IOError as e:
print(e)
continue
#
print("Calculating daily mean 2-m air temperature")
air_temp = air_temp.resample("1D", dim="t", how="mean")
# use daily mean precip (to be consistent with the 2-meter air temperature)
precip_m_s = data_mngr.read_data_for_period(p, default_varname_mappings.TOTAL_PREC)
precip_m_s = precip_m_s.resample("1D", dim="t", how="mean")
# Calculate snowfall from the total precipitation and 2-meter air temperature
snfl = precip_m_s.copy()
snfl.name = default_varname_mappings.SNOWFALL_RATE
snfl.values = base_utils.get_snow_fall_m_per_s(precip_m_per_s=precip_m_s.values, tair_deg_c=air_temp.values)
print("===========air temp ranges=======")
# print(air_temp.min(), " .. ", air_temp.max())
print("Snowfall values ranges: ")
# print(snfl.min(), snfl.max(), common_params.lower_limit_of_daily_snowfall)
# save snowfall total
snfl_total = snfl.copy()
snfl_total *= timedelta(days=1).total_seconds()
snfl_total.attrs["units"] = "M/day"
# set to 0 snowfall lower than 1 cm/day
snfl.values[snfl.values <= common_params.lower_limit_of_daily_snowfall] = 0
snfl *= timedelta(days=1).total_seconds()
snfl.attrs["units"] = "M/day"
years_index.append(start.year)
if reg_of_interest is None:
lons, lats = snfl.coords["lon"].values, snfl.coords["lat"].values
# convert longitudes to the 0..360 range
lons[lons < 0] += 360
reg_of_interest = common_params.great_lakes_limits.get_mask_for_coords(lons, lats)
# temporary
lake_mask = get_mask(lons, lats, shp_path=common_params.GL_COAST_SHP_PATH) > 0.1
# print("lake_mask shape", lake_mask.shape)
# mask lake points
reg_of_interest &= ~lake_mask
# get the KDTree for interpolation purposes
ktree = KDTree(
np.array(list(zip(*lat_lon.lon_lat_to_cartesian(lon=lons.flatten(), lat=lats.flatten()))))
)
# define the ~200km near lake zone
near_lake_x_km_zone_mask = get_zone_around_lakes_mask(lons=lons, lats=lats, lake_mask=lake_mask,
ktree=ktree, dist_km=lake_effect_zone_radius)
reg_of_interest &= near_lake_x_km_zone_mask
lons_rad = np.radians(lons)
lats_rad = np.radians(lats)
# check the winds
print("Reading the winds into memory")
u_we = data_mngr.read_data_for_period(p, default_varname_mappings.U_WE)
u_we = u_we.resample("1D", dim="t", how="mean")
v_sn = data_mngr.read_data_for_period(p, default_varname_mappings.V_SN)
v_sn = v_sn.resample("1D", dim="t", how="mean")
print("Successfully imported wind components")
assert len(v_sn.t) == len(np.unique(v_sn.t[:]))
# Try to get the lake ice fractions
lake_ice_fraction = None
try:
lake_ice_fraction = data_mngr.read_data_for_period(p, default_varname_mappings.LAKE_ICE_FRACTION)
lake_ice_fraction = lake_ice_fraction.resample("1D", dim="t", how="mean") # Calculate the daily means
lake_ice_fraction = lake_ice_fraction.sel(t=v_sn.coords["t"], method="nearest")
# update the time coordinates as well
lake_ice_fraction.coords["t"] = v_sn.coords["t"][:]
lake_ice_fraction = lake_ice_fraction.where((lake_ice_fraction <= 1) & (lake_ice_fraction >= 0))
# at this point shapes of the arrays should be the same
assert lake_ice_fraction.shape == u_we.shape
print(lake_ice_fraction.coords["t"][0], lake_ice_fraction.coords["t"][-1])
except Exception as e:
print(e)
print("WARNING: Could not find lake fraction in {}, "
"diagnosing lake-effect snow without lake ice "
"(NOTE: this could be OK for the months when there is no ice usually)".format(data_mngr.base_folder))
lake_ice_fraction = snfl * 0
# raise e
# take into account the wind direction
wind_blows_from_lakes = winds.get_wind_blows_from_lakes_mask(lons, lats, u_we.values, v_sn.values, lake_mask,
ktree=ktree,
region_of_interest=reg_of_interest,
dt_secs=secs_per_day, nneighbours=4,
lake_ice_fraction=lake_ice_fraction,
lons_rad=lons_rad, lats_rad=lats_rad)
print("wind_blows_from_lakes.shape = ", wind_blows_from_lakes.shape)
print("snfl.shape = ", snfl.shape)
snfl = wind_blows_from_lakes * snfl
# take into account nonlocal amplification due to lakes
if ktree_for_nonlocal_snowfall_calculations is None:
xs_nnlc, ys_nnlcl, zs_nnlcl = lat_lon.lon_lat_to_cartesian(lons.flatten(), lats.flatten())
ktree_for_nonlocal_snowfall_calculations = KDTree(list(zip(xs_nnlc, ys_nnlcl, zs_nnlcl)))
snfl_nonlocal = get_nonlocal_mean_snowfall(lons=lons, lats=lats, region_of_interest=reg_of_interest,
kdtree=ktree_for_nonlocal_snowfall_calculations,
snowfall=snfl, lake_mask=lake_mask, outer_radius_km=500)
snfl = (snfl > (common_params.snfl_local_amplification_m_per_s + snfl_nonlocal)).values * snfl
# save daily data to file
i_arr, j_arr = np.where(reg_of_interest)
i_min, i_max = i_arr.min(), i_arr.max()
j_min, j_max = j_arr.min(), j_arr.max()
ds = snfl.loc[:, i_min:i_max + 1, j_min:j_max + 1].to_dataset(name="hles_snow")
# import pickle
# pickle.dump(lake_ice_fraction, open(str(out_file_daily) + ".bin", "wb"))
ds["lake_ice_fraction"] = lake_ice_fraction.loc[:, i_min:i_max + 1, j_min:j_max + 1]
ds["u_we"] = u_we.loc[:, i_min:i_max + 1, j_min:j_max + 1]
ds["v_sn"] = v_sn.loc[:, i_min:i_max + 1, j_min:j_max + 1]
ds["total_snowfall"] = snfl_total.loc[:, i_min:i_max + 1, j_min:j_max + 1]
ds.to_netcdf(str(out_file_daily))
# count the number of hles events
snfl_eventcount = snfl.copy()
snfl_eventcount.values[snfl.values > 1e-5] = 1
snfl_eventcount.values[snfl.values <= 1e-5] = 0
snfl_eventcount = snfl_eventcount.diff("t", n=1)
snfl_eventcount = (snfl_eventcount < 0).sum(dim="t")
lkeff_snow_fall_eventcount.append(snfl_eventcount)
# count the number of days with lake effect snowfall
lkeff_snow_fall_days.append((snfl > 0).sum(dim="t"))
# Get the accumulation of the lake effect snowfall
snfl_acc = snfl.sum(dim="t")
# takes into account the 100km zone near lakes
# snfl_acc.values = np.ma.masked_where((~reg_of_interest) | (~near_lake_x_km_zone_mask), snfl_acc)
snfl_acc.values = np.ma.masked_where((~reg_of_interest), snfl_acc)
lkeff_snow_falls.append(snfl_acc)
del snfl
del snfl_nonlocal
if len(years_index) == 0:
print("Nothing to plot, exiting.")
return
# concatenate the yearly accumulated snowfall and save the result to a netcdf file
# select the region of interest before saving calculated fields to the file
years_index = DataArray(years_index, name="year", dims="year")
i_arr, j_arr = np.where(reg_of_interest)
i_min, i_max = i_arr.min(), i_arr.max()
j_min, j_max = j_arr.min(), j_arr.max()
snfl_yearly = xarray.concat([arr.loc[i_min: i_max + 1, j_min: j_max + 1] for arr in lkeff_snow_falls],
dim=years_index)
snfl_yearly.attrs["units"] = "m"
snfl_days_yearly = xarray.concat([arr.loc[i_min: i_max + 1, j_min: j_max + 1] for arr in lkeff_snow_fall_days],
dim=years_index)
snfl_days_yearly.attrs["units"] = "days"
snfl_eventcounts_yearly = xarray.concat(
[arr.loc[i_min: i_max + 1, j_min: j_max + 1] for arr in lkeff_snow_fall_eventcount],
dim=years_index)
snfl_eventcounts_yearly.attrs["units"] = "number of events"
ds = snfl_yearly.to_dataset()
assert isinstance(ds, xarray.Dataset)
ds["lkeff_snowfall_days"] = (("year", "x", "y"), snfl_days_yearly)
ds["lkeff_snowfall_eventcount"] = (("year", "x", "y"), snfl_eventcounts_yearly)
ds.to_netcdf(str(out_file))
ds.close()
# do the plotting
plot_acc_snowfall_map(data_path=out_file, label=label, period=period, out_folder=out_folder,
months_of_interest=period.months_of_interest)
def calculate_enh_lakeffect_snowfall_for_a_datasource(data_mngr,
label="",
period=None,
out_folder: Path = Path(
".")):
months_of_interest = period.months_of_interest
out_file = "{}_lkeff_snfl_{}-{}_m{:02d}-{:02d}.nc".format(
label, period.start.year, period.end.year, months_of_interest[0],
months_of_interest[-1], out_folder)
lake_effect_zone_radius = DEFAULT_LAKE_EFFECT_ZONE_RADIUS_KM
out_file = out_folder.joinpath(out_file)
if out_file.exists():
print("{} already exists, won't redo!".format(out_file))
# plot_acc_snowfall_map(data_path=out_file, label=label, period=period, out_folder=out_folder,
# months_of_interest=months_of_interest)
return
# for each period
# 1. get daily snowfall
# 2. get sum of daily snowfalls
lkeff_snow_falls = []
lkeff_snow_fall_days = []
lkeff_snow_fall_eventcount = []
years_index = []
reg_of_interest = None
lons = None
lats = None
lons_rad = None
lats_rad = None
ktree = None
lake_mask = None
near_lake_x_km_zone_mask = None
ktree_for_nonlocal_snowfall_calculations = None
secs_per_day = timedelta(days=1).total_seconds()
for start in period.range("years"):
end_date = start.add(months=len(months_of_interest))
end_date = min(period.end, end_date)
end_date = end_date.subtract(seconds=1)
sys.stderr.write(
"start_date={}, end_date={}, period.end={}, months_of_interest={}\n"
.format(start, end_date, period.end, months_of_interest))
p = Period(start, end_date)
# build the name of the daily output file
out_file_daily = "{}_lkeff_snfl_{}-{}_m{:02d}-{:02d}_daily.nc".format(
label, p.start.year, p.end.year, months_of_interest[0],
months_of_interest[-1], out_folder)
out_file_daily = out_folder.joinpath(out_file_daily)
print(f"Processing {p.start} ... {p.end} period")
# try to read snowfall if not available, try to calculate from total precip
try:
snfl = data_mngr.read_data_for_period(
p, default_varname_mappings.SNOWFALL_RATE)
snfl = snfl.resample(t="1D", restore_coord_dims=True).mean(dim="t")
rhosn = base_utils.get_snow_density_kg_per_m3(
tair_deg_c=air_temp.values)
# convert from water depth to snow depth
snfl *= base_utils.WATER_DENSITY_KG_PER_M3 / rhosn
except (IOError, KeyError, Exception):
print(f"Could not find snowfall rate in {data_mngr.base_folder}")
print(
"Calculating from 2-m air temperature and total precipitation."
)
try:
air_temp = data_mngr.read_data_for_period(
p, default_varname_mappings.T_AIR_2M)
except IOError as e:
print(e)
continue
#
print("Calculating daily mean 2-m air temperature")
air_temp = air_temp.resample(t="1D",
restore_coord_dims=True).mean(dim="t")
# use daily mean precip (to be consistent with the 2-meter air temperature)
precip_m_s = data_mngr.read_data_for_period(
p, default_varname_mappings.TOTAL_PREC)
precip_m_s = precip_m_s.resample(t="1D",
restore_coord_dims=True,
keep_attrs=True).mean(dim="t")
# Calculate snowfall from the total precipitation and 2-meter air temperature
snfl = precip_m_s.copy()
snfl.name = default_varname_mappings.SNOWFALL_RATE
snfl.values = base_utils.get_snow_fall_m_per_s(
precip_m_per_s=precip_m_s.values, tair_deg_c=air_temp.values)
sys.stderr.write(f"{precip_m_s}\n")
# print("===========air temp ranges=======")
# print(air_temp.min(), " .. ", air_temp.max())
print("Snowfall values ranges: ")
# print(snfl.min(), snfl.max(), common_params.lower_limit_of_daily_snowfall)
# save snowfall total
snfl_total = snfl.copy()
snfl_total *= timedelta(days=1).total_seconds()
snfl_total.attrs["units"] = "M/day"
# set to 0 snowfall lower than 1 cm/day
snfl.values[
snfl.values <= common_params.lower_limit_of_daily_snowfall] = 0
snfl *= timedelta(days=1).total_seconds()
snfl.attrs["units"] = "M/day"
years_index.append(start.year)
if reg_of_interest is None:
lons, lats = snfl.coords["lon"].values, snfl.coords["lat"].values
# convert longitudes to the 0..360 range
lons[lons < 0] += 360
reg_of_interest = common_params.great_lakes_limits.get_mask_for_coords(
lons, lats)
# temporary
lake_mask = get_mask(
lons, lats, shp_path=common_params.GL_COAST_SHP_PATH) > 0.1
# print("lake_mask shape", lake_mask.shape)
# mask lake points
reg_of_interest &= ~lake_mask
# get the KDTree for interpolation purposes
ktree = KDTree(
np.array(
list(
zip(*lat_lon.lon_lat_to_cartesian(
lon=lons.flatten(), lat=lats.flatten())))))
# define the ~200km near lake zone
near_lake_x_km_zone_mask = get_zone_around_lakes_mask(
lons=lons,
lats=lats,
lake_mask=lake_mask,
ktree=ktree,
dist_km=lake_effect_zone_radius)
reg_of_interest &= near_lake_x_km_zone_mask
lons_rad = np.radians(lons)
lats_rad = np.radians(lats)
# check the winds
print("Reading the winds into memory")
u_we = data_mngr.read_data_for_period(p, default_varname_mappings.U_WE)
u_we = u_we.resample(t="1D", restore_coord_dims=True).mean(dim="t")
v_sn = data_mngr.read_data_for_period(p, default_varname_mappings.V_SN)
v_sn = v_sn.resample(t="1D", restore_coord_dims=True).mean(dim="t")
print("Successfully imported wind components")
assert len(v_sn.t) == len(np.unique(v_sn.t[:]))
# Try to get the lake ice fractions
lake_ice_fraction = None
try:
lake_ice_fraction = data_mngr.read_data_for_period(
p, default_varname_mappings.LAKE_ICE_FRACTION)
lake_ice_fraction = lake_ice_fraction.resample(
t="1D", restore_coord_dims=True).mean(
dim="t") # Calculate the daily means
lake_ice_fraction = lake_ice_fraction.sel(t=v_sn.coords["t"],
method="nearest")
# update the time coordinates as well
lake_ice_fraction.coords["t"] = v_sn.coords["t"][:]
lake_ice_fraction = lake_ice_fraction.where(
(lake_ice_fraction <= 1) & (lake_ice_fraction >= 0))
# at this point shapes of the arrays should be the same
assert lake_ice_fraction.shape == u_we.shape
print(lake_ice_fraction.coords["t"][0],
lake_ice_fraction.coords["t"][-1])
except Exception as e:
print(e)
print(
"WARNING: Could not find lake fraction in {}, "
"diagnosing lake-effect snow without lake ice "
"(NOTE: this could be OK for the months when there is no ice usually)"
.format(data_mngr.base_folder))
lake_ice_fraction = snfl * 0
# raise e
# take into account the wind direction
wind_blows_from_lakes = winds.get_wind_blows_from_lakes_mask(
lons,
lats,
u_we.values,
v_sn.values,
lake_mask,
ktree=ktree,
region_of_interest=reg_of_interest,
dt_secs=secs_per_day,
nneighbours=4,
lake_ice_fraction=lake_ice_fraction,
lons_rad=lons_rad,
lats_rad=lats_rad)
print("wind_blows_from_lakes.shape = ", wind_blows_from_lakes.shape)
print("snfl.shape = ", snfl.shape)
snfl = wind_blows_from_lakes * snfl
# take into account nonlocal amplification due to lakes
if ktree_for_nonlocal_snowfall_calculations is None:
xs_nnlc, ys_nnlcl, zs_nnlcl = lat_lon.lon_lat_to_cartesian(
lons.flatten(), lats.flatten())
ktree_for_nonlocal_snowfall_calculations = KDTree(
list(zip(xs_nnlc, ys_nnlcl, zs_nnlcl)))
snfl_nonlocal = get_nonlocal_mean_snowfall(
lons=lons,
lats=lats,
region_of_interest=reg_of_interest,
kdtree=ktree_for_nonlocal_snowfall_calculations,
snowfall=snfl,
lake_mask=lake_mask,
outer_radius_km=500)
snfl = (snfl > (common_params.snfl_local_amplification_m_per_s +
snfl_nonlocal)).values * snfl
# save daily data to file
i_arr, j_arr = np.where(reg_of_interest)
i_min, i_max = i_arr.min(), i_arr.max()
j_min, j_max = j_arr.min(), j_arr.max()
ds = snfl.loc[:, i_min:i_max + 1,
j_min:j_max + 1].to_dataset(name="hles_snow")
# import pickle
# pickle.dump(lake_ice_fraction, open(str(out_file_daily) + ".bin", "wb"))
ds["lake_ice_fraction"] = lake_ice_fraction.loc[:, i_min:i_max + 1,
j_min:j_max + 1]
ds["u_we"] = u_we.loc[:, i_min:i_max + 1, j_min:j_max + 1]
ds["v_sn"] = v_sn.loc[:, i_min:i_max + 1, j_min:j_max + 1]
ds["total_snowfall"] = snfl_total.loc[:, i_min:i_max + 1,
j_min:j_max + 1]
ds.to_netcdf(str(out_file_daily))
# count the number of hles events
snfl_eventcount = snfl.copy()
snfl_eventcount.values[snfl.values > 1e-5] = 1
snfl_eventcount.values[snfl.values <= 1e-5] = 0
snfl_eventcount = snfl_eventcount.diff("t", n=1)
snfl_eventcount = (snfl_eventcount < 0).sum(dim="t")
lkeff_snow_fall_eventcount.append(snfl_eventcount)
# count the number of days with lake effect snowfall
lkeff_snow_fall_days.append((snfl > 0).sum(dim="t"))
# Get the accumulation of the lake effect snowfall
snfl_acc = snfl.sum(dim="t")
# takes into account the 100km zone near lakes
# snfl_acc.values = np.ma.masked_where((~reg_of_interest) | (~near_lake_x_km_zone_mask), snfl_acc)
snfl_acc.values = np.ma.masked_where((~reg_of_interest), snfl_acc)
lkeff_snow_falls.append(snfl_acc)
del snfl
del snfl_nonlocal
if len(years_index) == 0:
print("Nothing to plot, exiting.")
return
# concatenate the yearly accumulated snowfall and save the result to a netcdf file
# select the region of interest before saving calculated fields to the file
years_index = DataArray(years_index, name="year", dims="year")
i_arr, j_arr = np.where(reg_of_interest)
i_min, i_max = i_arr.min(), i_arr.max()
j_min, j_max = j_arr.min(), j_arr.max()
snfl_yearly = xarray.concat([
arr.loc[i_min:i_max + 1, j_min:j_max + 1] for arr in lkeff_snow_falls
],
dim=years_index)
snfl_yearly.attrs["units"] = "m"
snfl_days_yearly = xarray.concat([
arr.loc[i_min:i_max + 1, j_min:j_max + 1]
for arr in lkeff_snow_fall_days
],
dim=years_index)
snfl_days_yearly.attrs["units"] = "days"
snfl_eventcounts_yearly = xarray.concat([
arr.loc[i_min:i_max + 1, j_min:j_max + 1]
for arr in lkeff_snow_fall_eventcount
],
dim=years_index)
snfl_eventcounts_yearly.attrs["units"] = "number of events"
ds = snfl_yearly.to_dataset()
assert isinstance(ds, xarray.Dataset)
ds["lkeff_snowfall_days"] = (("year", "x", "y"), snfl_days_yearly)
ds["lkeff_snowfall_eventcount"] = (("year", "x", "y"),
snfl_eventcounts_yearly)
ds.to_netcdf(str(out_file))
ds.close()
# do the plotting
plot_acc_snowfall_map(data_path=out_file,
label=label,
period=period,
out_folder=out_folder,
months_of_interest=period.months_of_interest)
def main():
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
season_to_months = OrderedDict([
("Winter (DJF)", (1, 2, 12)),
("Spring (MAM)", range(3, 6)),
("Summer (JJA)", range(6, 9)),
("Fall (SON)", range(9, 12)),
])
varnames = ["TT", "PR"]
plot_utils.apply_plot_params(font_size=10,
width_pt=None,
width_cm=20,
height_cm=17)
# reanalysis_driven_config = RunConfig(data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
# start_year=1980, end_year=2010, label="ERAI-CRCM5-L")
#
reanalysis_driven_config = RunConfig(
data_path=
"/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.4_crcm5-hcd-rl.hdf5",
start_year=1980,
end_year=2010,
label="ERAI-CRCM5-L(0.4)")
nx_agg_model = 1
ny_agg_model = 1
nx_agg_anusplin = 4
ny_agg_anusplin = 4
gcm_driven_config = RunConfig(
data_path=
"/RESCUE/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5",
start_year=1980,
end_year=2010,
label="CanESM2-CRCM5-L")
bmp_info = analysis.get_basemap_info(r_config=reanalysis_driven_config)
xx, yy = bmp_info.get_proj_xy()
field_cmap = cm.get_cmap("jet", 10)
vname_to_clevels = {
"TT": np.arange(-30, 32, 2),
"PR": np.arange(0, 6.5, 0.5)
}
vname_to_anusplin_path = {
"TT": "/home/huziy/skynet3_rech1/anusplin_links",
"PR": "/home/huziy/skynet3_rech1/anusplin_links"
}
vname_to_cru_path = {
"TT":
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc",
"PR":
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc"
}
for vname in varnames:
fig = plt.figure()
ncols = len(season_to_months)
gs = GridSpec(4, ncols + 1, width_ratios=ncols * [
1.,
] + [
0.09,
])
clevels = vname_to_clevels[vname]
# get anusplin obs climatology
season_to_obs_anusplin = plot_performance_err_with_anusplin.get_seasonal_clim_obs_data(
rconfig=reanalysis_driven_config,
vname=vname,
season_to_months=season_to_months,
bmp_info=bmp_info,
n_agg_x=nx_agg_anusplin,
n_agg_y=ny_agg_anusplin)
row = 0
# Plot CRU values-------------------------
bmp_info_agg, season_to_obs_cru = plot_performance_err_with_cru.get_seasonal_clim_obs_data(
rconfig=reanalysis_driven_config,
bmp_info=bmp_info,
season_to_months=season_to_months,
obs_path=vname_to_cru_path[vname],
vname=vname)
# Mask out the Great Lakes
cru_mask = get_mask(bmp_info_agg.lons,
bmp_info_agg.lats,
shp_path=os.path.join(GL_SHP_FOLDER, "gl_cst.shp"))
for season in season_to_obs_cru:
season_to_obs_cru[season] = np.ma.masked_where(
cru_mask > 0.5, season_to_obs_cru[season])
ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
cs = None
xx_agg, yy_agg = bmp_info_agg.get_proj_xy()
for j, (season, obs_field) in enumerate(season_to_obs_cru.items()):
ax = ax_list[j]
cs = bmp_info_agg.basemap.contourf(xx_agg,
yy_agg,
obs_field.copy(),
levels=clevels,
ax=ax)
bmp_info.basemap.drawcoastlines(ax=ax)
bmp_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4],
"basin",
ax=ax)
ax.set_title(season)
ax_list[0].set_ylabel("CRU")
# plt.colorbar(cs, caax=ax_list[-1])
row += 1
# Plot ANUSPLIN values-------------------------
ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
cs = None
for j, (season,
obs_field) in enumerate(season_to_obs_anusplin.items()):
ax = ax_list[j]
cs = bmp_info.basemap.contourf(xx,
yy,
obs_field,
levels=clevels,
ax=ax)
bmp_info.basemap.drawcoastlines(ax=ax)
bmp_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4],
"basin",
ax=ax)
ax.set_title(season)
ax_list[0].set_ylabel("Hopkinson")
cb = plt.colorbar(cs, cax=fig.add_subplot(gs[:2, -1]))
cb.ax.set_xlabel(infovar.get_units(vname))
_format_axes(ax_list, vname=vname)
row += 1
# Plot model (CRCM) values-------------------------
# ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
# cs = None
#
# season_to_field_crcm = analysis.get_seasonal_climatology_for_runconfig(run_config=reanalysis_driven_config,
# varname=vname, level=0,
# season_to_months=season_to_months)
#
# for j, (season, crcm_field) in enumerate(season_to_field_crcm.items()):
# ax = ax_list[j]
# cs = bmp_info.basemap.contourf(xx, yy, crcm_field * 1000 * 24 * 3600, levels=clevels, ax=ax)
# bmp_info.basemap.drawcoastlines(ax=ax)
# bmp_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax)
# ax.set_title(season)
#
# ax_list[0].set_ylabel(reanalysis_driven_config.label)
# cb = plt.colorbar(cs, cax=fig.add_subplot(gs[:2, -1]))
# cb.ax.set_xlabel(infovar.get_units(vname))
# _format_axes(ax_list, vname=vname)
# row += 1
# Plot (Model - CRU) Performance biases-------------------------
ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
cs = plot_performance_err_with_cru.compare_vars(
vname_model=vname,
vname_obs=None,
r_config=reanalysis_driven_config,
season_to_months=season_to_months,
obs_path=vname_to_cru_path[vname],
bmp_info_agg=bmp_info_agg,
diff_axes_list=ax_list,
mask_shape_file=os.path.join(GL_SHP_FOLDER, "gl_cst.shp"),
nx_agg_model=nx_agg_model,
ny_agg_model=ny_agg_model)
ax_list[0].set_ylabel(
"{label}\n--\nCRU".format(label=reanalysis_driven_config.label))
_format_axes(ax_list, vname=vname)
row += 1
# Plot performance+BFE errors with respect to CRU (Model - CRU)-------------------------
# ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
# plot_performance_err_with_cru.compare_vars(vname, vname_obs=None, obs_path=vname_to_cru_path[vname],
# r_config=gcm_driven_config,
# bmp_info_agg=bmp_info_agg, season_to_months=season_to_months,
# axes_list=ax_list)
# _format_axes(ax_list, vname=vname)
# ax_list[0].set_ylabel("{label}\nvs\nCRU".format(label=gcm_driven_config.label))
# row += 1
# Plot performance errors with respect to ANUSPLIN (Model - ANUSPLIN)-------------------------
ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
plot_performance_err_with_anusplin.compare_vars(
vname, {vname: season_to_obs_anusplin},
r_config=reanalysis_driven_config,
bmp_info_agg=bmp_info,
season_to_months=season_to_months,
axes_list=ax_list)
_format_axes(ax_list, vname=vname)
ax_list[0].set_ylabel("{label}\n--\nHopkinson".format(
label=reanalysis_driven_config.label))
row += 1
# Plot performance+BFE errors with respect to ANUSPLIN (Model - ANUSPLIN)-------------------------
# ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
# plot_performance_err_with_anusplin.compare_vars(vname, {vname: season_to_obs_anusplin},
# r_config=gcm_driven_config,
# bmp_info_agg=bmp_info, season_to_months=season_to_months,
# axes_list=ax_list)
# _format_axes(ax_list, vname=vname)
# ax_list[0].set_ylabel("{label}\nvs\nHopkinson".format(label=gcm_driven_config.label))
cb = plt.colorbar(cs, cax=fig.add_subplot(gs[-2:, -1]))
cb.ax.set_xlabel(infovar.get_units(vname))
# Save the plot
img_file = "{vname}_{sy}-{ey}_{sim_label}.png".format(
vname=vname,
sy=reanalysis_driven_config.start_year,
ey=reanalysis_driven_config.end_year,
sim_label=reanalysis_driven_config.label)
img_file = img_folder.joinpath(img_file)
with img_file.open("wb") as f:
fig.savefig(f, bbox_inches="tight")
plt.close(fig)
def compare_vars(vname_model="TT", vname_obs="tmp", r_config=None,
season_to_months=None,
obs_path=None, nx_agg=5, ny_agg=5, bmp_info_agg=None,
diff_axes_list=None, obs_axes_list=None,
model_axes_list=None, bmp_info_model=None,
mask_shape_file=None):
"""
if obs_axes_list is not None, plot observation data in those
:param mask_shape_file:
:param bmp_info_model: basemap info native to the model
:param model_axes_list: Axes to plot model outputs
:param vname_model:
:param vname_obs:
:param r_config:
:param season_to_months:
:param obs_path:
:param nx_agg:
:param ny_agg:
:param bmp_info_agg:
:param diff_axes_list: if it is None the plots for each variable is done in separate figures
"""
if vname_obs is None:
vname_model_to_vname_obs = {"TT": "tmp", "PR": "pre"}
vname_obs = vname_model_to_vname_obs[vname_model]
seasonal_clim_fields_model = analysis.get_seasonal_climatology_for_runconfig(run_config=r_config,
varname=vname_model, level=0,
season_to_months=season_to_months)
season_to_clim_fields_model_agg = OrderedDict()
for season, field in seasonal_clim_fields_model.items():
print(field.shape)
season_to_clim_fields_model_agg[season] = aggregate_array(field, nagg_x=nx_agg, nagg_y=ny_agg)
if vname_model == "PR":
season_to_clim_fields_model_agg[season] *= 1.0e3 * 24 * 3600
if vname_obs in ["SWE", ]:
obs_manager = SweDataManager(path=obs_path, var_name=vname_obs)
elif obs_path is None:
obs_manager = CRUDataManager(var_name=vname_obs)
else:
obs_manager = CRUDataManager(var_name=vname_obs, path=obs_path)
seasonal_clim_fields_obs = obs_manager.get_seasonal_means(season_name_to_months=season_to_months,
start_year=r_config.start_year,
end_year=r_config.end_year)
seasonal_clim_fields_obs_interp = OrderedDict()
# Derive the mask from a shapefile if provided
if mask_shape_file is not None:
the_mask = get_mask(bmp_info_agg.lons, bmp_info_agg.lats, shp_path=mask_shape_file)
else:
the_mask = np.zeros_like(bmp_info_agg.lons)
for season, obs_field in seasonal_clim_fields_obs.items():
obs_field = obs_manager.interpolate_data_to(obs_field,
lons2d=bmp_info_agg.lons,
lats2d=bmp_info_agg.lats,
nneighbours=1)
obs_field = np.ma.masked_where(the_mask > 0.5, obs_field)
seasonal_clim_fields_obs_interp[season] = obs_field
# assert hasattr(seasonal_clim_fields_obs_interp[season], "mask")
season_to_err = OrderedDict()
print("-------------var: {} (PE with CRU)---------------------".format(vname_model))
for season in seasonal_clim_fields_obs_interp:
seasonal_clim_fields_obs_interp[season] = np.ma.masked_where(np.isnan(seasonal_clim_fields_obs_interp[season]),
seasonal_clim_fields_obs_interp[season])
season_to_err[season] = season_to_clim_fields_model_agg[season] - seasonal_clim_fields_obs_interp[season]
if vname_model in ["I5"]:
lons = bmp_info_agg.lons.copy()
lons[lons > 180] -= 360
season_to_err[season] = maskoceans(lons, bmp_info_agg.lats, season_to_err[season])
good_vals = season_to_err[season]
good_vals = good_vals[~good_vals.mask]
print("{}: min={}; max={}; avg={}".format(season,
good_vals.min(),
good_vals.max(),
good_vals.mean()))
print("---------percetages --- CRU ---")
print("{}: {} \%".format(season, good_vals.mean() / seasonal_clim_fields_obs_interp[season][~season_to_err[season].mask].mean() * 100))
cs = plot_seasonal_mean_biases(season_to_error_field=season_to_err,
varname=vname_model,
basemap_info=bmp_info_agg,
axes_list=diff_axes_list)
if obs_axes_list is not None and vname_model in ["I5"]:
clevs = [0, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 350, 400, 500]
cs_obs = None
xx, yy = bmp_info_agg.get_proj_xy()
lons = bmp_info_agg.lons.copy()
lons[lons > 180] -= 360
lons_model = None
xx_model, yy_model = None, None
cs_mod = None
norm = BoundaryNorm(clevs, 256)
for col, (season, obs_field) in enumerate(seasonal_clim_fields_obs_interp.items()):
# Obsrved fields
ax = obs_axes_list[col]
if bmp_info_agg.should_draw_basin_boundaries:
bmp_info_agg.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax)
to_plot = maskoceans(lons, bmp_info_agg.lats, obs_field)
cs_obs = bmp_info_agg.basemap.contourf(xx, yy, to_plot, levels=clevs, ax=ax, norm=norm, extend="max")
bmp_info_agg.basemap.drawcoastlines(ax=ax, linewidth=0.3)
ax.set_title(season)
# Model outputs
if model_axes_list is not None:
ax = model_axes_list[col]
if bmp_info_agg.should_draw_basin_boundaries:
bmp_info_agg.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax)
if lons_model is None:
lons_model = bmp_info_model.lons.copy()
lons_model[lons_model > 180] -= 360
xx_model, yy_model = bmp_info_model.basemap(lons_model, bmp_info_model.lats)
model_field = seasonal_clim_fields_model[season]
to_plot = maskoceans(lons_model, bmp_info_model.lats, model_field)
cs_mod = bmp_info_agg.basemap.contourf(xx_model, yy_model, to_plot, levels=cs_obs.levels, ax=ax,
norm=cs_obs.norm, cmap=cs_obs.cmap, extend="max")
bmp_info_agg.basemap.drawcoastlines(ax=ax, linewidth=0.3)
plt.colorbar(cs_obs, cax=obs_axes_list[-1])
return cs
def compare_vars(vname_model="TT",
vname_obs="tmp",
r_config=None,
season_to_months=None,
obs_path=None,
nx_agg_model=5,
ny_agg_model=5,
bmp_info_agg=None,
diff_axes_list=None,
obs_axes_list=None,
model_axes_list=None,
bmp_info_model=None,
mask_shape_file=None,
nx_agg_obs=1,
ny_agg_obs=1):
"""
if obs_axes_list is not None, plot observation data in those
:param mask_shape_file:
:param bmp_info_model: basemap info native to the model
:param model_axes_list: Axes to plot model outputs
:param vname_model:
:param vname_obs:
:param r_config:
:param season_to_months:
:param obs_path:
:param nx_agg_model:
:param ny_agg_model:
:param bmp_info_agg:
:param diff_axes_list: if it is None the plots for each variable is done in separate figures
"""
if vname_obs is None:
vname_model_to_vname_obs = {"TT": "tmp", "PR": "pre"}
vname_obs = vname_model_to_vname_obs[vname_model]
seasonal_clim_fields_model = analysis.get_seasonal_climatology_for_runconfig(
run_config=r_config,
varname=vname_model,
level=0,
season_to_months=season_to_months)
season_to_clim_fields_model_agg = OrderedDict()
for season, field in seasonal_clim_fields_model.items():
print(field.shape)
season_to_clim_fields_model_agg[season] = aggregate_array(
field, nagg_x=nx_agg_model, nagg_y=ny_agg_model)
if vname_model == "PR":
season_to_clim_fields_model_agg[season] *= 1.0e3 * 24 * 3600
if vname_obs in [
"SWE",
]:
obs_manager = SweDataManager(path=obs_path, var_name=vname_obs)
elif obs_path is None:
obs_manager = CRUDataManager(var_name=vname_obs)
else:
obs_manager = CRUDataManager(var_name=vname_obs, path=obs_path)
seasonal_clim_fields_obs = obs_manager.get_seasonal_means(
season_name_to_months=season_to_months,
start_year=r_config.start_year,
end_year=r_config.end_year)
seasonal_clim_fields_obs_interp = OrderedDict()
# Derive the mask from a shapefile if provided
if mask_shape_file is not None:
the_mask = get_mask(bmp_info_agg.lons,
bmp_info_agg.lats,
shp_path=mask_shape_file)
else:
the_mask = np.zeros_like(bmp_info_agg.lons)
for season, obs_field in seasonal_clim_fields_obs.items():
obs_field = obs_manager.interpolate_data_to(obs_field,
lons2d=bmp_info_agg.lons,
lats2d=bmp_info_agg.lats,
nneighbours=nx_agg_obs *
ny_agg_obs)
obs_field = np.ma.masked_where(the_mask > 0.5, obs_field)
seasonal_clim_fields_obs_interp[season] = obs_field
# assert hasattr(seasonal_clim_fields_obs_interp[season], "mask")
season_to_err = OrderedDict()
print("-------------var: {} (PE with CRU)---------------------".format(
vname_model))
for season in seasonal_clim_fields_obs_interp:
seasonal_clim_fields_obs_interp[season] = np.ma.masked_where(
np.isnan(seasonal_clim_fields_obs_interp[season]),
seasonal_clim_fields_obs_interp[season])
season_to_err[season] = season_to_clim_fields_model_agg[
season] - seasonal_clim_fields_obs_interp[season]
if vname_model in ["I5"]:
lons = bmp_info_agg.lons.copy()
lons[lons > 180] -= 360
season_to_err[season] = maskoceans(lons, bmp_info_agg.lats,
season_to_err[season])
good_vals = season_to_err[season]
good_vals = good_vals[~good_vals.mask]
print("{}: min={}; max={}; avg={}".format(season, good_vals.min(),
good_vals.max(),
good_vals.mean()))
print("---------percetages --- CRU ---")
print("{}: {} \%".format(
season,
good_vals.mean() / seasonal_clim_fields_obs_interp[season]
[~season_to_err[season].mask].mean() * 100))
cs = plot_seasonal_mean_biases(season_to_error_field=season_to_err,
varname=vname_model,
basemap_info=bmp_info_agg,
axes_list=diff_axes_list)
if obs_axes_list is not None and vname_model in ["I5"]:
clevs = [0, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 350, 400, 500]
cs_obs = None
xx, yy = bmp_info_agg.get_proj_xy()
lons = bmp_info_agg.lons.copy()
lons[lons > 180] -= 360
lons_model = None
xx_model, yy_model = None, None
cs_mod = None
norm = BoundaryNorm(clevs, 256)
for col, (season, obs_field) in enumerate(
seasonal_clim_fields_obs_interp.items()):
# Obsrved fields
ax = obs_axes_list[col]
if bmp_info_agg.should_draw_basin_boundaries:
bmp_info_agg.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4],
"basin",
ax=ax)
to_plot = maskoceans(lons, bmp_info_agg.lats, obs_field)
cs_obs = bmp_info_agg.basemap.contourf(xx,
yy,
to_plot,
levels=clevs,
ax=ax,
norm=norm,
extend="max")
bmp_info_agg.basemap.drawcoastlines(ax=ax, linewidth=0.3)
ax.set_title(season)
# Model outputs
if model_axes_list is not None:
ax = model_axes_list[col]
if bmp_info_agg.should_draw_basin_boundaries:
bmp_info_agg.basemap.readshapefile(
BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax)
if lons_model is None:
lons_model = bmp_info_model.lons.copy()
lons_model[lons_model > 180] -= 360
xx_model, yy_model = bmp_info_model.basemap(
lons_model, bmp_info_model.lats)
model_field = seasonal_clim_fields_model[season]
to_plot = maskoceans(lons_model, bmp_info_model.lats,
model_field)
cs_mod = bmp_info_agg.basemap.contourf(xx_model,
yy_model,
to_plot,
levels=cs_obs.levels,
ax=ax,
norm=cs_obs.norm,
cmap=cs_obs.cmap,
extend="max")
bmp_info_agg.basemap.drawcoastlines(ax=ax, linewidth=0.3)
plt.colorbar(cs_obs, cax=obs_axes_list[-1])
return cs