Figure 9.¶
Snapshot of top-of-atmosphere outgoing longwave radiation (W m−2) in the Trappist-1e simulation at 102 d: from the global model (a) over the whole planet, (b) zoomed in on the HighRes domain; and (c) from the HighRes simulation. The dark blue box in (a) shows the location of the HighRes domain.
Import the necessary libraries.
In [1]:
import warnings
warnings.filterwarnings("ignore")
Progress bar
In [2]:
from fastprogress import progress_bar
In [3]:
from datetime import datetime, timedelta
Scientific stack
In [4]:
import cartopy.crs as ccrs
import iris
import numpy as np
from matplotlib import gridspec
import matplotlib.pyplot as plt
In [5]:
from aeolus.coord_utils import UM_LATLON
from aeolus.core import Run
from aeolus.plot import GeoAxesGrid, label_global_map_gridlines
from aeolus.region import Region
from aeolus.util import fmt_lonlat, subplot_label_generator
Local modules
In [6]:
from commons import (
DT_FMT,
FCST_DIM_CNSTR,
FCST_PRD_CNSTR,
GLM_RUNID,
NS_COLORS,
NS_MODEL_TYPES,
NS_RUN_ALIASES,
NS_RUNID,
PLANET_ALIASES,
)
import mypaths
from plot_func import use_style, CART_KW
from proc_um_output import process_cubes
Global stylesheet for figures.
In [7]:
use_style()
A few local definitions
In [8]:
planet = "trap1e"
run_key = "grcs"
OUTPUT_NAME_PREFIX = f"{planet}_{run_key}"
Define the region to zoom in to in the figure.
In [9]:
ss_region = Region(-10, 30, -20, 20, "HighRes domain")
extent = [i["value"] for i in ss_region]
Set metadata for the variable.
In [10]:
varname = "toa_outgoing_longwave_flux"
short_name = "TOA OLR"
tex_units = "$W$ $m^{-2}$"
plt_kw = dict(cmap="Greys", vmin=60, vmax=270)
Load data¶
Change file mask to load only the files with TOA radiation data.
In [11]:
NS_MODEL_TYPES["global"]["path"] = (
NS_MODEL_TYPES["global"]["path"].parent / f"{GLM_RUNID}_pd*"
)
NS_MODEL_TYPES["lam"]["path"] = NS_MODEL_TYPES["lam"]["path"].parent / f"{NS_RUNID}_pa*"
Create a dictionary of Run objects with UM data.
In [12]:
runs = {}
for model_type, model_specs in progress_bar(NS_MODEL_TYPES.items()):
subdir = f"{planet}_{run_key}"
label = f"{planet}_{run_key}_{model_type}"
fpath = mypaths.nsdir / subdir / "*" / model_specs["path"]
# Load data
run = Run(
files=fpath,
name=label,
planet=planet,
model_type=model_type,
timestep=model_specs["timestep"],
)
# Regrid & interpolate data
run.proc_data(
process_cubes,
timestep=run.timestep,
extract_mean=False,
regrid_multi_lev=False,
)
# Select domain
run.proc = run.proc.extract(FCST_PRD_CNSTR)
# Save to dict
runs[label] = run
100.00% [2/2 00:11<00:00]
Extract cubes of TOA OLR along with longitude and latitude from the global and HighRes simulations.
In [13]:
cubes = {}
lats = {}
lons = {}
for model_type in NS_MODEL_TYPES.keys():
cubes[model_type] = runs[f"{planet}_{run_key}_{model_type}"].proc.extract_strict(varname)
lats[model_type] = cubes[model_type].coord(UM_LATLON[0]).points
lons[model_type] = cubes[model_type].coord(UM_LATLON[1]).points
Choose the time slice.
In [14]:
hour0 = cubes["lam"].coord("time").points[0]
In [15]:
FCST_TIME_CNSTR = iris.Constraint(
forecast_reference_time=lambda x: (x.point.day == 28) & (x.point.month == 7)
) # 102 days
In [16]:
ndays = (
cubes["lam"].extract(FCST_TIME_CNSTR).coord("time").points[0]
- hour0
) / 24
ndays
Out[16]:
102.0
Plot the results¶
Set axes tick locations.
In [17]:
xlocs_ss = np.arange(-20, 50, 10)
ylocs_ss = np.arange(-30, 40, 10)
xlocs = np.arange(-180, 181, 60)
ylocs = np.arange(-90, 91, 30)
Set title styles.
In [18]:
ttl_kw = dict(fontsize="small", pad=5, loc="left")
cb_ttl_kw = dict(fontsize="x-small", pad=5)
Assemble the plot.
In [19]:
ncols = 3
nrows = 1
iletters = subplot_label_generator()
fig = plt.figure(figsize=(ncols * 8, nrows * 6),)
gs = gridspec.GridSpec(1, 4)
axs = []
model_type = "global"
ax = fig.add_subplot(gs[:2], projection=ccrs.Robinson())
axs.append(ax)
ax.pcolormesh(
lons[model_type],
lats[model_type],
cubes[model_type].extract(FCST_TIME_CNSTR).data,
**plt_kw,
**CART_KW,
)
ss_region.add_to_ax(
ax, edgecolor=NS_COLORS[run_key]["lam"], facecolor="none", linewidth=5, **CART_KW
)
label_global_map_gridlines(
fig, ax, xlocs[1:-1], ylocs[1:-1], degree=True, size="x-small", xoff=-15
)
ax.gridlines(xlocs=xlocs, ylocs=ylocs, crs=ccrs.PlateCarree())
ax.set_title(
f"{NS_MODEL_TYPES[model_type]['title']} model, full domain ({PLANET_ALIASES[planet]})",
fontsize="medium",
pad=5,
loc="center",
)
ax = fig.add_subplot(gs[2], projection=ccrs.PlateCarree())
axs.append(ax)
ax.pcolormesh(
lons[model_type],
lats[model_type],
cubes[model_type].extract(FCST_TIME_CNSTR).data,
**plt_kw,
**CART_KW,
)
ax.set_title(
f"{NS_MODEL_TYPES[model_type]['title']} model, zoomed in",
fontsize="medium",
pad=5,
loc="center",
)
model_type = "lam"
ax = fig.add_subplot(gs[3], projection=ccrs.PlateCarree())
axs.append(ax)
h = ax.pcolormesh(
lons[model_type],
lats[model_type],
cubes[model_type].extract(FCST_TIME_CNSTR).data,
rasterized=True,
**plt_kw,
**CART_KW,
)
ax.set_title(
f"{NS_MODEL_TYPES[model_type]['title']} model",
fontsize="medium",
pad=5,
loc="center",
)
for ax in fig.axes[1:]:
ax.set_extent(extent)
ax.gridlines(xlocs=xlocs_ss, ylocs=ylocs_ss, crs=ccrs.PlateCarree())
label_global_map_gridlines(
fig, ax, xlocs_ss[1:-1], ylocs_ss[1:-1], degree=True, size="x-small", xoff=-15
)
for ax in fig.axes:
ax.set_title(f"({next(iletters)})", **ttl_kw)
cb = fig.colorbar(h, ax=axs, orientation="vertical", shrink=0.82, pad=0.02)
cb.ax.set_ylabel(f"{short_name} [{tex_units}]")
plt.close() # Show the figure in a separate cell
Show the figure¶
In [20]:
fig
Out[20]:
And save it.
In [21]:
imgname = mypaths.plotdir / f"{planet}__{run_key}__toa_olr_map__{ndays:.0f}d.png"
In [22]:
fig.savefig(imgname, dpi=200)
print(f"Saved to ../{imgname.relative_to(mypaths.topdir)}")
Saved to ../plots/trap1e__grcs__toa_olr_map__102d.png