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@RoyCaitSith
RoyCaitSith committed Aug 1, 2023
1 parent 1045ac0 commit 2db68c1
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Showing 10 changed files with 477 additions and 236 deletions.
4 changes: 2 additions & 2 deletions functions/cpex_metnav.py
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Original file line number Diff line number Diff line change
Expand Up @@ -127,10 +127,10 @@ def draw_metnav_dc8(data_library_name, dir_case, case_name, wrf_domain=False, wr
max_flight_lon = np.max(flight_lon)

if wrf_domain:
domain = 'wrf'
domain = 'd01_d02'
extent = [lon_d01[0,0], lon_d01[-1,-1], lat_d01[0,0], lat_d01[-1,-1]]
else:
domain = 'highlight'
domain = 'auto'
clon = 5*(min_flight_lon + max_flight_lon)/2.0//5
clat = 5*(min_flight_lat + max_flight_lat)/2.0//5
half_extent = np.max([5*abs(max_flight_lon - clon)//5+5, 5*abs(max_flight_lat - clat)//5+5, 15])
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0 colormaps/cpt_convert.py → functions/cpt_convert.py
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File renamed without changes.
8 changes: 4 additions & 4 deletions functions/data_library_cpex.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@

attributes[('/08_CPEX/01_CV_RF07_AEW06_IAN', 'Ian')] = {
'dir_exp': '/uufs/chpc.utah.edu/common/home/zpu-group16/cfeng/08_CPEX/01_CV_RF07_AEW06_IAN',
'dir_ScientificColourMaps7': '/uufs/chpc.utah.edu/common/home/u1237353/climetlab-my-plugin/colormaps/ScientificColourMaps7',
'dir_colormaps': '/uufs/chpc.utah.edu/common/home/u1237353/climetlab-my-plugin/colormaps',
'dir_namelists': '/uufs/chpc.utah.edu/common/home/u1237353/CPEX/01_CV_RF07_AEW06_IAN/cycling_da/namelists',
'NHC_best_track': '2022_09L_Ian.csv',
'AEW_best_track': '2022_AEW06.csv',
Expand All @@ -33,7 +33,7 @@

attributes[('/08_CPEX/02_AW_RF01_AEW01', 'AEW01')] = {
'dir_exp': '/uufs/chpc.utah.edu/common/home/zpu-group16/cfeng/08_CPEX/02_AW_RF01_AEW01',
'dir_ScientificColourMaps7': '/uufs/chpc.utah.edu/common/home/u1237353/climetlab-my-plugin/colormaps/ScientificColourMaps7',
'dir_colormaps': '/uufs/chpc.utah.edu/common/home/u1237353/climetlab-my-plugin/colormaps',
'dir_namelists': '/uufs/chpc.utah.edu/common/home/u1237353/CPEX/02_AW_RF01_AEW01/cycling_da/namelists',
'IMERG_version': {'run': 'HHR', 'version': 'V06B'},
'CMORPH_version': '8km-30min',
Expand All @@ -56,7 +56,7 @@

attributes[('/08_CPEX/03_AW_RF02_AEW02', 'AEW02')] = {
'dir_exp': '/uufs/chpc.utah.edu/common/home/zpu-group16/cfeng/08_CPEX/03_AW_RF02_AEW02',
'dir_ScientificColourMaps7': '/uufs/chpc.utah.edu/common/home/u1237353/climetlab-my-plugin/colormaps/ScientificColourMaps7',
'dir_colormaps': '/uufs/chpc.utah.edu/common/home/u1237353/climetlab-my-plugin/colormaps',
'dir_namelists': '/uufs/chpc.utah.edu/common/home/u1237353/CPEX/03_AW_RF02_AEW02/cycling_da/namelists',
'IMERG_version': {'run': 'HHR', 'version': 'V06B'},
'CMORPH_version': '8km-30min',
Expand All @@ -79,7 +79,7 @@

attributes[('/08_CPEX/04_AW_RF07_Larry', 'Larry')] = {
'dir_exp': '/uufs/chpc.utah.edu/common/home/zpu-group16/cfeng/08_CPEX/04_AW_RF07_Larry',
'dir_ScientificColourMaps7': '/uufs/chpc.utah.edu/common/home/u1237353/climetlab-my-plugin/colormaps/ScientificColourMaps7',
'dir_colormaps': '/uufs/chpc.utah.edu/common/home/u1237353/climetlab-my-plugin/colormaps',
'dir_namelists': '/uufs/chpc.utah.edu/common/home/u1237353/CPEX/04_AW_RF07_Larry/cycling_da/namelists',
'IMERG_version': {'run': 'HHR', 'version': 'V06B'},
'CMORPH_version': '8km-30min',
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217 changes: 214 additions & 3 deletions functions/goes_satellite.py
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Original file line number Diff line number Diff line change
@@ -1,8 +1,20 @@
import os
import re
import glob
import importlib
import subprocess
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from cpt_convert import loadCPT
from netCDF4 import Dataset
from datetime import datetime, timedelta
from tqdm.notebook import tqdm
from google.cloud import storage
from matplotlib.backends.backend_pdf import PdfPages
from mpl_toolkits.basemap import Basemap
from IPython.display import Image as IPImage
from matplotlib.colors import LinearSegmentedColormap

def download_goes_date(download_start_time, n_days, dir_GOES, data_set='ABI-L2-CMIPF'):
"""
Expand Down Expand Up @@ -66,14 +78,14 @@ def download_goes_case(data_library_names, dir_cases, case_names, data_sets=['AB
history_interval = attributes[(dir_case, case_name)]['history_interval']
initial_time = datetime(*itime)

dir_data=os.path.join(dir_exp, 'data')
dir_data = os.path.join(dir_exp, 'data')
dir_GOES = os.path.join(dir_data, 'GOES')
os.makedirs(dir_GOES, exist_ok=True)

anl_start_time = initial_time + timedelta(hours=cycling_interval)
n_time = int((cycling_interval*(total_da_cycles-1) + forecast_hours + 6)/history_interval + 1)

for da_cycle in tqdm(range(1, total_da_cycles+1), desc='DA Cycles', leave=False, unit="files", bar_format="{desc}: {n}/{total} da cycles | {elapsed}<{remaining}"):
for da_cycle in tqdm(range(0, total_da_cycles+1), desc='DA Cycles', leave=False, unit="files", bar_format="{desc}: {n}/{total} da cycles | {elapsed}<{remaining}"):

time_now = initial_time + timedelta(hours=cycling_interval*da_cycle)
time_now_str = time_now.strftime('%Y%m%d')
Expand Down Expand Up @@ -125,4 +137,203 @@ def download_goes_case(data_library_names, dir_cases, case_names, data_sets=['AB
local_path = os.path.join(dir_day, file_name)
# Download the file from GCS to the local machine
with open(local_path, "wb") as f:
blob.download_to_file(f)
blob.download_to_file(f)

def draw_goes_images(data_library_name, dir_case, case_name, channel=8, goes_levels=np.arange(190.0, 270.1, 1.0),
flight_track=False, dropsonde=False, tc_track=False, aew_track=False,
anl_start_time=datetime(2022, 9, 16, 0, 0, 0), anl_end_time=datetime(2022, 9, 16, 0, 0, 0), time_interval=1.0,
region_type='d01', wrf_domain_exp_name='CTRL', domain_specified=[-60, -10, -10, 30]):

# Import the necessary library
module = importlib.import_module(f"data_library_{data_library_name}")
attributes = getattr(module, 'attributes')

itime = attributes[(dir_case, case_name)]['itime']
initial_time = datetime(*itime)
dir_exp = attributes[(dir_case, case_name)]['dir_exp']
dir_colormaps = attributes[(dir_case, case_name)]['dir_colormaps']
total_da_cycles = attributes[(dir_case, case_name)]['total_da_cycles']

dir_data = os.path.join(dir_exp, 'data')
dir_MetNav = os.path.join(dir_data, 'MetNav')
dir_abi = os.path.join(dir_data, 'GOES')
dir_goes = os.path.join(dir_exp, 'goes')
dir_ScientificColourMaps7 = os.path.join(dir_colormaps, 'ScientificColourMaps7')
grayC_cm_data = np.loadtxt(os.path.join(dir_ScientificColourMaps7, 'grayC', 'grayC.txt'))
cpt, cpt_r = loadCPT(os.path.join(dir_colormaps, 'GOES-R_BT.rgb'))
cpt_convert = LinearSegmentedColormap('cpt', cpt)

if region_type == 'd01' or region_type == 'd02':

dir_wrfout = os.path.join(dir_exp, 'cycling_da', f"{case_name}_{wrf_domain_exp_name}_C{str(total_da_cycles).zfill(2)}", 'bkg')
wrfout_format = 'wrfout_{dom}_{ctime:%Y-%m-%d_%H:%M:00}'
file_wrfout_d01 = os.path.join(dir_wrfout, wrfout_format.format(dom='d01', ctime=initial_time))
file_wrfout_d02 = os.path.join(dir_wrfout, wrfout_format.format(dom='d02', ctime=initial_time))

wrfout_d01 = Dataset(file_wrfout_d01)
lat_d01 = wrfout_d01.variables['XLAT'][0,:,:]
lon_d01 = wrfout_d01.variables['XLONG'][0,:,:]
wrfout_d01.close()

lat_d02_box = []
lon_d02_box = []
wrfout_d02 = Dataset(file_wrfout_d02)
lat_d02 = wrfout_d02.variables['XLAT'][0,:,:]
lon_d02 = wrfout_d02.variables['XLONG'][0,:,:]
n_we_d02 = len(lat_d02[0, :])
n_sn_d02 = len(lat_d02[:, 0])
lat_d02_box.extend(list(lat_d02[0, 0:n_we_d02-1:1]))
lat_d02_box.extend(list(lat_d02[0:n_sn_d02-1, n_we_d02-1]))
lat_d02_box.extend(list(lat_d02[n_sn_d02-1, n_we_d02-1:0:-1]))
lat_d02_box.extend(list(lat_d02[n_sn_d02-1:0:-1, 0]))
lon_d02_box.extend(list(lon_d02[0, 0:n_we_d02-1:1]))
lon_d02_box.extend(list(lon_d02[0:n_sn_d02-1, n_we_d02-1]))
lon_d02_box.extend(list(lon_d02[n_sn_d02-1, n_we_d02-1:0:-1]))
lon_d02_box.extend(list(lon_d02[n_sn_d02-1:0:-1, 0]))
lat_d02_box.append(lat_d02[0, 0])
lon_d02_box.append(lon_d02[0, 0])
wrfout_d02.close()

if region_type == 'flight' or flight_track:
filenames = glob.glob(os.path.join(dir_MetNav, '*flight_track*csv'))
for filename in filenames:
df = pd.read_csv(filename)
flight_time = df['Time']
flight_lat = df['LAT']
flight_lon = df['LON']
min_flight_lat = np.min(flight_lat)
max_flight_lat = np.max(flight_lat)
min_flight_lon = np.min(flight_lon)
max_flight_lon = np.max(flight_lon)
numbers = re.findall(r'\d+', filename)
index_flight_time = flight_time%10000 == 0
index_flight_time_00 = flight_time%1000000 == 0
del df

if region_type == 'd01': extent = [lon_d01[0,0], lon_d01[-1,-1], lat_d01[0,0], lat_d01[-1,-1]]
if region_type == 'd02': extent = [lon_d02[0,0], lon_d02[-1,-1], lat_d02[0,0], lat_d02[-1,-1]]
if region_type == 'specified': extent = [domain_specified[0], domain_specified[1], domain_specified[2], domain_specified[3]]
if region_type == 'flight':
clon = 5*(min_flight_lon + max_flight_lon)/2.0//5
clat = 5*(min_flight_lat + max_flight_lat)/2.0//5
half_extent = np.max([5*abs(max_flight_lon - clon)//5+5, 5*abs(max_flight_lat - clat)//5+5, 15])
extent = [clon-half_extent+5.0, clon+half_extent, clat-half_extent+5.0, clat+half_extent]

duration = anl_end_time - anl_start_time
duration_in_second = duration.total_seconds()
duration_in_hour = duration_in_second/3600
n_time = int(duration_in_hour/time_interval)+1

for idt in tqdm(range(n_time), desc='Times', unit='files', bar_format="{desc}: {n}/{total} | {elapsed}<{remaining}"):

time_now = anl_start_time + timedelta(hours=idt*time_interval)
time_now_str = time_now.strftime('%Y%m%d%H%M%S')
time_now_YYYYMMDD = time_now.strftime('%Y%m%d')
time_now_HH = time_now.strftime('%H')

output_filename = (
f"{time_now_str}_ch{str(channel).zfill(2)}_"
f"flight_track_{flight_track}_dropsonde_{dropsonde}_"
f"tc_track_{tc_track}_aew_track_{aew_track}_"
f"{region_type}"
)

pdfname = os.path.join(dir_goes, output_filename+'.pdf')
pngname = os.path.join(dir_goes, output_filename+'.png')

with PdfPages(pdfname) as pdf:

fig_width = 2.75*np.abs(extent[1]-extent[0])/np.abs(extent[3]-extent[2])
fig_height = 2.75+0.75
clb_aspect = 25*np.abs(extent[1]-extent[0])/np.abs(extent[3]-extent[2])

fig, axs = plt.subplots(1, 1, figsize=(fig_width, fig_height))

ax = axs
m = Basemap(projection='cyl', llcrnrlat=extent[2], llcrnrlon=extent[0], urcrnrlat=extent[3], urcrnrlon=extent[1], resolution='h', ax=ax)
m.drawcoastlines(linewidth=0.2, color='k')

abiname = f"OR_ABI-L2-CMIPF-M6C{str(channel).zfill(2)}_G16_s*{time_now_HH}00*"
abi = '/'.join([dir_abi, time_now_YYYYMMDD, abiname])
fileabi = os.popen('ls ' + abi).read().split()

ncfile = Dataset(fileabi[0])
CMI = ncfile.variables['CMI'][:,:]
x = ncfile.variables['x'][:]
y = ncfile.variables['y'][:]

gip = ncfile.variables['goes_imager_projection']
r_eq = gip.semi_major_axis
r_pol = gip.semi_minor_axis
H = gip.perspective_point_height + gip.semi_major_axis
phi_0 = gip.latitude_of_projection_origin
lambda_0 = gip.longitude_of_projection_origin

x, y = np.meshgrid(x, y, indexing='xy')
sin_x = np.sin(x)
cos_x = np.cos(x)
sin_y = np.sin(y)
cos_y = np.cos(y)
a = np.power(sin_x, 2) + np.power(cos_x, 2)*(np.power(cos_y, 2)+np.power(r_eq*sin_y/r_pol, 2))
b = -2.0*H*cos_x*cos_y
c = np.power(H, 2) - np.power(r_eq, 2)
r_s = (-1.0*b - np.sqrt(np.power(b, 2)-4*a*c))/(2*a)
s_x = r_s*cos_x*cos_y
s_y = -1.0*r_s*sin_x
s_z = r_s*cos_x*sin_y
abi_lat = np.degrees(np.arctan(np.power(r_eq/r_pol, 2)*s_z/np.sqrt(np.power(H-s_x, 2)+np.power(s_y, 2))))
abi_lon = lambda_0 - np.degrees(np.arctan(s_y/(H-s_x)))

x_abi_lon, y_abi_lat = m(abi_lon, abi_lat, inverse=False)
pcm = ax.contourf(x_abi_lon, y_abi_lat, CMI, levels=goes_levels, cmap=cpt_convert, extend='both', zorder=1)

if flight_track: ax.plot(flight_lon, flight_lat, '-', color='k', linewidth=1.00, label='DC8', zorder=3)

if region_type == 'd01':
x_lon_d02_box, y_lat_d02_box = m(lon_d02_box, lat_d02_box, inverse=False)
ax.plot(x_lon_d02_box, y_lat_d02_box, '-', color='k', linewidth=1.00, zorder=7)
ax.text(np.max(lon_d01)-2.00, np.min(lat_d01)+1.50, 'D01', ha='right', va='bottom', color='k', fontsize=10.0, zorder=7)
ax.text(np.max(lon_d02)-2.00, np.min(lat_d02)+1.50, 'D02', ha='right', va='bottom', color='k', fontsize=10.0, zorder=7)

ax.set_xticks(np.arange(-180, 181, 10))
ax.set_yticks(np.arange(-90, 91, 10))
ax.set_xticklabels(["$\\mathrm{{{0}^\\circ {1}}}$".format(abs(x), "W" if x < 0 else ("E" if x > 0 else "")) for x in range(int(-180), int(180)+1, 10)])
ax.set_yticklabels(["$\\mathrm{{{0}^\\circ {1}}}$".format(abs(x), "S" if x < 0 else ("N" if x > 0 else "")) for x in range(int(-90), int(90)+1, 10)])
ax.tick_params('both', direction='in', labelsize=10.0)
ax.axis(extent)
ax.grid(True, linewidth=0.5, color=grayC_cm_data[53])
ax.legend(loc='upper right', fontsize=5.0, handlelength=2.5).set_zorder(102)

# if tc_track:
# clb1 = fig.colorbar(pcm, ax=axs, ticks=np.arange(190, 250.1, 5.0), orientation='horizontal', pad=-0.025, aspect=50, shrink=1.00)
# clb1.set_label('GOES-R Channel 8 BTs (K) at ' + time_now.strftime('%H UTC %d Aug %Y'), fontsize=10.0, labelpad=4.0)
# clb1.ax.tick_params(axis='both', direction='in', pad=4.0, length=3.0, labelsize=10.0)

# grade = [20, 33, 63, 82, 95, 112, 125]
# cat = ['TD', 'TS', 'Cat1', 'Cat2', 'Cat3', 'Cat4']
# clb2 = plt.colorbar(sc2, ticks=grade, orientation='horizontal', pad=0.050, aspect=50, shrink=1.00)
# clb2.set_ticklabels(grade)
# clb2.set_label('MWS (Knot)', fontsize=10.0)
# clb2.ax.tick_params(axis='both', direction='in', labelsize=10.0)
# for idx, lab in enumerate(cat):
# clb2.ax.text(0.5*(grade[idx+1]+grade[idx]), -95.0, lab, ha='center', va='center', color='k', fontsize=10.0)
# else:
# clb1 = fig.colorbar(pcm, ax=axs, ticks=goes_levels[::10], orientation='horizontal', pad=0.075, aspect=clb_aspect, shrink=1.00)
# clb1.set_label(f"BTs of channel {str(channel)} (K)", fontsize=10.0, labelpad=4.0)
# clb1.ax.tick_params(axis='both', direction='in', pad=4.0, length=3.0, labelsize=10.0)

clb1 = fig.colorbar(pcm, ax=axs, ticks=goes_levels[::10], orientation='horizontal', pad=0.075, aspect=clb_aspect, shrink=1.00)
clb1.set_label(f"BTs of channel {str(channel)} (K)", fontsize=10.0, labelpad=4.0)
clb1.ax.tick_params(axis='both', direction='in', pad=4.0, length=3.0, labelsize=10.0)

plt.tight_layout()
plt.savefig(pngname, dpi=600)
pdf.savefig(fig)
plt.cla()
plt.clf()
plt.close()

command = f"convert {pngname} -trim {pngname}"
subprocess.run(command, shell=True)
image = IPImage(filename=pngname)
display(image)
8 changes: 4 additions & 4 deletions functions/set_parameters.py
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Original file line number Diff line number Diff line change
Expand Up @@ -198,13 +198,13 @@ def set_variables(var):
information.update({'unit': 'null'})
information.update({'lb_title': 'Relative humidity (%)'})
information.update({'factor': 1.0})
information.update({'extend': 'max'})
information.update({'extend': 'both'})
information.update({'GFS': 'Relative humidity'})

levels = {}
levels.update({925: [[f'{float(i/ 1):.0f}' for i in range(0, 17, 2)], cmaps.imola]})
levels.update({850: [[f'{float(i/ 1):.0f}' for i in range(0, 17, 2)], cmaps.imola]})
levels.update({700: [[f'{float(i/ 2):.0f}' for i in range(0, 25, 3)], cmaps.imola]})
levels.update({925: [[f'{float(i/ 1):.0f}' for i in range(30, 101, 5)], cmaps.div3_green_brown_r]})
levels.update({850: [[f'{float(i/ 1):.0f}' for i in range(30, 101, 5)], cmaps.div3_green_brown_r]})
levels.update({700: [[f'{float(i/ 1):.0f}' for i in range(30, 101, 5)], cmaps.div3_green_brown_r]})
levels.update({500: [[f'{float(i/ 1):.0f}' for i in range(0, 9, 1)], cmaps.imola]})
levels.update({300: [[f'{float(i/ 10):.1f}' for i in range(0, 9, 1)], cmaps.imola]})
levels.update({200: [[f'{float(i/100):.2f}' for i in range(0, 9, 1)], cmaps.imola]})
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