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kuchaale · May 12, 2022 · 390b626 · 390b626
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diff --git a/code/EPFD_lagA_again.ipynb b/code/EPFD_lagA_again.ipynb
diff --git a/code/EPFD_vs_OGWD_correlation.ipynb b/code/EPFD_vs_OGWD_correlation.ipynb
diff --git a/code/GRL_reproduce_Fig1_Himalayas_lagA.ipynb b/code/GRL_reproduce_Fig1_Himalayas_lagA.ipynb
diff --git a/code/NAM_CMAM_variance.ipynb b/code/NAM_CMAM_variance.ipynb
diff --git a/code/NAM_lagA.ipynb b/code/NAM_lagA.ipynb
diff --git a/code/OGWD+refr_index_himalayas_composite_lagA.ipynb b/code/OGWD+refr_index_himalayas_composite_lagA.ipynb
diff --git a/code/OGWD_himalayas_composite_lagA.ipynb b/code/OGWD_himalayas_composite_lagA.ipynb
diff --git a/code/OGWD_trendanalysis_CCMI-SD.ipynb b/code/OGWD_trendanalysis_CCMI-SD.ipynb
diff --git a/code/Plumb_flux_analysis_Himalayas.ipynb b/code/Plumb_flux_analysis_Himalayas.ipynb
diff --git a/code/VMFC_ERA5_composite.ipynb b/code/VMFC_ERA5_composite.ipynb
diff --git a/code/bootstrap_statistical_significance@height-visualization-contourswithclimatology.ipynb b/code/bootstrap_statistical_significance@height-visualization-contourswithclimatology.ipynb
diff --git a/code/chap3_vykreslovani_dizertace_new-DJFonly-tropo-strato-meso.ipynb b/code/chap3_vykreslovani_dizertace_new-DJFonly-tropo-strato-meso.ipynb
diff --git a/code/effective_diffusivity_HI_composite-pv-xcontour.ipynb b/code/effective_diffusivity_HI_composite-pv-xcontour.ipynb
diff --git a/code/geoapps_vs_xcontour.ipynb b/code/geoapps_vs_xcontour.ipynb
diff --git a/code/influx_figure_using_composites.ipynb b/code/influx_figure_using_composites.ipynb
diff --git a/code/interpeak_times_analysis.ipynb b/code/interpeak_times_analysis.ipynb
diff --git a/code/moment_calculation_distribution_in_CMAM30-sd_composites.ipynb b/code/moment_calculation_distribution_in_CMAM30-sd_composites.ipynb
diff --git a/code/moments_fast_example.py b/code/moments_fast_example.py
@@ -0,0 +1,57 @@
+from netCDF4 import Dataset
+import matplotlib.pyplot as plt
+import numpy as np
+import vor_fast
+import vor_fast_setup
+import xarray as xr
+import sys
+
+#input variables
+plot = False
+save2netcdf = True
+resolution = 'full'
+
+# Read in NetCDF file with geopotential height values
+print('opening')
+ncin = xr.open_dataset('zg_daily_CMAM_CMAM30-SD_r1i1p1_19790101-20101231_10hPa.nc')
+gph = ncin.zg.values
+lons = ncin.lon.values
+lats = ncin.lat.values
+days = ncin.time.values#[:100]
+ncin.close()
+
+# Set up cartesian mapping xypoints and restrict to NH
+gph_nh, lats_nh, xypoints = vor_fast_setup.setup(gph,lats,lons,'NH')
+
+
+
+# Calculate diagnostics for each day
+print('Calculating for resolution: '+resolution)
+for iday, day in enumerate(days):
+    if iday % 1000 == 0:
+        print('Calculating moments for day '+str(iday), day)
+    moments = vor_fast.calc_moments(gph_nh[iday,:,:],lats_nh,lons,xypoints,
+                                    hemisphere='NH',field_type='GPH',
+                                    edge=3.02e4,resolution=resolution)
+    if iday == 0:
+        ds = xr.Dataset(moments, coords={'time': [day]})
+    else:
+        temp = xr.Dataset(moments, coords={'time': [day]})
+        ds = xr.concat([ds,temp], dim='time')
+
+
+if save2netcdf:
+    print('saving')
+    ds.to_netcdf('moment_calculation_w_obj-area_CMAM.nc')
+
+# Plot timeseries
+if plot:
+    print('plotting')
+    fig, axes = plt.subplots(nrows=2)
+    ds['aspect_ratio'].plot(ax = axes[0])
+    ds['aspect_ratio'].where(ds.aspect_ratio >= 2.4).plot(ax = axes[0], color= 'red')
+    ds['centroid_latitude'].plot(ax = axes[1])
+    ds['centroid_latitude'].where(ds.centroid_latitude < 66).plot(ax = axes[1])
+    plt.tight_layout()
+    plt.savefig('ar_centroid_full.pdf', bbox_inches = 'tight')
+
diff --git a/code/predictability_wavenumbers.ipynb b/code/predictability_wavenumbers.ipynb
diff --git a/code/refraction_index_calc.py b/code/refraction_index_calc.py
@@ -0,0 +1,37 @@
+import xarray as xr
+import glob
+from aostools.climate import ComputeRefractiveIndex
+
+
+cesta = '/mnt/4data/CMAM/0A.daily/'
+ta_list = sorted(glob.glob(cesta+'ta/ta_6hrPlev_CMAM_CMAM30-SD_r1i1p1_*-*18.nc'))
+ua_list = sorted(glob.glob(cesta+'ua/ua_6hrPlev_CMAM_CMAM30-SD_r1i1p1_*-*18.nc'))
+Earth_radius = 6.378e+6
+
+for i,(ta_file, ua_file) in enumerate(zip(ta_list[:], ua_list[:])):
+    print(ta_file, ua_file)
+    suffix = ta_file[len(cesta)+5:]
+    print(suffix)
+    ds_ta = xr.open_dataset(ta_file)
+    ds_ua = xr.open_dataset(ua_file)
+
+    if i==0:
+        lat = ds_ta.lat.values
+        pres = ds_ta.plev.values/100.
+
+    uz = ds_ua.ua.mean(['lon'])#
+    Tz = ds_ta.ta.mean(['lon'])
+
+    n_k_ls = []
+    for k in range(1,4):
+        n_k = ComputeRefractiveIndex(lat,pres,uz,Tz,k)
+        n_k_ls.append(n_k)
+
+    n_k_xa = xr.concat(n_k_ls, dim='wavenumber')
+    n_k_xa['wavenumber'] = range(1,4)
+
+    n_k_xa.name = 'refr_index'
+    outfile = '{0}refr_index/refr_index{1}'.format(cesta, suffix)
+    print(outfile)
+    n_k_xa.to_netcdf(outfile)
+