def calc_QOIs(df):
"""
Calculate derived quantities (IN PLACE)
"""
df['wspd'], df['wdir'] = calc_wind(df)
df['u*'] = (df['uw']**2 + df['vw']**2)**0.25
df['TKE'] = 0.5 * (df['uu'] + df['vv'] + df['ww'])
ang = np.arctan2(df['v'], df['u'])
df['TI'] = df['uu'] * np.cos(ang)**2 + 2 * df['uv'] * np.sin(ang) * np.cos(
ang) + df['vv'] * np.sin(ang)**2
df['TI'] = np.sqrt(df['TI']) / df['wspd']
def load_sowfa_data(dpath, times, heights, interval='1h', window_size='10min'):
"""
Load and process data of a particular SOWFA simulation
Data loaded includes:
- Planar averages
- Virutal tower data
- Source history data
"""
# Load planar average data
# ------------------------
fpath = os.path.join(dpath, 'postProcessing/planarAverages')
df_pavg = reader_planar_average(fpath)
# Calculate 10-min averages and quantities of interest
df_pavg_10min = df_pavg.unstack().resample('10min').mean().stack()
calc_QOIs(df_pavg_10min)
# Calculate hourly averages
df_pavg_60min = df_pavg_10min.unstack().resample('60min').mean().stack()
# Load virtual tower data
# -----------------------
fpath = os.path.join(dpath, 'postProcessing/probe1')
df_prob = reader_probe(fpath)
df_prob['wspd'], df_prob['wdir'] = calc_wind(df_prob)
# Calculate 10-min statistics and quantities of interest
df_prob_10min = calc_stats(df_prob)
calc_QOIs(df_prob_10min)
# Calculate hourly averages
df_prob_60min = df_prob_10min.unstack().resample('60min').mean().stack()
# Calculate frequency spectrum
df_prob_hgts = interpolate_to_heights(df_prob, heights)
df_prob_hgts = reindex_if_needed(df_prob_hgts)
df_prob_spectra = calc_spectra(df_prob_hgts, times, heights, interval,
window_size)
# Load source history data
# ------------------------
fpath = os.path.join(dpath, 'postProcessing/sourceHistory')
df_srch = reader_source_history(fpath)
# Rescale budget components
rescale_budget_components(df_srch)
# Calculate 10-min and hourly averages
df_srch_10min = df_srch.unstack().resample('10min').mean().stack()
df_srch_60min = df_srch.unstack().resample('60min').mean().stack()
return df_prob_10min, df_prob_60min, df_prob_spectra, df_pavg_10min, df_pavg_60min, df_srch, df_srch_10min, df_srch_60min
def load_tower_reference_spectra(fpath, times, heights, interval, window_size):
"""
Load TTU tower 1-Hz data and compute spectra
"""
# Load data
tower = pd.read_csv(fpath,
parse_dates=True,
index_col=['datetime', 'height'])
# Calculate some QoI
tower['wspd'], tower['wdir'] = calc_wind(tower)
tower['theta'] = theta(tower['Ts'], tower['p'])
# Interpolate data to specified heights
tower_hgt = interpolate_to_heights(tower, heights)
# Reindex if needed
tower_hgt = reindex_if_needed(tower_hgt)
# Compute spectra
tower_spectra = calc_spectra(tower_hgt, times, heights, interval,
window_size)
return tower_spectra
def load_wrf_reference_data(dpath):
"""
Load WRF reference data from SOWFA input files
"""
# Load wrf field data
# ------------------
wrf_pavg_10min = load_sowfa_input_file(os.path.join(dpath, 'fieldTable'))
# Drop value at z=0
wrf_pavg_10min = wrf_pavg_10min.loc[(
slice(None), wrf_pavg_10min.index.get_level_values(1) > 0), :]
# Calculate wind speed and direction
wrf_pavg_10min['wspd'], wrf_pavg_10min['wdir'] = calc_wind(wrf_pavg_10min)
# Calculate hourly averages
wrf_pavg_60min = wrf_pavg_10min.unstack().resample('60min').mean().stack()
return wrf_pavg_10min, wrf_pavg_60min
def load_wrf_reference_data(dpath):
"""
Load WRF reference data from SOWFA input files
"""
# Load wrf field data
# ------------------
wrf_pavg_10min = load_sowfa_input_file(os.path.join(dpath, 'fieldTable'))
# Drop value at z=0
wrf_pavg_10min = wrf_pavg_10min.loc[(
slice(None), wrf_pavg_10min.index.get_level_values(1) > 0), :]
# Calculate wind speed and direction
wrf_pavg_10min['wspd'], wrf_pavg_10min['wdir'] = calc_wind(wrf_pavg_10min)
# Calculate hourly averages
wrf_pavg_60min = wrf_pavg_10min.unstack().resample('60min').mean().stack()
# Load wrf forcing data
# ---------------------
wrf_srch_10min = load_sowfa_input_file(os.path.join(dpath, 'forcingTable'))
# Drop value at z=0
wrf_srch_10min = wrf_srch_10min.loc[(
slice(None), wrf_srch_10min.index.get_level_values(1) > 0), :]
# Rename columns and rescale budget components
wrf_srch_10min.rename({
'u': 'Fu',
'v': 'Fv',
'w': 'Fw',
'theta': 'Ft'
},
axis='columns',
inplace=True)
rescale_budget_components(wrf_srch_10min)
# Calculate hourly averages
wrf_srch_60min = wrf_srch_10min.unstack().resample('60min').mean().stack()
return wrf_pavg_10min, wrf_pavg_60min, wrf_srch_10min, wrf_srch_60min
def load_wrf_reference_data(fpath):
"""
Load WRF reference data
"""
# Load data with xarray
xa = xarray.open_dataset(fpath)
# Convert to pandas dataframe
wrf = xa.to_dataframe()
# Convert to standard names
wrf.rename({
'U': 'u',
'V': 'v',
'W': 'w',
'UST': 'u*'
},
axis='columns',
inplace=True)
# Compute wind speed and wind direction
wrf['wspd'], wrf['wdir'] = calc_wind(wrf)
return wrf
ny = len(wrfout.south_north)
dx = wrfout.DX/1000.0
dy = wrfout.DY/1000.0
x = np.arange(0,nx)*dx
y = np.arange(0,nx)*dy
xy,yx = np.meshgrid(x,y)
t2 = wrfout.T2.where(land_mask == 1.0)
u10 = wrfout.U10.where(land_mask == 1.0)
v10 = wrfout.V10.where(land_mask == 1.0)
sfcP = wrfout.PSFC.where(land_mask == 1.0)
temp = np.squeeze(wrfout.T)
z_f = (np.squeeze(wrfout.PH) + np.squeeze(wrfout.PHB))/9.8 - np.squeeze(wrfout.HGT)
zs_f = 0.5*(z_f[1:,:,:]+z_f[:-1,:,:])
vel10,dir10 = calc_wind(wrfout,u='U10',v='V10')
vel10 = vel10.where(land_mask == 1.0)
dir10 = dir10.where(land_mask == 1.0)
top_ind = 18
bot_ind = 0
u = wrfout.U[top_ind,:,:].data
v = wrfout.V[top_ind,:,:].data
u = 0.5*(u[:,1:] + u[:,:-1])
v = 0.5*(v[1:,:] + v[:-1,:])
wdir1km = 180. + np.degrees(np.arctan2(u, v))
obs_dir = '/glade/u/home/hawbecke/Research/Chesapeake/Data/Obs/'
awos_ds = xr.open_dataset('{}AWOS_2019to2020.nc'.format(obs_dir))