4. Roughness parameters (SuPy)

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import supy as sp
import warnings
warnings.filterwarnings('ignore')

4.1. Loading data, cleaning and getting ready for optimization

name_of_site='US-MMS'
years=[2010,2012,2016]


df_attr=pd.read_csv('all_attrs.csv')
z_meas=df_attr[df_attr.site==name_of_site].meas.values[0]
h_sfc=df_zmeas=df_attr[df_attr.site==name_of_site].height.values[0]
folder='data/data_csv_zip_clean_roughness/'
site_file = folder+'/'+name_of_site + '_clean.csv.gz'
df_data = pd.read_csv(site_file, index_col='time', parse_dates=['time'])
# Rain
bb=pd.DataFrame(~np.isin(df_data.index.date,df_data[df_data.P!=0].index.date))
bb.index=df_data.index
df_data=df_data[bb.values]


df_data=df_data[(df_data['WS']!=0)]

df_years=df_data.loc[f'{years[0]}']
for i in years[1:]:
    df_years=df_years.append(df_data.loc[f'{i}'])

df_val = df_years.loc[:, ['H', 'USTAR', 'TA', 'RH', 'PA', 'WS']].dropna()
df_val.head()

	H	USTAR	TA	RH	PA	WS
time
2010-01-01 00:00:00	21.873	0.739	-8.08	70.503	98.836	3.695
2010-01-01 01:00:00	41.819	0.855	-9.17	72.757	98.880	3.928
2010-01-01 02:00:00	-6.078	0.699	-9.63	72.611	98.910	3.088
2010-01-01 03:00:00	-16.788	0.581	-10.03	73.868	98.970	3.623
2010-01-01 04:00:00	5.006	0.562	-10.36	74.242	99.030	3.474

4.2. Running supy to calculate z0 and d

z0,d,ser_ws,ser_ustar=sp.util.optimize_MO(df_val,z_meas,h_sfc)

4.3. Calculating model wind speed using logarithmic law

def uz(z0,d,ser_ustar,z_meas):
    z = z_meas
    k = 0.4
    uz = (ser_ustar / k) * np.log((z - d) / z0)
    return uz

ws_model=uz(z0,d,ser_ustar,z_meas)

plt.scatter(ser_ws,ws_model,color='k')
plt.xlabel('WS-obs')
plt.ylabel('WS-model')
plt.title(f'z0={np.round(z0,2)} & d={np.round(d,2)}')
plt.plot([0,10],[0,10],color='r',linestyle='--')
plt.ylim([0,10])
plt.xlim([0,10])

(0.0, 10.0)