Meteorology#
Warning
This is not meant to be a standalone notebook. This notebook is part of the process we have for adding entries to the NCL Index and is not meant to be used as tutorial or example code.
Functions covered#
NCL code#
; dewtemp_trh
; Adapted from https://www.ncl.ucar.edu/Document/Functions/Built-in/dewtemp_trh.shtml
; ncl -n dewtemp_trh.ncl >> dewtemp_trh_output.txt
print("Temperature (K), Relative Humidity (%), Dew Temperature (C)")
do tk=273,374
do rh=1,100
begin
dewtemp = dewtemp_trh(tk,rh)-273.15
print (tk +","+ rh +","+ dewtemp)
end
end do
end do
; daylight_fao56
; Adapted from https://www.ncl.ucar.edu/Document/Functions/Crop/daylight_fao56.shtml
; ncl -n daylight_fao56.ncl >> daylight_fao56_output.txt
print("DOY, Latitude (Degrees), Daylight Hours")
do doy=0,365
do lat=-66,66
begin
daylight_hours = daylight_fao56(doy, lat)
print (doy +","+ lat +","+ daylight_hours)
end
end do
end do
Python Functionality#
dewtemp_trh#
#### Collect NCL values for dewtemp_trh from geocat-datafiles
import geocat.datafiles as gdf
import numpy as np
dewtemp_data = gdf.get('applications_files/ncl_outputs/dewtemp_trh_output.txt')
dewtemp_data = np.loadtxt(dewtemp_data, delimiter=',', skiprows=6)
Downloading file 'applications_files/ncl_outputs/dewtemp_trh_output.txt' from 'https://github.com/NCAR/GeoCAT-datafiles/raw/main/applications_files/ncl_outputs/dewtemp_trh_output.txt' to '/home/runner/.cache/geocat'.
### Collect NCL `dewtemp` value and associated (temperature_kelvin, relative humidity) values
ncl_dewtemp = {}
tk_rh = tuple(map(tuple, dewtemp_data[::, 0:2]))
dewtemp_values = dewtemp_data[::, 2]
ncl_dewtemp = dict(zip(tk_rh, dewtemp_values))
### Collect Temperature (Kelvin) and Relative Humidity Pairs
tk_rh = []
for tk in range(273, 374 + 1):
for rh in range(1, 100 + 1):
tk_rh.append((tk, rh))
### Calculate GeoCAT-Comp `dewtemp` value and tk/rh
from geocat.comp import dewtemp
geocat_dewtemp = {}
for i, pair in enumerate(tk_rh):
tk, rh = pair
geocat_dewtemp[pair] = dewtemp(tk, rh) - 273.15
daylight_fao56#
#### Collect NCL values for daylight_fao56 from geocat-datafiles
import geocat.datafiles as gdf
import numpy as np
daylight_data = gdf.get('applications_files/ncl_outputs/daylight_fao56_output.txt')
daylight_data = np.loadtxt(daylight_data, delimiter=',', skiprows=6)
Downloading file 'applications_files/ncl_outputs/daylight_fao56_output.txt' from 'https://github.com/NCAR/GeoCAT-datafiles/raw/main/applications_files/ncl_outputs/daylight_fao56_output.txt' to '/home/runner/.cache/geocat'.
### Collect NCL `daylight_fao56` value and associated (doy, latitude) values
ncl_daylight = {}
doy_lat = tuple(map(tuple, daylight_data[::, 0:2]))
daylight_values = daylight_data[::, 2]
ncl_daylight = dict(zip(doy_lat, daylight_values))
### Collect DOY and Latitude Pairs
doy_lat = []
for doy in range(0, 365 + 1):
for lat in range(-66, 66 + 1):
doy_lat.append((doy, lat))
### Calculate GeoCAT-Comp `daylight_fao56` value and doy/lat
from geocat.comp import max_daylight
geocat_daylight = {}
for i, pair in enumerate(doy_lat):
doy, lat = pair
geocat_daylight[pair] = max_daylight(doy, lat)
/home/runner/micromamba/envs/geocat-applications/lib/python3.12/site-packages/geocat/comp/meteorology.py:1058: UserWarning: WARNING: max_daylight has limited validity for abs(lat) > 55
warnings.warn(
Comparison#
import math
for pair in ncl_dewtemp.keys():
try:
assert math.isclose(
ncl_dewtemp[pair], geocat_dewtemp[pair], rel_tol=1e-04
) # within 4 decimal points
except Exception:
assert math.isclose(
ncl_dewtemp[pair], geocat_dewtemp[pair], rel_tol=1e-02
) # within 2 decimal points
print(f"{pair}:")
print(f"\t{ncl_dewtemp[pair]}, {geocat_dewtemp[pair]}")
print(f"\tDifference: {ncl_dewtemp[pair] - geocat_dewtemp[pair]}")
(274.0, 94.0):
-0.0055542, -0.005570036309563875
Difference: 1.5836309563875377e-05
(275.0, 87.0):
-0.0839233, -0.08393959272956408
Difference: 1.6292729564076902e-05
(275.0, 88.0):
0.073761, 0.07374617196580857
Difference: 1.482803419142198e-05
(277.0, 76.0):
0.00259399, 0.0025903565796738803
Difference: 3.633420326119678e-06
(278.0, 71.0):
0.0261536, 0.026158530424595483
Difference: -4.930424595483984e-06
(279.0, 66.0):
-0.0278931, -0.027897415813697535
Difference: 4.3158136975342265e-06
(281.0, 58.0):
0.0712891, 0.07128017477202775
Difference: 8.925227972245153e-06
(282.0, 54.0):
0.0129395, 0.01291856405322278
Difference: 2.0935946777218828e-05
(283.0, 50.0):
-0.130432, -0.13044636060385528
Difference: 1.4360603855290144e-05
(283.0, 51.0):
0.144928, 0.14490829422311435
Difference: 1.9705776885647897e-05
(284.0, 47.0):
-0.0726318, -0.07262340593854333
Difference: -8.39406145666799e-06
(285.0, 44.0):
-0.0798035, -0.07982300528288988
Difference: 1.950528288988118e-05
(286.0, 41.0):
-0.160156, -0.1601734585760255
Difference: 1.7458576025503048e-05
(287.0, 39.0):
0.0386658, 0.0386467450997543
Difference: 1.905490024570189e-05
(290.0, 32.0):
-0.0795288, -0.07954541765832346
Difference: 1.6617658323461737e-05
(291.0, 30.0):
-0.115204, -0.11521555297190389
Difference: 1.1552971903888709e-05
(294.0, 25.0):
-0.110413, -0.11042499197856159
Difference: 1.1991978561595729e-05
(295.0, 24.0):
0.156677, 0.15665929836796977
Difference: 1.7701632030242553e-05
(297.0, 21.0):
-0.0614929, -0.06151243710161225
Difference: 1.953710161224642e-05
(299.0, 19.0):
0.162537, 0.16251872398265732
Difference: 1.8276017342666595e-05
(302.0, 16.0):
0.138275, 0.1382598702660971
Difference: 1.5129733902913278e-05
(303.0, 15.0):
0.0130615, 0.013067891707237322
Difference: -6.391707237322214e-06
for pair in ncl_daylight.keys():
assert math.isclose(
ncl_daylight[pair], geocat_daylight[pair].flatten()[0], rel_tol=1e-05
) # within 5 decimal points