satelliteCoverage/cloud_fraction_anual_mean.py at main · ApSTL/satelliteCoverage · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
from datetime import datetime, timedelta
from classes import Location
from ground import find_city_location
from netCDF4 import Dataset
import numpy as np

# botched together to average out a years worth of daily averaged cloud data.


Targets = ["Solway firth", "Madrid", "Vilnius", "Bobo-Dioulasso"]

cf=0
cf_total={}

for t in Targets:
    cf_total[t]=0

cfmean={}
day_count=0
oneday=timedelta(days=1)


date = datetime(2021, 1, 1, 0, 0, 0)
datestring=date.strftime("%Y%m%d")
startyear=date.strftime("%Y")
year=startyear

while year == startyear:

    for target in Targets:

        #for each target grab the lat/longs, get the cloud fraction on that day then add it to the total

        target_location=Location(target, find_city_location(target, "lat_lon_data/coverage_lat_lng.csv"))
        lat=target_location.location.latitude.degrees
        lon=target_location.location.longitude.degrees

        nc_f = f"Global_Cloud_Data_{year}/CFCdm{datestring}000040019AVPOS01GL.nc"  # Your filename

        # 2018 file names are different, not sure why
        if year=='2018':
            nc_f = f"Global_Cloud_Data_{year}/CFCdm{datestring}000000219AVPOSE1GL.nc"  # Your filename

        nc_fid = Dataset(nc_f, 'r')  # Dataset is the class behavior to open the file and create an instance of the ncCDF4 class

        lats = nc_fid.variables['lat'][:]  # extract/copy the data
        lons = nc_fid.variables['lon'][:]

        cfc = nc_fid.variables['cfc_day'][:]

        minlat = lat - 0.2
        maxlat = lat + 0.2

        minlon = lon - 0.2
        maxlon = lon + 0.2

        indlat = np.where((lats < maxlat) & (lats > minlat))
        indlon = np.where((lons < maxlon) & (lons > minlon))

        cf = np.mean(cfc[0,indlat[:],indlon[:]])

        mask_check = isinstance(cf, float)

        if mask_check == False:
            continue


        cf_total[target]=cf_total[target]+cf

    # incriment the day and go again
    day_count+=1
    date += oneday
    year=date.strftime("%Y")
    datestring=date.strftime("%Y%m%d")

# When everything is counted, print the results.
# Print total contacts
print(f"Annual Mean Cloud Fraction of {startyear}:")
for target in Targets:
    print(f"==> {target} = {cf_total[target]/day_count}")