-
Notifications
You must be signed in to change notification settings - Fork 3
/
makegefssnowcsv.py
executable file
·169 lines (161 loc) · 7.34 KB
/
makegefssnowcsv.py
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
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
#!/bin/usr/env python
import pygrib
import numpy.ma as ma
import csv
import datetime
import ncepy
import numpy as np
import matplotlib
import math
import subprocess
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from scipy import interpolate
import sys
ymdh = str(sys.argv[1])
def find_nearest(array,value):
idx=(np.abs(array-value)).argmin()
return idx
slist=[]
slats=[]
slons=[]
with open('gfsxstations.txt','r') as f:
for row in f:
x=row.split(',')
slist.append(x[0])
slats.append(float(x[1]))
slons.append(float(x[2]))
members=['time','date','c00','p01','p02','p03','p04','p05','p06','p07','p08','p09','p10','p11','p12','p13','p14','p15','p16','p17','p18','p19','p20','p21','p22','p23','p24','p25','p26','p27','p28','p29','p30','GFS']
type=['rain','snow','freezing rain','ice pellets']
membertype=['time','date','rain','snow','freezing rain','ice pellets']
closest=0 #starting range of forecast hour
furthest=195 #3 hours more than the actual ending forecast hour you want
ymd=ymdh[0:8]
year=int(ymdh[0:4])
month=int(ymdh[4:6])
day=int(ymdh[6:8])
hour=int(ymdh[8:10])
print(year, month , day, hour)
dtime=datetime.datetime(year,month,day,hour,0)
date_list = [dtime + datetime.timedelta(hours=x) for x in range(closest,furthest,3)]
lastcycle=dtime - datetime.timedelta(hours=6)
lastymd=lastcycle.strftime("%Y%m%d")
lasthour=lastcycle.strftime("%H")
fhours1=list(range(closest,furthest,3))
nmbtotal=np.empty((len(slist),len(fhours1),len(members)+1),dtype='object')
nmbrain=np.empty((len(slist),len(fhours1),len(members)+1),dtype='object')
nmbsnow=np.empty((len(slist),len(fhours1),len(members)+1),dtype='object')
nmbfreezing=np.empty((len(slist),len(fhours1),len(members)+1),dtype='object')
nmbice=np.empty((len(slist),len(fhours1),len(members)+1),dtype='object')
print(nmbtotal.shape)
for i in range(len(members)):
print(members[i])
ptotal=0
for j in range(len(fhours1)):
if i==0:
nmbtotal[:,j,i]=fhours1[j]
elif i==1:
nmbtotal[:,j,i]=date_list[j].strftime("%m-%d-%Y:%H")
elif i>1 and members[i]!='GFS':
if j==0:
nmbtotal[:,j,i]=0.0
snowtotal=np.zeros((361,720))
continue
elif (j%2)!=0:
grbind = pygrib.index('/gpfs/dell4/nco/ops/com/gefs/prod/gefs.'+str(ymd)+'/'+str(hour).zfill(2)+'/atmos/pgrb2ap5/ge'+members[i]+'.t'+str(hour).zfill(2)+'z.pgrb2a.0p50.f'+str(fhours1[j]).zfill(3),'name')
precip=grbind.select(name='Total Precipitation')[0].values*.03937
catsnow=grbind.select(name='Categorical snow')[0].values
precip=np.asarray(precip[::-1,:])
print(precip.shape)
catsnow=np.asarray(catsnow[::-1,:])
else:
grbindprev = pygrib.index('/gpfs/dell4/nco/ops/com/gefs/prod/gefs.'+str(ymd)+'/'+str(hour).zfill(2)+'/atmos/pgrb2ap5/ge'+members[i]+'.t'+str(hour).zfill(2)+'z.pgrb2a.0p50.f'+str(fhours1[j-1]).zfill(3),'name')
grbind = pygrib.index('/gpfs/dell4/nco/ops/com/gefs/prod/gefs.'+str(ymd)+'/'+str(hour).zfill(2)+'/atmos/pgrb2ap5/ge'+members[i]+'.t'+str(hour).zfill(2)+'z.pgrb2a.0p50.f'+str(fhours1[j]).zfill(3),'name')
precipnewc=grbind.select(name='Total Precipitation')[0].values*.03937
precipnewp=grbindprev.select(name='Total Precipitation')[0].values*.03937
catsnow=grbind.select(name='Categorical snow')[0].values
precipnewc=np.asarray(precipnewc[::-1,:])
precipnewp=np.asarray(precipnewp[::-1,:])
catsnow=np.asarray(catsnow[::-1,:])
precip=precipnewc-precipnewp
lats,lons = grbind.select(name='Total Precipitation')[0].latlons()
latlist=lats[::-1,0]
lonlist=lons[0,:]
lonlist=np.asarray(lonlist)
latlist=np.asarray(latlist)
precip[catsnow==0]=0
snowtotal=snowtotal+precip
f=interpolate.interp2d(lonlist,latlist,snowtotal,kind='linear')
for k in range(len(slats)):
nearestlat=find_nearest(latlist,slats[k])
nearestlon=find_nearest(lonlist,slons[k]+360)
thisprecip=precip[nearestlat,nearestlon]
thissnow=catsnow[nearestlat,nearestlon]
if thissnow==1 and thisprecip>0.01:
znew=f((360+slons[k]),slats[k])*10.0
if j>0:
if nmbtotal[k,j-1,i]>np.round(np.absolute(znew),5):
nmbtotal[k,j-1,i]=np.round(np.absolute(znew),5)
nmbtotal[k,j,i]=np.round(np.absolute(znew),5)
print("bad things")
else:
nmbtotal[k,j,i]=np.round(np.absolute(znew),5)
else:
nmbtotal[k,j,i]=nmbtotal[k,j-1,i]
else:
if j==0:
nmbtotal[:,j,34]=0.0
snowtotal=np.zeros((361,720))
continue
elif (j%2)!=0:
grbind = pygrib.index('/gpfs/dell1/nco/ops/com/gfs/prod/gfs.'+str(ymd)+'/'+str(hour).zfill(2)+'/atmos/gfs.t'+str(hour).zfill(2)+'z.pgrb2.0p50.f'+str(fhours1[j]).zfill(3),'name')
precip=grbind.select(name='Total Precipitation')[0].values*.03937
catsnow=grbind.select(name='Categorical snow')[0].values
precip=np.asarray(precip[::-1,:])
catsnow=np.asarray(catsnow[::-1,:])
else:
grbindprev = pygrib.index('/gpfs/dell1/nco/ops/com/gfs/prod/gfs.'+str(ymd)+'/'+str(hour).zfill(2)+'/atmos/gfs.t'+str(hour).zfill(2)+'z.pgrb2.0p50.f'+str(fhours1[j-1]).zfill(3),'name')
grbind = pygrib.index('/gpfs/dell1/nco/ops/com/gfs/prod/gfs.'+str(ymd)+'/'+str(hour).zfill(2)+'/atmos/gfs.t'+str(hour).zfill(2)+'z.pgrb2.0p50.f'+str(fhours1[j]).zfill(3),'name')
precipnewc=grbind.select(name='Total Precipitation')[0].values*.03937
precipnewp=grbindprev.select(name='Total Precipitation')[0].values*.03937
catsnow=grbind.select(name='Categorical snow')[0].values
precipnewc=np.asarray(precipnewc[::-1,:])
precipnewp=np.asarray(precipnewp[::-1,:])
catsnow=np.asarray(catsnow[::-1,:])
precip=precipnewc-precipnewp
lats,lons = grbind.select(name='Total Precipitation')[0].latlons()
latlist=lats[::-1,0]
lonlist=lons[0,:]
lonlist=np.asarray(lonlist)
latlist=np.asarray(latlist)
precip[catsnow==0]=0
snowtotal=snowtotal+precip
f=interpolate.interp2d(lonlist,latlist,snowtotal,kind='linear')
for k in range(len(slats)):
nearestlat=find_nearest(latlist,slats[k])
nearestlon=find_nearest(lonlist,slons[k]+360)
thisprecip=precip[nearestlat,nearestlon]
thissnow=catsnow[nearestlat,nearestlon]
if thissnow==1 and thisprecip>0.01:
znew=f((360+slons[k]),slats[k])*10.0
if j>0:
if nmbtotal[k,j-1,34]>np.round(np.absolute(znew),5):
nmbtotal[k,j-1,34]=np.round(np.absolute(znew),5)
nmbtotal[k,j,34]=np.round(np.absolute(znew),5)
print("bad things")
else:
nmbtotal[k,j,34]=np.round(np.absolute(znew),5)
else:
nmbtotal[k,j,34]=nmbtotal[k,j-1,34]
for k in range(len(slats)):
for j in range(len(fhours1)):
nmbtotal[k,j,33]=np.round(np.sum(nmbtotal[k,j,2:33])/31.0,5)
for k in range(len(slats)):
f = open("GEFS"+slist[k]+ymdh+"snow.csv","wt")
try:
writer = csv.writer(f)
writer.writerow(('time','date','c0','p1','p2','p3','p4','p5','p6','p7','p8','p9','p10','p11','p12','p13','p14','p15','p16','p17','p18','p19','p20','p21','p22','p23','p24','p25','p26','p27','p28','p29','p30','mean','GFS'))
for i in range(nmbtotal.shape[1]):
writer.writerow((str(m).replace("[","")).replace("]","") for m in nmbtotal[k,i,:])
finally:
f.close()