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Commit 29f6950e authored by sbiri's avatar sbiri
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- fixed sea ice concentration mask 

- added qmeth, L options as input
- added option to add noise to spd, T, sst, Td/rh
parent ab9085f1
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Showing with 80 additions and 41 deletions
toy_ASFC.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
example of running AirSeaFluxCode with
1. R/V data (data_all.csv) or
2. one day era5 hourly data (era5_r360x180.nc)
compute fluxes
output NetCDF4
and statistics in stats.txt
output NetCDF4 or csv
and statistics in "outS".txt
@author: sbiri
"""
......@@ -23,7 +25,7 @@ def reject_outliers(data, m=2):
return x
def toy_ASFC(inF, outF, outS, gustIn, cskinIn, tolIn, meth):
def toy_ASFC(inF, outF, outS, gustIn, cskinIn, tolIn, meth, qmIn, LIn, stdIn):
"""
Example routine of how to run AirSeaFluxCode with the test data given
and save output either as .csv or NetCDF
......@@ -62,9 +64,13 @@ def toy_ASFC(inF, outF, outS, gustIn, cskinIn, tolIn, meth):
lon = np.asarray(inDt["Longitude"])
lat = np.asarray(inDt["Latitude"])
spd = np.asarray(inDt["Wind speed"])
spd = spd+np.random.normal(0, stdIn[0], spd.shape)
t = np.asarray(inDt["Air temperature"])
t = t+np.random.normal(0, stdIn[1], t.shape)
sst = np.asarray(inDt["SST"])
sst = sst+np.random.normal(0, stdIn[2], sst.shape)
rh = np.asarray(inDt["RH"])
rh = rh+np.random.normal(0, stdIn[3], rh.shape)
p = np.asarray(inDt["P"])
sw = np.asarray(inDt["Rs"])
hu = np.asarray(inDt["zu"])
......@@ -74,7 +80,7 @@ def toy_ASFC(inF, outF, outS, gustIn, cskinIn, tolIn, meth):
#%% run AirSeaFluxCode
res = AirSeaFluxCode(spd, t, sst, lat=lat, hum=['rh', rh], P=p,
hin=hin, Rs=sw, tol=tolIn, gust=gustIn,
cskin=cskinIn, meth=meth, L="ecmwf", n=30)
cskin=cskinIn, meth=meth, qmeth=qmIn, L=LIn, n=30)
flg = res["flag"]
elif (inF == 'era5_r360x180.nc'):
......@@ -86,12 +92,15 @@ def toy_ASFC(inF, outF, outS, gustIn, cskinIn, tolIn, meth):
lsm = np.array(fid.variables["lsm"])
icon = np.array(fid.variables["siconc"])
lsm = np.where(lsm > 0, np.nan, 1) # reverse 0 on land 1 over ocean
icon = np.where(icon < 0, np.nan, 1)
icon = np.where(icon == 0, 1, np.nan) # keep only ice-free regions
msk = lsm*icon
T = np.array(fid.variables["t2m"])*msk
T = T+np.random.normal(0, stdIn[1], T.shape)
Td = np.array(fid.variables["d2m"])*msk
Td = Td+np.random.normal(0, stdIn[3], T.shape)
sst = np.array(fid.variables["sst"])
sst = np.where(sst < -100, np.nan, sst)*msk
sst = sst+np.random.normal(0, stdIn[2], sst.shape)
p = np.array(fid.variables["msl"])*msk/100 # to set hPa
lw = np.array(fid.variables["strd"])*msk/60/60
sw = np.array(fid.variables["ssrd"])*msk/60/60
......@@ -99,6 +108,7 @@ def toy_ASFC(inF, outF, outS, gustIn, cskinIn, tolIn, meth):
v = np.array(fid.variables["v10"])
fid.close()
spd = np.sqrt(np.power(u, 2)+np.power(v, 2))*msk
spd = spd+np.random.normal(0, stdIn[0], spd.shape)
del u, v, fid
hin = np.array([10, 2, 2])
latIn = np.tile(lat, (len(lon), 1)).T.reshape(len(lon)*len(lat))
......@@ -109,7 +119,7 @@ def toy_ASFC(inF, outF, outS, gustIn, cskinIn, tolIn, meth):
flg = np.empty((len(tim),len(lon)*len(lat)), dtype="object")
# reshape input and run code
for x in range(len(tim)):
temp = AirSeaFluxCode(spd.reshape(len(tim), len(lon)*len(lat))[x, :],
a = AirSeaFluxCode(spd.reshape(len(tim), len(lon)*len(lat))[x, :],
T.reshape(len(tim), len(lon)*len(lat))[x, :],
sst.reshape(len(tim), len(lon)*len(lat))[x, :],
lat=latIn,
......@@ -119,11 +129,11 @@ def toy_ASFC(inF, outF, outS, gustIn, cskinIn, tolIn, meth):
Rs=sw.reshape(len(tim), len(lon)*len(lat))[x, :],
Rl=lw.reshape(len(tim), len(lon)*len(lat))[x, :],
gust=gustIn, cskin=cskinIn, tol=tolIn,
qmeth='Buck2', meth=meth, n=30, L="ecmwf")
a = temp.loc[:,"tau":"rh"]
a = a.to_numpy()
flg[x, :] = temp["flag"]
res[x, :, :] = a
meth=meth, qmeth=qmIn, n=30, L=LIn)
temp = a.loc[:,"tau":"rh"]
temp = temp.to_numpy()
flg[x, :] = a["flag"]
res[x, :, :] = temp
del a, temp
n = np.shape(res)
res = np.asarray([res[:, :, i]*msk.reshape(n[0], n[1])
......@@ -505,6 +515,17 @@ else:
meth = meth #[meth]
ext = meth+"_"
#------------------------------------------------------------------------------
qmIn = input("Give prefered method for specific humidity: \n")
if (qmIn == ''):
qmIn = 'Buck2' # default
while qmIn not in ["HylandWexler", "Hardy", "Preining", "Wexler",
"GoffGratch", "WMO", "MagnusTetens", "Buck", "Buck2",
"WMO2018", "Sonntag", "Bolton", "IAPWS", "MurphyKoop"]:
print("method unknown")
meth = input("Give prefered method: \n")
else:
qmIn = qmIn
#------------------------------------------------------------------------------
gustIn = input("Give gustiness option (to use default press enter): \n")
if (gustIn == ''):
gustIn = None
......@@ -520,8 +541,8 @@ cskinIn = input("Give cool skin option (to use default press enter): \n")
if (cskinIn == ''):
cskinIn = None
if ((cskinIn == None) and (meth == "S80" or meth == "S88" or meth == "LP82"
or meth == "YT96" or meth == "UA" or
meth == "LY04")):
or meth == "YT96" or meth == "UA"
or meth == "LY04")):
cskinIn = 0
ext = ext+'noskin_'
elif ((cskinIn == None) and (meth == "C30" or meth == "C35"
......@@ -542,6 +563,21 @@ else:
tolIn = eval(tolIn)
ext = ext+'tol'+tolIn[0]
#------------------------------------------------------------------------------
LIn = input("Give prefered method for L (S80 or ecmwf): \n")
if (LIn == ''):
LIn = 'ecmwf' # default
elif LIn not in ["S80", "ecmwf"]:
LIn = 'ecmwf' # default
else:
LIn = LIn
#------------------------------------------------------------------------------
stdIn = input("Give noise std for spd, T, SST and Td/rh \n (e.g. [0.01, 0, 0, 0]"
" adds noise only to spd): \n")
if (stdIn == ''):
stdIn = [0, 0, 0, 0] # no noise added
else:
stdIn = eval(stdIn)
#------------------------------------------------------------------------------
outF = input("Give path and output file name: \n")
if ((outF == '') and (inF == "data_all.csv")):
outF = "out_"+inF[:-4]+"_"+ext+".csv"
......@@ -568,37 +604,38 @@ elif (outS[-4:] != '.txt'):
#------------------------------------------------------------------------------
print("\n run_ASFC.py, started for method "+meth)
res, lon, lat = toy_ASFC(inF, outF, outS, gustIn, cskinIn, tolIn, meth)
res, lon, lat = toy_ASFC(inF, outF, outS, gustIn, cskinIn, tolIn, meth, qmIn,
LIn, stdIn)
print("run_ASFC.py took ", np.round((time.perf_counter()-start_time)/60, 2),
"minutes to run")
#%% generate flux plots
if (inF == 'era5_r360x180.nc'):
cm = plt.cm.get_cmap('RdYlBu')
ttl = ["tau (Nm$^{-2}$)", "shf (Wm$^{-2}$)", "lhf (Wm$^{-2}$)"]
for i in range(3):
plt.figure()
plt.contourf(lon, lat,
np.nanmean(res[:, :, i], axis=0).reshape(len(lat),
len(lon)),
100, cmap=cm)
plt.colorbar()
plt.tight_layout()
plt.xlabel("Longitude")
plt.ylabel("Latitude")
plt.title(meth+', '+ttl[i])
plt.savefig('./'+ttl[i][:3]+'_'+ext+'.png', dpi=300, bbox_inches='tight')
elif (inF == "data_all.csv"):
ttl = ["tau (Nm$^{-2}$)", "shf (Wm$^{-2}$)", "lhf (Wm$^{-2}$)"]
for i in range(3):
plt.figure()
plt.plot(res[ttl[i][:3]],'.c', markersize=1)
plt.title(meth)
plt.xlabel("points")
plt.ylabel(ttl[i])
plt.savefig('./'+ttl[i][:3]+'_'+ext+'.png', dpi=300, bbox_inches='tight')
# if (inF == 'era5_r360x180.nc'):
# cm = plt.cm.get_cmap('RdYlBu')
# ttl = ["tau (Nm$^{-2}$)", "shf (Wm$^{-2}$)", "lhf (Wm$^{-2}$)"]
# for i in range(3):
# plt.figure()
# plt.contourf(lon, lat,
# np.nanmean(res[:, :, i], axis=0).reshape(len(lat),
# len(lon)),
# 100, cmap=cm)
# plt.colorbar()
# plt.tight_layout()
# plt.xlabel("Longitude")
# plt.ylabel("Latitude")
# plt.title(meth+', '+ttl[i])
# plt.savefig('./'+ttl[i][:3]+'_'+ext+'.png', dpi=300, bbox_inches='tight')
# elif (inF == "data_all.csv"):
# ttl = ["tau (Nm$^{-2}$)", "shf (Wm$^{-2}$)", "lhf (Wm$^{-2}$)"]
# for i in range(3):
# plt.figure()
# plt.plot(res[ttl[i][:3]],'.c', markersize=1)
# plt.title(meth)
# plt.xlabel("points")
# plt.ylabel(ttl[i])
# plt.savefig('./'+ttl[i][:3]+'_'+ext+'.png', dpi=300, bbox_inches='tight')
#%% generate txt file with statistic
#%% generate txt file with statistics
if ((cskinIn == None) and (meth == "S80" or meth == "S88" or meth == "LP82"
or meth == "YT96" or meth == "UA" or
meth == "LY04")):
......@@ -615,12 +652,14 @@ elif np.all(gustIn == None):
elif ((np.size(gustIn) < 3) and (gustIn == 0)):
gust = [0, 0, 0]
if (tolIn == None):
tolIn = ['flux', 0.01, 1, 1]
tolIn = ['all', 0.01, 0.01, 1e-05, 1e-3, 0.1, 0.1]
print("Input summary", file=open('./'+outS, 'a'))
print('input file name: {}, \n method: {}, \n gustiness: {}, \n cskin: {},'
' \n tolerance: {}'.format(inF, meth, gustIn, cskinIn, tolIn),
' \n tolerance: {}, \n qmethod: {}, \n L: {}'.format(inF, meth, gustIn,
cskinIn, tolIn,
qmIn, LIn),
file=open('./'+outS, 'a'))
ttl = np.asarray(["tau ", "shf ", "lhf ", "L ", "cd ", "cdn ",
"ct ", "ctn ", "cq ", "cqn ", "tsrv ", "tsr ",
......
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