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read-rlds-fluxnet2015.py
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import numpy as np
from netCDF4 import Dataset
import pylab as plt
import os
import time
import datetime
import netCDF4 as nc
import math
from urllib.request import urlretrieve
# set up Data directory
DataDir = "/Users/mingquan/newDATA"
# set up initial and final years of data
start_yr = 1991
end_yr = 2014
VarID = "rlds"
RawVarID = "LW_IN_F"
RawVarID_QC = RawVarID + "_QC"
long_name = "surface downward longwave radiation"
# Set general information for the data source
remote_source = "https://fluxnet.fluxdata.org/data/fluxnet2015-dataset/"
gist_source = "https://github.com/mmu2019/Datasets/blob/master/read-rlds-fluxnet2015.py"
local_source = DataDir + '/FLUXNET2015/TIER1/FULLSET/MM/FLX_STATIONNAME_FLUXNET2015_FULLSET_MM_YEAR.csv'
stamp1 = '2019-06-20'
datestr = str(datetime.datetime.now())
TmpStr = datestr.split(' ')
stamp2 = TmpStr[0]
instit1 = "FluxNet, AmeriFlux, AfriFlux, AsiaFlux, ChinaFlux, Fluxnet-Canada, and KoFlux"
instit2 = "CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, GreenGrass, and OzFlux-TERN"
instit3 = "LBA, NECC, ICOS, TCOS-Siberia, and USCCC"
period = period = str(start_yr) + "-01 through " + str(end_yr) + "-12"
origtr = "monthly"
origsr = "site"
origut = "W/m2"
finltr = "monthly"
finlsr = "site"
finlut = "W/m2"
# Create temporal dimension
nyears = end_yr - start_yr + 1
nmonth = 12
smonth = np.asarray(['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12'])
month_bnd = np.asarray([0,31,59,90,120,151,181,212,243,273,304,334,365],dtype=float)
tbnd = np.asarray([((np.arange(nyears)*365)[:,np.newaxis]+month_bnd[:-1]).flatten(),
((np.arange(nyears)*365)[:,np.newaxis]+month_bnd[+1:]).flatten()]).T
tbnd += (1991-1850)*365
tbnd.shape
t = tbnd.mean(axis=1)
t.shape
# Input all site info
SiteInfFile = DataDir + '/FLUXNET2015/README/FluxNet2015_sites_info.txt'
#infrec = np.genfromtxt(SiteInfFile, delimiter="\t", deletechars="#", invalid_raise = False, dtype=str)
infrec = np.genfromtxt(SiteInfFile, delimiter=" ", deletechars="#", dtype=str)
nsite = len(infrec)
headerstr = infrec[0].split('\t')
indx1 = headerstr.index("SITE_ID")
indx2 = headerstr.index("SITE_NAME")
indx3 = headerstr.index("LOCATION_LAT")
indx4 = headerstr.index("LOCATION_LONG")
indx5 = headerstr.index("LOCATION_ELEV")
indx6 = headerstr.index("IGBP")
AllSiteLats = np.ma.masked_array(np.random.rand(nsite-1))
AllSiteLons = np.ma.masked_array(np.random.rand(nsite-1))
AllSiteElev = np.ma.masked_array(np.random.rand(nsite-1))
AllSiteIDs = []
AllSiteNames = []
AllSiteIGBP = []
for i in range(nsite-1):
datastr = infrec[i+1].split('\t')
AllSiteIDs.append(datastr[indx1])
AllSiteNames.append(datastr[indx2])
AllSiteIGBP.append(datastr[indx6])
AllSiteLats[i] = float(datastr[indx3])
AllSiteLons[i] = float(datastr[indx4])
tempstr = datastr[indx5]
if tempstr=='':
tempstr = "-999"
AllSiteElev[i] = float(tempstr)
# Set data directory
ThisDir = DataDir + '/FLUXNET2015/TIER1/FULLSET/MM/'
# list all data files in directory ThisDir
AllFileNames = os.listdir(ThisDir)
nfile = len(AllFileNames)
lon = np.ma.masked_array(np.random.rand(nfile))
lat = np.ma.masked_array(np.random.rand(nfile))
lev = np.ma.masked_array(np.random.rand(nfile))
siteID = np.ma.masked_array(np.random.rand(nfile))
data = np.ma.masked_array(np.random.rand(t.size, nfile), fill_value=-999)
data2D = np.ma.masked_array(np.random.rand(nyears,nmonth), fill_value=-999)
lat[:] = -999
lon[:] = -999
data[:,:] = -999
site_id = []
site_name = []
site_igbp = []
ij = 0
for FileName in AllFileNames:
print(FileName)
data2D[:,:] = -999
tempstr = FileName.split("_")
SiteID = tempstr[1]
site_id.append(SiteID)
#indx0 = np.where(AllSiteIDs==SiteID)
indx0 = AllSiteIDs.index(SiteID)
lat[ij] = AllSiteLats[indx0]
lon[ij] = AllSiteLons[indx0]
lev[ij] = AllSiteElev[indx0]
site_name.append(AllSiteNames[indx0])
site_igbp.append(AllSiteIGBP[indx0])
datarec = np.genfromtxt(ThisDir+FileName, delimiter="\t", deletechars="#", dtype=str)
ndata = len(datarec)
datastr = datarec[0].split(',')
if RawVarID in datastr:
indx = datastr.index(RawVarID)
indx_qc = datastr.index(RawVarID_QC)
else:
indx = -1
if indx != -1:
for i in range(ndata-1):
datastr = datarec[i+1].split(',')
YYMM = int(datastr[0])
YY = int(YYMM/100)
MM = int(YYMM - YY*100)
tempdata = float(datastr[indx])
tempdata_qc = float(datastr[indx_qc])
if tempdata<=-990 or tempdata_qc<0.5 and tempdata_qc>=0:
# reset missing value
tempdata = -999.
# only data in the period from start_yr till end_yr are chosen.
if YY>=start_yr and YY<=end_yr:
iy = YY - start_yr
im = MM - 1
data2D[iy,im] = tempdata
ijk = 0
for iy in range(nyears):
for im in range(nmonth):
data[ijk,ij] = data2D[iy,im]
ijk = ijk + 1
siteID[ij] = ij + 1
ij = ij + 1
data_min = data.min()
data_max = data.max()
# Calculate climatology of burned area
mdata = data.mean(axis=0)
with Dataset(DataDir + "/rlds.nc", mode="w") as dset:
# dimensions
dset.createDimension("time", size=t.size)
dset.createDimension("data", size=nfile)
dset.createDimension("nb", size=2)
# time
T = dset.createVariable("time", t.dtype, ("time"))
T[...] = t
T.units = "days since 1850-01-01 00:00:00"
T.calendar = "noleap"
T.bounds = "time_bounds"
T.standard_name = "time"
T.long_name = "time"
# time bounds
TB = dset.createVariable("time_bounds", t.dtype, ("time", "nb"))
TB[...] = tbnd
# latitude
X = dset.createVariable("lat", lat.dtype, ("data"))
X[...] = lat
X.standard_name = "latitude"
X.long_name = "site latitude"
X.units = "degrees_north"
# longitude
Y = dset.createVariable("lon", lon.dtype, ("data"))
Y[...] = lon
Y.standard_name = "longitude"
Y.long_name = "site longitude"
Y.units = "degrees_east"
# elevation
Z = dset.createVariable("elevation", lev.dtype, ("data"))
Z[...] = lev
Z.units = "m"
Z.positive = "up"
# data
D = dset.createVariable(VarID, data.dtype, ("time", "data"), fill_value = -999)
D[...] = data
D.units = "W/m2"
D.standard_name = long_name
D.long_name = long_name
D.actual_range = np.asarray([data_min,data_max])
# site_info
S = dset.createVariable("site_info", int, ("data"))
S[...] = siteID
S.site_id = site_id
S.site_name = site_name
S.IGBP_class = site_igbp
dset.title = "FluxNet Tower eddy covariance measurements TIER1"
dset.version = "2015"
dset.institutions = "%s; %s; %s" % (instit1, instit2, instit3)
dset.source = "Longwave radiation, incoming, consolidated from LW_IN_F_MDS and LW_IN_ERA"
dset.history = """
%s: downloaded source from %s;
%s: converted to netCDF with %s""" % (stamp1, remote_source, stamp2, gist_source)
dset.references = """
@ARTICLE{Reichstein2007,
author = {Reichstein, M., D. Papale, R. Valentini, M. Aubinet, C. Bernhofer, A. Knohl, T. Laurila, A. Lindroth, E. Moors, K. Pilegaard, and G. Seufert},
title = {Determinants of terrestrialecosystem carbon balance inferred from European eddy covarianceflux sites},
journal = {Geophys. Res. Lett.},
year = {2007},
number = {34},
page = {L01402},
doi = {https://doi.org/doi:10.1029/2006GL027880}
}
@ARTICLE{Lasslop2010,
author = {Lasslop, G., M. Reichstein, D. Papale, A.D. Richardson, A. Arneth, A. Barr, P. Stoy, and G. Wohlfahrt},
title = {Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation},
journal = {Global Change Biology},
year = {2010},
number = {16},
page = {187-208},
doi = {https://doi.org/10.1111/j.1365-2486.2009.02041.x}
}
@ARTICLE{Knauer2018,
author = {Knauer, J., S. Zaehle, B.E. Medlyn, M. Reichstein, C.A. Williams, M. Migliavacca, M.G. De Kauwe, C. Werner, C. Keitel, P. Kolari, J.-M. Limousin, and M.-L. Linderson},
title = {Towards physiologically meaningful water use efficiency estimates from eddy covariance data},
journal = {Global Change Biology},
year = {2018},
number = {24(2)},
page = {694-710},
doi = {https://doi.org/10.1111/gcb.13893}
}"""
dset.comments = """
time_period: %s; original_temporal_resolution: %s; original_spatial_resolution: %s; original_units: %s; final_temporal_resolution: %s; final_spatial_resolution: %s; final_units: %s""" % (period, origtr, origsr, origut, finltr, finlsr, finlut)
dset.convention = "CF-1.7"