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UNM_RemoveBadData.m
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UNM_RemoveBadData.m
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function [] = UNM_RemoveBadData( sitecode, year, varargin )
% UNM_REMOVEBADDATA - remove bogus observations from UNM flux data and write
% filtered data to delimited ASCII files FOR SITE-YEARS 2012 AND LATER.
%
% This program was created by Krista Anderson Teixeira in July 2007
% Modified by John DeLong 2008 through 2009.
% Modifed by Timothy W. Hilton, 2011 through 2013
%
% The program reads site_fluxall_year delimited text files and pulls in a
% combination of matlab processed ts data and data logged average 30-min flux
% data. It then flags values based on a variety of criteria and writes out new
% files that do not have the identified bad values. It writes out a
% site_flux_all_qc file and a site_flux_all_for_gap_filling file to send to
% REddyProc for gapfilling and flux partitioning. It can be adjusted to make
% other subsetted files too.
%
% USAGE
% UNM_RemoveBadData( sitecode, year )
% UNM_RemoveBadData( sitecode, year, 'iteration', iteration )
% UNM_RemoveBadData( sitecode, year, ..., 'write_QC', write_QC )
% UNM_RemoveBadData( sitecode, year, ..., 'write_GF', write_GF )
% UNM_RemoveBadData( sitecode, year, ..., 'draw_plots', draw_plots )
%
% INPUTS
% sitecode: UNM_sites object or integer; specifies site to process
% year: integer; year to process
% PARAMETER-VALUE PAIRS
% iteration: integer 1-6 (Default is 6); defines which set of bad data tasks
% to perform (see code for details)
% write_QC: {true}|false; if true, writes flux_all_qc file
% write_GF: {true}|false; if true, writes flux_all_for_gapfilling file
% draw_plots: 0|{1}|2|3; determines extent of plotting. larger values
% cause more plots to be drawn.
% 0: draw no plots
% 1 (default): plot only NEE time series with NEE filter results
% 2: all plots from 1, plus:
% - PAR normalization results
% - radiation timing correction results
% - six-panel plot showing "fingerprints" for incoming shortwave
% (Rg), relative humidity (RH), air temperature (T), net ecosystem
% exchange (NEE), latent heat (LE), and sensible heat (H),
% 3: all plots from 1 and 2, plus:
% - latent heat diagnostic plot showing LE and PAR time series
% with the results of various filters (see plot legend)
% - four-panel plot showing time series for NEE, T, carbon
% dioxide concentration ([CO2]), and pcp
% - NEE vs wind speed scatter plot
% - NEE vs wind direction scatter plot (split by day/night)
% - NEE vs friction velocity (ustar) scatter plot
% - [CO2] time series, with results of various filters (see plot
% legend)
% - Burba cold temperature correction results
%
% OUTPUTS:
% This function has no outputs
%
% SEE ALSO
% UNM_RemoveBadData_pre2012
%
% author: Timothy W. Hilton, UNM, 2012-2013
%clear all
%close all
[ this_year, ~, ~ ] = datevec( now );
% -----
% define optional inputs, with defaults and typechecking
% -----
args = inputParser;
args.addRequired( 'sitecode', @(x) ( isintval( x ) | isa( x, 'UNM_sites' ) ) );
args.addRequired( 'year', ...
@(x) ( isintval( x ) & ( x >= 2006 ) & ( x <= this_year ) ) );
args.addParamValue( 'iteration', 6, ...
@(x) ( isintval( x ) & ( x >= 1 ) & ( x <= 6 ) ) );
args.addParamValue( 'write_QC', true, @islogical );
args.addParamValue( 'write_GF', true, @islogical );
args.addParamValue( 'old_fluxall', false, @islogical );
args.addParamValue( 'xls_fluxall', false, @islogical );
args.addParamValue( 'draw_plots', 1, ...
@(x) ( isnumeric( x ) & ismember( x, [ 0, 1, 2, 3 ] ) ) );
% parse optional inputs
args.parse( sitecode, year, varargin{ : } );
% place user arguments into variables
sitecode = args.Results.sitecode;
year_arg = args.Results.year;
site = char( sitecode );
iteration = int8( args.Results.iteration );
%true to write "[sitename].._qc", -- file with all variables & bad data removed
write_complete_out_file = args.Results.write_QC;
%true to write file for Reichstein's online gap-filling. SET U* LIM (including
%site- specific ones--comment out) TO 0!!!!!!!!!!
write_gap_filling_out_file = args.Results.write_GF;
% Parameters used to select between versions of fluxall files
use_old_fluxall = args.Results.old_fluxall;
use_xls_fluxall = args.Results.xls_fluxall;
draw_plots = args.Results.draw_plots;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% USTAR filter switch
% If this is set to false, the ustar filter for fluxes is turned off
% (this is desireable for making ameriflux files)
% See line 1272 to see where this takes affect (its pretty hacky)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
ustar_filter_switch = false;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
data_for_analyses = 0; %1 to output file with data sorted for specific analyses
ET_gap_filler = 0; %run ET gap-filler program
winter_co2_min = -100; %initialization -- will be set for specific sites later
obs_per_day = 48; % half-hourly observations
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Specify some details about sites and years
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if sitecode == UNM_sites.GLand; % grassland
ustar_lim = 0.06;
co2_min_by_month = -15; co2_max_by_month = 7.25;
n_SDs_filter_hi = 3.0; % how many std devs above the mean NEE to allow
n_SDs_filter_lo = 3.0; % how many std devs below the mean NEE to allow
sd_filter_windows = [ 1, 3 ];
sd_filter_thresh = 3.5;
wind_min = 330; wind_max = 30; % these are given a sonic_orient = 180;
Tdry_min = 240; Tdry_max = 320;
HS_min = -100; HS_max = 450;
HSmass_min = -100; HSmass_max = 450;
LH_min = -150; LH_max = 450;
rH_min = 0; rH_max = 1;
h2o_max = 30; h2o_min = 0;
elseif sitecode == UNM_sites.SLand; % shrubland
ustar_lim = 0.08;
co2_min_by_month = -15; co2_max_by_month = 6;
n_SDs_filter_hi = 3.0; % how many std devs above the mean NEE to allow
n_SDs_filter_lo = 3.0; % how many std devs below the mean NEE to allow
sd_filter_windows = [ 1, 3 ];
sd_filter_thresh = 3.5;
wind_min = 330; wind_max = 30; % these are given a sonic_orient = 180;
Tdry_min = 240; Tdry_max = 320;
HS_min = -100; HS_max = 450;
HSmass_min = -100; HSmass_max = 450;
LH_min = -150; LH_max = 450;
rH_min = 0; rH_max = 1;
h2o_max = 30; h2o_min = 0;
elseif sitecode == UNM_sites.JSav; % Juniper savanna
ustar_lim = 0.11;
co2_min_by_month = -11;
co2_max_by_month = [ repmat( 5, 1, 6 ), repmat( 10, 1, 6 ) ];
n_SDs_filter_hi = 3.0; % how many std devs above the mean NEE to allow
n_SDs_filter_lo = 3.0; % how many std devs below the mean NEE to allow
sd_filter_windows = [ 1, 3, 5 ];
sd_filter_thresh = 3;
wind_min = 15; wind_max = 75; % these are given a sonic_orient = 225;
Tdry_min = 240; Tdry_max = 320;
HS_min = -100; HS_max = 550;
HSmass_min = -100; HSmass_max = 550;
LH_min = -150; LH_max = 450;
rH_min = 0; rH_max = 1;
h2o_max = 30; h2o_min = 0;
press_min = 70; press_max = 130;
elseif sitecode == UNM_sites.PJ | sitecode == UNM_sites.TestSite; % Pinyon Juniper
ustar_lim = 0.22;
n_SDs_filter_hi = 3.0; % how many std devs above the mean NEE to allow
n_SDs_filter_lo = 3.0; % how many std devs below the mean NEE to allow
wind_min = 15; wind_max = 75; % these are given a sonic_orient = 225;
sd_filter_windows = [ 1.5, 3, 5 ];
sd_filter_thresh = 3;
co2_min_by_month = -10;
co2_max_by_month = 6;
Tdry_min = 240; Tdry_max = 310;
HS_min = -100; HS_max = 640;
HSmass_min = -100; HSmass_max = 640;
LH_min = -150; LH_max = 450;
rH_min = 0; rH_max = 1;
h2o_max = 30; h2o_min = 0;
press_min = 70; press_max = 130;
co2_max_by_month = [ 2.5, 2.5, 2.5, 3.5, 4, 5, 6, repmat( 6, 1, 5 ) ];
elseif sitecode == UNM_sites.PJ_girdle; % Pinyon Juniper girdle
ustar_lim = 0.16;
n_SDs_filter_hi = 3.0; % how many std devs above the mean NEE to allow
n_SDs_filter_lo = 3.0; % how many std devs below the mean NEE to allow
sd_filter_windows = [ 1.5, 3, 5 ];
sd_filter_thresh = 3;
wind_min = 15; wind_max = 75; % these are given a sonic_orient = 225;
Tdry_min = 240; Tdry_max = 310;
HS_min = -100; HS_max = 640;
HSmass_min = -100; HSmass_max = 640;
LH_min = -150; LH_max = 450;
rH_min = 0; rH_max = 1;
h2o_max = 30; h2o_min = 0;
press_min = 70; press_max = 130;
co2_min_by_month = -10;
co2_max_by_month = [ 2.5, 2.5, 2.5, 3.5, 4, 5, 6, 6, 6, 4, 4, 4 ];
%co2_max_by_month = [ 1.5, 1.5, 2, 2, 3, 4, 4, repmat( 6, 1, 5 ) ];
elseif sitecode == UNM_sites.New_GLand; % new Grassland
ustar_lim = 0.06;
n_SDs_filter_hi = 4.5; % how many std devs above the mean NEE to allow
n_SDs_filter_lo = 3.0; % how many std devs below the mean NEE to allow
sd_filter_windows = [ 1, 3 ];
sd_filter_thresh = 4;
co2_min_by_month = -15; co2_max_by_month = 7.25;
wind_min = 330; wind_max = 30; % these are given a sonic_orient = 180;
Tdry_min = 240; Tdry_max = 320;
HS_min = -100; HS_max = 450;
HSmass_min = -100; HSmass_max = 450;
LH_min = -150; LH_max = 450;
rH_min = 0; rH_max = 1;
h2o_max = 30; h2o_min = 0;
elseif sitecode == UNM_sites.PPine; % Ponderosa Pine
% site default values
%co2_min_by_month = [-6 -6 -15 -15 -15 -15 -15 -15 -15 -15 -15 -5];
%co2_max_by_month = [4 4 4 5 30 30 30 30 30 30 5 4];
co2_min_by_month = [-8 -8 -21 -21 -21 -21 -21 -21 -21 -21 -17 -10];
% Note that the sketchy NEE normalization that occurs relies on this:
co2_max_by_month = 30;
ustar_lim = 0.2;
n_SDs_filter_hi = 3.0; % how many std devs above the mean NEE to allow
n_SDs_filter_lo = 3.0; % how many std devs below the mean NEE to allow
sd_filter_windows = [ 2, 4, 7 ];
sd_filter_thresh = 3;
wind_min = 119; wind_max = 179; % these are given a sonic_orient = 329;
Tdry_min = 240; Tdry_max = 310;
HS_min = -200; HS_max = 800;
HSmass_min = -200; HSmass_max = 800;
LH_min = -50; LH_max = 550;
rH_min = 0; rH_max = 1;
h2o_max = 30; h2o_min = 0;
elseif sitecode == UNM_sites.MCon; % Mixed conifer
co2_min_by_month = [ -2.5, -2.5, repmat( -16, 1, 9 ), -2.5 ];%[ -1.5, -1.5, repmat( -12, 1, 9 ), -1.5 ];
co2_max_by_month = 6;
n_SDs_filter_hi = 2.0; % how many std devs above the mean NEE to allow
n_SDs_filter_lo = 3.0; % how many std devs below the mean NEE to allow
sd_filter_windows = [ 1, 3, 6 ];
sd_filter_thresh = 3;
wind_min = 153; wind_max = 213; % these are given a sonic_orient = 333;
Tdry_min = 250; Tdry_max = 300;
HS_min = -200; HS_max = 800;
HSmass_min = -200; HSmass_max = 800;
LH_min = -50; LH_max = 550;
rH_min = 0; rH_max = 1;
h2o_max = 30; h2o_min = 0;
ustar_lim = 0.2;
elseif sitecode == UNM_sites.MCon_SS; % New Mixed Conifer
warning('Filters copied from MCon, adjust for NMCon ');
co2_min_by_month = [ -6.5, -6.5, repmat( -16, 1, 9 ), -.5 ];%[ -1.5, -1.5, repmat( -12, 1, 9 ), -1.5 ];
co2_max_by_month = 6;
n_SDs_filter_hi = 2.0; % how many std devs above the mean NEE to allow
n_SDs_filter_lo = 3.0; % how many std devs below the mean NEE to allow
sd_filter_windows = [ 1, 3, 6 ];
sd_filter_thresh = 3;
% Note that these are copied from MCon (which may itself be off)
% There are two things to consider - distortion from wind behind the
% anemometer and from the tower - these will be in two different
% directions since the flux instruments are on a boom positioned a
% meter or so to the east of the tower and sonic is not facing straight
% out from tower.
wind_min = 153; wind_max = 213; % these are given a sonic_orient = 333;
Tdry_min = 250; Tdry_max = 300;
HS_min = -200; HS_max = 800;
HSmass_min = -200; HSmass_max = 800;
LH_min = -50; LH_max = 550;
rH_min = 0; rH_max = 1;
h2o_max = 30; h2o_min = 0;
ustar_lim = 0.2;
elseif sitecode == UNM_sites.TX;
ustar_lim = 0.11;
co2_min_by_month = -26;
switch args.Results.year
case 2011
co2_max_by_month = [ 4.0, 4, 4, 4, 9, 10, ...
10, 4, 4, 4, 4, 4.0 ];
case 2012
co2_max_by_month = [ 4.9, 6, 7, 8, 9, 12, ...
12, 12, 9, 6, 6, 4.9 ];
end
n_SDs_filter_hi = 3.0; % how many std devs above the mean NEE to allow
n_SDs_filter_lo = 3.0; % how many std devs below the mean NEE to allow
wind_min = 296; wind_max = 356; % these are given a sonic_orient = 146;
Tdry_min = 265; Tdry_max = 315;
HS_min = -200; HS_max = 800;
HSmass_min = -200; HSmass_max = 800;
LH_min = -150; LH_max = 550;
rH_min = 0; rH_max = 1;
h2o_max = 30; h2o_min = 0;
press_min = 70; press_max = 130;
elseif sitecode == UNM_sites.TX_forest;
ustar_lim = 0.11;
n_SDs_filter_hi = 3.0; % how many std devs above the mean NEE to allow
n_SDs_filter_lo = 3.0; % how many std devs below the mean NEE to allow
co2_min_by_month = -26; co2_max_by_month = 12;
wind_min = 300; wind_max = 360; % these are given a sonic_orient = ;
Tdry_min = 265; Tdry_max = 315;
HS_min = -200; HS_max = 800;
HSmass_min = -200; HSmass_max = 800;
LH_min = -150; LH_max = 550;
rH_min = 0; rH_max = 1;
h2o_max = 30; h2o_min = 0;
press_min = 70; press_max = 130;
elseif sitecode == UNM_sites.TX_grass;
ustar_lim = 0.11;
n_SDs_filter_hi = 3.0; % how many std devs above the mean NEE to allow
n_SDs_filter_lo = 3.0; % how many std devs below the mean NEE to allow
co2_min_by_month = -26; co2_max_by_month = 12;
wind_min = 300; wind_max = 360; % these are given a sonic_orient = ;
Tdry_min = 265; Tdry_max = 315;
HS_min = -200; HS_max = 800;
HSmass_min = -200; HSmass_max = 800;
LH_min = -150; LH_max = 550;
rH_min = 0; rH_max = 1;
h2o_max = 35; h2o_min = 0;
press_min = 70; press_max = 130;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% read the fluxall file
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if use_old_fluxall
pathname = fullfile( get_site_directory( sitecode ), ...
'old_fluxall');
fname = sprintf( '%s_FLUX_all_%d.txt', get_site_name( sitecode ), ...
year_arg );
else
pathname = fullfile( get_site_directory( sitecode ));
fname = sprintf( '%s_%d_fluxall.txt', get_site_name( sitecode ), ...
year_arg );
end
if use_xls_fluxall
fname = sprintf( '%s_FLUX_all_%d.xls', get_site_name( sitecode ), ...
year_arg );
data = UNM_parse_fluxall_xls_file( sitecode, year_arg, ...
'file', fullfile( pathname, fname ));
else
data = UNM_parse_fluxall_txt_file( sitecode, year_arg, ...
'file', fullfile( pathname, fname ));
data_orig = parse_fluxall_txt_file( sitecode, year_arg, 'file', ...
fullfile( pathname, fname ));
% In 2007 the existing GLand and SLand sites and were acquired by
% Marcy. The sites were then revamped (new programs and data handling)
% in late May/early June 2007. Data from before the revamp is present
% in old fluxall files, but I am unsure what had to be done to get this
% data into the old files. For now merge the old 2007 fluxall
% files into the current dataset and proceed with the QC process.
if (sitecode == 1 || sitecode == 2 ) && year_arg == 2007
warning('Dataset to table conversions! FIX THESE!');
data = merge_2007_fluxall_files( dataset2table(data), sitecode );
data = table2dataset(data);
% These old data have duplicated SW_IN measurements
data = replacedata( data, ...
interp_duplicated_radiation( double( data ), ...
data.Properties.VarNames, data.timestamp ) );
end
% Fill fluxes with 30 minute data?
disp( 'WARNING: Filling fluxes with 30min data');
data = fill_30min_flux_spooler( data, sitecode, year_arg );
end
outfolder = fullfile( get_site_directory( sitecode ), ...
'processed_flux' );
headertext = data.Properties.VarNames;
timestamp = data.timestamp;
[year,month,day,hour,minute,second] = datevec( data.timestamp );
ncol = size( data, 2 );
filelength_n = size( data, 1 );
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% some siteyears have periods where the observed radition does not line
% up with sunrise. Fix this here so that the matched time/radiation
% propagates through the rest of the calculations
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
data = UNM_fix_datalogger_timestamps( sitecode, year_arg, ...
dataset2table( data ),...
'debug', args.Results.draw_plots > 1 );
data = table2dataset( data );
% data.timestamp = [];
% This was an issue with old MCon fluxall files where SW_IN appears to be
% filled with hourly data from off-site (which is mistakely duplicated
% each half hour instead of averaged)
if ( sitecode == UNM_sites.MCon )
% if ( year == 2007 | year == 2008)
% data = replacedata( data, ...
% revise_MCon_duplicated_Rg( double( data ), ...
% headertext, ...
% timestamp ) );
% end
end
shift_t_str = 'shifted';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Read in Matlab processed ts data
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
jday = data.jday;
iok = data.iok ;
Tdry = data.tdry ;
%wnd_dir_compass = data.wind_direction ;
wnd_spd = data.speed ;
%u_star = data.ustar ;
%CO2_mean = data.CO2_mean ;
%CO2_std = data.CO2_std ;
%H2O_mean = data.H2O_mean ;
%H2O_std = data.H2O_std ;
%u_mean = data.u_mean_unrot ;
t_mean = data.temp_mean;
t_meanK = t_mean + 273.15;
fc_raw = data.Fc_raw ;
fc_raw_massman = data.Fc_raw_massman ;
fc_water_term = data.Fc_water_term ;
fc_heat_term_massman = data.Fc_heat_term_massman ;
%fc_raw_massman_wpl = data.Fc_raw_massman_ourwpl;
E_raw = data.E_raw ;
E_raw_massman = data.E_raw_massman ;
E_water_term = data.E_water_term;
E_heat_term_massman = data.E_heat_term_massman;
E_wpl_massman = data.E_wpl_massman;
%HSdry = data.SensibleHeat_dry ;
%HSdry_massman = data.HSdry_massman;
%HL_raw = data.LatentHeat_raw;
%HL_wpl_massman = data.LatentHeat_raw_massman ; % Is this correct?
%HL_wpl_massman_un = repmat( NaN, size( data, 1 ), 1 );
%rhoa_dry = data.rhoa_dry_air_molar_density;
decimal_day = ( datenum( year, month, day, hour, minute, second ) - ...
datenum( year, 1, 1 ) + 1 );
year_arg = year(2);
%initialize some variables to a NaN array
dummy = repmat( NaN, size( data, 1), 1 );
rH = dummy;
precip = dummy;
% Some sites are missing radiation measurements
sw_incoming = dummy;
lw_incoming = dummy;
sw_outgoing = dummy;
lw_outgoing = dummy;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Read in 30-min data, variable order and names in flux_all files are not
% consistent so match headertext
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% convert CNR1 temperature from centrigrade to Kelvins
% FIXME - this needs to handle different header names from early years
CNR1TK = [];
CNR1_var = regexp_header_vars( data, 'CNR1*|Temp_C_Avg' );
if ~isempty(CNR1_var)
CNR1TK = data.( CNR1_var{ 1 } ) + 273.15;
end
h2oflag_1 = 0;
data = double( data );
for i=1:numel( headertext );
if strcmp('agc_Avg',headertext{i}) == 1
agc_Avg = data(:,i);
elseif strcmp('h2o_hmp_Avg', headertext{i}) == 1 || ...
strcmp('h2o_hmp_mean', headertext{i}) == 1 || ...
strcmp('h2o_hmp_mean_Avg', headertext{i}) == 1
h2o_hmp = data( :, i );
elseif strcmp('wind_direction', headertext{i}) == 1 || ...
strcmp('windDirection_theta', headertext{i}) == 1
wnd_dir_compass = data( :, i );
elseif strcmp('ustar', headertext{i}) == 1 || ...
strcmp('ustar_frictionVelocity_M_s', headertext{i}) == 1
u_star = data( :, i );
% filter out absurd u_star values
u_star( u_star > 50 ) = NaN;
elseif strcmp('u_mean_unrot', headertext{i}) == 1 || ...
strcmp('u_mean', headertext{i}) == 1
u_mean = data( :, i );
elseif strcmp('CO2_mean', headertext{i}) == 1 || ...
strcmp('CO2_mean_umol_molDryAir', headertext{i}) == 1
CO2_mean = data( :, i );
elseif strcmp('CO2_std', headertext{i}) == 1 || ...
strcmp('CO2_std_umol_molDryAir', headertext{i}) == 1
CO2_std = data( :, i );
% This should come in from 10hz data in g/m3, but some fluxall
% have it in mmol/mol, so convert it below
elseif strcmp('H2O_mean', headertext{i}) == 1
H2O_mean = data( :, i );
elseif strcmp('H2O_mean_mmol_molDryAir', headertext{i}) == 1
H2O_mean = data( :, i );
h2oflag_1 = 1;
elseif strcmp('H2O_std', headertext{i}) == 1 || ...
strcmp('H2O_std_mmol_molDryAir', headertext{i}) == 1
H2O_std = data( :, i );
elseif strcmp('Fc_raw_massman_ourwpl', headertext{i}) == 1 || ...
strcmp('Fc_raw_massman_wpl', headertext{i}) == 1
fc_raw_massman_wpl = data( :, i );
elseif strcmp('SensibleHeat_dry', headertext{i}) == 1 || ...
strcmp('HSdry_WM2', headertext{i}) == 1
HSdry = data( :, i );
elseif strcmp('HSdry_massman', headertext{i}) == 1 || ...
strcmp('HSdry_massman_WM2', headertext{i}) == 1
HSdry_massman = data( :, i );
elseif strcmp('LatentHeat_raw', headertext{i}) == 1 || ...
strcmp('HL_raw_WM2', headertext{i}) == 1
HL_raw = data( :, i );
elseif strcmp('LatentHeat_raw_massman', headertext{i}) == 1 || ...
strcmp('HL_raw_massman_WM2', headertext{i}) == 1
HL_wpl_massman = data( :, i );% Is this correct? Its raw, not wpl
HL_wpl_massman_un = repmat( NaN, size( data, 1 ), 1 );
% Half hourly data filler only produces uncorrected HL_wpl_massman,
% but use these where available as very similar values
HL_wpl_massman( isnan( HL_wpl_massman ) & ...
~isnan( HL_wpl_massman_un ) ) = ...
HL_wpl_massman_un( isnan( HL_wpl_massman ) & ...
~isnan( HL_wpl_massman_un ) );
elseif strcmp('rhoa_dry_air_molar_density', headertext{i}) == 1 || ...
strcmp('rhoa_dryAirMolarDensity_mols_m3MoistAir', headertext{i}) == 1 || ...
strcmp('rhoa_dryAirMolarDensity_g_m3MoistAir', headertext{i}) == 1% Is this correct? Convert?
rhoa_dry = data( :, i );
% Input all the different relative humidity variables and change to 0-1.
elseif strcmp('rH', headertext{i}) == 1 || ...
strcmp('RH_Avg', headertext{i}) == 1 || ...
strcmp('RH_4p5_Avg',headertext{i}) == 1 || ...
strcmp('RH_4p5',headertext{i}) == 1 || ...
strcmp('rh_hmp', headertext{i}) == 1 || ...
strcmp('rh_hmp_4_Avg', headertext{i}) == 1 || ...
strcmp('RH',headertext{i}) == 1 || ...
strcmp('RH_2_Avg',headertext{i}) == 1 || ...
strcmp('RH_10_Avg',headertext{i}) == 1 || ...
strcmp('RH_6p85_Avg', headertext{i})==1 || ...
strcmp('RH_24_Avg', headertext{i})==1
rH = data(:,i);
%strcmp('RH_3p7_Avg',headertext{i}) == 1 || ...
% strcmp('RH_2',headertext{i}) == 1 || ...
%Fixed scaling the rH now on a per rH value to account for
%the scale changes associated with program changes in the
%fluxall.
scale = find(rH > 1.1);
rH(scale) = rH(scale) ./ 100;
% Now convert back to percent
%rH = rH * 100;
elseif strcmp('Ts_mean', headertext{i}) == 1 || ...
strcmp('Ts_Avg', headertext{i}) == 1
Tair_TOA5 = data(:,i);
elseif strcmp('5point_precip', headertext{i}) == 1 || ...
strcmp('rain_Tot', headertext{i}) == 1 || ...
strcmp('precip', headertext{i}) == 1 || ...
strcmp('precip(in)', headertext{i}) == 1 || ...
strcmp('ppt', headertext{i}) == 1 || ...
strcmp('Precipitation', headertext{i}) == 1
precip = data(:,i);
elseif strcmp( 'press_mean', headertext{i}) == 1 || ...
strcmp('press_Avg', headertext{i}) == 1 || ...
strcmp('press_a', headertext{i}) == 1
atm_press = data(:,i);
elseif strcmp('par_correct_Avg', headertext{i}) == 1 || ...
strcmp('par_Avg(1)', headertext{i}) == 1 || ...
strcmp('par_Avg_1', headertext{i}) == 1 || ...
strcmp('par_Avg', headertext{i}) == 1 || ...
strcmp('par_licor', headertext{i}) == 1 || ...
strcmp('par_up_Avg', headertext{i}) == 1 || ...
strcmp('par_face_up_Avg', headertext{i}) == 1 || ...
strcmp('par_faceup_Avg', headertext{i}) == 1 || ...
strcmp('par_incoming_Avg', headertext{i}) == 1 || ...
strcmp('par_lite_Avg', headertext{i}) == 1
Par_Avg = data(:,i);
elseif strcmp('t_hmp_mean', headertext{i})==1 || ...
strcmp('AirTC_Avg', headertext{i})==1 || ...
strcmp('AirTC_2_Avg', headertext{i})==1 || ...
strcmp('AirTC_10_Avg', headertext{i})==1 || ...
strcmp('AirTC_4p5_Avg', headertext{i})==1 || ...
strcmp('t_hmp_3_Avg', headertext{i})==1 || ...
strcmp('pnl_tmp_a', headertext{i})==1 || ...
strcmp('t_hmp_Avg', headertext{i})==1 || ...
strcmp('t_hmp_4_Avg', headertext{i})==1 || ...
strcmp('t_hmp_top_Avg', headertext{i})==1| ...
strcmp('AirTC_6p85_Avg', headertext{i})==1| ...
strcmp('AirTC_24_Avg', headertext{i})==1
air_temp_hmp = data(:,i);
elseif strcmp('Tsoil',headertext{i}) == 1 || ...
strcmp('Tsoil_avg',headertext{i}) == 1 || ...
strcmp('soilT_Avg(1)',headertext{i}) == 1
Tsoil = data(:,i);
elseif strcmp('Rn_correct_Avg',headertext{i})==1 || ...
strcmp('NR_surf_AVG', headertext{i})==1 || ...
strcmp('NetTot_Avg_corrected', headertext{i})==1 || ...
strcmp('NetTot_Avg', headertext{i})==1 || ...
strcmp('Rn_Avg',headertext{i})==1 || ...
strcmp('Rn_total_Avg',headertext{i})==1
NR_tot = data(:,i);
elseif strcmp('Rad_short_Up_Avg', headertext{i}) || ...
strcmp('pyrr_incoming_Avg', headertext{i})
sw_incoming = data(:,i);
elseif strcmp('Rad_short_Dn_Avg', headertext{i})==1 || ...
strcmp('pyrr_outgoing_Avg', headertext{i})==1
sw_outgoing = data(:,i);
elseif strcmp('Rad_long_Up_Avg', headertext{i}) == 1 || ...
strcmp('Rad_long_Up__Avg', headertext{i}) == 1
lw_incoming = data(:,i);
elseif strcmp('CG3UpCo_Avg', headertext{i})==1 || ...
strcmp('Rad_long_Up_TCor_Avg', headertext{i})==1
lw_incoming_Co = data(:, i);
elseif strcmp('Rad_long_Dn_Avg', headertext{i})==1 || ...
strcmp('Rad_long_Dn__Avg', headertext{i})==1
lw_outgoing = data(:,i);
elseif strcmp('CG3DnCo_Avg', headertext{i})==1 || ...
strcmp('Rad_long_Dn_TCor_Avg', headertext{i})==1
lw_outgoing_Co = data(:, i);
elseif strcmp('VW_Avg', headertext{i})==1 || ...
strcmp('SWC_Avg_1', headertext{i})==1 || ...
strcmp('SWC_P1_5_Avg', headertext{i})==1
VWC = data(:,i);
elseif strcmp('shf_Avg(1)', headertext{i})==1 || ...
strcmp('shf_Avg_1', headertext{i})==1 || ...
strcmp('shf_pinon_1_Avg', headertext{i})==1
soil_heat_flux_1 = data(:,i);
disp('FOUND shf_pinon_1_Avg');
elseif any( strcmp( headertext{i}, ...
{ 'hfp_grass_1_Avg', 'hfp01_grass_Avg' } ) )
soil_heat_flux_1 = data(:,i);
disp('FOUND hfp_grass_1_Avg');
elseif any( strcmp( headertext( i ), ...
{ 'hfp_grass_2_Avg', 'hft3_grass_Avg' } ) )
soil_heat_flux_2 = data(:,i);
disp('FOUND hfp_grass_2_Avg');
elseif strcmp('shf_Avg(2)', headertext{i})==1 || ...
strcmp('shf_Avg_2', headertext{i})==1 || ...
strcmp('shf_jun_1_Avg', headertext{i})==1
soil_heat_flux_2 = data(:,i);
elseif strcmp('hfpopen_1_Avg', headertext{i})==1 % only for TX
soil_heat_flux_open = data(:,i);
elseif strcmp('hfpmescan_1_Avg', headertext{i})==1 % only for TX
soil_heat_flux_mescan = data(:,i);
elseif strcmp('hfpjuncan_1_Avg', headertext{i})==1 % only for TX
soil_heat_flux_juncan = data(:,i);
%Shrubland flux plates 2009 onwards
elseif strcmp('hfp01_1_Avg', headertext{i})==1
soil_heat_flux_1 = data(:,i);
elseif strcmp('hfp01_2_Avg', headertext{i})==1
soil_heat_flux_2 = data(:,i);
elseif strcmp('hfp01_3_Avg', headertext{i})==1
soil_heat_flux_3 = data(:,i);
elseif strcmp('hfp01_4_Avg', headertext{i})==1
soil_heat_flux_4 = data(:,i);
elseif strcmp('hfp01_5_Avg', headertext{i})==1
soil_heat_flux_5 = data(:,i);
elseif strcmp('hfp01_6_Avg', headertext{i})==1
soil_heat_flux_6 = data(:,i);
elseif strcmp('shf_Avg(3)', headertext{i})==1 |...
strcmp('shf_Avg_3', headertext{i})==1
soil_heat_flux_3 = data(:,i);
elseif strcmp('shf_Avg(4)', headertext{i})==1 |...
strcmp('shf_Avg_4', headertext{i})==1
soil_heat_flux_4 = data(:,i);
end
end
% Fix selected atmospheric water content measuremnts
if h2oflag_1
H2O_mean = H2O_mean .* ( 18 * ( 1 ./ ...
( 8.3143e-3 .* ( t_meanK ./ atm_press ) ) ) ./ 1000);
end
% remove absurd precipitation measurements
precip( precip > 1000 ) = NaN;
% In 2009 at GLand and SLand, LiCor PAR sensors (Par_Avg) are scaled to
% Kipp & Zonen sensors (Par_lite). Par_lite favored thereafter.
if ismember( sitecode, [ 1, 2 ] ) & year_arg == 2009
Par_Avg = combine_PARavg_PARlite( headertext, data );
end
% PJ girdle, calculate relative humidity from hmp obs using helper
% function. Not needed starting 1/10/2014
if sitecode == 10
rH = thmp_and_h2ohmp_2_rhhmp( air_temp_hmp, h2o_hmp ) ./ 100.0;
end
% Calculate VPD in millibars (hPa) using Teten's equation - GEM 5/2015
% see here:
% https://en.wikipedia.org/wiki/Clausius-Clapeyron_relation#Meteorology_and_climatology
% or here:
% http://andrewsforest.oregonstate.edu/data/studies/ms01/dewpt_vpd_calculations.pdf
tair_temp = Tdry - 273.15;
rH_100 = rH * 100;
es = 6.1078 .* exp( (17.269 .* tair_temp )./(237.3 + tair_temp) );
vpd = es - ( rH_100 .* es ./ 100 );
% vpd = 6.1078 * (1 - rH) .* exp(17.08085*tair_temp./(234.175+tair_temp));
%es = 0.6108*exp(17.27*air_temp_hmp./(air_temp_hmp+237.3));
%ea = rH .* es ;
%vpd2 = ea - es;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Site-specific steps for soil temperature
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if sitecode == 1 %GLand added TWH, 27 Oct 2011
for i=1:ncol;
if strcmp('TCAV_grass_Avg',headertext(i)) == 1
Tsoil = data(:,i-1);
end
end
% find soil heat flux plate measurements
SHF_idx = find( cellfun( @(x) ~isempty(x), ...
regexp( headertext, 'hfp.*[Aa]vg' ) ) );
if numel( SHF_idx ) ~= 2
%error( 'could not find two soil heat flux observations' );
end
soil_heat_flux = data( :, SHF_idx );
SHF_labels = headertext( SHF_idx );
SHF_labels = regexprep( SHF_labels, 'hfp01_(.*)', 'SHF_$1');
elseif sitecode == 2 %SLand changed TWH, 30 Oct 2012
% regular expression to identify SHF variables
re_SHF = '.*([Ss][Hh][Ff]).*|.*([Hh][Ff][Pp]).*';
SHF_idx = find( ~cellfun( @isempty, regexp( headertext, re_SHF ) ) );
SHF_labels = headertext( SHF_idx );
soil_heat_flux = data( :, SHF_idx );
elseif sitecode == 3 %JSav added TWH, 7 May 2012
SHF_cols = find( ~cellfun( @isempty, regexp( headertext, 'shf_Avg.*' ) ) );
soil_heat_flux = data( :, SHF_cols - 1 );
if isempty( soil_heat_flux )
soil_heat_flux = repmat( NaN, size( data, 1 ), 4 );
soil_heat_flux_1 = soil_heat_flux( :, 1 );
soil_heat_flux_2 = soil_heat_flux( :, 2 );
soil_heat_flux_3 = soil_heat_flux( :, 3 );
soil_heat_flux_4 = soil_heat_flux( :, 4 );
end
SHF_labels = { 'SHF_1', 'SHF_2', 'SHF_3', 'SHF_4' };
elseif sitecode == 4 | sitecode == 14 %PJ/TestSite
for i=1:ncol;
if strcmp('tcav_pinon_1_Avg',headertext(i)) == 1
Tsoil1 = data(:,i-1);
elseif strcmp('tcav_jun_1_Avg',headertext(i)) == 1
Tsoil2 = data(:,i-1);
end
end
if exist( 'Tsoil1' ) == 1 & exist( 'Tsoil2' ) == 1
Tsoil = (Tsoil1 + Tsoil2) ./ 2;
else
Tsoil = repmat( NaN, size( data, 1 ), 1 );
end
soil_heat_flux_1 = repmat( NaN, size( data, 1 ), 1 );
soil_heat_flux_2 = repmat( NaN, size( data, 1 ), 1 );
SHF_labels = { 'soil_heat_flux_1', 'soil_heat_flux_2' };
soil_heat_flux = [ soil_heat_flux_1, soil_heat_flux_2 ];
% related lines 678-682: corrections for site 4 (PJ) soil_heat_flux_1 and soil_heat_flux_2
%Tsoil=sw_incoming .* NaN; %MF: note, this converts all values in Tsoil to NaN. Not sure if this was intended.
elseif ismember( sitecode, [ UNM_sites.PPine, UNM_sites.MCon ] )
SHF_labels = { 'soil_heat_flux_1', 'soil_heat_flux_2', 'soil_heat_flux_3' };
soil_heat_flux = repmat( NaN, size( data, 1 ), 3 );
Tsoil = repmat( NaN, size( data, 1 ), 1 );
elseif ismember( sitecode, [ UNM_sites.MCon_SS ] )
SHF_labels = { 'shf_pit_1_Avg', 'shf_pit_2_Avg', 'shf_pit_3_Avg' };
soil_heat_flux = repmat( NaN, size( data, 1 ), 3 );
Tsoil = repmat( NaN, size( data, 1 ), 1 );
elseif sitecode == 7 % Texas Freeman
for i=1:ncol;
if strcmp('Tsoil_Avg_2',headertext(i)) == 1
open_5cm = data(:,i-1);
elseif strcmp('Tsoil_Avg_3',headertext(i)) == 1
open_10cm = data(:,i-1);
elseif strcmp('Tsoil_Avg_5',headertext(i)) == 1
Mesquite_5cm = data(:,i-1);
elseif strcmp('Tsoil_Avg_6',headertext(i)) == 1
Mesquite_10cm = data(:,i-1);
elseif strcmp('Tsoil_Avg_8',headertext(i)) == 1
Juniper_5cm = data(:,i-1);
elseif strcmp('Tsoil_Avg_9',headertext(i)) == 1
Juniper_10cm = data(:,i-1);
end
end
if year_arg == 2005 % juniper probes on-line after 5/19/05
% before 5/19
canopy_5cm = Mesquite_5cm(find(decimal_day < 139.61));
canopy_10cm = Mesquite_10cm(find(decimal_day < 139.61));
% after 5/19
canopy_5cm(find(decimal_day >= 139.61)) = ...
(Mesquite_5cm(find(decimal_day >= 139.61)) + ...
Juniper_5cm(find(decimal_day >= 139.61))) ./ 2;
canopy_10cm(find(decimal_day >= 139.61)) = ...
(Mesquite_10cm(find(decimal_day >= 139.61)) + ...
Juniper_10cm(find(decimal_day >= 139.61))) ./ 2;
% clean strange 0 values
canopy_5cm(find(canopy_5cm == 0)) = NaN;
canopy_10cm(find(canopy_10cm == 0)) = NaN;
Tsoil = (open_5cm + canopy_5cm) ./ 2;
else
canopy_5cm = (Mesquite_5cm + Juniper_5cm) ./ 2;
canopy_10cm = (Mesquite_10cm + Juniper_10cm) ./ 2;
Tsoil = (open_5cm + canopy_5cm) ./ 2;
end
elseif sitecode == 10 || sitecode == 11
Tsoil=sw_incoming .* NaN;
soil_heat_flux_1 =sw_incoming .* NaN;
soil_heat_flux_2 =sw_incoming .* NaN;
SHF_labels = { 'soil_heat_flux_1', 'soil_heat_flux_2' };
soil_heat_flux = [ soil_heat_flux_1, soil_heat_flux_2 ];
end
% Juniper S heat flux plates need multiplying by calibration factors
if sitecode == 3
soil_heat_flux_1 = soil_heat_flux_1 .* 32.27;
soil_heat_flux_2 = soil_heat_flux_2 .* 33.00;
soil_heat_flux_3 = soil_heat_flux_3 .* 31.60;
soil_heat_flux_4 = soil_heat_flux_4 .* 32.20;
end
% Pinon Juniper heat flux plates need multiplying by calibration factors
if sitecode == 4
soil_heat_flux_1 = soil_heat_flux_1 .* 35.2;
soil_heat_flux_2 = soil_heat_flux_2 .* 32.1;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% data processing and fixing datalogger & instrument errors
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% fix incorrect precipitation values
precip_uncorr= precip;
precip = fix_incorrect_precip_factors( sitecode, year_arg, ...
timestamp, precip );
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Radiation corrections
% RJL added the lw_incoming and lw_outgoing variables to output on 01172014
% Variables lw_incoming, lw_outgoing, NR_sw, NR_lw, and NR_tot are now
% corrected for temperature. New data logger programs starting 2014
% will have lw_incomingCo and lw_outgoingCo corrected variables.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% In 2007 & 2010 the thermocouple in the CNR1 at GLand failed. Use air temp.
if sitecode == 1 && year_arg==2007;
CNR1TempK = CNR1TK;
idx1 = find(decimal_day > 156.71 & decimal_day < 162.52 )
CNR1TempK( idx1 ) = air_temp_hmp( idx1 ) + 273.15;
elseif sitecode == 1 && year_arg==2010;
CNR1TempK = air_temp_hmp + 273.15;
% Same at shrub in 2007
elseif sitecode == 2 && year_arg==2007;
CNR1TempK = CNR1TK;
idx1 = find(decimal_day >= 150.75 & decimal_day < 162.44);
CNR1TempK( idx1 ) = air_temp_hmp( idx1 ) + 273.15;
% In 2007 the thermocouple in the CNR1 at PPine failed for a while in
% the spring. Use air temp.
elseif sitecode == 5 && year_arg==2007;
CNR1TempK = CNR1TK;
idx = DOYidx( 155.5 ):DOYidx( 198.55 );
CNR1TempK( idx ) = air_temp_hmp( idx ) + 273.15;
elseif sitecode == 5 & year_arg==2013;
CNR1TempK = CNR1TK;
idx = DOYidx( 353.18 ):17520;
CNR1TempK( idx ) = air_temp_hmp( idx ) + 273.15;
elseif sitecode == 5 & year_arg==2014;
CNR1TempK = CNR1TK;
idx = [ DOYidx( 1 ):DOYidx( 3.6 ), DOYidx( 83.81 ):DOYidx( 106.36 ) ] ;
CNR1TempK( idx ) = air_temp_hmp( idx ) + 273.15;
% In 2014 the thermocouple in the CNR1 at NewGLand failed for a while in
% the spring. Use air temp.
elseif sitecode == 11 & year_arg==2014;
CNR1TempK = CNR1TK;
idx = DOYidx( 17.645 ):DOYidx( 63.335 );
CNR1TempK( idx ) = air_temp_hmp( idx ) + 273.15;
% In 2013 the thermocouple in the CNR1 at PJ failed for a while in
% the fall. Use air temp.
elseif sitecode == 4 & year_arg==2013;
CNR1TempK = CNR1TK;
idx = DOYidx( 240.53 ):DOYidx( 282.53 );
CNR1TempK( idx ) = air_temp_hmp( idx ) + 273.15;
else
CNR1TempK = CNR1TK;
end
[ sw_incoming, sw_outgoing, ...
lw_incoming, lw_outgoing, Par_Avg ] = ...
UNM_RBD_apply_radiation_calibration_factors( sitecode, ...
year_arg, ...
decimal_day, ...
sw_incoming, ...
sw_outgoing, ...
lw_incoming, ...
lw_outgoing, ...
Par_Avg, ...
NR_tot, ...
wnd_spd, ...
CNR1TempK );
% These next 2 removals were formerly in
% UNM_RBD_apply_radiation_calibration_factors
% Applies to all sites and all years
% remove negative Rg_out values
sw_outgoing( sw_outgoing < -50 ) = NaN;
isnight = ( Par_Avg < 20.0 ) | ( sw_incoming < 20 );
%remove nighttime Rg and RgOut values outside of [ -5, 5 ]
% added 13 May 2013 in response to problems noted by Bai Yang
sw_incoming( isnight & ( abs( sw_incoming ) > 5 ) ) = NaN;
sw_outgoing( isnight & ( abs( sw_outgoing ) > 5 ) ) = NaN;
[ NR_sw, NR_lw, NR_tot ] = ...
UNM_RBD_calculate_net_radiation( sitecode, year_arg, ...
sw_incoming, sw_outgoing, ...
lw_incoming, lw_outgoing, ...
NR_tot, wnd_spd, decimal_day );
% normalize PAR to account for calibration problems at some sites
Par_Avg = normalize_PAR_wrapper( sitecode, year_arg, decimal_day, Par_Avg, ...
draw_plots >= 0 );
% Diagnostic plot for checking corrected radiation data
plot_qc_radiation( sitecode, ...
year_arg, ...
timestamp, ...
sw_incoming, ...
sw_outgoing, ...
lw_incoming, ...
lw_outgoing, ...
Par_Avg, ...
NR_tot );
% Diagnostic plot for checking corrected Met data
plot_qc_meteorology( sitecode, ...
year_arg, ...
timestamp, ...
air_temp_hmp, ...
rH_100, ...
vpd, ...
Tdry, ...
H2O_mean, ...
precip );
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Apply Burba 2008 correction for sensible heat conducted from 7500
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% define some constants
R = 8.3143e-3; % universal gas constant [J / umol / K ]
MWd = 28.97; % dry air molecular weight [g / mol]
Cp = 1004.67; % specific heat capacity of dry air @ constant pres [J / kg / K]
% This factor for conversion from mumol/mol to mg/m3 for CO2
% Essentially works as: ppm CO2 * ( MolecularWt CO2 / Vol of 1mol Air )
hh = 44.01 .* (1 ./ ( R .* ( t_meanK ./ atm_press ) .* 1000 ) );
CO2_mg = CO2_mean .* hh;
% This is the conversion from mmol/mol to g/m3 for H2O
gg = 18 * ( 1 ./ ...
( R .* ( t_meanK ./ atm_press ) ) ) ./ 1000;
% I don't think it is necessary to convert this (see notes above)
H2O_g = H2O_mean;% .* gg;
% Convert dry air density from mol/m3 to kg/m3
rhoa_dry_kg = ( rhoa_dry .* MWd ) ./ 1000; %