-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathextract_O2_netCDF.m
More file actions
223 lines (173 loc) · 6.97 KB
/
Copy pathextract_O2_netCDF.m
File metadata and controls
223 lines (173 loc) · 6.97 KB
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
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
%% Import NetCDF data
ncid = netcdf.open('wv_dial05.181020.Python.nc');
% Read the number of dimensions and number of variables in the file
[numdims, numvars, numglobalatts, unlimdimID] = netcdf.inq(ncid);
% Read in information about each dimension
dim_names = {};
dim_sz = [];
data = [];
for i = 0:numdims-1
[dimname, dimlen] = netcdf.inqDim(ncid,i);
dim_names{i+1} = dimname;
dim_sz = [dim_sz, dimlen];
end
% Read variables
var_names = {};
var_types = [];
var_dims = {};
var_natts = [];
var_data = {};
for i =0:numvars-1
% Inquire about variables
[varname,xtype,dimids,natts] = netcdf.inqVar(ncid,i);
var_names{i+1} = varname;
var_types = [var_types, xtype];
var_dims{i+1} = dimids;
var_natts = [var_natts, natts];
% Read in data
count = dim_sz(dimids+1);
start = 0 * count;
data = netcdf.getVar(ncid,i,start,count);
var_data{i+1} = data;
end
% ----- Time and Range profiles -----
% Set maximum range [m] to use.
r_max = 6000;
% Temperature Time and Range profiles
t_name = dim_names{30};
t_ind = find(strcmp(var_names, t_name));
t_T = double(var_data{t_ind}); % Time [sec]
t_hr_T = t_T./3600; % Time [hr]
r_name = dim_names{31};
r_index = find(strcmp(var_names, r_name));
r_T = double(var_data{r_index});
ind_r_T = r_T <= r_max;
r_T = r_T(ind_r_T); % Range up to r_max [m]
% Pressure Time and Range profiles
t_name = dim_names{32};
t_ind = find(strcmp(var_names, t_name));
t_P = double(var_data{t_ind}); % Time [sec]
t_hr_P = t_P./3600; % Time [hr]
r_name = dim_names{33};
r_index = find(strcmp(var_names, r_name));
r_P = double(var_data{r_index});
ind_r_P = r_P <= r_max;
r_P = r_P(ind_r_P); % Range up to r_max [m]
% Online O2
t_name = dim_names{19};
t_ind = find(strcmp(var_names, t_name));
t_o2on = double(var_data{t_ind});%#ok<*FNDSB> % Time [sec]
t_hr_o2on = t_o2on./3600; % Time [hr]
r_name = dim_names{20};
r_index = find(strcmp(var_names, r_name));
r_o2on = double(var_data{r_index});
ind_r_o2on = r_o2on >= r_T(2) & r_o2on<= r_max;
r_o2on = r_o2on(ind_r_o2on); % Range from 2nd element of r_T up to r_max
% Offline O2 (same as online)
t_name = dim_names{22};
t_ind = find(strcmp(var_names, t_name));
t_o2off = double(var_data{t_ind});%#ok<*FNDSB> % Time [sec]
t_hr_o2off = t_o2off./3600; % Time [hr]
r_name = dim_names{23};
r_index = find(strcmp(var_names, r_name));
r_o2off = double(var_data{r_index});
ind_r_o2off = r_o2off >= r_T(2) & r_o2off<= r_max;
r_o2off = r_o2off(ind_r_o2off); % Range from 2nd element of r_T up to r_max
% Absolute Humidity Time and Range profiles
t_name = dim_names{24};
t_ind = find(strcmp(var_names, t_name));
t_wv = double(var_data{t_ind}); % Time [sec]
t_hr_wv = t_wv./3600; % Time [hr]
r_name = dim_names{25};
r_index = find(strcmp(var_names, r_name));
r_wv = double(var_data{r_index});
ind_r_wv = r_wv >= r_T(2) & r_wv<= r_max;
r_wv = r_wv(ind_r_wv); % Range from 2nd element of r_T up to r_max
% Denoised Aerosol Backscatter Coefficient Time and Range profiles
t_name = dim_names{38};
t_ind = find(strcmp(var_names, t_name));
t_ABC = double(var_data{t_ind}); % Time [sec]
t_hr_ABC = t_ABC./3600; % Time [hr]
r_name = dim_names{39};
r_index = find(strcmp(var_names, r_name));
r_ABC = double(var_data{r_index});
ind_r_ABC = r_ABC <= r_max;
r_ABC = r_ABC(ind_r_ABC); % Range up to r_max [m]
% Denoised Backscatter Ratio Time and Range profiles
t_name = dim_names{42};
t_ind = find(strcmp(var_names, t_name));
t_BR = double(var_data{t_ind}); % Time [sec]
t_hr_BR = t_BR./3600; % Time [hr]
r_name = dim_names{43};
r_index = find(strcmp(var_names, r_name));
r_BR = double(var_data{r_index});
ind_r_BR = r_BR <= r_max;
r_BR = r_BR(ind_r_BR); % Range up to r_max [m]
%% ----- Read in Temperature and Pressure profiles -----
T_name = 'Temperature';
T_index = find(strcmp(var_names, T_name));
T_data = var_data{T_index};
T_dims = var_dims{T_index};
T = double(T_data);
T = T(ind_r_T,:); % Temperature [K]
P_name = 'Pressure';
P_index = find(strcmp(var_names, P_name));
P_data = var_data{P_index};
P_dims = var_dims{P_index};
P = double(P_data);
P = P(ind_r_P,:); % Pressure [atm]
%% ----- O2 Backscatter Channel Raw Data -----------------
% Online
o2on_name = 'O2_Online_Backscatter_Channel_Raw_Data';
o2on_index = find(strcmp(var_names, o2on_name));
o2on_data = var_data{o2on_index};
o2on_dims = var_dims{o2on_index};
o2on = double(o2on_data);
o2on = o2on(ind_r_o2on,:); % Online return signal
% Offline
o2off_name = 'O2_Offline_Backscatter_Channel_Raw_Data';
o2off_index = find(strcmp(var_names, o2off_name));
o2off_data = var_data{o2off_index};
o2off_dims = var_dims{o2off_index};
o2off = double(o2off_data);
o2off = o2off(ind_r_o2off,:); % Offline return signal
%% ----- Water Vapor Number Density --------------------------
wv_name = 'Absolute_Humidity';
wv_index = find(strcmp(var_names, wv_name));
wv_data = var_data{wv_index};
wv_dims = var_dims{wv_index};
wvm_name = 'Absolute_Humidity_mask';
wvm_index = find(strcmp(var_names, wvm_name));
wvm_data = var_data{wvm_index};
wvm_dims = var_dims{wvm_index};
wvm_data = double(wvm_data);
wv_profile = wv_data.*abs((1-wvm_data)); % Apply mask
wv_profile(wv_profile<0) = 0;
wv_profile = double(wv_profile);
wv_profile = wv_profile(ind_r_wv,:); % WV profile [g/m^3]
m_h2o = 18.015/6.022E23; % Mass of H2O molecule [g]
wv_num = wv_profile./m_h2o; % WV number density [#/m^3]
%% ----- Denoised Aerosol Backscatter Coefficient ---------------------
dabc_name = 'Denoised_Aerosol_Backscatter_Coefficient';
dabc_index = find(strcmp(var_names, dabc_name));
dabc_data = var_data{dabc_index};
dabc_dims = var_dims{dabc_index};
dabcm_name = 'Denoised_Aerosol_Backscatter_Coefficient_mask';
dabcm_index = find(strcmp(var_names, dabcm_name));
dabcm_data = var_data{dabcm_index};
dabcm_dims = var_dims{dabcm_index};
dabcm_data = double(dabcm_data);
betaa = double(dabc_data.*(1-dabcm_data)); % Apply mask
betaa = betaa(ind_r_ABC,:); % Aerosol Backscatter Coefficient
%% ----- Denoised Backscatter Ratio ----------------------------
dbr_name = 'Denoised_Backscatter_Ratio';
dbr_index = find(strcmp(var_names, dbr_name));
dbr_data = var_data{dbr_index};
dbr_dims = var_dims{dbr_index};
dbrm_name = 'Denoised_Aerosol_Backscatter_Coefficient_mask';
dbrm_index = find(strcmp(var_names, dbrm_name));
dbrm_data = var_data{dbrm_index};
dbrm_dims = var_dims{dbrm_index};
dbrm_data = double(dbrm_data);
br = double(dbr_data.*(1-dbrm_data)); % Apply mask
br = br(ind_r_BR,:); % Backscatter Ratio