cate = c("aircraft-pfp",
"aircraft-insitu",
"aircraft-flask",
"surface-insitu",
"surface-flask",
"surface-pfp",
"tower-insitu",
"aircore",
"shipboard-insitu",
"shipboard-flask")
obs <- "Z:/torf/obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08/data/nc/"
index <- obs_summary(obs = obs,
categories = cate)#> Number of files of index: 479
#> sector N
#> <char> <int>
#> 1: aircraft-pfp 42
#> 2: aircraft-insitu 17
#> 3: surface-flask 106
#> 4: surface-insitu 121
#> 5: aircraft-flask 4
#> 6: aircore 1
#> 7: surface-pfp 33
#> 8: tower-insitu 150
#> 9: shipboard-flask 4
#> 10: shipboard-insitu 1
#> 11: Total sectors 479
#> Detected 203 files with agl
#> Detected 276 files without aglNow we read the aircraft-flask using the function
obs_read_nc. To this date, solar_time is not
included for aircraft, so we FALSE that argument.
datasetid <- "aircraft-flask"
df <- obs_read_nc(index = index,
categories = datasetid,
solar_time = FALSE,
verbose = T)#> Searching aircraft-flask...
#> 1: ch4_aia_aircraft-flask_2_representative.nc
#> 2: ch4_aoa_aircraft-flask_19_allvalid.nc
#> 3: ch4_con_aircraft-flask_42_allvalid.nc
#> 4: ch4_iagos-caribic_aircraft-flask_457_allvalid.ncNow we check the data
df#> year month day hour minute second time start_time
#> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1: 1991 6 24 0 5 0 677721900 677721900
#> 2: 1991 6 24 0 18 0 677722680 677722680
#> 3: 1991 6 24 0 20 0 677722800 677722800
#> 4: 1991 6 24 0 45 0 677724300 677724300
#> 5: 1991 6 24 0 50 0 677724600 677724600
#> ---
#> 16351: 2020 3 4 14 49 4 1583333344 1583333344
#> 16352: 2020 3 4 15 33 4 1583335984 1583335984
#> 16353: 2020 3 4 16 17 2 1583338622 1583338622
#> 16354: 2020 3 4 17 1 8 1583341268 1583341268
#> 16355: 2020 3 4 17 45 7 1583343907 1583343907
#> midpoint_time datetime time_decimal time_interval
#> <int> <char> <num> <int>
#> 1: 677721900 1991-06-24T00:05:00Z 1991.477 3600
#> 2: 677722680 1991-06-24T00:18:00Z 1991.477 3600
#> 3: 677722800 1991-06-24T00:20:00Z 1991.477 3600
#> 4: 677724300 1991-06-24T00:45:00Z 1991.477 3600
#> 5: 677724600 1991-06-24T00:50:00Z 1991.477 3600
#> ---
#> 16351: 1583333344 2020-03-04T14:49:04Z 2020.174 NA
#> 16352: 1583335984 2020-03-04T15:33:04Z 2020.174 NA
#> 16353: 1583338622 2020-03-04T16:17:02Z 2020.174 NA
#> 16354: 1583341268 2020-03-04T17:01:08Z 2020.174 NA
#> 16355: 1583343907 2020-03-04T17:45:07Z 2020.174 NA
#> value nvalue value_std_dev latitude longitude altitude elevation
#> <num> <int> <num> <num> <num> <num> <num>
#> 1: 1.686550e-06 1 NA -38.931 145.152 4270 0
#> 2: 1.684910e-06 1 NA -39.436 145.276 4270 0
#> 3: 1.692130e-06 1 NA -39.404 145.268 4270 0
#> 4: 1.682670e-06 1 NA -40.394 144.721 4270 0
#> 5: 1.683830e-06 1 NA -40.699 144.710 4270 0
#> ---
#> 16351: 1.862239e-06 NA NA 21.430 11.310 12040 NA
#> 16352: 1.861981e-06 NA NA 26.210 9.000 11970 NA
#> 16353: 1.856997e-06 NA NA 31.120 8.940 11870 NA
#> 16354: 1.858964e-06 NA NA 36.020 10.450 12380 NA
#> 16355: 1.858041e-06 NA NA 41.150 11.560 12280 NA
#> qcflag obs_flag obspack_num
#> <char> <int> <int>
#> 1: ... 1 4504993
#> 2: ... 1 4504994
#> 3: ... 1 4504995
#> 4: ... 1 4504996
#> 5: ... 1 4504997
#> ---
#> 16351: <NA> 1 8431291
#> 16352: <NA> 1 8431292
#> 16353: <NA> 1 8431293
#> 16354: <NA> 1 8431294
#> 16355: <NA> 1 8431295
#> obspack_id
#> <char>
#> 1: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aia_aircraft-flask_2_representative~4504993
#> 2: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aia_aircraft-flask_2_representative~4504994
#> 3: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aia_aircraft-flask_2_representative~4504995
#> 4: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aia_aircraft-flask_2_representative~4504996
#> 5: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aia_aircraft-flask_2_representative~4504997
#> ---
#> 16351: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_iagos-caribic_aircraft-flask_457_allvalid~8431291
#> 16352: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_iagos-caribic_aircraft-flask_457_allvalid~8431292
#> 16353: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_iagos-caribic_aircraft-flask_457_allvalid~8431293
#> 16354: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_iagos-caribic_aircraft-flask_457_allvalid~8431294
#> 16355: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_iagos-caribic_aircraft-flask_457_allvalid~8431295
#> unique_sample_location_num scale site_elevation_unit
#> <int> <char> <char>
#> 1: 5959263 WMO CH4 X2004A masl
#> 2: 5959132 WMO CH4 X2004A masl
#> 3: 5959137 WMO CH4 X2004A masl
#> 4: 5958950 WMO CH4 X2004A masl
#> 5: 5957693 WMO CH4 X2004A masl
#> ---
#> 16351: 52172274 WMO CH4 X2004A <NA>
#> 16352: 52172545 WMO CH4 X2004A <NA>
#> 16353: 52172779 WMO CH4 X2004A <NA>
#> 16354: 52173055 WMO CH4 X2004A <NA>
#> 16355: 52173483 WMO CH4 X2004A <NA>
#> dataset_project dataset_selection_tag
#> <char> <char>
#> 1: aircraft-flask representative
#> 2: aircraft-flask representative
#> 3: aircraft-flask representative
#> 4: aircraft-flask representative
#> 5: aircraft-flask representative
#> ---
#> 16351: aircraft-flask allvalid
#> 16352: aircraft-flask allvalid
#> 16353: aircraft-flask allvalid
#> 16354: aircraft-flask allvalid
#> 16355: aircraft-flask allvalid
#> site_name site_elevation
#> <char> <num>
#> 1: Bass Strait/Cape Grim, Tasmania 0e+00
#> 2: Bass Strait/Cape Grim, Tasmania 0e+00
#> 3: Bass Strait/Cape Grim, Tasmania 0e+00
#> 4: Bass Strait/Cape Grim, Tasmania 0e+00
#> 5: Bass Strait/Cape Grim, Tasmania 0e+00
#> ---
#> 16351: In-service Aircraft for a Global Observing System -1e+34
#> 16352: In-service Aircraft for a Global Observing System -1e+34
#> 16353: In-service Aircraft for a Global Observing System -1e+34
#> 16354: In-service Aircraft for a Global Observing System -1e+34
#> 16355: In-service Aircraft for a Global Observing System -1e+34
#> site_latitude site_longitude site_country site_code site_utc2lst
#> <num> <num> <char> <char> <num>
#> 1: -4.053e+01 1.443e+02 Australia AIA 10
#> 2: -4.053e+01 1.443e+02 Australia AIA 10
#> 3: -4.053e+01 1.443e+02 Australia AIA 10
#> 4: -4.053e+01 1.443e+02 Australia AIA 10
#> 5: -4.053e+01 1.443e+02 Australia AIA 10
#> ---
#> 16351: -1.000e+34 -1.000e+34 <NA> IAGOS NA
#> 16352: -1.000e+34 -1.000e+34 <NA> IAGOS NA
#> 16353: -1.000e+34 -1.000e+34 <NA> IAGOS NA
#> 16354: -1.000e+34 -1.000e+34 <NA> IAGOS NA
#> 16355: -1.000e+34 -1.000e+34 <NA> IAGOS NA
#> lab_1_abbr dataset_calibration_scale altitude_final type_altitude
#> <char> <char> <num> <num>
#> 1: CSIRO WMO CH4 X2004A 4270 1
#> 2: CSIRO WMO CH4 X2004A 4270 1
#> 3: CSIRO WMO CH4 X2004A 4270 1
#> 4: CSIRO WMO CH4 X2004A 4270 1
#> 5: CSIRO WMO CH4 X2004A 4270 1
#> ---
#> 16351: KIT/IMK-ASF WMO CH4 X2004A 12040 1
#> 16352: KIT/IMK-ASF WMO CH4 X2004A 11970 1
#> 16353: KIT/IMK-ASF WMO CH4 X2004A 11870 1
#> 16354: KIT/IMK-ASF WMO CH4 X2004A 12380 1
#> 16355: KIT/IMK-ASF WMO CH4 X2004A 12280 1
#> instrument intake_height method pressure
#> <char> <num> <char> <num>
#> 1: <NA> NA <NA> NA
#> 2: <NA> NA <NA> NA
#> 3: <NA> NA <NA> NA
#> 4: <NA> NA <NA> NA
#> 5: <NA> NA <NA> NA
#> ---
#> 16351: <NA> NA <NA> 205.08
#> 16352: <NA> NA <NA> 205.06
#> 16353: <NA> NA <NA> 205.07
#> 16354: <NA> NA <NA> 185.72
#> 16355: <NA> NA <NA> 185.68
#> source_id value_unc
#> <char> <num>
#> 1: <NA> NA
#> 2: <NA> NA
#> 3: <NA> NA
#> 4: <NA> NA
#> 5: <NA> NA
#> ---
#> 16351: GHG_20200304_591_CPT_MUC_V02.txt~83 8.846e-10
#> 16352: GHG_20200304_591_CPT_MUC_V02.txt~84 8.844e-10
#> 16353: GHG_20200304_591_CPT_MUC_V02.txt~85 8.821e-10
#> 16354: GHG_20200304_591_CPT_MUC_V02.txt~86 8.830e-10
#> 16355: GHG_20200304_591_CPT_MUC_V02.txt~87 8.826e-10Now we can process the data. We first filter for observations within our spatial domain:
Checks and definitions
north <- 80
south <- 10
west <- -170
east <- -50
max_altitude <- 8000
yy <- 2022We check altitude, intake_height, altitude_final and elevation. altitude_final is a column from intake_height, added to match column from obs_read text files.
df[, c("altitude", "altitude_final", "intake_height", "elevation")]#> altitude altitude_final intake_height elevation
#> <num> <num> <num> <num>
#> 1: 4270 4270 NA 0
#> 2: 4270 4270 NA 0
#> 3: 4270 4270 NA 0
#> 4: 4270 4270 NA 0
#> 5: 4270 4270 NA 0
#> ---
#> 16351: 12040 12040 NA NA
#> 16352: 11970 11970 NA NA
#> 16353: 11870 11870 NA NA
#> 16354: 12380 12380 NA NA
#> 16355: 12280 12280 NA NAThe temporal range of data is
range(df$year)#> [1] 1991 2022We also check for dimensions of data
dim(df)#> [1] 16355 45Filters
df <- df[year == yy]
# df <- df[altitude_final < max_altitude &
# latitude < north &
# latitude > south &
# longitude < east &
# longitude > west]
dim(df)Adding time as POSIXct class
df <- obs_addtime(df)
df[, "timeUTC"]#> timeUTC
#> <POSc>
#> 1: 2020-01-22 00:01:30
#> 2: 2020-01-22 00:09:30
#> 3: 2020-01-22 00:19:30
#> 4: 2020-01-22 00:30:30
#> 5: 2020-01-22 00:39:30
#> ---
#> 413: 2020-03-04 14:49:04
#> 414: 2020-03-04 15:33:04
#> 415: 2020-03-04 16:17:02
#> 416: 2020-03-04 17:01:08
#> 417: 2020-03-04 17:45:07Cut time
now we can cut time every 20 seconds. We can chosse other frequency as well.
#> second sec2
#> <int> <num>
#> 1: 30 20
#> 2: 30 20
#> 3: 30 20
#> 4: 30 20
#> 5: 30 20
#> ---
#> 413: 4 0
#> 414: 4 0
#> 415: 2 0
#> 416: 8 0
#> 417: 7 0Aggregate data
now we need to add the column key_time, that it will be used to aggregate other variables
df$key_time <- ISOdatetime(year = df$year,
month = df$month,
day = df$day,
hour = df$hour,
min = df$minute,
sec = df$sec2,
tz = "UTC")
df[, c("timeUTC", "key_time")]#> timeUTC key_time
#> <POSc> <POSc>
#> 1: 2020-01-22 00:01:30 2020-01-22 00:01:20
#> 2: 2020-01-22 00:09:30 2020-01-22 00:09:20
#> 3: 2020-01-22 00:19:30 2020-01-22 00:19:20
#> 4: 2020-01-22 00:30:30 2020-01-22 00:30:20
#> 5: 2020-01-22 00:39:30 2020-01-22 00:39:20
#> ---
#> 413: 2020-03-04 14:49:04 2020-03-04 14:49:00
#> 414: 2020-03-04 15:33:04 2020-03-04 15:33:00
#> 415: 2020-03-04 16:17:02 2020-03-04 16:17:00
#> 416: 2020-03-04 17:01:08 2020-03-04 17:01:00
#> 417: 2020-03-04 17:45:07 2020-03-04 17:45:00Add local time
Now we add local time
df3 <- obs_addltime(df2)
setorderv(df3, cols = c("site_code", "timeUTC"),
order = c(-1, 1))
df3#> timeUTC site_code lab_1_abbr dataset_calibration_scale year
#> <POSc> <char> <char> <char> <int>
#> 1: 2020-01-09 02:14:20 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 2: 2020-01-09 02:58:20 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 3: 2020-01-09 03:42:20 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 4: 2020-01-09 04:26:20 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 5: 2020-01-09 05:10:20 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> ---
#> 413: 2020-12-10 04:48:20 AOA JMA WMO CH4 X2004A 2020
#> 414: 2020-12-10 05:01:20 AOA JMA WMO CH4 X2004A 2020
#> 415: 2020-12-10 05:11:20 AOA JMA WMO CH4 X2004A 2020
#> 416: 2020-12-10 05:23:20 AOA JMA WMO CH4 X2004A 2020
#> 417: 2020-12-10 05:34:20 AOA JMA WMO CH4 X2004A 2020
#> month day hour minute second time time_decimal value
#> <int> <char> <int> <int> <int> <num> <num> <num>
#> 1: 1 09 2 14 20 1578536060 2020.022 1.897204e-06
#> 2: 1 09 2 58 20 1578538700 2020.022 1.879403e-06
#> 3: 1 09 3 42 20 1578541340 2020.022 1.874217e-06
#> 4: 1 09 4 26 20 1578543980 2020.022 1.900391e-06
#> 5: 1 09 5 10 20 1578546620 2020.022 1.878125e-06
#> ---
#> 413: 12 10 4 48 20 1607575700 2020.940 1.918800e-06
#> 414: 12 10 5 1 20 1607576480 2020.940 1.901700e-06
#> 415: 12 10 5 11 20 1607577080 2020.940 1.916000e-06
#> 416: 12 10 5 23 20 1607577800 2020.941 1.926800e-06
#> 417: 12 10 5 34 20 1607578460 2020.941 1.929900e-06
#> latitude longitude altitude_final elevation intake_height type_altitude
#> <num> <num> <num> <num> <num> <num>
#> 1: 35.150 -116.260 10340 NaN NaN 1
#> 2: 38.900 -110.190 10550 NaN NaN 1
#> 3: 43.410 -104.470 10460 NaN NaN 1
#> 4: 47.810 -97.360 10400 NaN NaN 1
#> 5: 51.860 -89.110 10260 NaN NaN 1
#> ---
#> 413: 30.093 139.568 7000 -999.999 -999.999 1
#> 414: 31.069 139.712 7600 -999.999 -999.999 1
#> 415: 31.870 139.856 7600 -999.999 -999.999 1
#> 416: 32.814 139.976 7500 -999.999 -999.999 1
#> 417: 33.746 140.312 5100 -999.999 -999.999 1
#> local_time lh
#> <POSc> <int>
#> 1: 2020-01-08 18:29:17 18
#> 2: 2020-01-08 19:37:34 19
#> 3: 2020-01-08 20:44:27 20
#> 4: 2020-01-08 21:56:53 21
#> 5: 2020-01-08 23:13:53 23
#> ---
#> 413: 2020-12-10 14:06:36 14
#> 414: 2020-12-10 14:20:10 14
#> 415: 2020-12-10 14:30:45 14
#> 416: 2020-12-10 14:43:14 14
#> 417: 2020-12-10 14:55:34 14Master
Before generating the receptors list, we have the databe with all the required information
master <- df3We may replace missing values with a nine nines. Here is commented
#master[is.na(master)] <- 999999999
We transform the time variables to character and round coordinates with 4 digits
master$timeUTC <- as.character(master$timeUTC)
master$local_time <- as.character(master$local_time)
master$latitude <- round(master$latitude, 4)
master$longitude <- round(master$longitude, 4)Save master
Finally we save the master file
out <- tempfile()txt
message(paste0(out,"_", datasetid, ".txt\n"))
fwrite(master,
paste0(out,"_", datasetid, ".txt"),
sep = " ")#> C:\Users\sibarrae\AppData\Local\Temp\RtmpIP4sJV\file71b065447a30_aircraft-flask.txtcsv
message(paste0(out,"_", datasetid, ".csv\n"))
fwrite(master,
paste0(out,"_", datasetid, ".csv"),
sep = ",")#> C:\Users\sibarrae\AppData\Local\Temp\RtmpIP4sJV\file71b065447a30_aircraft-flask.csvcsvy
CSVY are csv files with a YAML header to include metadata in tabulated text files
cat("\nAdding notes in csvy:\n")
notes <- c(paste0("sector: ", datasetid),
paste0("timespan: ", yy),
paste0("spatial_limits: north = ", north, ", south = ", south, ", east = ", east, ", west = ", west),
"data: Data averaged every 20 seconds",
paste0("altitude: < ", max_altitude),
"hours: All",
"local_time: if var `site_utc2lst` is not available, calculated as",
"longitude/15*60*60 (John Miller)")
cat(notes, sep = "\n")
message(paste0(out,"_", datasetid, ".csvy\n"))
obs_write_csvy(dt = master,
notes = notes,
out = paste0(out,"_", datasetid, ".csvy"))#> sector: aircraft-flask
#> timespan: 2020
#> spatial_limits: north = 80, south = 10, east = -50, west = -170
#> data: Data averaged every 20 seconds
#> altitude: < 8000
#> hours: All
#> local_time: if var `site_utc2lst` is not available, calculated as
#> longitude/15*60*60 (John Miller)
#> C:\Users\sibarrae\AppData\Local\Temp\RtmpIP4sJV\file71b065447a30_aircraft-flask.csvy
obs_read_csvy(paste0(out,"_", datasetid, ".csvy"))#> [1] "---"
#> [2] "name: Metadata "
#> [3] "sector: aircraft-flask"
#> [4] "timespan: 2020"
#> [5] "spatial_limits: north = 80, south = 10, east = -50, west = -170"
#> [6] "data: Data averaged every 20 seconds"
#> [7] "altitude: < 8000"
#> [8] "hours: All"
#> [9] "local_time: if var `site_utc2lst` is not available, calculated as"
#> [10] "longitude/15*60*60 (John Miller)"
#> [11] "structure: "
#> [12] "Classes 'data.table' and 'data.frame':\t417 obs. of 21 variables:"
#> [13] " $ timeUTC : chr \"2020-01-09 02:14:20\" \".."
#> [14] " $ site_code : chr \"IAGOS\" \"IAGOS\" ..."
#> [15] " $ lab_1_abbr : chr \"KIT/IMK-ASF\" \"KIT/IMK\".."
#> [16] " $ dataset_calibration_scale: chr \"WMO CH4 X2004A\" \"WMO \".."
#> [17] " $ year : int 2020 2020 2020 2020 202.."
#> [18] " $ month : int 1 1 1 1 1 ..."
#> [19] " $ day : chr \"09\" \"09\" ..."
#> [20] " $ hour : int 2 2 3 4 5 ..."
#> [21] " $ minute : int 14 58 42 26 10 ..."
#> [22] " $ second : int 20 20 20 20 20 ..."
#> [23] " $ time : num 1.58e+09 1.58e+09 ..."
#> [24] " $ time_decimal : num 2020 2020 ..."
#> [25] " $ value : num 1.90e-06 1.88e-06 ..."
#> [26] " $ latitude : num 35.1 38.9 ..."
#> [27] " $ longitude : num -116 -110 ..."
#> [28] " $ altitude_final : num 10340 10550 ..."
#> [29] " $ elevation : num NaN NaN NaN NaN NaN ..."
#> [30] " $ intake_height : num NaN NaN NaN NaN NaN ..."
#> [31] " $ type_altitude : num 1 1 1 1 1 ..."
#> [32] " $ local_time : chr \"2020-01-08 18:29:17.5\".."
#> [33] " $ lh : int 18 19 20 21 23 ..."
#> [34] " - attr(*, \".internal.selfref\")=<externalptr> "
#> [35] "NULL"
#> [36] "---"
#> timeUTC site_code lab_1_abbr dataset_calibration_scale year
#> <POSc> <char> <char> <char> <int>
#> 1: 2020-01-09 02:14:20 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 2: 2020-01-09 02:58:20 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 3: 2020-01-09 03:42:20 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 4: 2020-01-09 04:26:20 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 5: 2020-01-09 05:10:20 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> ---
#> 413: 2020-12-10 04:48:20 AOA JMA WMO CH4 X2004A 2020
#> 414: 2020-12-10 05:01:20 AOA JMA WMO CH4 X2004A 2020
#> 415: 2020-12-10 05:11:20 AOA JMA WMO CH4 X2004A 2020
#> 416: 2020-12-10 05:23:20 AOA JMA WMO CH4 X2004A 2020
#> 417: 2020-12-10 05:34:20 AOA JMA WMO CH4 X2004A 2020
#> month day hour minute second time time_decimal value
#> <int> <int> <int> <int> <int> <int> <num> <num>
#> 1: 1 9 2 14 20 1578536060 2020.022 1.897204e-06
#> 2: 1 9 2 58 20 1578538700 2020.022 1.879403e-06
#> 3: 1 9 3 42 20 1578541340 2020.022 1.874217e-06
#> 4: 1 9 4 26 20 1578543980 2020.022 1.900391e-06
#> 5: 1 9 5 10 20 1578546620 2020.022 1.878125e-06
#> ---
#> 413: 12 10 4 48 20 1607575700 2020.940 1.918800e-06
#> 414: 12 10 5 1 20 1607576480 2020.940 1.901700e-06
#> 415: 12 10 5 11 20 1607577080 2020.940 1.916000e-06
#> 416: 12 10 5 23 20 1607577800 2020.941 1.926800e-06
#> 417: 12 10 5 34 20 1607578460 2020.941 1.929900e-06
#> latitude longitude altitude_final elevation intake_height type_altitude
#> <num> <num> <int> <num> <num> <int>
#> 1: 35.150 -116.260 10340 NA NA 1
#> 2: 38.900 -110.190 10550 NA NA 1
#> 3: 43.410 -104.470 10460 NA NA 1
#> 4: 47.810 -97.360 10400 NA NA 1
#> 5: 51.860 -89.110 10260 NA NA 1
#> ---
#> 413: 30.093 139.568 7000 -999.999 -999.999 1
#> 414: 31.069 139.712 7600 -999.999 -999.999 1
#> 415: 31.870 139.856 7600 -999.999 -999.999 1
#> 416: 32.814 139.976 7500 -999.999 -999.999 1
#> 417: 33.746 140.312 5100 -999.999 -999.999 1
#> local_time lh
#> <POSc> <int>
#> 1: 2020-01-08 18:29:17 18
#> 2: 2020-01-08 19:37:34 19
#> 3: 2020-01-08 20:44:27 20
#> 4: 2020-01-08 21:56:53 21
#> 5: 2020-01-08 23:13:53 23
#> ---
#> 413: 2020-12-10 14:06:36 14
#> 414: 2020-12-10 14:20:10 14
#> 415: 2020-12-10 14:30:45 14
#> 416: 2020-12-10 14:43:14 14
#> 417: 2020-12-10 14:55:34 14Receptors
Now we can do the last step which is generating the receptor list files. Now we filter selected columns
receptor <- master[, c("site_code",
"year",
"month",
"day",
"hour",
"minute",
"second",
"latitude",
"longitude",
"altitude_final",
"type_altitude")]We can round altitude also
receptor$altitude_final <- round(receptor$altitude_final)Now we can format time variables with two digits
receptor <- obs_format(receptor,
spf = c("month", "day",
"hour", "minute", "second"))We have a column that indicate AGL or ASL
receptor_agl <- receptor[type_altitude == 0]
receptor_asl <- receptor[type_altitude == 1]Finally, we save the receptors
if(nrow(receptor_agl) > 0) {
message(paste0(out, "_", datasetid, "_receptor_AGL.txt"), "\n")
fwrite(x = receptor_agl,
file = paste0(out, "_", datasetid, "_receptor_AGL.txt"),
sep = " ")
}
if(nrow(receptor_asl) > 0) {
message(paste0(out, "_", datasetid, "_receptor_ASL.txt"), "\n")
fwrite(x = receptor_asl,
file = paste0(out, "_", datasetid, "receptor_ASL.txt"),
sep = " ")
}#> C:\Users\sibarrae\AppData\Local\Temp\RtmpIP4sJV\file71b065447a30_aircraft-flask_receptor_ASL.txtPlot
Finally, we just plot some data, run it locally
obs_plot(df3, time = "timeUTC", yfactor = 1e9)#> Found the following sites:
#> [1] IAGOS CON AOA
#> Plotting the following sites:
#> [1] IAGOS CON
#> png
#> 2
Times Series
library(sf)
x <- st_as_sf(df3, coords = c("longitude", "latitude"), crs = 4326)
plot(x["value"], axes = T, reset = F)
maps::map(add = T)
Map
x <- df3
x$ch4 <- x$value*1e+9
obs_plot(x,
time = "ch4",
y = "altitude_final",
colu = "month", #n = c(1L, 3L, 6L, 8L, 9L, 11L, 12L),
type = "b",
xlab = expression(CH[4]~ppb),
ylab = "altitude (m)")
Vertical profiles