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 aircore using the function
obs_read_nc. To this date, solar_time is not
included for aircraft, so we FALSE that argument.
datasetid <- "aircore"
df <- obs_read_nc(index = index,
categories = datasetid,
solar_time = FALSE,
verbose = T)#> Searching aircore...
#> 1: ch4_aircorenoaa_aircore_1_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: 2012 1 14 20 16 52 1326572212 1326572212
#> 2: 2012 1 14 20 16 54 1326572214 1326572214
#> 3: 2012 1 14 20 16 57 1326572217 1326572217
#> 4: 2012 1 14 20 16 59 1326572219 1326572219
#> 5: 2012 1 14 20 17 1 1326572221 1326572221
#> ---
#> 72003: 2021 7 28 18 48 8 1627498088 1627498088
#> 72004: 2021 7 28 18 48 10 1627498090 1627498090
#> 72005: 2021 7 28 18 48 11 1627498091 1627498091
#> 72006: 2021 7 28 18 48 14 1627498094 1627498094
#> 72007: 2021 7 28 18 48 17 1627498097 1627498097
#> midpoint_time datetime time_decimal time_interval
#> <int> <char> <num> <int>
#> 1: 1326572212 2012-01-14T20:16:52.500000Z 2012.038 1
#> 2: 1326572214 2012-01-14T20:16:54.500000Z 2012.038 1
#> 3: 1326572217 2012-01-14T20:16:57.500000Z 2012.038 1
#> 4: 1326572219 2012-01-14T20:16:59.500000Z 2012.038 1
#> 5: 1326572221 2012-01-14T20:17:01.500000Z 2012.038 1
#> ---
#> 72003: 1627498088 2021-07-28T18:48:08.500000Z 2021.572 1
#> 72004: 1627498090 2021-07-28T18:48:10.500000Z 2021.572 1
#> 72005: 1627498091 2021-07-28T18:48:11.500000Z 2021.572 1
#> 72006: 1627498094 2021-07-28T18:48:14.500000Z 2021.572 1
#> 72007: 1627498097 2021-07-28T18:48:17.500000Z 2021.572 1
#> value value_unc latitude longitude altitude pressure temperature
#> <num> <num> <num> <num> <num> <num> <num>
#> 1: 1.28550e-06 1.88e-09 36.85327 -98.20967 21685.83 42.22 NA
#> 2: 1.28640e-06 1.88e-09 36.85339 -98.20983 21626.17 42.63 NA
#> 3: 1.28762e-06 1.87e-09 36.85342 -98.21008 21565.31 43.03 NA
#> 4: 1.28904e-06 1.87e-09 36.85336 -98.21020 21508.82 43.43 NA
#> 5: 1.29071e-06 1.87e-09 36.85347 -98.21030 21453.79 43.81 NA
#> ---
#> 72003: 1.92660e-06 7.00e-10 39.95629 -104.16959 1801.49 820.30 303.02
#> 72004: 1.92784e-06 7.00e-10 39.95627 -104.16962 1787.17 828.84 303.20
#> 72005: 1.92688e-06 7.00e-10 39.95625 -104.16962 1776.80 822.42 303.32
#> 72006: 1.92769e-06 7.00e-10 39.95626 -104.16955 1757.47 824.24 303.39
#> 72007: 1.92808e-06 7.00e-10 39.95625 -104.16945 1735.74 826.09 303.66
#> source_id profile_id flight_id obs_flag obspack_num
#> <char> <char> <char> <int> <int>
#> 1: NOAA AirCore ACBIG002_AC ACBIG002 1 1174889
#> 2: NOAA AirCore ACBIG002_AC ACBIG002 1 1174890
#> 3: NOAA AirCore ACBIG002_AC ACBIG002 1 1174891
#> 4: NOAA AirCore ACBIG002_AC ACBIG002 1 1174892
#> 5: NOAA AirCore ACBIG002_AC ACBIG002 1 1174893
#> ---
#> 72003: NOAA AirCore ACGMD009_AC ACGMD009 1 1246891
#> 72004: NOAA AirCore ACGMD008_AC ACGMD008 1 1246892
#> 72005: NOAA AirCore ACGMD009_AC ACGMD009 1 1246893
#> 72006: NOAA AirCore ACGMD009_AC ACGMD009 1 1246894
#> 72007: NOAA AirCore ACGMD009_AC ACGMD009 1 1246895
#> obspack_id
#> <char>
#> 1: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aircorenoaa_aircore_1_allvalid~1174889
#> 2: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aircorenoaa_aircore_1_allvalid~1174890
#> 3: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aircorenoaa_aircore_1_allvalid~1174891
#> 4: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aircorenoaa_aircore_1_allvalid~1174892
#> 5: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aircorenoaa_aircore_1_allvalid~1174893
#> ---
#> 72003: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aircorenoaa_aircore_1_allvalid~1246891
#> 72004: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aircorenoaa_aircore_1_allvalid~1246892
#> 72005: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aircorenoaa_aircore_1_allvalid~1246893
#> 72006: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aircorenoaa_aircore_1_allvalid~1246894
#> 72007: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aircorenoaa_aircore_1_allvalid~1246895
#> unique_sample_location_num scale dataset_project
#> <int> <char> <char>
#> 1: 24769354 WMO CH4 X2004 aircore
#> 2: 24769358 WMO CH4 X2004 aircore
#> 3: 24769357 WMO CH4 X2004 aircore
#> 4: 24769356 WMO CH4 X2004 aircore
#> 5: 24769364 WMO CH4 X2004 aircore
#> ---
#> 72003: 46264921 WMO CH4 X2004 aircore
#> 72004: 46264920 WMO CH4 X2004 aircore
#> 72005: 46264901 WMO CH4 X2004 aircore
#> 72006: 46264919 WMO CH4 X2004 aircore
#> 72007: 46264902 WMO CH4 X2004 aircore
#> dataset_selection_tag site_name site_elevation site_latitude
#> <char> <char> <num> <num>
#> 1: allvalid NOAA AirCore -1e+34 -1e+34
#> 2: allvalid NOAA AirCore -1e+34 -1e+34
#> 3: allvalid NOAA AirCore -1e+34 -1e+34
#> 4: allvalid NOAA AirCore -1e+34 -1e+34
#> 5: allvalid NOAA AirCore -1e+34 -1e+34
#> ---
#> 72003: allvalid NOAA AirCore -1e+34 -1e+34
#> 72004: allvalid NOAA AirCore -1e+34 -1e+34
#> 72005: allvalid NOAA AirCore -1e+34 -1e+34
#> 72006: allvalid NOAA AirCore -1e+34 -1e+34
#> 72007: allvalid NOAA AirCore -1e+34 -1e+34
#> site_longitude site_country site_code lab_1_abbr
#> <num> <char> <char> <char>
#> 1: -1e+34 United States AirCoreNOAA NOAA
#> 2: -1e+34 United States AirCoreNOAA NOAA
#> 3: -1e+34 United States AirCoreNOAA NOAA
#> 4: -1e+34 United States AirCoreNOAA NOAA
#> 5: -1e+34 United States AirCoreNOAA NOAA
#> ---
#> 72003: -1e+34 United States AirCoreNOAA NOAA
#> 72004: -1e+34 United States AirCoreNOAA NOAA
#> 72005: -1e+34 United States AirCoreNOAA NOAA
#> 72006: -1e+34 United States AirCoreNOAA NOAA
#> 72007: -1e+34 United States AirCoreNOAA NOAA
#> dataset_calibration_scale altitude_final type_altitude
#> <char> <num> <num>
#> 1: WMO CH4 X2004 21685.83 1
#> 2: WMO CH4 X2004 21626.17 1
#> 3: WMO CH4 X2004 21565.31 1
#> 4: WMO CH4 X2004 21508.82 1
#> 5: WMO CH4 X2004 21453.79 1
#> ---
#> 72003: WMO CH4 X2004 1801.49 1
#> 72004: WMO CH4 X2004 1787.17 1
#> 72005: WMO CH4 X2004 1776.80 1
#> 72006: WMO CH4 X2004 1757.47 1
#> 72007: WMO CH4 X2004 1735.74 1Now 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 <- 2020We 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")]#> altitude altitude_final
#> <num> <num>
#> 1: 21685.83 21685.83
#> 2: 21626.17 21626.17
#> 3: 21565.31 21565.31
#> 4: 21508.82 21508.82
#> 5: 21453.79 21453.79
#> ---
#> 72003: 1801.49 1801.49
#> 72004: 1787.17 1787.17
#> 72005: 1776.80 1776.80
#> 72006: 1757.47 1757.47
#> 72007: 1735.74 1735.74The temporal range of data is
range(df$year)#> [1] 2012 2021We also check for dimensions of data
dim(df)#> [1] 72007 39Filters
df <- df[year == yy]
df <- df[altitude_final < max_altitude &
latitude < north &
latitude > south &
longitude < east &
longitude > west]
dim(df)#> [1] 3256 39Adding time as POSIXct class
df <- obs_addtime(df)
df[, "timeUTC"]#> timeUTC
#> <POSc>
#> 1: 2020-01-30 19:37:28
#> 2: 2020-01-30 19:37:32
#> 3: 2020-01-30 19:37:36
#> 4: 2020-01-30 19:37:40
#> 5: 2020-01-30 19:37:43
#> ---
#> 3252: 2020-12-17 18:56:09
#> 3253: 2020-12-17 18:56:10
#> 3254: 2020-12-17 18:56:12
#> 3255: 2020-12-17 18:56:12
#> 3256: 2020-12-17 18:56:15Cut time
now we can cut time every 20 seconds. We can chosse other frequency as well.
#> second sec2
#> <int> <num>
#> 1: 28 20
#> 2: 32 20
#> 3: 36 20
#> 4: 40 20
#> 5: 43 40
#> ---
#> 3252: 9 0
#> 3253: 10 0
#> 3254: 12 0
#> 3255: 12 0
#> 3256: 15 0Aggregate data
now we need to add the column key_time, that it will be used to aggregate other variables
df$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: 2020-01-30 19:37:28 2020-01-30 19:37:20
#> 2: 2020-01-30 19:37:32 2020-01-30 19:37:20
#> 3: 2020-01-30 19:37:36 2020-01-30 19:37:20
#> 4: 2020-01-30 19:37:40 2020-01-30 19:37:20
#> 5: 2020-01-30 19:37:43 2020-01-30 19:37:40
#> ---
#> 3252: 2020-12-17 18:56:09 2020-12-17 18:56:00
#> 3253: 2020-12-17 18:56:10 2020-12-17 18:56:00
#> 3254: 2020-12-17 18:56:12 2020-12-17 18:56:00
#> 3255: 2020-12-17 18:56:12 2020-12-17 18:56:00
#> 3256: 2020-12-17 18:56:15 2020-12-17 18:56:00Add 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: 2020-01-30 19:37:20 AirCoreNOAA NOAA WMO CH4 X2004 2020
#> 2: 2020-01-30 19:37:40 AirCoreNOAA NOAA WMO CH4 X2004 2020
#> 3: 2020-01-30 19:38:00 AirCoreNOAA NOAA WMO CH4 X2004 2020
#> 4: 2020-01-30 19:38:20 AirCoreNOAA NOAA WMO CH4 X2004 2020
#> 5: 2020-01-30 19:38:40 AirCoreNOAA NOAA WMO CH4 X2004 2020
#> ---
#> 334: 2020-12-17 18:54:40 AirCoreNOAA NOAA WMO CH4 X2004 2020
#> 335: 2020-12-17 18:55:00 AirCoreNOAA NOAA WMO CH4 X2004 2020
#> 336: 2020-12-17 18:55:20 AirCoreNOAA NOAA WMO CH4 X2004 2020
#> 337: 2020-12-17 18:55:40 AirCoreNOAA NOAA WMO CH4 X2004 2020
#> 338: 2020-12-17 18:56:00 AirCoreNOAA NOAA WMO CH4 X2004 2020
#> month day hour minute second time time_decimal value
#> <int> <char> <int> <int> <int> <num> <num> <num>
#> 1: 1 30 19 37 20 1580413040 2020.081 1.912662e-06
#> 2: 1 30 19 37 40 1580413060 2020.081 1.913034e-06
#> 3: 1 30 19 38 0 1580413080 2020.081 1.913344e-06
#> 4: 1 30 19 38 20 1580413100 2020.081 1.913346e-06
#> 5: 1 30 19 38 40 1580413120 2020.081 1.913906e-06
#> ---
#> 334: 12 17 18 54 40 1608231280 2020.961 1.958356e-06
#> 335: 12 17 18 55 0 1608231300 2020.961 1.958784e-06
#> 336: 12 17 18 55 20 1608231320 2020.961 1.959021e-06
#> 337: 12 17 18 55 40 1608231340 2020.961 1.958858e-06
#> 338: 12 17 18 56 0 1608231360 2020.961 1.959788e-06
#> latitude longitude altitude_final type_altitude local_time lh
#> <num> <num> <num> <num> <POSc> <int>
#> 1: 39.68173 -103.7711 7926.282 1 2020-01-30 12:42:14 12
#> 2: 39.67923 -103.7686 7795.292 1 2020-01-30 12:42:35 12
#> 3: 39.67596 -103.7657 7647.354 1 2020-01-30 12:42:56 12
#> 4: 39.67261 -103.7625 7500.024 1 2020-01-30 12:43:16 12
#> 5: 39.66949 -103.7596 7357.494 1 2020-01-30 12:43:37 12
#> ---
#> 334: 39.47656 -103.8517 2353.402 1 2020-12-17 11:59:15 11
#> 335: 39.47749 -103.8506 2233.901 1 2020-12-17 11:59:35 11
#> 336: 39.47844 -103.8497 2121.761 1 2020-12-17 11:59:56 11
#> 337: 39.47984 -103.8488 2007.779 1 2020-12-17 12:00:16 12
#> 338: 39.48127 -103.8481 1905.712 1 2020-12-17 12:00:36 12Master
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\RtmpQ33K7x\file8b4862a53ca7_aircore.txtcsv
message(paste0(out,"_", datasetid, ".csv\n"))
fwrite(master,
paste0(out,"_", datasetid, ".csv"),
sep = ",")#> C:\Users\sibarrae\AppData\Local\Temp\RtmpQ33K7x\file8b4862a53ca7_aircore.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"))#>
#> Adding notes in csvy:
#> sector: aircore
#> 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\RtmpQ33K7x\file8b4862a53ca7_aircore.csvy
obs_read_csvy(paste0(out,"_", datasetid, ".csvy"))#> [1] "---"
#> [2] "name: Metadata "
#> [3] "sector: aircore"
#> [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':\t338 obs. of 19 variables:"
#> [13] " $ timeUTC : chr \"2020-01-30 19:37:20\" \".."
#> [14] " $ site_code : chr \"AirCoreNOAA\" \"AirCore\".."
#> [15] " $ lab_1_abbr : chr \"NOAA\" \"NOAA\" ..."
#> [16] " $ dataset_calibration_scale: chr \"WMO CH4 X2004\" \"WMO C\".."
#> [17] " $ year : int 2020 2020 2020 2020 202.."
#> [18] " $ month : int 1 1 1 1 1 ..."
#> [19] " $ day : chr \"30\" \"30\" ..."
#> [20] " $ hour : int 19 19 19 19 19 ..."
#> [21] " $ minute : int 37 37 38 38 38 ..."
#> [22] " $ second : int 20 40 0 20 40 ..."
#> [23] " $ time : num 1.58e+09 1.58e+09 ..."
#> [24] " $ time_decimal : num 2020 2020 ..."
#> [25] " $ value : num 1.91e-06 1.91e-06 ..."
#> [26] " $ latitude : num 39.7 39.7 ..."
#> [27] " $ longitude : num -104 -104 ..."
#> [28] " $ altitude_final : num 7926 7795 ..."
#> [29] " $ type_altitude : num 1 1 1 1 1 ..."
#> [30] " $ local_time : chr \"2020-01-30 12:42:14.9\".."
#> [31] " $ lh : int 12 12 12 12 12 ..."
#> [32] " - attr(*, \".internal.selfref\")=<externalptr> "
#> [33] "NULL"
#> [34] "---"
#> timeUTC site_code lab_1_abbr dataset_calibration_scale year
#> <POSc> <char> <char> <char> <int>
#> 1: 2020-01-30 19:37:20 AirCoreNOAA NOAA WMO CH4 X2004 2020
#> 2: 2020-01-30 19:37:40 AirCoreNOAA NOAA WMO CH4 X2004 2020
#> 3: 2020-01-30 19:38:00 AirCoreNOAA NOAA WMO CH4 X2004 2020
#> 4: 2020-01-30 19:38:20 AirCoreNOAA NOAA WMO CH4 X2004 2020
#> 5: 2020-01-30 19:38:40 AirCoreNOAA NOAA WMO CH4 X2004 2020
#> ---
#> 334: 2020-12-17 18:54:40 AirCoreNOAA NOAA WMO CH4 X2004 2020
#> 335: 2020-12-17 18:55:00 AirCoreNOAA NOAA WMO CH4 X2004 2020
#> 336: 2020-12-17 18:55:20 AirCoreNOAA NOAA WMO CH4 X2004 2020
#> 337: 2020-12-17 18:55:40 AirCoreNOAA NOAA WMO CH4 X2004 2020
#> 338: 2020-12-17 18:56:00 AirCoreNOAA NOAA WMO CH4 X2004 2020
#> month day hour minute second time time_decimal value
#> <int> <int> <int> <int> <int> <int> <num> <num>
#> 1: 1 30 19 37 20 1580413040 2020.081 1.912662e-06
#> 2: 1 30 19 37 40 1580413060 2020.081 1.913034e-06
#> 3: 1 30 19 38 0 1580413080 2020.081 1.913344e-06
#> 4: 1 30 19 38 20 1580413100 2020.081 1.913346e-06
#> 5: 1 30 19 38 40 1580413120 2020.081 1.913906e-06
#> ---
#> 334: 12 17 18 54 40 1608231280 2020.961 1.958356e-06
#> 335: 12 17 18 55 0 1608231300 2020.961 1.958784e-06
#> 336: 12 17 18 55 20 1608231320 2020.961 1.959021e-06
#> 337: 12 17 18 55 40 1608231340 2020.961 1.958858e-06
#> 338: 12 17 18 56 0 1608231360 2020.961 1.959788e-06
#> latitude longitude altitude_final type_altitude local_time lh
#> <num> <num> <num> <int> <POSc> <int>
#> 1: 39.6817 -103.7711 7926.282 1 2020-01-30 12:42:14 12
#> 2: 39.6792 -103.7686 7795.292 1 2020-01-30 12:42:35 12
#> 3: 39.6760 -103.7657 7647.354 1 2020-01-30 12:42:56 12
#> 4: 39.6726 -103.7625 7500.024 1 2020-01-30 12:43:16 12
#> 5: 39.6695 -103.7596 7357.494 1 2020-01-30 12:43:37 12
#> ---
#> 334: 39.4766 -103.8517 2353.402 1 2020-12-17 11:59:15 11
#> 335: 39.4775 -103.8506 2233.901 1 2020-12-17 11:59:35 11
#> 336: 39.4784 -103.8497 2121.761 1 2020-12-17 11:59:56 11
#> 337: 39.4798 -103.8488 2007.779 1 2020-12-17 12:00:16 12
#> 338: 39.4813 -103.8481 1905.712 1 2020-12-17 12:00:36 12Receptors
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\RtmpQ33K7x\file8b4862a53ca7_aircore_receptor_ASL.txtPlot
Finally, we just plot some data, run it locally
obs_plot(df3, time = "timeUTC", yfactor = 1e9)#> Found the following sites:
#> [1] AirCoreNOAA
#> Plotting the following sites:
#> [1] AirCoreNOAA
#> png
#> 2
Time 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+9obs_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