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-insitu using the function
obs_read_nc. To this date, solar_time is not
included for aircraft, so we FALSE that argument.
datasetid <- "aircraft-insitu"
df <- obs_read_nc(index = index,
categories = datasetid,
solar_time = FALSE,
verbose = T)#> Searching aircraft-insitu...
#> 1: ch4_above_aircraft-insitu_1_allvalid.nc
#> 2: ch4_act_aircraft-insitu_428_allvalid-b200.nc
#> 3: ch4_act_aircraft-insitu_428_allvalid-c130.nc
#> 4: ch4_ajax_aircraft-insitu_429_allvalid.nc
#> 5: ch4_cob2003b_aircraft-insitu_59_allvalid.nc
#> 6: ch4_eco_aircraft-insitu_1_allvalid.nc
#> 7: ch4_hip_aircraft-insitu_59_allvalid.nc
#> 8: ch4_iagos-caribic_aircraft-insitu_457_allvalid.nc
#> 9: ch4_iagos-core_aircraft-insitu_45_allvalid.nc
#> 10: ch4_korus-aq_aircraft-insitu_428_allvalid-dc8.nc
#> 11: ch4_man_aircraft-insitu_1_allvalid.nc
#> 12: ch4_orc_aircraft-insitu_3_allvalid-merge10.nc
#> 13: ch4_seac4rs_aircraft-insitu_428_allvalid-ER2.nc
#> 14: ch4_seac4rs_aircraft-insitu_428_allvalid-dc8.nc
#> 15: ch4_start08_aircraft-insitu_59_allvalid.nc
#> 16: ch4_tom_aircraft-insitu_1_allvalid.nc
#> 17: ch4_ugd_aircraft-insitu_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: 2017 4 26 15 32 10 1493220730 1493220730
#> 2: 2017 4 26 15 32 20 1493220740 1493220740
#> 3: 2017 4 26 15 32 30 1493220750 1493220750
#> 4: 2017 4 26 15 32 40 1493220760 1493220760
#> 5: 2017 4 26 15 32 50 1493220770 1493220770
#> ---
#> 3158219: 2021 12 10 11 37 45 1639136265 1639136260
#> 3158220: 2021 12 10 11 37 55 1639136275 1639136270
#> 3158221: 2021 12 10 11 38 5 1639136285 1639136280
#> 3158222: 2021 12 10 11 38 15 1639136295 1639136290
#> 3158223: 2021 12 10 11 38 25 1639136305 1639136300
#> midpoint_time datetime time_decimal value value_unc
#> <int> <char> <num> <num> <num>
#> 1: 1493220730 2017-04-26T15:32:10Z 2017.317 1.96430e-06 5e-10
#> 2: 1493220740 2017-04-26T15:32:20Z 2017.317 1.96176e-06 5e-10
#> 3: 1493220750 2017-04-26T15:32:30Z 2017.317 1.96173e-06 5e-10
#> 4: 1493220760 2017-04-26T15:32:40Z 2017.317 1.96131e-06 5e-10
#> 5: 1493220770 2017-04-26T15:32:50Z 2017.317 1.96199e-06 5e-10
#> ---
#> 3158219: 1639136265 2021-12-10T11:37:45Z 2021.941 1.97308e-06 1e-09
#> 3158220: 1639136275 2021-12-10T11:37:55Z 2021.941 1.97495e-06 1e-09
#> 3158221: 1639136285 2021-12-10T11:38:05Z 2021.941 1.97649e-06 1e-09
#> 3158222: 1639136295 2021-12-10T11:38:15Z 2021.941 1.98205e-06 1e-09
#> 3158223: 1639136305 2021-12-10T11:38:25Z 2021.941 1.97504e-06 1e-09
#> nvalue value_std_dev latitude longitude altitude pressure u v
#> <int> <num> <num> <num> <num> <num> <num> <num>
#> 1: 4 1.23e-09 40.0391 -105.2320 1617.03 836.39 -2.39 0.16
#> 2: 5 3.10e-10 40.0391 -105.2290 1617.04 836.78 -1.45 0.33
#> 3: 5 4.30e-10 40.0391 -105.2240 1617.03 836.79 -1.40 0.94
#> 4: 4 2.40e-10 40.0391 -105.2190 1630.93 835.28 -3.37 1.82
#> 5: 5 1.16e-09 40.0389 -105.2120 1650.95 832.78 -2.25 2.68
#> ---
#> 3158219: 3 2.96e-09 0.2176 32.5360 1239.50 865.98 NA NA
#> 3158220: 3 1.46e-09 0.2142 32.5387 1213.20 867.65 NA NA
#> 3158221: 4 3.31e-09 0.2109 32.5413 1199.20 869.33 NA NA
#> 3158222: 3 4.50e-09 0.2076 32.5440 1181.10 871.76 NA NA
#> 3158223: 4 1.62e-09 0.2044 32.5466 1163.50 873.92 NA NA
#> temperature profile_id flight_id obs_flag obspack_num
#> <num> <char> <char> <int> <int>
#> 1: 282.02 0 20170426 1 11109646
#> 2: 281.79 0 20170426 1 11109647
#> 3: 281.34 0 20170426 1 11109648
#> 4: 280.92 1 20170426 1 11109649
#> 5: 280.43 1 20170426 1 11109650
#> ---
#> 3158219: 296.70 12 20211210_27100 1 2042566
#> 3158220: 296.90 12 20211210_27100 1 2042567
#> 3158221: 297.00 12 20211210_27100 1 2042568
#> 3158222: 297.20 12 20211210_27100 1 2042569
#> 3158223: 297.40 12 20211210_27100 1 2042570
#> obspack_id
#> <char>
#> 1: obspack_ch4_1_GLOBALVIEWplus_v6.0_2023-12-01~ch4_above_aircraft-insitu_1_allvalid~11109646
#> 2: obspack_ch4_1_GLOBALVIEWplus_v6.0_2023-12-01~ch4_above_aircraft-insitu_1_allvalid~11109647
#> 3: obspack_ch4_1_GLOBALVIEWplus_v6.0_2023-12-01~ch4_above_aircraft-insitu_1_allvalid~11109648
#> 4: obspack_ch4_1_GLOBALVIEWplus_v6.0_2023-12-01~ch4_above_aircraft-insitu_1_allvalid~11109649
#> 5: obspack_ch4_1_GLOBALVIEWplus_v6.0_2023-12-01~ch4_above_aircraft-insitu_1_allvalid~11109650
#> ---
#> 3158219: obspack_ch4_1_GLOBALVIEWplus_v6.0_2023-12-01~ch4_ugd_aircraft-insitu_1_allvalid~2042566
#> 3158220: obspack_ch4_1_GLOBALVIEWplus_v6.0_2023-12-01~ch4_ugd_aircraft-insitu_1_allvalid~2042567
#> 3158221: obspack_ch4_1_GLOBALVIEWplus_v6.0_2023-12-01~ch4_ugd_aircraft-insitu_1_allvalid~2042568
#> 3158222: obspack_ch4_1_GLOBALVIEWplus_v6.0_2023-12-01~ch4_ugd_aircraft-insitu_1_allvalid~2042569
#> 3158223: obspack_ch4_1_GLOBALVIEWplus_v6.0_2023-12-01~ch4_ugd_aircraft-insitu_1_allvalid~2042570
#> unique_sample_location_num scale dataset_project
#> <int> <char> <char>
#> 1: 33175520 WMO CH4 X2004A aircraft-insitu
#> 2: 33175521 WMO CH4 X2004A aircraft-insitu
#> 3: 33175522 WMO CH4 X2004A aircraft-insitu
#> 4: 33175523 WMO CH4 X2004A aircraft-insitu
#> 5: 33175519 WMO CH4 X2004A aircraft-insitu
#> ---
#> 3158219: 51772833 WMO CH4 X2004A aircraft-insitu
#> 3158220: 51772776 WMO CH4 X2004A aircraft-insitu
#> 3158221: 51772721 WMO CH4 X2004A aircraft-insitu
#> 3158222: 51772671 WMO CH4 X2004A aircraft-insitu
#> 3158223: 51772623 WMO CH4 X2004A aircraft-insitu
#> dataset_selection_tag
#> <char>
#> 1: allvalid
#> 2: allvalid
#> 3: allvalid
#> 4: allvalid
#> 5: allvalid
#> ---
#> 3158219: allvalid
#> 3158220: allvalid
#> 3158221: allvalid
#> 3158222: allvalid
#> 3158223: allvalid
#> site_name
#> <char>
#> 1: Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE)
#> 2: Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE)
#> 3: Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE)
#> 4: Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE)
#> 5: Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE)
#> ---
#> 3158219: Kajjansi Airfield, Kampala, Uganda
#> 3158220: Kajjansi Airfield, Kampala, Uganda
#> 3158221: Kajjansi Airfield, Kampala, Uganda
#> 3158222: Kajjansi Airfield, Kampala, Uganda
#> 3158223: Kajjansi Airfield, Kampala, Uganda
#> site_elevation site_latitude site_longitude site_country site_code
#> <num> <num> <num> <char> <char>
#> 1: -1.000e+34 -1e+34 -1.000e+34 United States CRV
#> 2: -1.000e+34 -1e+34 -1.000e+34 United States CRV
#> 3: -1.000e+34 -1e+34 -1.000e+34 United States CRV
#> 4: -1.000e+34 -1e+34 -1.000e+34 United States CRV
#> 5: -1.000e+34 -1e+34 -1.000e+34 United States CRV
#> ---
#> 3158219: 1.141e+03 2e-01 3.255e+01 Uganda UGD
#> 3158220: 1.141e+03 2e-01 3.255e+01 Uganda UGD
#> 3158221: 1.141e+03 2e-01 3.255e+01 Uganda UGD
#> 3158222: 1.141e+03 2e-01 3.255e+01 Uganda UGD
#> 3158223: 1.141e+03 2e-01 3.255e+01 Uganda UGD
#> site_utc2lst lab_1_abbr dataset_calibration_scale altitude_final
#> <num> <char> <char> <num>
#> 1: -8 NOAA WMO CH4 X2004A 1617.03
#> 2: -8 NOAA WMO CH4 X2004A 1617.04
#> 3: -8 NOAA WMO CH4 X2004A 1617.03
#> 4: -8 NOAA WMO CH4 X2004A 1630.93
#> 5: -8 NOAA WMO CH4 X2004A 1650.95
#> ---
#> 3158219: 3 NOAA WMO CH4 X2004A 1239.50
#> 3158220: 3 NOAA WMO CH4 X2004A 1213.20
#> 3158221: 3 NOAA WMO CH4 X2004A 1199.20
#> 3158222: 3 NOAA WMO CH4 X2004A 1181.10
#> 3158223: 3 NOAA WMO CH4 X2004A 1163.50
#> type_altitude air_flag bl_bt_flag elevation event_number flight_flag
#> <num> <int> <int> <num> <char> <int>
#> 1: 1 NA NA NA <NA> NA
#> 2: 1 NA NA NA <NA> NA
#> 3: 1 NA NA NA <NA> NA
#> 4: 1 NA NA NA <NA> NA
#> 5: 1 NA NA NA <NA> NA
#> ---
#> 3158219: 1 NA NA 1138 251934 NA
#> 3158220: 1 NA NA 1141 251935 NA
#> 3158221: 1 NA NA 1143 251936 NA
#> 3158222: 1 NA NA 1140 251937 NA
#> 3158223: 1 NA NA 1138 251938 NA
#> gps_altitude h2o instrument intake_height maneuver_flag
#> <num> <num> <char> <num> <int>
#> 1: NA NA <NA> NA NA
#> 2: NA NA <NA> NA NA
#> 3: NA NA <NA> NA NA
#> 4: NA NA <NA> NA NA
#> 5: NA NA <NA> NA NA
#> ---
#> 3158219: NA NA Picarro_CFKBDS2302 101.5 NA
#> 3158220: NA NA Picarro_CFKBDS2302 72.2 NA
#> 3158221: NA NA Picarro_CFKBDS2302 56.2 NA
#> 3158222: NA NA Picarro_CFKBDS2302 41.1 NA
#> 3158223: NA NA Picarro_CFKBDS2302 25.5 NA
#> maneuver_flagqc pressure_altitude qcflag rh site_elevation_unit
#> <int> <num> <char> <num> <char>
#> 1: NA NA <NA> NA <NA>
#> 2: NA NA <NA> NA <NA>
#> 3: NA NA <NA> NA <NA>
#> 4: NA NA <NA> NA <NA>
#> 5: NA NA <NA> NA <NA>
#> ---
#> 3158219: NA NA ..P 79.9 masl
#> 3158220: NA NA ..P 79.8 masl
#> 3158221: NA NA ..P 78.3 masl
#> 3158222: NA NA ..P 78.9 masl
#> 3158223: NA NA ..P 78.6 masl
#> source_id
#> <char>
#> 1: <NA>
#> 2: <NA>
#> 3: <NA>
#> 4: <NA>
#> 5: <NA>
#> ---
#> 3158219: KampalaExecutiveAviation_CessnaCaravan208B_5XKEB~20211210_27100~12
#> 3158220: KampalaExecutiveAviation_CessnaCaravan208B_5XKEB~20211210_27100~12
#> 3158221: KampalaExecutiveAviation_CessnaCaravan208B_5XKEB~20211210_27100~12
#> 3158222: KampalaExecutiveAviation_CessnaCaravan208B_5XKEB~20211210_27100~12
#> 3158223: KampalaExecutiveAviation_CessnaCaravan208B_5XKEB~20211210_27100~12
#> time_interval value_original_scale
#> <int> <num>
#> 1: NA NA
#> 2: NA NA
#> 3: NA NA
#> 4: NA NA
#> 5: NA NA
#> ---
#> 3158219: NA NA
#> 3158220: NA NA
#> 3158221: NA NA
#> 3158222: NA NA
#> 3158223: NA NANow 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", "intake_height", "elevation")]#> altitude altitude_final intake_height elevation
#> <num> <num> <num> <num>
#> 1: 1617.03 1617.03 NA NA
#> 2: 1617.04 1617.04 NA NA
#> 3: 1617.03 1617.03 NA NA
#> 4: 1630.93 1630.93 NA NA
#> 5: 1650.95 1650.95 NA NA
#> ---
#> 3158219: 1239.50 1239.50 101.5 1138
#> 3158220: 1213.20 1213.20 72.2 1141
#> 3158221: 1199.20 1199.20 56.2 1143
#> 3158222: 1181.10 1181.10 41.1 1140
#> 3158223: 1163.50 1163.50 25.5 1138The temporal range of data is
range(df$year)#> [1] 2003 2021We also check for dimensions of data
dim(df)#> [1] 2041758 59Filters
df <- df[year == yy]
df <- df[altitude_final < max_altitude &
latitude < north &
latitude > south &
longitude < east &
longitude > west]
dim(df)#> [1] 236 59Adding time as POSIXct class
df <- obs_addtime(df)
df[, "timeUTC"]#> timeUTC
#> <POSc>
#> 1: 2020-01-08 22:59:55
#> 2: 2020-01-08 23:00:05
#> 3: 2020-01-08 23:00:15
#> 4: 2020-01-08 23:00:25
#> 5: 2020-01-08 23:00:35
#> ---
#> 232: 2020-03-02 22:56:15
#> 233: 2020-03-02 22:56:25
#> 234: 2020-03-02 22:56:35
#> 235: 2020-03-02 22:56:45
#> 236: 2020-03-02 22:56:55Cut time
now we can cut time every 20 seconds. We can chosse other frequency as well.
#> second sec2
#> <int> <num>
#> 1: 55 40
#> 2: 5 0
#> 3: 15 0
#> 4: 25 20
#> 5: 35 20
#> ---
#> 232: 15 0
#> 233: 25 20
#> 234: 35 20
#> 235: 45 40
#> 236: 55 40Aggregate data
now we need to add the column key_time, that it will be used to aggregate other variables
df$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: 2020-01-08 22:59:55 2020-01-08 22:59:40
#> 2: 2020-01-08 23:00:05 2020-01-08 23:00:00
#> 3: 2020-01-08 23:00:15 2020-01-08 23:00:00
#> 4: 2020-01-08 23:00:25 2020-01-08 23:00:20
#> 5: 2020-01-08 23:00:35 2020-01-08 23:00:20
#> ---
#> 232: 2020-03-02 22:56:15 2020-03-02 22:56:00
#> 233: 2020-03-02 22:56:25 2020-03-02 22:56:20
#> 234: 2020-03-02 22:56:35 2020-03-02 22:56:20
#> 235: 2020-03-02 22:56:45 2020-03-02 22:56:40
#> 236: 2020-03-02 22:56:55 2020-03-02 22:56:40
df2 <- obs_agg(df, cols = c("year",
"month",
"day",
"hour",
"minute",
"second",
"time",
"time_decimal",
"value",
"latitude",
"longitude",
"altitude_final",
"elevation",
"intake_height",
"gps_altitude",
"pressure",
"pressure_altitude",
"u", "v", "temperature",
"type_altitude"))#> Adding timeAdd 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: 2020-01-08 22:59:40 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 2: 2020-01-08 23:00:00 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 3: 2020-01-08 23:00:20 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 4: 2020-01-08 23:00:40 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 5: 2020-01-08 23:01:00 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> ---
#> 115: 2020-03-02 22:55:20 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 116: 2020-03-02 22:55:40 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 117: 2020-03-02 22:56:00 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 118: 2020-03-02 22:56:20 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 119: 2020-03-02 22:56:40 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> month day hour minute second time time_decimal value
#> <int> <char> <int> <int> <int> <num> <num> <num>
#> 1: 1 08 22 59 40 1578524380 2020.022 1.90980e-06
#> 2: 1 08 23 0 0 1578524400 2020.022 1.90885e-06
#> 3: 1 08 23 0 20 1578524420 2020.022 1.90775e-06
#> 4: 1 08 23 0 40 1578524440 2020.022 1.90815e-06
#> 5: 1 08 23 1 0 1578524460 2020.022 1.90780e-06
#> ---
#> 115: 3 02 22 55 20 1583189720 2020.169 1.94435e-06
#> 116: 3 02 22 55 40 1583189740 2020.169 1.94785e-06
#> 117: 3 02 22 56 0 1583189760 2020.169 1.95025e-06
#> 118: 3 02 22 56 20 1583189780 2020.169 1.95040e-06
#> 119: 3 02 22 56 40 1583189800 2020.169 1.94940e-06
#> latitude longitude altitude_final elevation intake_height gps_altitude
#> <num> <num> <num> <num> <num> <num>
#> 1: 34.4790 -116.4090 7983.10 NaN NaN NaN
#> 2: 34.4675 -116.4360 7907.00 NaN NaN NaN
#> 3: 34.4525 -116.4720 7796.40 NaN NaN NaN
#> 4: 34.4375 -116.5075 7615.25 NaN NaN NaN
#> 5: 34.4225 -116.5430 7413.90 NaN NaN NaN
#> ---
#> 115: 33.9955 -118.2480 1113.10 NaN NaN NaN
#> 116: 33.9935 -118.2705 983.70 NaN NaN NaN
#> 117: 33.9915 -118.2925 855.05 NaN NaN NaN
#> 118: 33.9895 -118.3145 753.00 NaN NaN NaN
#> 119: 33.9875 -118.3340 648.10 NaN NaN NaN
#> pressure pressure_altitude u v temperature type_altitude
#> <num> <num> <num> <num> <num> <num>
#> 1: 481.30 NaN 20.82002449 -4.235882 257.65 1
#> 2: 485.65 NaN 21.46781063 -3.550151 258.30 1
#> 3: 491.60 NaN 22.83116627 -2.518281 259.05 1
#> 4: 505.15 NaN 25.19994354 -1.720480 260.95 1
#> 5: 517.75 NaN 26.27869606 -3.928331 262.50 1
#> ---
#> 115: 947.15 NaN 0.40699802 -4.795110 288.90 1
#> 116: 961.05 NaN 0.16893618 -4.854677 290.10 1
#> 117: 976.05 NaN 0.05610672 -6.043770 291.35 1
#> 118: 978.35 NaN -0.99867356 -5.014345 291.60 1
#> 119: 981.70 NaN -1.99368972 -5.770015 291.90 1
#> local_time lh
#> <POSc> <int>
#> 1: 2020-01-08 15:14:01 15
#> 2: 2020-01-08 15:14:15 15
#> 3: 2020-01-08 15:14:26 15
#> 4: 2020-01-08 15:14:38 15
#> 5: 2020-01-08 15:14:49 15
#> ---
#> 115: 2020-03-02 15:02:20 15
#> 116: 2020-03-02 15:02:35 15
#> 117: 2020-03-02 15:02:49 15
#> 118: 2020-03-02 15:03:04 15
#> 119: 2020-03-02 15:03:19 15Master
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\RtmpCmgiyH\file7abc7b936bd2_aircraft-insitu.txtcsv
message(paste0(out,"_", datasetid, ".csv\n"))
fwrite(master,
paste0(out,"_", datasetid, ".csv"),
sep = ",")#> C:\Users\sibarrae\AppData\Local\Temp\RtmpCmgiyH\file7abc7b936bd2_aircraft-insitu.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-insitu
#> 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\RtmpCmgiyH\file7abc7b936bd2_aircraft-insitu.csvy
obs_read_csvy(paste0(out,"_", datasetid, ".csvy"))#> [1] "---"
#> [2] "name: Metadata "
#> [3] "sector: aircraft-insitu"
#> [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':\t119 obs. of 27 variables:"
#> [13] " $ timeUTC : chr \"2020-01-08 22:59:40\" \".."
#> [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 \"08\" \"08\" ..."
#> [20] " $ hour : int 22 23 23 23 23 ..."
#> [21] " $ minute : int 59 0 0 0 1 ..."
#> [22] " $ second : int 40 0 20 40 0 ..."
#> [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 34.5 34.5 ..."
#> [27] " $ longitude : num -116 -116 ..."
#> [28] " $ altitude_final : num 7983 7907 ..."
#> [29] " $ elevation : num NaN NaN NaN NaN NaN ..."
#> [30] " $ intake_height : num NaN NaN NaN NaN NaN ..."
#> [31] " $ gps_altitude : num NaN NaN NaN NaN NaN ..."
#> [32] " $ pressure : num 481 486 ..."
#> [33] " $ pressure_altitude : num NaN NaN NaN NaN NaN ..."
#> [34] " $ u : num 20.8 21.5 ..."
#> [35] " $ v : num -4.24 -3.55 ..."
#> [36] " $ temperature : num 258 258 ..."
#> [37] " $ type_altitude : num 1 1 1 1 1 ..."
#> [38] " $ local_time : chr \"2020-01-08 15:14:01.8\".."
#> [39] " $ lh : int 15 15 15 15 15 ..."
#> [40] " - attr(*, \".internal.selfref\")=<externalptr> "
#> [41] "NULL"
#> [42] "---"
#> timeUTC site_code lab_1_abbr dataset_calibration_scale year
#> <POSc> <char> <char> <char> <int>
#> 1: 2020-01-08 22:59:40 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 2: 2020-01-08 23:00:00 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 3: 2020-01-08 23:00:20 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 4: 2020-01-08 23:00:40 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 5: 2020-01-08 23:01:00 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> ---
#> 115: 2020-03-02 22:55:20 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 116: 2020-03-02 22:55:40 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 117: 2020-03-02 22:56:00 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 118: 2020-03-02 22:56:20 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> 119: 2020-03-02 22:56:40 IAGOS KIT/IMK-ASF WMO CH4 X2004A 2020
#> month day hour minute second time time_decimal value
#> <int> <int> <int> <int> <int> <int> <num> <num>
#> 1: 1 8 22 59 40 1578524380 2020.022 1.90980e-06
#> 2: 1 8 23 0 0 1578524400 2020.022 1.90885e-06
#> 3: 1 8 23 0 20 1578524420 2020.022 1.90775e-06
#> 4: 1 8 23 0 40 1578524440 2020.022 1.90815e-06
#> 5: 1 8 23 1 0 1578524460 2020.022 1.90780e-06
#> ---
#> 115: 3 2 22 55 20 1583189720 2020.169 1.94435e-06
#> 116: 3 2 22 55 40 1583189740 2020.169 1.94785e-06
#> 117: 3 2 22 56 0 1583189760 2020.169 1.95025e-06
#> 118: 3 2 22 56 20 1583189780 2020.169 1.95040e-06
#> 119: 3 2 22 56 40 1583189800 2020.169 1.94940e-06
#> latitude longitude altitude_final elevation intake_height gps_altitude
#> <num> <num> <num> <lgcl> <lgcl> <lgcl>
#> 1: 34.4790 -116.4090 7983.10 NA NA NA
#> 2: 34.4675 -116.4360 7907.00 NA NA NA
#> 3: 34.4525 -116.4720 7796.40 NA NA NA
#> 4: 34.4375 -116.5075 7615.25 NA NA NA
#> 5: 34.4225 -116.5430 7413.90 NA NA NA
#> ---
#> 115: 33.9955 -118.2480 1113.10 NA NA NA
#> 116: 33.9935 -118.2705 983.70 NA NA NA
#> 117: 33.9915 -118.2925 855.05 NA NA NA
#> 118: 33.9895 -118.3145 753.00 NA NA NA
#> 119: 33.9875 -118.3340 648.10 NA NA NA
#> pressure pressure_altitude u v temperature type_altitude
#> <num> <lgcl> <num> <num> <num> <int>
#> 1: 481.30 NA 20.82002449 -4.235882 257.65 1
#> 2: 485.65 NA 21.46781063 -3.550151 258.30 1
#> 3: 491.60 NA 22.83116627 -2.518281 259.05 1
#> 4: 505.15 NA 25.19994354 -1.720480 260.95 1
#> 5: 517.75 NA 26.27869606 -3.928331 262.50 1
#> ---
#> 115: 947.15 NA 0.40699802 -4.795110 288.90 1
#> 116: 961.05 NA 0.16893618 -4.854677 290.10 1
#> 117: 976.05 NA 0.05610672 -6.043770 291.35 1
#> 118: 978.35 NA -0.99867356 -5.014345 291.60 1
#> 119: 981.70 NA -1.99368972 -5.770015 291.90 1
#> local_time lh
#> <POSc> <int>
#> 1: 2020-01-08 15:14:01 15
#> 2: 2020-01-08 15:14:15 15
#> 3: 2020-01-08 15:14:26 15
#> 4: 2020-01-08 15:14:38 15
#> 5: 2020-01-08 15:14:49 15
#> ---
#> 115: 2020-03-02 15:02:20 15
#> 116: 2020-03-02 15:02:35 15
#> 117: 2020-03-02 15:02:49 15
#> 118: 2020-03-02 15:03:04 15
#> 119: 2020-03-02 15:03:19 15Receptors
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\RtmpCmgiyH\file7abc7b936bd2_aircraft-insitu_receptor_ASL.txtPlot
Finally, we just plot some data, run it locally
obs_plot(df3, time = "timeUTC", yfactor = 1e9)
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+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