cate = c("aircraft-pfp",
"aircraft-insitu",
"aircraft-flask",
"surface-insitu",
"surface-flask",
"surface-pfp",
"tower-insitu",
"aircore",
"shipboard-insitu",
"shipboard-flask")
obsch4 <- "Z:/obspack/obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08/data/nc/"
indexch4 <- obs_summary(obs = obsch4,
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 agl
obsco2 <- "Z:/obspack/obspack_co2_1_GLOBALVIEWplus_v9.1_2023-12-08/data/nc/"
indexco2 <- obs_summary(obs = obsco2,
categories = cate)Number of files of index: 617
sector N
<char> <int>
1: surface-insitu 160
2: aircraft-pfp 41
3: aircraft-insitu 41
4: surface-flask 138
5: shipboard-insitu 14
6: aircraft-flask 6
7: aircore 1
8: surface-pfp 29
9: tower-insitu 183
10: shipboard-flask 4
11: Total sectors 617
Detected 231 files with agl
Detected 386 files without aglWe can merge co2 and ch4
index <- rbind(indexch4,
indexco2)Now we read the aircraft-pfp using the function
obs_read_nc. To this date, solar_time is not
included for aircraft, so we FALSE that argument.
datasetid <- "aircraft-pfp"
df <- obs_read_nc(index = index,
categories = datasetid,
solar_time = FALSE,
verbose = T)Searching aircraft-pfp...
1: ch4_aao_aircraft-pfp_1_allvalid.nc
2: ch4_acg_aircraft-pfp_1_allvalid.nc
3: ch4_act_aircraft-pfp_1_allvalid-b200.nc
4: ch4_act_aircraft-pfp_1_allvalid-c130.nc
5: ch4_alf_aircraft-pfp_26_representative.nc
6: ch4_bgi_aircraft-pfp_1_allvalid.nc
7: ch4_bne_aircraft-pfp_1_allvalid.nc
8: ch4_car_aircraft-pfp_1_allvalid.nc
9: ch4_cma_aircraft-pfp_1_allvalid.nc
10: ch4_crv_aircraft-pfp_1_allvalid.nc
11: ch4_dnd_aircraft-pfp_1_allvalid.nc
12: ch4_esp_aircraft-pfp_1_allvalid.nc
13: ch4_etl_aircraft-pfp_1_allvalid.nc
14: ch4_ftl_aircraft-pfp_1_allvalid.nc
15: ch4_fwi_aircraft-pfp_1_allvalid.nc
16: ch4_haa_aircraft-pfp_1_allvalid.nc
17: ch4_hfm_aircraft-pfp_1_allvalid.nc
18: ch4_hil_aircraft-pfp_1_allvalid.nc
19: ch4_hip_aircraft-pfp_1_allvalid.nc
20: ch4_inx_aircraft-pfp_1_allvalid.nc
21: ch4_lef_aircraft-pfp_1_allvalid.nc
22: ch4_man_aircraft-pfp_26_representative.nc
23: ch4_mci_aircraft-pfp_1_allvalid.nc
24: ch4_mrc_aircraft-pfp_1_allvalid.nc
25: ch4_nha_aircraft-pfp_1_allvalid.nc
26: ch4_oil_aircraft-pfp_1_allvalid.nc
27: ch4_pfa_aircraft-pfp_1_allvalid.nc
28: ch4_rba-b_aircraft-pfp_26_representative.nc
29: ch4_rta_aircraft-pfp_1_allvalid.nc
30: ch4_sah_aircraft-pfp_26_representative.nc
31: ch4_sam_aircraft-pfp_1_allvalid.nc
32: ch4_san_aircraft-pfp_1_allvalid.nc
33: ch4_san_aircraft-pfp_26_representative.nc
34: ch4_sca_aircraft-pfp_1_allvalid.nc
35: ch4_sgp_aircraft-pfp_1_allvalid.nc
36: ch4_tab_aircraft-pfp_26_representative.nc
37: ch4_tgc_aircraft-pfp_1_allvalid.nc
38: ch4_thd_aircraft-pfp_1_allvalid.nc
39: ch4_ulb_aircraft-pfp_1_allvalid.nc
40: ch4_wbi_aircraft-pfp_1_allvalid.nc
41: co2_aao_aircraft-pfp_1_allvalid.nc
42: co2_above_aircraft-pfp_1_allvalid.nc
43: co2_acg_aircraft-pfp_1_allvalid.nc
44: co2_act_aircraft-pfp_1_allvalid-b200.nc
45: co2_act_aircraft-pfp_1_allvalid-c130.nc
46: co2_alf_aircraft-pfp_433_representative.nc
47: co2_bgi_aircraft-pfp_1_allvalid.nc
48: co2_bne_aircraft-pfp_1_allvalid.nc
49: co2_car_aircraft-pfp_1_allvalid.nc
50: co2_cma_aircraft-pfp_1_allvalid.nc
51: co2_cob_aircraft-pfp_1_allvalid.nc
52: co2_crv_aircraft-pfp_1_allvalid.nc
53: co2_dnd_aircraft-pfp_1_allvalid.nc
54: co2_esp_aircraft-pfp_1_allvalid.nc
55: co2_etl_aircraft-pfp_1_allvalid.nc
56: co2_ftl_aircraft-pfp_1_allvalid.nc
57: co2_fwi_aircraft-pfp_1_allvalid.nc
58: co2_haa_aircraft-pfp_1_allvalid.nc
59: co2_hfm_aircraft-pfp_1_allvalid.nc
60: co2_hil_aircraft-pfp_1_allvalid.nc
61: co2_inx_aircraft-pfp_1_allvalid.nc
62: co2_lef_aircraft-pfp_1_allvalid.nc
63: co2_mci_aircraft-pfp_1_allvalid.nc
64: co2_mrc_aircraft-pfp_1_allvalid.nc
65: co2_nha_aircraft-pfp_1_allvalid.nc
66: co2_oil_aircraft-pfp_1_allvalid.nc
67: co2_pan_aircraft-pfp_433_representative.nc
68: co2_pfa_aircraft-pfp_1_allvalid.nc
69: co2_rba-b_aircraft-pfp_433_representative.nc
70: co2_rta_aircraft-pfp_1_allvalid.nc
71: co2_sam_aircraft-pfp_1_allvalid.nc
72: co2_san_aircraft-pfp_1_allvalid.nc
73: co2_san_aircraft-pfp_433_representative.nc
74: co2_sca_aircraft-pfp_1_allvalid.nc
75: co2_sgp_aircraft-pfp_1_allvalid.nc
76: co2_tab_aircraft-pfp_433_representative.nc
77: co2_tef_aircraft-pfp_433_representative.nc
78: co2_tgc_aircraft-pfp_1_allvalid.nc
79: co2_thd_aircraft-pfp_1_allvalid.nc
80: co2_ulb_aircraft-pfp_1_allvalid.nc
81: co2_wbi_aircraft-pfp_1_allvalid.ncNow we check the data
df year month day hour minute second time start_time midpoint_time
<int> <int> <int> <int> <int> <int> <int> <int> <int>
1: 2006 6 7 22 11 47 1149718307 1149718307 1149718307
2: 2006 6 7 22 28 22 1149719302 1149719302 1149719302
3: 2006 6 7 22 41 24 1149720084 1149720084 1149720084
4: 2006 6 7 22 55 31 1149720931 1149720931 1149720931
5: 2006 6 7 23 22 23 1149722543 1149722543 1149722543
---
176349: 2022 12 21 19 12 27 1671649947 1671649947 1671649947
176350: 2022 12 21 19 15 1 1671650101 1671650101 1671650101
176351: 2022 12 21 19 17 32 1671650252 1671650252 1671650252
176352: 2022 12 21 19 19 55 1671650395 1671650395 1671650395
176353: 2022 12 21 19 23 3 1671650583 1671650583 1671650583
datetime time_decimal time_interval value value_unc nvalue
<char> <num> <int> <num> <num> <int>
1: 2006-06-07T22:11:47Z 2006.433 3600 1.81998e-06 1.7e-09 1
2: 2006-06-07T22:28:22Z 2006.433 3600 1.81748e-06 1.7e-09 1
3: 2006-06-07T22:41:24Z 2006.433 3600 1.83108e-06 1.7e-09 1
4: 2006-06-07T22:55:31Z 2006.433 3600 1.83283e-06 1.7e-09 1
5: 2006-06-07T23:22:23Z 2006.433 3600 1.84134e-06 1.7e-09 1
---
176349: 2022-12-21T19:12:27Z 2022.972 3600 4.22600e-04 4.1e-08 1
176350: 2022-12-21T19:15:01Z 2022.972 3600 4.22920e-04 4.1e-08 1
176351: 2022-12-21T19:17:32Z 2022.972 3600 4.22120e-04 4.1e-08 1
176352: 2022-12-21T19:19:55Z 2022.972 3600 4.28270e-04 4.1e-08 1
176353: 2022-12-21T19:23:03Z 2022.972 3600 4.27920e-04 4.1e-08 1
value_std_dev latitude longitude altitude elevation intake_height qcflag
<num> <num> <num> <num> <num> <num> <char>
1: NA 40.1000 -88.5500 4758.10 210 4548.10 ...
2: NA 40.1000 -88.5500 3801.47 210 3591.47 ...
3: NA 40.1000 -88.5500 3170.38 210 2960.38 ...
4: NA 40.1000 -88.5500 2539.71 210 2329.71 ...
5: NA 40.1000 -88.5500 1905.77 210 1695.77 ...
---
176349: NA 41.7190 -91.5463 3008.99 227 2781.99 ...
176350: NA 41.6885 -91.5333 2387.80 240 2147.80 ...
176351: NA 41.6865 -91.3657 1763.88 230 1533.88 ...
176352: NA 41.6935 -91.2090 1124.71 247 877.71 ...
176353: NA 41.7063 -91.0566 950.98 219 731.98 ...
instrument analysis_datetime method event_number air_sample_container_id
<char> <char> <char> <char> <char>
1: H4 2006-06-16T17:06:00 A 218410 3141-01
2: H4 2006-06-16T17:21:00 A 218411 3141-02
3: H4 2006-06-16T17:36:00 A 218412 3141-03
4: H4 2006-06-16T17:51:00 A 218413 3141-04
5: H4 2006-06-16T18:20:00 A 218415 3141-06
---
176349: PC2 2023-01-03T22:20:27 R 512757 3036-08
176350: PC2 2023-01-03T22:35:42 R 512758 3036-09
176351: PC2 2023-01-03T22:50:56 R 512759 3036-10
176352: PC2 2023-01-03T23:06:10 R 512760 3036-11
176353: PC2 2023-01-03T23:21:24 R 512761 3036-12
obs_flag obspack_num
<int> <int>
1: 1 959079
2: 1 959080
3: 1 959081
4: 1 959082
5: 1 959083
---
176349: 1 1827113
176350: 1 1827114
176351: 1 1827115
176352: 1 1827116
176353: 1 1827117
obspack_id
<char>
1: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aao_aircraft-pfp_1_allvalid~959079
2: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aao_aircraft-pfp_1_allvalid~959080
3: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aao_aircraft-pfp_1_allvalid~959081
4: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aao_aircraft-pfp_1_allvalid~959082
5: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aao_aircraft-pfp_1_allvalid~959083
---
176349: obspack_co2_1_GLOBALVIEWplus_v9.1_2023-12-08~co2_wbi_aircraft-pfp_1_allvalid~1827113
176350: obspack_co2_1_GLOBALVIEWplus_v9.1_2023-12-08~co2_wbi_aircraft-pfp_1_allvalid~1827114
176351: obspack_co2_1_GLOBALVIEWplus_v9.1_2023-12-08~co2_wbi_aircraft-pfp_1_allvalid~1827115
176352: obspack_co2_1_GLOBALVIEWplus_v9.1_2023-12-08~co2_wbi_aircraft-pfp_1_allvalid~1827116
176353: obspack_co2_1_GLOBALVIEWplus_v9.1_2023-12-08~co2_wbi_aircraft-pfp_1_allvalid~1827117
unique_sample_location_num scale site_elevation_unit dataset_project
<int> <char> <char> <char>
1: 2718218 WMO CH4 X2004A masl aircraft-pfp
2: 2718192 WMO CH4 X2004A masl aircraft-pfp
3: 2718174 WMO CH4 X2004A masl aircraft-pfp
4: 2718138 WMO CH4 X2004A masl aircraft-pfp
5: 2718094 WMO CH4 X2004A masl aircraft-pfp
---
176349: 55771244 WMO CO2 X2019 masl aircraft-pfp
176350: 55771219 WMO CO2 X2019 masl aircraft-pfp
176351: 55771217 WMO CO2 X2019 masl aircraft-pfp
176352: 55771222 WMO CO2 X2019 masl aircraft-pfp
176353: 55771235 WMO CO2 X2019 masl aircraft-pfp
dataset_selection_tag site_name
<char> <char>
1: allvalid Airborne Aerosol Observatory, Bondville, Illinois
2: allvalid Airborne Aerosol Observatory, Bondville, Illinois
3: allvalid Airborne Aerosol Observatory, Bondville, Illinois
4: allvalid Airborne Aerosol Observatory, Bondville, Illinois
5: allvalid Airborne Aerosol Observatory, Bondville, Illinois
---
176349: allvalid West Branch, Iowa
176350: allvalid West Branch, Iowa
176351: allvalid West Branch, Iowa
176352: allvalid West Branch, Iowa
176353: allvalid West Branch, Iowa
site_elevation site_latitude site_longitude site_country site_code site_utc2lst
<num> <num> <num> <char> <char> <num>
1: 230.0 40.0500 -88.3700 United States AAO -6
2: 230.0 40.0500 -88.3700 United States AAO -6
3: 230.0 40.0500 -88.3700 United States AAO -6
4: 230.0 40.0500 -88.3700 United States AAO -6
5: 230.0 40.0500 -88.3700 United States AAO -6
---
176349: 241.7 41.7248 -91.3529 United States WBI -6
176350: 241.7 41.7248 -91.3529 United States WBI -6
176351: 241.7 41.7248 -91.3529 United States WBI -6
176352: 241.7 41.7248 -91.3529 United States WBI -6
176353: 241.7 41.7248 -91.3529 United States WBI -6
lab_1_abbr dataset_calibration_scale altitude_final type_altitude air_flag
<char> <char> <num> <num> <int>
1: NOAA WMO CH4 X2004A 4758.10 1 NA
2: NOAA WMO CH4 X2004A 3801.47 1 NA
3: NOAA WMO CH4 X2004A 3170.38 1 NA
4: NOAA WMO CH4 X2004A 2539.71 1 NA
5: NOAA WMO CH4 X2004A 1905.77 1 NA
---
176349: NOAA WMO CO2 X2019 3008.99 1 NA
176350: NOAA WMO CO2 X2019 2387.80 1 NA
176351: NOAA WMO CO2 X2019 1763.88 1 NA
176352: NOAA WMO CO2 X2019 1124.71 1 NA
176353: NOAA WMO CO2 X2019 950.98 1 NA
bl_bt_flag CT_assim CT_may_localize CT_may_reject CT_MDM CT_RMSE
<int> <int> <int> <int> <num> <num>
1: NA NA NA NA NA NA
2: NA NA NA NA NA NA
3: NA NA NA NA NA NA
4: NA NA NA NA NA NA
5: NA NA NA NA NA NA
---
176349: NA 1 1 1 7.204303e-07 7.583477e-07
176350: NA 1 1 1 9.783121e-07 1.029802e-06
176351: NA 1 1 1 1.614815e-06 1.699805e-06
176352: NA 1 1 1 1.614815e-06 1.699805e-06
176353: NA 1 1 1 2.754005e-06 2.898953e-06
CT_sampling_strategy flight_flag flight_id gps_altitude h2o maneuver_flag
<int> <int> <char> <num> <num> <int>
1: NA NA <NA> NA NA NA
2: NA NA <NA> NA NA NA
3: NA NA <NA> NA NA NA
4: NA NA <NA> NA NA NA
5: NA NA <NA> NA NA NA
---
176349: 2 NA <NA> NA NA NA
176350: 2 NA <NA> NA NA NA
176351: 2 NA <NA> NA NA NA
176352: 2 NA <NA> NA NA NA
176353: 2 NA <NA> NA NA NA
maneuver_flagqc pressure pressure_altitude profile_id source_id temperature u
<int> <num> <num> <char> <char> <num> <num>
1: NA NA NA <NA> <NA> NA NA
2: NA NA NA <NA> <NA> NA NA
3: NA NA NA <NA> <NA> NA NA
4: NA NA NA <NA> <NA> NA NA
5: NA NA NA <NA> <NA> NA NA
---
176349: NA NA NA <NA> <NA> NA NA
176350: NA NA NA <NA> <NA> NA NA
176351: NA NA NA <NA> <NA> NA NA
176352: NA NA NA <NA> <NA> NA NA
176353: NA NA NA <NA> <NA> NA NA
v value_original_scale
<num> <num>
1: NA NA
2: NA NA
3: NA NA
4: NA NA
5: NA NA
---
176349: NA NA
176350: NA NA
176351: NA NA
176352: NA NA
176353: 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: 4758.10 4758.10 4548.10 210
2: 3801.47 3801.47 3591.47 210
3: 3170.38 3170.38 2960.38 210
4: 2539.71 2539.71 2329.71 210
5: 1905.77 1905.77 1695.77 210
---
176349: 3008.99 3008.99 2781.99 227
176350: 2387.80 2387.80 2147.80 240
176351: 1763.88 1763.88 1533.88 230
176352: 1124.71 1124.71 877.71 247
176353: 950.98 950.98 731.98 219The temporal range of data is
range(df$year)[1] 1992 2021We also check for dimensions of data
dim(df)[1] 176353 68Filters
df <- df[year == yy]
df <- df[altitude_final < max_altitude &
latitude < north &
latitude > south &
longitude < east &
longitude > west]
dim(df)[1] 5615 68Adding time as POSIXct class
df <- obs_addtime(df)
df[, "timeUTC"] timeUTC
<POSc>
1: 2020-01-02 19:50:20
2: 2020-01-02 19:59:18
3: 2020-01-02 20:08:11
4: 2020-01-02 20:16:34
5: 2020-01-02 20:23:47
---
5611: 2020-12-28 20:21:43
5612: 2020-12-28 20:23:59
5613: 2020-12-28 20:26:07
5614: 2020-12-28 20:28:09
5615: 2020-12-28 20:30:23Cut time
now we can cut time every 20 seconds. We can chosse other frequency as well.
second sec2
<int> <num>
1: 20 0
2: 18 0
3: 11 0
4: 34 20
5: 47 40
---
5611: 43 40
5612: 59 40
5613: 7 0
5614: 9 0
5615: 23 20Aggregate data
now we need to add the column key_time, that it will be used to aggregate other variables
df$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: 2020-01-02 19:50:20 2020-01-02 19:50:00
2: 2020-01-02 19:59:18 2020-01-02 19:59:00
3: 2020-01-02 20:08:11 2020-01-02 20:08:00
4: 2020-01-02 20:16:34 2020-01-02 20:16:20
5: 2020-01-02 20:23:47 2020-01-02 20:23:40
---
5611: 2020-12-28 20:21:43 2020-12-28 20:21:40
5612: 2020-12-28 20:23:59 2020-12-28 20:23:40
5613: 2020-12-28 20:26:07 2020-12-28 20:26:00
5614: 2020-12-28 20:28:09 2020-12-28 20:28:00
5615: 2020-12-28 20:30:23 2020-12-28 20:30:20
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"))Processed 5615 groups out of 5615. 100% done. Time elapsed: 4s. ETA: 0s.
Adding timeAdd local time
Now we add local time
df3 <- obs_addltime(df2)
setorderv(df3, cols = c("site_code", "timeUTC", "dataset_calibration_scale"),
order = c(-1, 1, 1))
df3 key_time site_code altitude_final type_altitude lab_1_abbr
<POSc> <char> <num> <num> <char>
1: 2020-01-14 18:28:20 WBI 7618.48 1 NOAA
2: 2020-01-14 18:33:00 WBI 6958.89 1 NOAA
3: 2020-01-14 18:37:40 WBI 5685.43 1 NOAA
4: 2020-01-14 18:40:40 WBI 4987.14 1 NOAA
5: 2020-01-14 18:43:20 WBI 4333.95 1 NOAA
---
5611: 2020-12-25 21:09:20 CAR 2926.69 1 NOAA
5612: 2020-12-25 21:20:20 CAR 2453.94 1 NOAA
5613: 2020-12-25 21:27:20 CAR 2138.78 1 NOAA
5614: 2020-12-25 21:33:00 CAR 1832.76 1 NOAA
5615: 2020-12-25 21:35:40 CAR 1668.78 1 NOAA
dataset_calibration_scale year month day hour minute second time
<char> <int> <int> <char> <int> <int> <int> <num>
1: WMO CH4 X2004A 2020 1 14 18 28 20 1579026500
2: WMO CH4 X2004A 2020 1 14 18 33 0 1579026780
3: WMO CH4 X2004A 2020 1 14 18 37 40 1579027060
4: WMO CH4 X2004A 2020 1 14 18 40 40 1579027240
5: WMO CH4 X2004A 2020 1 14 18 43 20 1579027400
---
5611: WMO CO2 X2019 2020 12 25 21 9 20 1608930560
5612: WMO CO2 X2019 2020 12 25 21 20 20 1608931220
5613: WMO CO2 X2019 2020 12 25 21 27 20 1608931640
5614: WMO CO2 X2019 2020 12 25 21 33 0 1608931980
5615: WMO CO2 X2019 2020 12 25 21 35 40 1608932140
time_decimal value latitude longitude altitude_final elevation intake_height
<num> <num> <num> <num> <num> <num> <num>
1: 2020.038 1.91295e-06 41.7505 -91.0213 7618.48 242 7376.48
2: 2020.038 1.92525e-06 41.7427 -91.1791 6958.89 234 6724.89
3: 2020.038 1.92408e-06 41.7232 -91.3675 5685.43 236 5449.43
4: 2020.038 1.91808e-06 41.7275 -91.4877 4987.14 244 4743.14
5: 2020.038 1.92311e-06 41.7177 -91.5966 4333.95 235 4098.95
---
5611: 2020.983 4.16040e-04 40.7275 -104.6853 2926.69 1559 1367.69
5612: 2020.983 4.16090e-04 40.7813 -104.6506 2453.94 1590 863.94
5613: 2020.983 4.15690e-04 40.7955 -104.6787 2138.78 1599 539.78
5614: 2020.983 4.15970e-04 40.6879 -104.6850 1832.76 1543 289.76
5615: 2020.983 4.18430e-04 40.7908 -104.6855 1668.78 1614 54.78
gps_altitude pressure pressure_altitude u v temperature type_altitude
<num> <num> <num> <num> <num> <num> <num>
1: NaN NaN NaN NaN NaN NaN 1
2: NaN NaN NaN NaN NaN NaN 1
3: NaN NaN NaN NaN NaN NaN 1
4: NaN NaN NaN NaN NaN NaN 1
5: NaN NaN NaN NaN NaN NaN 1
---
5611: NaN NaN NaN NaN NaN NaN 1
5612: NaN NaN NaN NaN NaN NaN 1
5613: NaN NaN NaN NaN NaN NaN 1
5614: NaN NaN NaN NaN NaN NaN 1
5615: NaN NaN NaN NaN NaN NaN 1
timeUTC local_time lh
<POSc> <POSc> <int>
1: 2020-01-14 18:28:20 2020-01-14 12:24:14 12
2: 2020-01-14 18:33:00 2020-01-14 12:28:17 12
3: 2020-01-14 18:37:40 2020-01-14 12:32:11 12
4: 2020-01-14 18:40:40 2020-01-14 12:34:42 12
5: 2020-01-14 18:43:20 2020-01-14 12:36:56 12
---
5611: 2020-12-25 21:09:20 2020-12-25 14:10:35 14
5612: 2020-12-25 21:20:20 2020-12-25 14:21:43 14
5613: 2020-12-25 21:27:20 2020-12-25 14:28:37 14
5614: 2020-12-25 21:33:00 2020-12-25 14:34:15 14
5615: 2020-12-25 21:35:40 2020-12-25 14:36:55 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\RtmpAj3Z3p\file7f9c18b239ca_aircraft-pfp.txtcsv
message(paste0(out,"_", datasetid, ".csv\n"))
fwrite(master,
paste0(out,"_", datasetid, ".csv"),
sep = ",")C:\Users\sibarrae\AppData\Local\Temp\RtmpAj3Z3p\file7f9c18b239ca_aircraft-pfp.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),
"gas: CO2 and CH4",
"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-pfp
timespan: 2020
spatial_limits: north = 80, south = 10, east = -50, west = -170
gas: CO2 and CH4
data: Data averaged every 20 seconds
altitude: < 8000
C:\Users\sibarrae\AppData\Local\Temp\RtmpAj3Z3p\file7f9c18b239ca_aircraft-pfp.csvy
obs_read_csvy(paste0(out,"_", datasetid, ".csvy")) [1] "---"
[2] "name: Metadata "
[3] "sector: aircraft-pfp"
[4] "timespan: 2020"
[5] "spatial_limits: north = 80, south = 10, east = -50, west = -170"
[6] "gas: CO2 and CH4"
[7] "data: Data averaged every 20 seconds"
[8] "altitude: < 8000"
[9] "structure: "
[10] "Classes ‘data.table’ and 'data.frame':\t5615 obs. of 30 variables:"
[11] " $ key_time : POSIXct, format: \"2020-01-1\".."
[12] " $ site_code : chr \"WBI\" \"WBI\" ..."
[13] " $ altitude_final : num 7618 7618 ..."
[14] " $ type_altitude : num 1 1 1 1 1 ..."
[15] " $ lab_1_abbr : chr \"NOAA\" \"NOAA\" ..."
[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 \"14\" \"14\" ..."
[20] " $ hour : int 18 18 18 18 18 ..."
[21] " $ minute : int 28 28 33 33 37 ..."
[22] " $ second : int 20 20 0 0 40 ..."
[23] " $ time : num 1.58e+09 1.58e+09 ..."
[24] " $ time_decimal : num 2020 2020 ..."
[25] " $ value : num 1.91e-06 4.14e-04 ..."
[26] " $ latitude : num 41.8 41.8 ..."
[27] " $ longitude : num -91 -91 ..."
[28] " $ altitude_final : num 7618 7618 ..."
[29] " $ elevation : num 242 242 234 234 236 ..."
[30] " $ intake_height : num 7376 7376 ..."
[31] " $ gps_altitude : num NaN NaN NaN NaN NaN ..."
[32] " $ pressure : num NaN NaN NaN NaN NaN ..."
[33] " $ pressure_altitude : num NaN NaN NaN NaN NaN ..."
[34] " $ u : num NaN NaN NaN NaN NaN ..."
[35] " $ v : num NaN NaN NaN NaN NaN ..."
[36] " $ temperature : num NaN NaN NaN NaN NaN ..."
[37] " $ type_altitude : num 1 1 1 1 1 ..."
[38] " $ timeUTC : chr \"2020-01-14 18:28:20\" \".."
[39] " $ local_time : chr \"2020-01-14 12:24:14.8\".."
[40] " $ lh : int 12 12 12 12 12 ..."
[41] " - attr(*, \".internal.selfref\")=<externalptr> "
[42] "NULL"
[43] "---"
key_time site_code altitude_final type_altitude lab_1_abbr
<POSc> <char> <num> <int> <char>
1: 2020-01-14 18:28:20 WBI 7618.48 1 NOAA
2: 2020-01-14 18:28:20 WBI 7618.48 1 NOAA
3: 2020-01-14 18:33:00 WBI 6958.89 1 NOAA
4: 2020-01-14 18:33:00 WBI 6958.89 1 NOAA
5: 2020-01-14 18:37:40 WBI 5685.43 1 NOAA
---
5611: 2020-12-25 21:27:20 CAR 2138.78 1 NOAA
5612: 2020-12-25 21:33:00 CAR 1832.76 1 NOAA
5613: 2020-12-25 21:33:00 CAR 1832.76 1 NOAA
5614: 2020-12-25 21:35:40 CAR 1668.78 1 NOAA
5615: 2020-12-25 21:35:40 CAR 1668.78 1 NOAA
dataset_calibration_scale year month day hour minute second time
<char> <int> <int> <int> <int> <int> <int> <int>
1: WMO CH4 X2004A 2020 1 14 18 28 20 1579026500
2: WMO CO2 X2019 2020 1 14 18 28 20 1579026500
3: WMO CH4 X2004A 2020 1 14 18 33 0 1579026780
4: WMO CO2 X2019 2020 1 14 18 33 0 1579026780
5: WMO CH4 X2004A 2020 1 14 18 37 40 1579027060
---
5611: WMO CO2 X2019 2020 12 25 21 27 20 1608931640
5612: WMO CH4 X2004A 2020 12 25 21 33 0 1608931980
5613: WMO CO2 X2019 2020 12 25 21 33 0 1608931980
5614: WMO CH4 X2004A 2020 12 25 21 35 40 1608932140
5615: WMO CO2 X2019 2020 12 25 21 35 40 1608932140
time_decimal value latitude longitude altitude_final elevation intake_height
<num> <num> <num> <num> <num> <int> <num>
1: 2020.038 1.91295e-06 41.7505 -91.0213 7618.48 242 7376.48
2: 2020.038 4.13850e-04 41.7505 -91.0213 7618.48 242 7376.48
3: 2020.038 1.92525e-06 41.7427 -91.1791 6958.89 234 6724.89
4: 2020.038 4.14660e-04 41.7427 -91.1791 6958.89 234 6724.89
5: 2020.038 1.92408e-06 41.7232 -91.3675 5685.43 236 5449.43
---
5611: 2020.983 4.15690e-04 40.7955 -104.6787 2138.78 1599 539.78
5612: 2020.983 1.93514e-06 40.6879 -104.6850 1832.76 1543 289.76
5613: 2020.983 4.15970e-04 40.6879 -104.6850 1832.76 1543 289.76
5614: 2020.983 2.00509e-06 40.7908 -104.6855 1668.78 1614 54.78
5615: 2020.983 4.18430e-04 40.7908 -104.6855 1668.78 1614 54.78
gps_altitude pressure pressure_altitude u v temperature type_altitude
<lgcl> <lgcl> <lgcl> <lgcl> <lgcl> <lgcl> <int>
1: NA NA NA NA NA NA 1
2: NA NA NA NA NA NA 1
3: NA NA NA NA NA NA 1
4: NA NA NA NA NA NA 1
5: NA NA NA NA NA NA 1
---
5611: NA NA NA NA NA NA 1
5612: NA NA NA NA NA NA 1
5613: NA NA NA NA NA NA 1
5614: NA NA NA NA NA NA 1
5615: NA NA NA NA NA NA 1
timeUTC local_time lh
<POSc> <POSc> <int>
1: 2020-01-14 18:28:20 2020-01-14 12:24:14 12
2: 2020-01-14 18:28:20 2020-01-14 12:24:14 12
3: 2020-01-14 18:33:00 2020-01-14 12:28:17 12
4: 2020-01-14 18:33:00 2020-01-14 12:28:17 12
5: 2020-01-14 18:37:40 2020-01-14 12:32:11 12
---
5611: 2020-12-25 21:27:20 2020-12-25 14:28:37 14
5612: 2020-12-25 21:33:00 2020-12-25 14:34:15 14
5613: 2020-12-25 21:33:00 2020-12-25 14:34:15 14
5614: 2020-12-25 21:35:40 2020-12-25 14:36:55 14
5615: 2020-12-25 21:35:40 2020-12-25 14:36:55 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\RtmpAj3Z3p\file7f9c18b239ca_aircraft-pfp_receptor_ASL.txtPlot
Finally, we just plot some data, run it locally
obs_plot(df3[dataset_calibration_scale == "WMO CO2 X2019"],
time = "timeUTC",
yfactor = 1e6)Found the following sites:
[1] WBI THD TGC SGP SCA PFA NHA LEF HIL ETL ESP CMA CAR
Plotting the following sites:
[1] WBI THD
Time series
library(sf)
x <- df3[dataset_calibration_scale == "WMO CO2 X2019",
mean(value*1e+6),
by = .(site_code,
latitude,
longitude)]
x <- st_as_sf(x,
coords = c("longitude",
"latitude"),
crs = 4326)
plot(x["V1"],
axes = T,
reset = F)
maps::map(add = T)
Map
x <- df3[dataset_calibration_scale == "WMO CO2 X2019"]
x$co2 <- x$value*1e+6
obs_plot(x,
time = "co2",
y = "altitude_final",
colu = "site_code", #n = c(1L, 3L, 6L, 8L, 9L, 11L, 12L),
type = "p",
xlab = expression(CO[2]~ppm),
ylab = "altitude (m)")
Vertical profiles