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: 429
#> sector N
#> <char> <int>
#> 1: aircraft-pfp 40
#> 2: aircraft-insitu 15
#> 3: surface-flask 106
#> 4: surface-insitu 174
#> 5: aircraft-flask 4
#> 6: aircore 1
#> 7: surface-pfp 33
#> 8: tower-insitu 51
#> 9: shipboard-flask 4
#> 10: shipboard-insitu 1
#> 11: Total sectors 429
#> Detected 190 files with agl
#> Detected 239 files without aglNow we read the surface-pfp using the function
obs_read_nc. solar_time is included for
surface, so we TRUE that argument.
datasetid <- "surface-pfp"
df <- obs_read_nc(index = index,
categories = datasetid,
solar_time = TRUE,
verbose = TRUE)#> Searching surface-pfp...
#> 1: ch4_amt_surface-pfp_1_allvalid-107magl.nc
#> 2: ch4_bao_surface-pfp_1_allvalid-300magl.nc
#> 3: ch4_crv_surface-pfp_1_allvalid-32magl.nc
#> 4: ch4_inx_surface-pfp_1_allvalid-121magl.nc
#> 5: ch4_inx_surface-pfp_1_allvalid-125magl.nc
#> 6: ch4_inx_surface-pfp_1_allvalid-129magl.nc
#> 7: ch4_inx_surface-pfp_1_allvalid-130magl.nc
#> 8: ch4_inx_surface-pfp_1_allvalid-137magl.nc
#> 9: ch4_inx_surface-pfp_1_allvalid-39magl.nc
#> 10: ch4_inx_surface-pfp_1_allvalid-40magl.nc
#> 11: ch4_inx_surface-pfp_1_allvalid-54magl.nc
#> 12: ch4_klm_surface-pfp_1_allvalid-4magl.nc
#> 13: ch4_lef_surface-pfp_1_allvalid-244magl.nc
#> 14: ch4_lef_surface-pfp_1_allvalid-396magl.nc
#> 15: ch4_lew_surface-pfp_1_allvalid-95magl.nc
#> 16: ch4_mbo_surface-pfp_1_allvalid-11magl.nc
#> 17: ch4_mrc_surface-pfp_1_allvalid-east.nc
#> 18: ch4_mrc_surface-pfp_1_allvalid-south.nc
#> 19: ch4_msh_surface-pfp_1_allvalid-46magl.nc
#> 20: ch4_mvy_surface-pfp_1_allvalid-16magl.nc
#> 21: ch4_mwo_surface-pfp_1_allvalid-46magl.nc
#> 22: ch4_nwf_surface-pfp_1_allvalid-23magl.nc
#> 23: ch4_nwf_surface-pfp_1_allvalid-2magl.nc
#> 24: ch4_nwr_surface-pfp_1_allvalid-3magl.nc
#> 25: ch4_sct_surface-pfp_1_allvalid-305magl.nc
#> 26: ch4_sgp_surface-pfp_1_allvalid-60magl.nc
#> 27: ch4_sgp_surface-pfp_1_allvalid-9magl.nc
#> 28: ch4_str_surface-pfp_1_allvalid-232magl.nc
#> 29: ch4_wbi_surface-pfp_1_allvalid-379magl.nc
#> 30: ch4_wgc_surface-pfp_1_allvalid-484magl.nc
#> 31: ch4_wgc_surface-pfp_1_allvalid-89magl.nc
#> 32: ch4_wkt_surface-pfp_1_allvalid-122magl.nc
#> 33: ch4_wkt_surface-pfp_1_allvalid-457magl.ncNow we check the data
df#> year month day hour minute second time start_time
#> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1: 2008 11 23 16 22 49 1227457369 1227457369
#> 2: 2008 11 23 16 29 19 1227457759 1227457759
#> 3: 2008 11 23 16 35 54 1227458154 1227458154
#> 4: 2008 11 23 16 40 21 1227458421 1227458421
#> 5: 2008 11 23 16 44 47 1227458687 1227458687
#> ---
#> 43159: 2021 12 23 20 45 5 1640292305 1640292305
#> 43160: 2021 12 25 20 45 8 1640465108 1640465108
#> 43161: 2021 12 27 20 45 6 1640637906 1640637906
#> 43162: 2021 12 29 20 45 13 1640810713 1640810713
#> 43163: 2021 12 31 20 45 10 1640983510 1640983510
#> midpoint_time datetime time_decimal time_interval
#> <int> <char> <num> <int>
#> 1: 1227457369 2008-11-23T16:22:49Z 2008.895 3600
#> 2: 1227457759 2008-11-23T16:29:19Z 2008.895 3600
#> 3: 1227458154 2008-11-23T16:35:54Z 2008.895 3600
#> 4: 1227458421 2008-11-23T16:40:21Z 2008.895 3600
#> 5: 1227458687 2008-11-23T16:44:47Z 2008.895 3600
#> ---
#> 43159: 1640292305 2021-12-23T20:45:05Z 2021.978 3600
#> 43160: 1640465108 2021-12-25T20:45:08Z 2021.983 3600
#> 43161: 1640637906 2021-12-27T20:45:06Z 2021.989 3600
#> 43162: 1640810713 2021-12-29T20:45:13Z 2021.994 3600
#> 43163: 1640983510 2021-12-31T20:45:10Z 2022.000 3600
#> value value_unc nvalue value_std_dev latitude longitude altitude
#> <num> <num> <int> <num> <num> <num> <num>
#> 1: 1.87433e-06 1.31e-09 1 NA 45.0345 -68.6821 160.4
#> 2: 1.87468e-06 1.31e-09 1 NA 45.0345 -68.6821 160.4
#> 3: 1.87651e-06 1.31e-09 1 NA 45.0345 -68.6821 160.4
#> 4: 1.87619e-06 1.31e-09 1 NA 45.0345 -68.6821 160.4
#> 5: 1.87548e-06 1.31e-09 1 NA 45.0345 -68.6821 160.4
#> ---
#> 43159: 2.04697e-06 5.75e-10 1 NA 31.3149 -97.3269 708.0
#> 43160: 2.01324e-06 5.75e-10 1 NA 31.3149 -97.3269 708.0
#> 43161: 1.99720e-06 5.75e-10 2 2.828427e-11 31.3149 -97.3269 708.0
#> 43162: 2.03372e-06 5.75e-10 1 NA 31.3149 -97.3269 708.0
#> 43163: 2.03007e-06 5.75e-10 1 NA 31.3149 -97.3269 708.0
#> elevation intake_height qcflag instrument analysis_datetime method
#> <num> <num> <char> <char> <char> <char>
#> 1: 52.4 108 .S. H6 2008-12-03T00:35:00 A
#> 2: 52.4 108 .S. H6 2008-12-03T00:51:00 A
#> 3: 52.4 108 .S. H6 2008-12-03T01:07:00 A
#> 4: 52.4 108 .S. H6 2008-12-03T01:23:00 A
#> 5: 52.4 108 .S. H6 2008-12-03T01:39:00 A
#> ---
#> 43159: 251.0 457 ... PC2 2022-01-08T07:20:56 B
#> 43160: 251.0 457 ... PC2 2022-01-08T07:36:12 B
#> 43161: 251.0 457 ... PC2 2022-01-08T08:06:44 B
#> 43162: 251.0 457 ... PC2 2022-01-08T08:22:01 B
#> 43163: 251.0 457 ... PC2 2022-01-08T08:37:18 B
#> event_number air_sample_container_id obs_flag obspack_num
#> <char> <char> <int> <int>
#> 1: 264955 3065-03 0 1131726
#> 2: 264956 3065-04 0 1131727
#> 3: 264957 3065-05 0 1131728
#> 4: 264958 3065-06 0 1131729
#> 5: 264959 3065-07 0 1131730
#> ---
#> 43159: 498370 3939-03 1 1172783
#> 43160: 498372 3939-05 1 1172784
#> 43161: 498374,498375 3939-07,3939-08 1 1172785
#> 43162: 498376 3939-09 1 1172786
#> 43163: 498378 3939-11 1 1172787
#> obspack_id
#> <char>
#> 1: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_amt_surface-pfp_1_allvalid-107magl~1131726
#> 2: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_amt_surface-pfp_1_allvalid-107magl~1131727
#> 3: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_amt_surface-pfp_1_allvalid-107magl~1131728
#> 4: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_amt_surface-pfp_1_allvalid-107magl~1131729
#> 5: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_amt_surface-pfp_1_allvalid-107magl~1131730
#> ---
#> 43159: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_wkt_surface-pfp_1_allvalid-457magl~1172783
#> 43160: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_wkt_surface-pfp_1_allvalid-457magl~1172784
#> 43161: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_wkt_surface-pfp_1_allvalid-457magl~1172785
#> 43162: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_wkt_surface-pfp_1_allvalid-457magl~1172786
#> 43163: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_wkt_surface-pfp_1_allvalid-457magl~1172787
#> unique_sample_location_num year_st month_st day_st hour_st minute_st
#> <int> <int> <int> <int> <int> <int>
#> 1: 50239326 2008 11 23 12 1
#> 2: 50239327 2008 11 23 12 7
#> 3: 50239328 2008 11 23 12 14
#> 4: 50239329 2008 11 23 12 18
#> 5: 50239330 2008 11 23 12 23
#> ---
#> 43159: 48733613 2021 12 23 14 16
#> 43160: 48733614 2021 12 25 14 15
#> 43161: 48733615 2021 12 27 14 14
#> 43162: 48733616 2021 12 29 14 13
#> 43163: 48733617 2021 12 31 14 12
#> second_st scale site_elevation_unit dataset_project
#> <int> <char> <char> <char>
#> 1: 4 WMO CH4 X2004A masl surface-pfp
#> 2: 34 WMO CH4 X2004A masl surface-pfp
#> 3: 9 WMO CH4 X2004A masl surface-pfp
#> 4: 36 WMO CH4 X2004A masl surface-pfp
#> 5: 2 WMO CH4 X2004A masl surface-pfp
#> ---
#> 43159: 33 WMO CH4 X2004A masl surface-pfp
#> 43160: 40 WMO CH4 X2004A masl surface-pfp
#> 43161: 43 WMO CH4 X2004A masl surface-pfp
#> 43162: 55 WMO CH4 X2004A masl surface-pfp
#> 43163: 57 WMO CH4 X2004A masl surface-pfp
#> dataset_selection_tag site_name site_elevation site_latitude
#> <char> <char> <num> <num>
#> 1: allvalid-107magl Argyle, Maine 52.4 45.0345
#> 2: allvalid-107magl Argyle, Maine 52.4 45.0345
#> 3: allvalid-107magl Argyle, Maine 52.4 45.0345
#> 4: allvalid-107magl Argyle, Maine 52.4 45.0345
#> 5: allvalid-107magl Argyle, Maine 52.4 45.0345
#> ---
#> 43159: allvalid-457magl Moody, Texas 251.0 31.3149
#> 43160: allvalid-457magl Moody, Texas 251.0 31.3149
#> 43161: allvalid-457magl Moody, Texas 251.0 31.3149
#> 43162: allvalid-457magl Moody, Texas 251.0 31.3149
#> 43163: allvalid-457magl Moody, Texas 251.0 31.3149
#> site_longitude site_country site_code site_utc2lst lab_1_abbr
#> <num> <char> <char> <num> <char>
#> 1: -68.6821 United States AMT -5 NOAA
#> 2: -68.6821 United States AMT -5 NOAA
#> 3: -68.6821 United States AMT -5 NOAA
#> 4: -68.6821 United States AMT -5 NOAA
#> 5: -68.6821 United States AMT -5 NOAA
#> ---
#> 43159: -97.3269 United States WKT -6 NOAA
#> 43160: -97.3269 United States WKT -6 NOAA
#> 43161: -97.3269 United States WKT -6 NOAA
#> 43162: -97.3269 United States WKT -6 NOAA
#> 43163: -97.3269 United States WKT -6 NOAA
#> dataset_calibration_scale altitude_final type_altitude
#> <char> <num> <num>
#> 1: WMO CH4 X2004A 108 NA
#> 2: WMO CH4 X2004A 108 NA
#> 3: WMO CH4 X2004A 108 NA
#> 4: WMO CH4 X2004A 108 NA
#> 5: WMO CH4 X2004A 108 NA
#> ---
#> 43159: WMO CH4 X2004A 457 0
#> 43160: WMO CH4 X2004A 457 0
#> 43161: WMO CH4 X2004A 457 0
#> 43162: WMO CH4 X2004A 457 0
#> 43163: WMO CH4 X2004A 457 0
#> dataset_intake_ht_unit
#> <char>
#> 1: <NA>
#> 2: <NA>
#> 3: <NA>
#> 4: <NA>
#> 5: <NA>
#> ---
#> 43159: magl
#> 43160: magl
#> 43161: magl
#> 43162: magl
#> 43163: maglNow 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 <- 2020
evening <- 14We 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",
"dataset_selection_tag",
"site_name")]#> altitude altitude_final intake_height elevation dataset_selection_tag
#> <num> <num> <num> <num> <char>
#> 1: 160.4 108 108 52.4 allvalid-107magl
#> 2: 160.4 108 108 52.4 allvalid-107magl
#> 3: 160.4 108 108 52.4 allvalid-107magl
#> 4: 160.4 108 108 52.4 allvalid-107magl
#> 5: 160.4 108 108 52.4 allvalid-107magl
#> ---
#> 43159: 708.0 457 457 251.0 allvalid-457magl
#> 43160: 708.0 457 457 251.0 allvalid-457magl
#> 43161: 708.0 457 457 251.0 allvalid-457magl
#> 43162: 708.0 457 457 251.0 allvalid-457magl
#> 43163: 708.0 457 457 251.0 allvalid-457magl
#> site_name
#> <char>
#> 1: Argyle, Maine
#> 2: Argyle, Maine
#> 3: Argyle, Maine
#> 4: Argyle, Maine
#> 5: Argyle, Maine
#> ---
#> 43159: Moody, Texas
#> 43160: Moody, Texas
#> 43161: Moody, Texas
#> 43162: Moody, Texas
#> 43163: Moody, TexasThe temporal range of data is
range(df$year)#> [1] 2005 2021We also check for dimensions of data
dim(df)#> [1] 43163 53Filters
df <- df[year == yy]
df <- df[altitude_final < max_altitude &
latitude < north &
latitude > south &
longitude < east &
longitude > west]
dim(df)#> [1] 2205 53Towers can have observations at different heights. Here we need to
select one site with the observations registered at the highest height.
The column with the height is named altitude_final and the
max altitude was named max_altitude.
#> site_code max_altitude
#> <char> <num>
#> 1: AMT 108.00
#> 2: CRV 31.70
#> 3: LEF 396.00
#> 4: LEW 95.00
#> 5: MBO 11.30
#> 6: MRC 60.97
#> 7: MSH 46.30
#> 8: MWO 45.90
#> 9: NWR 3.20
#> 10: SCT 304.80
#> 11: SGP 60.00
#> 12: WBI 378.90
#> 13: WGC 89.10
#> 14: WKT 122.00Key Time
Here we need to start time columns. The function
obs_addtime adds time columns timeUTC,
timeUTC_start which shows the start time of each
observation and timeUTC_end which shows the end time for
each observation.
df2 <- obs_addtime(df)#> Adding timeUTC
#> Adding timeUTC_start
#> Adding timeUTC_end
#> Found time_intervalThen we need a key_time to aggregate data. This can be done using UTC, solar, or local time. The normal approach is using afternoon solar or local time.
Hierarchy of solar or local time
- Solar time
- Local time with columns
site_utc2lst - Local time longitude
solar time (default)
Here we select the hours of interest and then aggregate data by year, month and day of solar time. In this way, we will have one information per day. however this approach is not appropriate for aircraft which are aggregated every 10 or 20 seconds. Hence we need to aggregate data by one time column. Also, this helps to generate the receptor info files including hour, minute and second. Hence, we need to add solar or local time column.
df2$solar_time <- obs_addstime(df2)local time with column
site_utc2lst
Then we need to identify the local time with the function
add_ltime. This is important because to identifying
observations in the evening in local time. add_ltime uses
two methods, first identify the time difference with utc by identifying
the metadata column “site_utc2lst”. If solar time is not available #now
we need to cut solar time for the frequency needed. As we will work
with
local time longitude
If this information is not available, with the aircrafts for instance, the local time is calculated with an approximation based on longitude:
Where is the local time, the time, the coordinate. Then, the time is cut every two hours. Now, we identify the local time to select evening hours.
Cut time
Now we have they key column time, we can cut it accordingly.
df2$solar_time_cut <- cut(x = df2$solar_time,
breaks = "1 hour") |>
as.character()How we can check the solar time and the cut solar time. Please note that solar_time_cut, the column that it will be used to aggregate data
How we filter for the required solar time, in this case 14.
#> solar_time solar_time_cut
#> <POSc> <char>
#> 1: 2020-01-09 14:43:43 2020-01-09 14:00:00
#> 2: 2020-01-10 14:43:23 2020-01-10 14:00:00
#> 3: 2020-03-13 14:46:01 2020-03-13 14:00:00
#> 4: 2020-03-15 14:46:20 2020-03-15 14:00:00
#> 5: 2020-03-17 14:47:01 2020-03-17 14:00:00
#> ---
#> 1047: 2020-12-22 14:14:08 2020-12-22 14:00:00
#> 1048: 2020-12-24 14:13:21 2020-12-24 14:00:00
#> 1049: 2020-12-26 14:12:29 2020-12-26 14:00:00
#> 1050: 2020-12-28 14:11:32 2020-12-28 14:00:00
#> 1051: 2020-12-30 14:10:46 2020-12-30 14:00:00At this point we can calculate the averages of several columns by the
cut time. The function obs_agg does this aggregation as
shown in the following lines of code. The argument gby
establish the function used to aggregate cols. I need to
aggregate the data by date (year, month, date), because it is already
filtered by the hours of interest. Then, I would have 1 observation per
day.
As standard, let us define key_time as
solar_time. The obs_agg function will
aggregate the desired data by that column.
df3$key_time <- df3$solar_time_cut
df4 <- obs_agg(dt = df3,
cols = c("value",
"latitude",
"longitude",
"site_utc2lst"),
verbose = T,
byalt = TRUE)#> Selecting by alt
#> Adding timeHere we add the column max_altitude to identify the max
altitude by site.
df4[,
max_altitude := max(altitude_final),
by = site_code]
df4[,
c("site_code",
"altitude_final",
"max_altitude")] |> unique()#> site_code altitude_final max_altitude
#> <char> <num> <num>
#> 1: AMT 108.00 108.00
#> 2: LEF 396.00 396.00
#> 3: LEW 95.00 95.00
#> 4: MRC 60.97 60.97
#> 5: MSH 46.30 46.30
#> 6: NWR 3.20 3.20
#> 7: SCT 304.80 304.80
#> 8: SGP 60.00 60.00
#> 9: WBI 378.90 378.90
#> 10: WGC 89.10 89.10
#> 11: WKT 122.00 122.00Master
Before generating the receptors list, we have the database with all the required information
master <- df4We 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\RtmpkdE8le\file844c7c2a3817_surface-pfp.txtcsv
message(paste0(out,"_", datasetid, ".csv\n"))
fwrite(master,
paste0(out,"_", datasetid, ".csv"),
sep = ",")#> C:\Users\sibarrae\AppData\Local\Temp\RtmpkdE8le\file844c7c2a3817_surface-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),
paste0("altitude: < ", max_altitude),
paste0("hours: ", evening),
"local_time: used solar_time")
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: surface-pfp
#> timespan: 2020
#> spatial_limits: north = 80, south = 10, east = -50, west = -170
#> data: Data averaged every 20 seconds
#> altitude: < 8000
#> hours: 14
#> local_time: used solar_time
#> C:\Users\sibarrae\AppData\Local\Temp\RtmpkdE8le\file844c7c2a3817_surface-pfp.csvy
obs_read_csvy(paste0(out,"_", datasetid, ".csvy"))#> [1] "---"
#> [2] "name: Metadata "
#> [3] "sector: surface-pfp"
#> [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: 14"
#> [9] "local_time: used solar_time"
#> [10] "structure: "
#> [11] "Classes 'data.table' and 'data.frame':\t1050 obs. of 20 variables:"
#> [12] " $ timeUTC : chr \"2020-01-09 14:00:00\" \".."
#> [13] " $ site_code : chr \"AMT\" \"AMT\" ..."
#> [14] " $ altitude_final : num 108 108 108 108 108 ..."
#> [15] " $ type_altitude : num NA NA NA NA NA ..."
#> [16] " $ lab_1_abbr : chr \"NOAA\" \"NOAA\" ..."
#> [17] " $ dataset_calibration_scale: chr \"WMO CH4 X2004A\" \"WMO \".."
#> [18] " $ value : num 1.97e-06 2.01e-06 ..."
#> [19] " $ latitude : num 45 45 ..."
#> [20] " $ longitude : num -68.7 -68.7 ..."
#> [21] " $ site_utc2lst : num -5 -5 -5 -5 -5 ..."
#> [22] " $ year : int 2020 2020 2020 2020 202.."
#> [23] " $ month : int 1 1 3 3 3 ..."
#> [24] " $ day : chr \"09\" \"10\" ..."
#> [25] " $ hour : int 14 14 14 14 14 ..."
#> [26] " $ minute : int 0 0 0 0 0 ..."
#> [27] " $ second : int 0 0 0 0 0 ..."
#> [28] " $ time : num 1.58e+09 1.58e+09 ..."
#> [29] " $ time_decimal : num 2020 2020 ..."
#> [30] " $ max_altitude : num 108 108 108 108 108 ..."
#> [31] " $ local_time : chr NA NA ..."
#> [32] " - attr(*, \".internal.selfref\")=<externalptr> "
#> [33] "NULL"
#> [34] "---"
#> timeUTC site_code altitude_final type_altitude lab_1_abbr
#> <POSc> <char> <num> <int> <char>
#> 1: 2020-01-09 14:00:00 AMT 108 NA NOAA
#> 2: 2020-01-10 14:00:00 AMT 108 NA NOAA
#> 3: 2020-03-13 14:00:00 AMT 108 NA NOAA
#> 4: 2020-03-15 14:00:00 AMT 108 NA NOAA
#> 5: 2020-03-17 14:00:00 AMT 108 NA NOAA
#> ---
#> 1046: 2020-12-22 14:00:00 WKT 122 0 NOAA
#> 1047: 2020-12-24 14:00:00 WKT 122 0 NOAA
#> 1048: 2020-12-26 14:00:00 WKT 122 0 NOAA
#> 1049: 2020-12-28 14:00:00 WKT 122 0 NOAA
#> 1050: 2020-12-30 14:00:00 WKT 122 0 NOAA
#> dataset_calibration_scale value latitude longitude site_utc2lst
#> <char> <num> <num> <num> <int>
#> 1: WMO CH4 X2004A 1.971160e-06 45.0345 -68.6821 -5
#> 2: WMO CH4 X2004A 2.005080e-06 45.0345 -68.6821 -5
#> 3: WMO CH4 X2004A 1.959600e-06 45.0345 -68.6821 -5
#> 4: WMO CH4 X2004A 1.961400e-06 45.0345 -68.6821 -5
#> 5: WMO CH4 X2004A 1.959420e-06 45.0345 -68.6821 -5
#> ---
#> 1046: WMO CH4 X2004A 2.067470e-06 31.3149 -97.3269 -6
#> 1047: WMO CH4 X2004A 1.987230e-06 31.3149 -97.3269 -6
#> 1048: WMO CH4 X2004A 1.990765e-06 31.3149 -97.3269 -6
#> 1049: WMO CH4 X2004A 2.183760e-06 31.3149 -97.3269 -6
#> 1050: WMO CH4 X2004A 2.084820e-06 31.3149 -97.3269 -6
#> year month day hour minute second time time_decimal
#> <int> <int> <int> <int> <int> <int> <int> <num>
#> 1: 2020 1 9 14 0 0 1578578400 2020.023
#> 2: 2020 1 10 14 0 0 1578664800 2020.026
#> 3: 2020 3 13 14 0 0 1584108000 2020.198
#> 4: 2020 3 15 14 0 0 1584280800 2020.204
#> 5: 2020 3 17 14 0 0 1584453600 2020.209
#> ---
#> 1046: 2020 12 22 14 0 0 1608645600 2020.974
#> 1047: 2020 12 24 14 0 0 1608818400 2020.980
#> 1048: 2020 12 26 14 0 0 1608991200 2020.985
#> 1049: 2020 12 28 14 0 0 1609164000 2020.991
#> 1050: 2020 12 30 14 0 0 1609336800 2020.996
#> max_altitude local_time
#> <num> <lgcl>
#> 1: 108 NA
#> 2: 108 NA
#> 3: 108 NA
#> 4: 108 NA
#> 5: 108 NA
#> ---
#> 1046: 122 NA
#> 1047: 122 NA
#> 1048: 122 NA
#> 1049: 122 NA
#> 1050: 122 NAReceptors
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",
"time_decimal")]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\RtmpkdE8le\file844c7c2a3817_surface-pfp_receptor_AGL.txtPlot
Finally, we just plot some data, run it locally
obs_plot(df4, time = "timeUTC", yfactor = 1e9)#> Found the following sites:
#> [1] AMT LEF LEW MRC MSH NWR SCT SGP WBI WGC WKT
#> Plotting the following sites:
#> [1] AMT LEF
#> png
#> 2
Time series
library(sf)
dx <- df4[,
lapply(.SD, mean),
.SDcols = "value",
by = .(latitude, longitude)]
x <- st_as_sf(dx, coords = c("longitude", "latitude"), crs = 4326)
plot(x["value"], axes = T, reset = F)
maps::map(add = T)
Map