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 tower-insitu using the function
obs_read_nc. solar_time is included for
surface, so we TRUE that argument.
datasetid <- "tower-insitu"
df <- obs_read_nc(index = index,
categories = datasetid,
solar_time = TRUE,
verbose = TRUE)#> Searching tower-insitu...
#> 1: ch4_azv_tower-insitu_20_allvalid-29magl.nc
#> 2: ch4_azv_tower-insitu_20_allvalid-50magl.nc
#> 3: ch4_brz_tower-insitu_20_allvalid-20magl.nc
#> 4: ch4_brz_tower-insitu_20_allvalid-40magl.nc
#> 5: ch4_brz_tower-insitu_20_allvalid-5magl.nc
#> 6: ch4_brz_tower-insitu_20_allvalid-80magl.nc
#> 7: ch4_bsd_tower-insitu_160_allvalid-108magl.nc
#> 8: ch4_bsd_tower-insitu_160_allvalid-248magl.nc
#> 9: ch4_bsd_tower-insitu_160_allvalid-42magl.nc
#> 10: ch4_crv_tower-insitu_1_allvalid-17magl.nc
#> 11: ch4_crv_tower-insitu_1_allvalid-32magl.nc
#> 12: ch4_crv_tower-insitu_1_allvalid-5magl.nc
#> 13: ch4_dem_tower-insitu_20_allvalid-45magl.nc
#> 14: ch4_dem_tower-insitu_20_allvalid-63magl.nc
#> 15: ch4_dvv_tower-insitu_60_allvalid.nc
#> 16: ch4_gci01_tower-insitu_60_allvalid.nc
#> 17: ch4_gci02_tower-insitu_60_allvalid.nc
#> 18: ch4_gci03_tower-insitu_60_allvalid.nc
#> 19: ch4_gci04_tower-insitu_60_allvalid.nc
#> 20: ch4_hun_tower-insitu_35_allvalid-10magl.nc
#> 21: ch4_hun_tower-insitu_35_allvalid-115magl.nc
#> 22: ch4_hun_tower-insitu_35_allvalid-48magl.nc
#> 23: ch4_hun_tower-insitu_35_allvalid-82magl.nc
#> 24: ch4_igr_tower-insitu_20_allvalid-24magl.nc
#> 25: ch4_igr_tower-insitu_20_allvalid-47magl.nc
#> 26: ch4_krs_tower-insitu_20_allvalid-35magl.nc
#> 27: ch4_krs_tower-insitu_20_allvalid-67magl.nc
#> 28: ch4_lef_tower-insitu_1_allvalid-122magl.nc
#> 29: ch4_lef_tower-insitu_1_allvalid-30magl.nc
#> 30: ch4_lef_tower-insitu_1_allvalid-396magl.nc
#> 31: ch4_mrc_tower-insitu_60_allvalid-south.nc
#> 32: ch4_noy_tower-insitu_20_allvalid-21magl.nc
#> 33: ch4_noy_tower-insitu_20_allvalid-43magl.nc
#> 34: ch4_rgl_tower-insitu_160_allvalid-45magl.nc
#> 35: ch4_rgl_tower-insitu_160_allvalid-90magl.nc
#> 36: ch4_sct_tower-insitu_1_allvalid-305magl.nc
#> 37: ch4_sct_tower-insitu_1_allvalid-31magl.nc
#> 38: ch4_sct_tower-insitu_1_allvalid-61magl.nc
#> 39: ch4_svv_tower-insitu_20_allvalid-27magl.nc
#> 40: ch4_svv_tower-insitu_20_allvalid-52magl.nc
#> 41: ch4_tac_tower-insitu_160_allvalid-100magl.nc
#> 42: ch4_tac_tower-insitu_160_allvalid-185magl.nc
#> 43: ch4_tac_tower-insitu_160_allvalid-54magl.nc
#> 44: ch4_vgn_tower-insitu_20_allvalid-42magl.nc
#> 45: ch4_vgn_tower-insitu_20_allvalid-85magl.nc
#> 46: ch4_wgc_tower-insitu_1_allvalid-30magl.nc
#> 47: ch4_wgc_tower-insitu_1_allvalid-483magl.nc
#> 48: ch4_wgc_tower-insitu_1_allvalid-91magl.nc
#> 49: ch4_wsd_tower-insitu_60_allvalid.nc
#> 50: ch4_yak_tower-insitu_20_allvalid-11magl.nc
#> 51: ch4_yak_tower-insitu_20_allvalid-77magl.ncNow we check the data
df#> year month day hour minute second time start_time
#> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1: 2009 10 7 5 30 0 1254893400 1254891600
#> 2: 2009 10 7 6 30 0 1254897000 1254895200
#> 3: 2009 10 7 7 30 0 1254900600 1254898800
#> 4: 2009 10 7 8 30 0 1254904200 1254902400
#> 5: 2009 10 7 9 30 0 1254907800 1254906000
#> ---
#> 2611288: 2013 12 13 1 30 0 1386898200 1386896400
#> 2611289: 2013 12 13 2 30 0 1386901800 1386900000
#> 2611290: 2013 12 13 3 30 0 1386905400 1386903600
#> 2611291: 2013 12 13 4 30 0 1386909000 1386907200
#> 2611292: 2013 12 13 5 30 0 1386912600 1386910800
#> midpoint_time datetime time_decimal time_interval
#> <int> <char> <num> <int>
#> 1: 1254893400 2009-10-07T05:30:00Z 2009.765 3600
#> 2: 1254897000 2009-10-07T06:30:00Z 2009.765 3600
#> 3: 1254900600 2009-10-07T07:30:00Z 2009.765 3600
#> 4: 1254904200 2009-10-07T08:30:00Z 2009.765 3600
#> 5: 1254907800 2009-10-07T09:30:00Z 2009.765 3600
#> ---
#> 2611288: 1386898200 2013-12-13T01:30:00Z 2013.948 3600
#> 2611289: 1386901800 2013-12-13T02:30:00Z 2013.948 3600
#> 2611290: 1386905400 2013-12-13T03:30:00Z 2013.948 3600
#> 2611291: 1386909000 2013-12-13T04:30:00Z 2013.948 3600
#> 2611292: 1386912600 2013-12-13T05:30:00Z 2013.949 3600
#> value latitude longitude altitude elevation intake_height obs_flag
#> <num> <num> <num> <num> <num> <num> <int>
#> 1: 1.907e-06 54.7050 73.0292 139 110 29 1
#> 2: 1.904e-06 54.7050 73.0292 139 110 29 1
#> 3: 1.903e-06 54.7050 73.0292 139 110 29 1
#> 4: 1.906e-06 54.7050 73.0292 139 110 29 1
#> 5: 1.903e-06 54.7050 73.0292 139 110 29 1
#> ---
#> 2611288: 1.938e-06 62.0886 129.3558 341 264 77 1
#> 2611289: 1.933e-06 62.0886 129.3558 341 264 77 1
#> 2611290: 1.943e-06 62.0886 129.3558 341 264 77 1
#> 2611291: 1.949e-06 62.0886 129.3558 341 264 77 1
#> 2611292: 1.947e-06 62.0886 129.3558 341 264 77 1
#> obspack_num
#> <int>
#> 1: 5856371
#> 2: 5856372
#> 3: 5856373
#> 4: 5856374
#> 5: 5856375
#> ---
#> 2611288: 6664376
#> 2611289: 6664377
#> 2611290: 6664378
#> 2611291: 6664379
#> 2611292: 6664380
#> obspack_id
#> <char>
#> 1: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_azv_tower-insitu_20_allvalid-29magl~5856371
#> 2: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_azv_tower-insitu_20_allvalid-29magl~5856372
#> 3: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_azv_tower-insitu_20_allvalid-29magl~5856373
#> 4: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_azv_tower-insitu_20_allvalid-29magl~5856374
#> 5: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_azv_tower-insitu_20_allvalid-29magl~5856375
#> ---
#> 2611288: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_yak_tower-insitu_20_allvalid-77magl~6664376
#> 2611289: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_yak_tower-insitu_20_allvalid-77magl~6664377
#> 2611290: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_yak_tower-insitu_20_allvalid-77magl~6664378
#> 2611291: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_yak_tower-insitu_20_allvalid-77magl~6664379
#> 2611292: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_yak_tower-insitu_20_allvalid-77magl~6664380
#> unique_sample_location_num year_st month_st day_st hour_st minute_st
#> <int> <int> <int> <int> <int> <int>
#> 1: 46849218 2009 10 7 10 34
#> 2: 46849219 2009 10 7 11 34
#> 3: 46849220 2009 10 7 12 34
#> 4: 46849221 2009 10 7 13 34
#> 5: 46849222 2009 10 7 14 34
#> ---
#> 2611288: 47506934 2013 12 13 10 12
#> 2611289: 47506935 2013 12 13 11 12
#> 2611290: 47506936 2013 12 13 12 12
#> 2611291: 47506937 2013 12 13 13 12
#> 2611292: 47506938 2013 12 13 14 12
#> second_st scale dataset_intake_ht_unit site_elevation_unit
#> <int> <char> <char> <char>
#> 1: 46 NIES 94 CH4 scale magl masl
#> 2: 46 NIES 94 CH4 scale magl masl
#> 3: 46 NIES 94 CH4 scale magl masl
#> 4: 46 NIES 94 CH4 scale magl masl
#> 5: 46 NIES 94 CH4 scale magl masl
#> ---
#> 2611288: 48 NIES 94 CH4 scale magl masl
#> 2611289: 48 NIES 94 CH4 scale magl masl
#> 2611290: 48 NIES 94 CH4 scale magl masl
#> 2611291: 48 NIES 94 CH4 scale magl masl
#> 2611292: 48 NIES 94 CH4 scale magl masl
#> dataset_project dataset_selection_tag site_name site_elevation
#> <char> <char> <char> <num>
#> 1: tower-insitu allvalid-29magl Azovo 110
#> 2: tower-insitu allvalid-29magl Azovo 110
#> 3: tower-insitu allvalid-29magl Azovo 110
#> 4: tower-insitu allvalid-29magl Azovo 110
#> 5: tower-insitu allvalid-29magl Azovo 110
#> ---
#> 2611288: tower-insitu allvalid-77magl Yakutsk 264
#> 2611289: tower-insitu allvalid-77magl Yakutsk 264
#> 2611290: tower-insitu allvalid-77magl Yakutsk 264
#> 2611291: tower-insitu allvalid-77magl Yakutsk 264
#> 2611292: tower-insitu allvalid-77magl Yakutsk 264
#> site_latitude site_longitude site_country site_code site_utc2lst
#> <num> <num> <char> <char> <num>
#> 1: 54.7050 73.0292 Russia AZV 6
#> 2: 54.7050 73.0292 Russia AZV 6
#> 3: 54.7050 73.0292 Russia AZV 6
#> 4: 54.7050 73.0292 Russia AZV 6
#> 5: 54.7050 73.0292 Russia AZV 6
#> ---
#> 2611288: 62.0886 129.3558 Russia YAK 9
#> 2611289: 62.0886 129.3558 Russia YAK 9
#> 2611290: 62.0886 129.3558 Russia YAK 9
#> 2611291: 62.0886 129.3558 Russia YAK 9
#> 2611292: 62.0886 129.3558 Russia YAK 9
#> lab_1_abbr dataset_calibration_scale altitude_final type_altitude
#> <char> <char> <num> <num>
#> 1: NIES NIES 94 CH4 scale 29 0
#> 2: NIES NIES 94 CH4 scale 29 0
#> 3: NIES NIES 94 CH4 scale 29 0
#> 4: NIES NIES 94 CH4 scale 29 0
#> 5: NIES NIES 94 CH4 scale 29 0
#> ---
#> 2611288: NIES NIES 94 CH4 scale 77 0
#> 2611289: NIES NIES 94 CH4 scale 77 0
#> 2611290: NIES NIES 94 CH4 scale 77 0
#> 2611291: NIES NIES 94 CH4 scale 77 0
#> 2611292: NIES NIES 94 CH4 scale 77 0
#> inst_repeatability instrument method nvalue qcflag value_std_dev
#> <num> <char> <char> <int> <char> <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
#> ---
#> 2611288: NA <NA> <NA> NA <NA> NA
#> 2611289: NA <NA> <NA> NA <NA> NA
#> 2611290: NA <NA> <NA> NA <NA> NA
#> 2611291: NA <NA> <NA> NA <NA> NA
#> 2611292: NA <NA> <NA> NA <NA> NA
#> value_unc
#> <num>
#> 1: NA
#> 2: NA
#> 3: NA
#> 4: NA
#> 5: NA
#> ---
#> 2611288: NA
#> 2611289: NA
#> 2611290: NA
#> 2611291: NA
#> 2611292: 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 <- 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: 139 29 29 110 allvalid-29magl
#> 2: 139 29 29 110 allvalid-29magl
#> 3: 139 29 29 110 allvalid-29magl
#> 4: 139 29 29 110 allvalid-29magl
#> 5: 139 29 29 110 allvalid-29magl
#> ---
#> 2611288: 341 77 77 264 allvalid-77magl
#> 2611289: 341 77 77 264 allvalid-77magl
#> 2611290: 341 77 77 264 allvalid-77magl
#> 2611291: 341 77 77 264 allvalid-77magl
#> 2611292: 341 77 77 264 allvalid-77magl
#> site_name
#> <char>
#> 1: Azovo
#> 2: Azovo
#> 3: Azovo
#> 4: Azovo
#> 5: Azovo
#> ---
#> 2611288: Yakutsk
#> 2611289: Yakutsk
#> 2611290: Yakutsk
#> 2611291: Yakutsk
#> 2611292: YakutskThe temporal range of data is
range(df$year)#> [1] 2004 2021We also check for dimensions of data
dim(df)#> [1] 2611292 51Filters
df <- df[year == yy]
df <- df[altitude_final < max_altitude &
latitude < north &
latitude > south &
longitude < east &
longitude > west]
dim(df)#> [1] 96106 51Towers can have observations at different heights. Here we need to
select one site with the observations registered at the highest height.
The column with the height is named altitude_final and the
max altitude was named max_altitude.
#> site_code max_altitude
#> <char> <num>
#> 1: CRV 31.7
#> 2: LEF 396.0
#> 3: SCT 304.8
#> 4: WGC 484.0Key Time
Here we need to start time columns. The function
obs_addtime adds time columns timeUTC,
timeUTC_start which shows the start time of each
observation and timeUTC_end which shows the end time for
each observation.
df2 <- obs_addtime(df)#> Adding timeUTC
#> Adding timeUTC_start
#> Adding timeUTC_end
#> Found time_intervalThen we need a key_time to aggregate data. This can be done using UTC, solar, or local time. The normal approach is using afternoon solar or local time.
Hierarchy of solar or local time
- Solar time
- Local time with columns
site_utc2lst - Local time longitude
solar time (default)
Here we select the hours of interest and then aggregate data by year, month and day of solar time. In this way, we will have one information per day. however this approach is not appropriate for aircraft which are aggregated every 10 or 20 seconds. Hence we need to aggregate data by one time column. Also, this helps to generate the receptor info files including hour, minute and second. Hence, we need to add solar or local time column.
df2$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: 2019-12-31 14:37:48 2019-12-31 14:00:00
#> 2: 2020-01-01 14:35:49 2020-01-01 14:00:00
#> 3: 2020-01-02 14:35:23 2020-01-02 14:00:00
#> 4: 2020-01-03 14:34:57 2020-01-03 14:00:00
#> 5: 2020-01-04 14:34:31 2020-01-04 14:00:00
#> ---
#> 3676: 2020-12-27 14:22:57 2020-12-27 14:00:00
#> 3677: 2020-12-28 14:22:30 2020-12-28 14:00:00
#> 3678: 2020-12-29 14:22:02 2020-12-29 14:00:00
#> 3679: 2020-12-30 14:21:35 2020-12-30 14:00:00
#> 3680: 2020-12-31 14:21:08 2020-12-31 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: CRV 17.1 31.7
#> 2: CRV 31.7 31.7
#> 3: CRV 4.9 31.7
#> 4: LEF 122.0 396.0
#> 5: LEF 396.0 396.0
#> 6: LEF 30.0 396.0
#> 7: SCT 304.8 304.8
#> 8: SCT 31.0 304.8
#> 9: SCT 61.0 304.8
#> 10: WGC 30.0 484.0
#> 11: WGC 484.0 484.0
#> 12: WGC 89.1 484.0Master
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\RtmpyqH3NI\file5ef420fc3b21_tower-insitu.txtcsv
message(paste0(out,"_", datasetid, ".csv\n"))
fwrite(master,
paste0(out,"_", datasetid, ".csv"),
sep = ",")#> C:\Users\sibarrae\AppData\Local\Temp\RtmpyqH3NI\file5ef420fc3b21_tower-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),
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: tower-insitu
#> 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\RtmpyqH3NI\file5ef420fc3b21_tower-insitu.csvy
obs_read_csvy(paste0(out,"_", datasetid, ".csvy"))#> [1] "---"
#> [2] "name: Metadata "
#> [3] "sector: tower-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: 14"
#> [9] "local_time: used solar_time"
#> [10] "structure: "
#> [11] "Classes 'data.table' and 'data.frame':\t3680 obs. of 20 variables:"
#> [12] " $ timeUTC : chr \"2019-12-31 14:00:00\" \".."
#> [13] " $ site_code : chr \"CRV\" \"CRV\" ..."
#> [14] " $ altitude_final : num 17.1 31.7 ..."
#> [15] " $ type_altitude : num 0 0 0 0 0 ..."
#> [16] " $ lab_1_abbr : chr \"NOAA\" \"NOAA\" ..."
#> [17] " $ dataset_calibration_scale: chr \"WMO CH4 X2004A\" \"WMO \".."
#> [18] " $ value : num 1.97e-06 1.97e-06 ..."
#> [19] " $ latitude : num 65 65 ..."
#> [20] " $ longitude : num -148 -148 ..."
#> [21] " $ site_utc2lst : num -8 -8 -8 -8 -8 ..."
#> [22] " $ year : int 2019 2019 2019 2020 202.."
#> [23] " $ month : int 12 12 12 1 1 ..."
#> [24] " $ day : chr \"31\" \"31\" ..."
#> [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 31.7 31.7 ..."
#> [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: 2019-12-31 14:00:00 CRV 17.1 0 NOAA
#> 2: 2019-12-31 14:00:00 CRV 31.7 0 NOAA
#> 3: 2019-12-31 14:00:00 CRV 4.9 0 NOAA
#> 4: 2020-01-01 14:00:00 CRV 17.1 0 NOAA
#> 5: 2020-01-01 14:00:00 CRV 31.7 0 NOAA
#> ---
#> 3676: 2020-12-30 14:00:00 WGC 484.0 0 NOAA
#> 3677: 2020-12-30 14:00:00 WGC 89.1 0 NOAA
#> 3678: 2020-12-31 14:00:00 WGC 30.0 0 NOAA
#> 3679: 2020-12-31 14:00:00 WGC 484.0 0 NOAA
#> 3680: 2020-12-31 14:00:00 WGC 89.1 0 NOAA
#> dataset_calibration_scale value latitude longitude site_utc2lst
#> <char> <num> <num> <num> <int>
#> 1: WMO CH4 X2004A 1.970275e-06 64.9863 -147.5980 -8
#> 2: WMO CH4 X2004A 1.967440e-06 64.9863 -147.5980 -8
#> 3: WMO CH4 X2004A 1.971806e-06 64.9863 -147.5980 -8
#> 4: WMO CH4 X2004A 1.988251e-06 64.9863 -147.5980 -8
#> 5: WMO CH4 X2004A 1.987424e-06 64.9863 -147.5980 -8
#> ---
#> 3676: WMO CH4 X2004A 2.004825e-06 38.2645 -121.4904 -8
#> 3677: WMO CH4 X2004A 2.238231e-06 38.2645 -121.4904 -8
#> 3678: WMO CH4 X2004A 2.048590e-06 38.2645 -121.4904 -8
#> 3679: WMO CH4 X2004A 2.007957e-06 38.2645 -121.4904 -8
#> 3680: WMO CH4 X2004A 2.048209e-06 38.2645 -121.4904 -8
#> year month day hour minute second time time_decimal
#> <int> <int> <int> <int> <int> <int> <int> <num>
#> 1: 2019 12 31 14 0 0 1577800800 2019.999
#> 2: 2019 12 31 14 0 0 1577800800 2019.999
#> 3: 2019 12 31 14 0 0 1577800800 2019.999
#> 4: 2020 1 1 14 0 0 1577887200 2020.002
#> 5: 2020 1 1 14 0 0 1577887200 2020.002
#> ---
#> 3676: 2020 12 30 14 0 0 1609336800 2020.996
#> 3677: 2020 12 30 14 0 0 1609336800 2020.996
#> 3678: 2020 12 31 14 0 0 1609423200 2020.999
#> 3679: 2020 12 31 14 0 0 1609423200 2020.999
#> 3680: 2020 12 31 14 0 0 1609423200 2020.999
#> max_altitude local_time
#> <num> <lgcl>
#> 1: 31.7 NA
#> 2: 31.7 NA
#> 3: 31.7 NA
#> 4: 31.7 NA
#> 5: 31.7 NA
#> ---
#> 3676: 484.0 NA
#> 3677: 484.0 NA
#> 3678: 484.0 NA
#> 3679: 484.0 NA
#> 3680: 484.0 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\RtmpyqH3NI\file5ef420fc3b21_tower-insitu_receptor_AGL.txtPlot
Finally, we just plot some data, run it locally
obs_plot(df4, time = "timeUTC", yfactor = 1e9)#> Found the following sites:
#> [1] CRV LEF SCT WGC
#> Plotting the following sites:
#> [1] CRV 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