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 shipboard-insitu using the function
obs_read_nc. solar_time is included for
surface, so we TRUE that argument.
datasetid <- "shipboard-insitu"
df <- obs_read_nc(index = index,
categories = datasetid,
solar_time = TRUE,
verbose = TRUE)#> Searching shipboard-insitu...
#> 1: ch4_rvrb_shipboard-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: 2019 3 2 1 19 37 1551489577 1551489577
#> 2: 2019 3 2 1 20 3 1551489603 1551489603
#> 3: 2019 3 2 1 21 38 1551489698 1551489698
#> 4: 2019 3 2 1 49 9 1551491349 1551491349
#> 5: 2019 3 2 2 3 7 1551492187 1551492187
#> ---
#> 73230: 2019 3 30 16 20 21 1553962821 1553962821
#> 73231: 2019 3 30 16 24 23 1553963063 1553963063
#> 73232: 2019 3 30 16 24 57 1553963097 1553963097
#> 73233: 2019 3 30 16 25 58 1553963158 1553963158
#> 73234: 2019 3 30 16 26 24 1553963184 1553963184
#> midpoint_time datetime time_decimal value latitude
#> <int> <char> <num> <num> <num>
#> 1: 1551489577 2019-03-02T01:19:37Z 2019.165 2.076981e-06 32.8521
#> 2: 1551489603 2019-03-02T01:20:03Z 2019.165 2.077718e-06 32.8521
#> 3: 1551489698 2019-03-02T01:21:38Z 2019.165 2.077695e-06 32.8521
#> 4: 1551491349 2019-03-02T01:49:09Z 2019.165 2.071150e-06 32.8521
#> 5: 1551492187 2019-03-02T02:03:07Z 2019.165 2.076558e-06 32.8522
#> ---
#> 73230: 1553962821 2019-03-30T16:20:21Z 2019.243 1.950464e-06 32.6589
#> 73231: 1553963063 2019-03-30T16:24:23Z 2019.243 1.949703e-06 32.6660
#> 73232: 1553963097 2019-03-30T16:24:57Z 2019.243 1.949468e-06 32.6669
#> 73233: 1553963158 2019-03-30T16:25:58Z 2019.243 1.949421e-06 32.6686
#> 73234: 1553963184 2019-03-30T16:26:24Z 2019.243 1.948835e-06 32.6694
#> longitude altitude elevation intake_height obs_flag obspack_num
#> <num> <num> <num> <num> <int> <int>
#> 1: -79.9457 15 0 15 1 1844069
#> 2: -79.9457 15 0 15 1 1844070
#> 3: -79.9457 15 0 15 1 1844071
#> 4: -79.9457 15 0 15 1 1844072
#> 5: -79.9457 15 0 15 1 1844073
#> ---
#> 73230: -79.6765 15 0 15 1 1917298
#> 73231: -79.6907 15 0 15 1 1917299
#> 73232: -79.6925 15 0 15 1 1917300
#> 73233: -79.6961 15 0 15 1 1917301
#> 73234: -79.6978 15 0 15 1 1917302
#> obspack_id
#> <char>
#> 1: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_rvrb_shipboard-insitu_1_allvalid~1844069
#> 2: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_rvrb_shipboard-insitu_1_allvalid~1844070
#> 3: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_rvrb_shipboard-insitu_1_allvalid~1844071
#> 4: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_rvrb_shipboard-insitu_1_allvalid~1844072
#> 5: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_rvrb_shipboard-insitu_1_allvalid~1844073
#> ---
#> 73230: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_rvrb_shipboard-insitu_1_allvalid~1917298
#> 73231: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_rvrb_shipboard-insitu_1_allvalid~1917299
#> 73232: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_rvrb_shipboard-insitu_1_allvalid~1917300
#> 73233: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_rvrb_shipboard-insitu_1_allvalid~1917301
#> 73234: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_rvrb_shipboard-insitu_1_allvalid~1917302
#> unique_sample_location_num year_st month_st day_st hour_st minute_st
#> <int> <int> <int> <int> <int> <int>
#> 1: 46806290 2019 3 1 19 47
#> 2: 46806291 2019 3 1 19 47
#> 3: 46806292 2019 3 1 19 49
#> 4: 46806293 2019 3 1 20 16
#> 5: 44831881 2019 3 1 20 30
#> ---
#> 73230: 46806283 2019 3 30 10 56
#> 73231: 46806284 2019 3 30 11 0
#> 73232: 46806285 2019 3 30 11 1
#> 73233: 46806286 2019 3 30 11 2
#> 73234: 46806287 2019 3 30 11 2
#> second_st scale site_elevation_unit dataset_project
#> <int> <char> <char> <char>
#> 1: 18 WMO CH4 X2004A masl shipboard-insitu
#> 2: 44 WMO CH4 X2004A masl shipboard-insitu
#> 3: 19 WMO CH4 X2004A masl shipboard-insitu
#> 4: 50 WMO CH4 X2004A masl shipboard-insitu
#> 5: 48 WMO CH4 X2004A masl shipboard-insitu
#> ---
#> 73230: 57 WMO CH4 X2004A masl shipboard-insitu
#> 73231: 55 WMO CH4 X2004A masl shipboard-insitu
#> 73232: 29 WMO CH4 X2004A masl shipboard-insitu
#> 73233: 29 WMO CH4 X2004A masl shipboard-insitu
#> 73234: 55 WMO CH4 X2004A masl shipboard-insitu
#> dataset_selection_tag site_name site_elevation site_latitude
#> <char> <char> <num> <num>
#> 1: allvalid R/V Ron Brown 0 -1e+34
#> 2: allvalid R/V Ron Brown 0 -1e+34
#> 3: allvalid R/V Ron Brown 0 -1e+34
#> 4: allvalid R/V Ron Brown 0 -1e+34
#> 5: allvalid R/V Ron Brown 0 -1e+34
#> ---
#> 73230: allvalid R/V Ron Brown 0 -1e+34
#> 73231: allvalid R/V Ron Brown 0 -1e+34
#> 73232: allvalid R/V Ron Brown 0 -1e+34
#> 73233: allvalid R/V Ron Brown 0 -1e+34
#> 73234: allvalid R/V Ron Brown 0 -1e+34
#> site_longitude site_code lab_1_abbr dataset_calibration_scale
#> <num> <char> <char> <char>
#> 1: -1e+34 RVRB NOAA WMO CH4 X2004A
#> 2: -1e+34 RVRB NOAA WMO CH4 X2004A
#> 3: -1e+34 RVRB NOAA WMO CH4 X2004A
#> 4: -1e+34 RVRB NOAA WMO CH4 X2004A
#> 5: -1e+34 RVRB NOAA WMO CH4 X2004A
#> ---
#> 73230: -1e+34 RVRB NOAA WMO CH4 X2004A
#> 73231: -1e+34 RVRB NOAA WMO CH4 X2004A
#> 73232: -1e+34 RVRB NOAA WMO CH4 X2004A
#> 73233: -1e+34 RVRB NOAA WMO CH4 X2004A
#> 73234: -1e+34 RVRB NOAA WMO CH4 X2004A
#> altitude_final type_altitude
#> <num> <lgcl>
#> 1: 15 NA
#> 2: 15 NA
#> 3: 15 NA
#> 4: 15 NA
#> 5: 15 NA
#> ---
#> 73230: 15 NA
#> 73231: 15 NA
#> 73232: 15 NA
#> 73233: 15 NA
#> 73234: 15 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: 15 15 15 0 allvalid
#> 2: 15 15 15 0 allvalid
#> 3: 15 15 15 0 allvalid
#> 4: 15 15 15 0 allvalid
#> 5: 15 15 15 0 allvalid
#> ---
#> 73230: 15 15 15 0 allvalid
#> 73231: 15 15 15 0 allvalid
#> 73232: 15 15 15 0 allvalid
#> 73233: 15 15 15 0 allvalid
#> 73234: 15 15 15 0 allvalid
#> site_name
#> <char>
#> 1: R/V Ron Brown
#> 2: R/V Ron Brown
#> 3: R/V Ron Brown
#> 4: R/V Ron Brown
#> 5: R/V Ron Brown
#> ---
#> 73230: R/V Ron Brown
#> 73231: R/V Ron Brown
#> 73232: R/V Ron Brown
#> 73233: R/V Ron Brown
#> 73234: R/V Ron BrownThe temporal range of data is
range(df$year)#> [1] 2019 2019We also check for dimensions of data
dim(df)#> [1] 73234 40Plot
Finally, we just plot some data, run it locally
obs_plot(df4, time = "timeUTC", yfactor = 1e9)#> Found the following sites:
#> [1] RVRB
#> Plotting the following sites:
#> [1] RVRB
#> png
#> 2
Time series
library(sf)
dx <- df[,
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