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-insitu using the function
obs_read_nc. solar_time is included for
surface, so we TRUE that argument.
datasetid <- "surface-insitu"
df <- obs_read_nc(index = index,
categories = datasetid,
solar_time = TRUE,
verbose = TRUE)#> Searching surface-insitu...
#> 1: ch4_abt_surface-insitu_6_allvalid.nc
#> 2: ch4_alt_surface-insitu_6_allvalid.nc
#> 3: ch4_amy_surface-insitu_61_allvalid.nc
#> 4: ch4_bck_surface-insitu_6_allvalid.nc
#> 5: ch4_bhd_surface-insitu_15_baseline.nc
#> 6: ch4_bir_surface-insitu_442_allvalid-10magl.nc
#> 7: ch4_bir_surface-insitu_442_allvalid-50magl.nc
#> 8: ch4_bir_surface-insitu_442_allvalid-75magl.nc
#> 9: ch4_blk_surface-insitu_6_allvalid.nc
#> 10: ch4_bra_surface-insitu_6_allvalid.nc
#> 11: ch4_brw_surface-insitu_1_allvalid.nc
#> 12: ch4_buc_surface-insitu_112_allvalid.nc
#> 13: ch4_carl_surface-insitu_60_allvalid-9magl.nc
#> 14: ch4_cbw_surface-insitu_118_allvalid.nc
#> 15: ch4_cby_surface-insitu_6_allvalid.nc
#> 16: ch4_cdl_surface-insitu_6_allvalid.nc
#> 17: ch4_chl_surface-insitu_6_allvalid.nc
#> 18: ch4_chm_surface-insitu_6_allvalid.nc
#> 19: ch4_cmn_surface-insitu_106_allvalid.nc
#> 20: ch4_cmn_surface-insitu_442_allvalid-8magl.nc
#> 21: ch4_cps_surface-insitu_6_allvalid.nc
#> 22: ch4_cpt_surface-insitu_36_marine.nc
#> 23: ch4_cvo_surface-insitu_456_allvalid.nc
#> 24: ch4_dnh_surface-insitu_112_allvalid.nc
#> 25: ch4_egb_surface-insitu_6_allvalid.nc
#> 26: ch4_ena_surface-insitu_64_allvalid-10magl.nc
#> 27: ch4_esp_surface-insitu_6_allvalid.nc
#> 28: ch4_est_surface-insitu_6_allvalid.nc
#> 29: ch4_etl_surface-insitu_6_allvalid.nc
#> 30: ch4_fne_surface-insitu_6_allvalid.nc
#> 31: ch4_fort_surface-insitu_60_allvalid-128magl.nc
#> 32: ch4_fsd_surface-insitu_6_allvalid.nc
#> 33: ch4_gat_surface-insitu_442_allvalid-132magl.nc
#> 34: ch4_gat_surface-insitu_442_allvalid-216magl.nc
#> 35: ch4_gat_surface-insitu_442_allvalid-30magl.nc
#> 36: ch4_gat_surface-insitu_442_allvalid-341magl.nc
#> 37: ch4_gat_surface-insitu_442_allvalid-60magl.nc
#> 38: ch4_ghg03_surface-insitu_112_allvalid.nc
#> 39: ch4_ghg06_surface-insitu_112_allvalid.nc
#> 40: ch4_ghg09_surface-insitu_112_allvalid.nc
#> 41: ch4_ghg10_surface-insitu_112_allvalid.nc
#> 42: ch4_hct_surface-insitu_112_allvalid.nc
#> 43: ch4_hel_surface-insitu_442_allvalid-110magl.nc
#> 44: ch4_hnp_surface-insitu_6_allvalid.nc
#> 45: ch4_hobb_surface-insitu_60_allvalid-91magl.nc
#> 46: ch4_hpb_surface-insitu_442_allvalid-131magl.nc
#> 47: ch4_hpb_surface-insitu_442_allvalid-50magl.nc
#> 48: ch4_hpb_surface-insitu_442_allvalid-93magl.nc
#> 49: ch4_htm_surface-insitu_442_allvalid-150magl.nc
#> 50: ch4_htm_surface-insitu_442_allvalid-30magl.nc
#> 51: ch4_htm_surface-insitu_442_allvalid-70magl.nc
#> 52: ch4_inu_surface-insitu_6_allvalid.nc
#> 53: ch4_inx01_surface-insitu_60_allhours-10magl.nc
#> 54: ch4_inx01_surface-insitu_60_allhours-121magl.nc
#> 55: ch4_inx01_surface-insitu_60_allhours-40magl.nc
#> 56: ch4_inx02_surface-insitu_60_allhours-10magl.nc
#> 57: ch4_inx02_surface-insitu_60_allhours-136magl.nc
#> 58: ch4_inx02_surface-insitu_60_allhours-40magl.nc
#> 59: ch4_inx03_surface-insitu_60_allhours-10magl.nc
#> 60: ch4_inx03_surface-insitu_60_allhours-20magl.nc
#> 61: ch4_inx03_surface-insitu_60_allhours-40magl.nc
#> 62: ch4_inx03_surface-insitu_60_allhours-54magl.nc
#> 63: ch4_inx04_surface-insitu_60_allhours-60magl.nc
#> 64: ch4_inx05_surface-insitu_60_allhours-125magl.nc
#> 65: ch4_inx07_surface-insitu_60_allhours-21magl.nc
#> 66: ch4_inx07_surface-insitu_60_allhours-58magl.nc
#> 67: ch4_inx08_surface-insitu_60_allhours-41magl.nc
#> 68: ch4_inx09_surface-insitu_60_allhours-130magl.nc
#> 69: ch4_inx10_surface-insitu_60_allhours-40magl.nc
#> 70: ch4_inx11_surface-insitu_60_allhours-130magl.nc
#> 71: ch4_inx13_surface-insitu_60_allhours-87magl.nc
#> 72: ch4_inx14_surface-insitu_60_allhours-76magl.nc
#> 73: ch4_inx15_surface-insitu_60_allhours-75magl.nc
#> 74: ch4_ipr_surface-insitu_442_allvalid-100magl.nc
#> 75: ch4_ipr_surface-insitu_442_allvalid-40magl.nc
#> 76: ch4_ipr_surface-insitu_442_allvalid-60magl.nc
#> 77: ch4_izo_surface-insitu_27_allvalid.nc
#> 78: ch4_jfj_surface-insitu_442_allvalid-5magl.nc
#> 79: ch4_jfj_surface-insitu_5_allvalid.nc
#> 80: ch4_jgs_surface-insitu_61_allvalid.nc
#> 81: ch4_jue_surface-insitu_442_allvalid-120magl.nc
#> 82: ch4_jue_surface-insitu_442_allvalid-50magl.nc
#> 83: ch4_jue_surface-insitu_442_allvalid-80magl.nc
#> 84: ch4_kit_surface-insitu_442_allvalid-100magl.nc
#> 85: ch4_kit_surface-insitu_442_allvalid-200magl.nc
#> 86: ch4_kit_surface-insitu_442_allvalid-30magl.nc
#> 87: ch4_kit_surface-insitu_442_allvalid-60magl.nc
#> 88: ch4_kre_surface-insitu_442_allvalid-10magl.nc
#> 89: ch4_kre_surface-insitu_442_allvalid-125magl.nc
#> 90: ch4_kre_surface-insitu_442_allvalid-250magl.nc
#> 91: ch4_kre_surface-insitu_442_allvalid-50magl.nc
#> 92: ch4_krk_surface-insitu_118_allvalid.nc
#> 93: ch4_lew_surface-insitu_112_allvalid.nc
#> 94: ch4_lin_surface-insitu_442_allvalid-10magl.nc
#> 95: ch4_lin_surface-insitu_442_allvalid-2.5magl.nc
#> 96: ch4_lin_surface-insitu_442_allvalid-40magl.nc
#> 97: ch4_lin_surface-insitu_442_allvalid-98magl.nc
#> 98: ch4_llb_surface-insitu_6_allvalid.nc
#> 99: ch4_lmp_surface-insitu_442_allvalid-8magl.nc
#> 100: ch4_lut_surface-insitu_118_allvalid.nc
#> 101: ch4_lut_surface-insitu_442_allvalid-60magl.nc
#> 102: ch4_malj_surface-insitu_60_allvalid-134magl.nc
#> 103: ch4_mlo_surface-insitu_1_allvalid.nc
#> 104: ch4_mnc_surface-insitu_112_allvalid.nc
#> 105: ch4_mnm_surface-insitu_19_representative.nc
#> 106: ch4_ndao_surface-insitu_45_allvalid-21magl.nc
#> 107: ch4_nor_surface-insitu_442_allvalid-100magl.nc
#> 108: ch4_nor_surface-insitu_442_allvalid-32magl.nc
#> 109: ch4_nor_surface-insitu_442_allvalid-58magl.nc
#> 110: ch4_notr_surface-insitu_60_allvalid-91magl.nc
#> 111: ch4_oli_surface-insitu_64_allvalid-10magl.nc
#> 112: ch4_ope_surface-insitu_442_allvalid-10magl.nc
#> 113: ch4_ope_surface-insitu_442_allvalid-120magl.nc
#> 114: ch4_ope_surface-insitu_442_allvalid-50magl.nc
#> 115: ch4_oxk_surface-insitu_442_allvalid-163magl.nc
#> 116: ch4_oxk_surface-insitu_442_allvalid-23magl.nc
#> 117: ch4_oxk_surface-insitu_442_allvalid-90magl.nc
#> 118: ch4_pal_surface-insitu_30_continental.nc
#> 119: ch4_pal_surface-insitu_30_marine.nc
#> 120: ch4_pal_surface-insitu_30_nonlocal.nc
#> 121: ch4_pal_surface-insitu_442_allvalid-12magl.nc
#> 122: ch4_prs_surface-insitu_442_allvalid-10magl.nc
#> 123: ch4_pui_surface-insitu_442_allvalid-47magl.nc
#> 124: ch4_pui_surface-insitu_442_allvalid-84magl.nc
#> 125: ch4_puy_surface-insitu_442_allvalid-10magl.nc
#> 126: ch4_rgl_surface-insitu_442_allvalid-45magl.nc
#> 127: ch4_rgl_surface-insitu_442_allvalid-90magl.nc
#> 128: ch4_ric_surface-insitu_112_allvalid.nc
#> 129: ch4_run_surface-insitu_442_allvalid-6magl.nc
#> 130: ch4_ryo_surface-insitu_19_representative.nc
#> 131: ch4_sac_surface-insitu_442_allvalid-100magl.nc
#> 132: ch4_sac_surface-insitu_442_allvalid-15magl.nc
#> 133: ch4_sac_surface-insitu_442_allvalid-50magl.nc
#> 134: ch4_sgp_surface-insitu_64_allvalid-60magl.nc
#> 135: ch4_sig_surface-insitu_112_allvalid.nc
#> 136: ch4_smr_surface-insitu_442_allvalid-125magl.nc
#> 137: ch4_smr_surface-insitu_442_allvalid-17magl.nc
#> 138: ch4_smr_surface-insitu_442_allvalid-67magl.nc
#> 139: ch4_snj_surface-insitu_112_allvalid.nc
#> 140: ch4_sno_surface-insitu_442_allvalid-20magl.nc
#> 141: ch4_sno_surface-insitu_442_allvalid-50magl.nc
#> 142: ch4_sno_surface-insitu_442_allvalid-85magl.nc
#> 143: ch4_ssl_surface-insitu_442_allvalid-12magl.nc
#> 144: ch4_ssl_surface-insitu_442_allvalid-35magl.nc
#> 145: ch4_ste_surface-insitu_442_allvalid-127magl.nc
#> 146: ch4_ste_surface-insitu_442_allvalid-187magl.nc
#> 147: ch4_ste_surface-insitu_442_allvalid-252magl.nc
#> 148: ch4_ste_surface-insitu_442_allvalid-32magl.nc
#> 149: ch4_ste_surface-insitu_442_allvalid-82magl.nc
#> 150: ch4_svb_surface-insitu_442_allvalid-150magl.nc
#> 151: ch4_svb_surface-insitu_442_allvalid-35magl.nc
#> 152: ch4_svb_surface-insitu_442_allvalid-85magl.nc
#> 153: ch4_syo_surface-insitu_8_representative.nc
#> 154: ch4_tao_surface-insitu_6_allvalid.nc
#> 155: ch4_thd_surface-insitu_112_allvalid.nc
#> 156: ch4_tik_surface-insitu_30_allvalid.nc
#> 157: ch4_toh_surface-insitu_442_allvalid-10magl.nc
#> 158: ch4_toh_surface-insitu_442_allvalid-110magl.nc
#> 159: ch4_toh_surface-insitu_442_allvalid-147magl.nc
#> 160: ch4_toh_surface-insitu_442_allvalid-76magl.nc
#> 161: ch4_tpd_surface-insitu_6_allvalid.nc
#> 162: ch4_trn_surface-insitu_442_allvalid-100magl.nc
#> 163: ch4_trn_surface-insitu_442_allvalid-180magl.nc
#> 164: ch4_trn_surface-insitu_442_allvalid-50magl.nc
#> 165: ch4_trn_surface-insitu_442_allvalid-5magl.nc
#> 166: ch4_uld_surface-insitu_61_allvalid.nc
#> 167: ch4_uny_surface-insitu_112_allvalid.nc
#> 168: ch4_uto_surface-insitu_442_allvalid-57magl.nc
#> 169: ch4_wao_surface-insitu_442_allvalid-10magl.nc
#> 170: ch4_wes_surface-insitu_442_allvalid-14magl.nc
#> 171: ch4_wsa_surface-insitu_6_allvalid.nc
#> 172: ch4_yon_surface-insitu_19_representative.nc
#> 173: ch4_zep_surface-insitu_442_allvalid-15magl.nc
#> 174: ch4_zsf_surface-insitu_442_allvalid-3magl.ncNow we check the data
df#> year month day hour minute second time start_time
#> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1: 2014 3 6 5 30 0 1394083800 1394082000
#> 2: 2014 3 6 6 30 0 1394087400 1394085600
#> 3: 2014 3 6 7 30 0 1394091000 1394089200
#> 4: 2014 3 6 8 30 0 1394094600 1394092800
#> 5: 2014 3 6 9 30 0 1394098200 1394096400
#> ---
#> 8281129: 2021 12 31 19 30 0 1640979000 1640977200
#> 8281130: 2021 12 31 20 30 0 1640982600 1640980800
#> 8281131: 2021 12 31 21 30 0 1640986200 1640984400
#> 8281132: 2021 12 31 22 30 0 1640989800 1640988000
#> 8281133: 2021 12 31 23 30 0 1640993400 1640991600
#> midpoint_time datetime time_decimal value nvalue
#> <int> <char> <num> <num> <int>
#> 1: 1394083800 2014-03-06T05:30:00Z 2014.176 1.93390e-06 53
#> 2: 1394087400 2014-03-06T06:30:00Z 2014.176 1.93930e-06 57
#> 3: 1394091000 2014-03-06T07:30:00Z 2014.176 1.95110e-06 57
#> 4: 1394094600 2014-03-06T08:30:00Z 2014.176 1.96890e-06 57
#> 5: 1394098200 2014-03-06T09:30:00Z 2014.176 1.99260e-06 57
#> ---
#> 8281129: 1640979000 2021-12-31T19:30:00Z 2021.999 1.93855e-06 60
#> 8281130: 1640982600 2021-12-31T20:30:00Z 2022.000 1.93909e-06 60
#> 8281131: 1640986200 2021-12-31T21:30:00Z 2022.000 1.93938e-06 60
#> 8281132: 1640989800 2021-12-31T22:30:00Z 2022.000 1.94089e-06 60
#> 8281133: 1640993400 2021-12-31T23:30:00Z 2022.000 1.94087e-06 26
#> value_std_dev latitude longitude altitude elevation intake_height
#> <num> <num> <num> <num> <num> <num>
#> 1: 4.20e-09 49.0114 -122.3353 93 60 33
#> 2: 8.40e-09 49.0114 -122.3353 93 60 33
#> 3: 5.70e-09 49.0114 -122.3353 93 60 33
#> 4: 2.18e-08 49.0114 -122.3353 93 60 33
#> 5: 9.40e-09 49.0114 -122.3353 93 60 33
#> ---
#> 8281129: 4.54e-10 47.4165 10.9796 2669 2666 3
#> 8281130: 3.38e-10 47.4165 10.9796 2669 2666 3
#> 8281131: 7.64e-10 47.4165 10.9796 2669 2666 3
#> 8281132: 4.25e-10 47.4165 10.9796 2669 2666 3
#> 8281133: 3.55e-10 47.4165 10.9796 2669 2666 3
#> obs_flag obspack_num
#> <int> <int>
#> 1: 0 2671976
#> 2: 0 2671977
#> 3: 0 2671978
#> 4: 0 2671979
#> 5: 0 2671980
#> ---
#> 8281129: 0 13341897
#> 8281130: 0 13341898
#> 8281131: 0 13341899
#> 8281132: 0 13341900
#> 8281133: 0 13341901
#> obspack_id
#> <char>
#> 1: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_abt_surface-insitu_6_allvalid~2671976
#> 2: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_abt_surface-insitu_6_allvalid~2671977
#> 3: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_abt_surface-insitu_6_allvalid~2671978
#> 4: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_abt_surface-insitu_6_allvalid~2671979
#> 5: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_abt_surface-insitu_6_allvalid~2671980
#> ---
#> 8281129: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_zsf_surface-insitu_442_allvalid-3magl~13341897
#> 8281130: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_zsf_surface-insitu_442_allvalid-3magl~13341898
#> 8281131: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_zsf_surface-insitu_442_allvalid-3magl~13341899
#> 8281132: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_zsf_surface-insitu_442_allvalid-3magl~13341900
#> 8281133: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_zsf_surface-insitu_442_allvalid-3magl~13341901
#> unique_sample_location_num year_st month_st day_st hour_st minute_st
#> <int> <int> <int> <int> <int> <int>
#> 1: 32763088 2014 3 5 21 8
#> 2: 32763089 2014 3 5 22 8
#> 3: 32763090 2014 3 5 23 8
#> 4: 32763091 2014 3 6 0 8
#> 5: 32763092 2014 3 6 1 8
#> ---
#> 8281129: 49987485 2021 12 31 20 11
#> 8281130: 49987486 2021 12 31 21 11
#> 8281131: 49987487 2021 12 31 22 11
#> 8281132: 49987488 2021 12 31 23 11
#> 8281133: 49987489 2022 1 1 0 11
#> second_st scale site_elevation_unit dataset_project
#> <int> <char> <char> <char>
#> 1: 58 WMO CH4 X2004A masl surface-insitu
#> 2: 58 WMO CH4 X2004A masl surface-insitu
#> 3: 58 WMO CH4 X2004A masl surface-insitu
#> 4: 58 WMO CH4 X2004A masl surface-insitu
#> 5: 58 WMO CH4 X2004A masl surface-insitu
#> ---
#> 8281129: 1 WMO CH4 X2019 masl surface-insitu
#> 8281130: 1 WMO CH4 X2019 masl surface-insitu
#> 8281131: 1 WMO CH4 X2019 masl surface-insitu
#> 8281132: 1 WMO CH4 X2019 masl surface-insitu
#> 8281133: 1 WMO CH4 X2019 masl surface-insitu
#> dataset_selection_tag site_name site_elevation
#> <char> <char> <num>
#> 1: allvalid Abbotsford, British Columbia 60
#> 2: allvalid Abbotsford, British Columbia 60
#> 3: allvalid Abbotsford, British Columbia 60
#> 4: allvalid Abbotsford, British Columbia 60
#> 5: allvalid Abbotsford, British Columbia 60
#> ---
#> 8281129: allvalid-3magl Zugspitze / Schneefernerhaus 2666
#> 8281130: allvalid-3magl Zugspitze / Schneefernerhaus 2666
#> 8281131: allvalid-3magl Zugspitze / Schneefernerhaus 2666
#> 8281132: allvalid-3magl Zugspitze / Schneefernerhaus 2666
#> 8281133: allvalid-3magl Zugspitze / Schneefernerhaus 2666
#> site_latitude site_longitude site_country site_code site_utc2lst
#> <num> <num> <char> <char> <num>
#> 1: 49.0114 -122.3353 Canada ABT -8
#> 2: 49.0114 -122.3353 Canada ABT -8
#> 3: 49.0114 -122.3353 Canada ABT -8
#> 4: 49.0114 -122.3353 Canada ABT -8
#> 5: 49.0114 -122.3353 Canada ABT -8
#> ---
#> 8281129: 47.4165 10.9796 Germany ZSF 0
#> 8281130: 47.4165 10.9796 Germany ZSF 0
#> 8281131: 47.4165 10.9796 Germany ZSF 0
#> 8281132: 47.4165 10.9796 Germany ZSF 0
#> 8281133: 47.4165 10.9796 Germany ZSF 0
#> lab_1_abbr dataset_calibration_scale altitude_final type_altitude
#> <char> <char> <num> <num>
#> 1: ECCC WMO CH4 X2004A 33 NA
#> 2: ECCC WMO CH4 X2004A 33 NA
#> 3: ECCC WMO CH4 X2004A 33 NA
#> 4: ECCC WMO CH4 X2004A 33 NA
#> 5: ECCC WMO CH4 X2004A 33 NA
#> ---
#> 8281129: ICOS-ATC WMO CH4 X2019 3 0
#> 8281130: ICOS-ATC WMO CH4 X2019 3 0
#> 8281131: ICOS-ATC WMO CH4 X2019 3 0
#> 8281132: ICOS-ATC WMO CH4 X2019 3 0
#> 8281133: ICOS-ATC WMO CH4 X2019 3 0
#> dataset_intake_ht_unit instrument method pressure qcflag temperature
#> <char> <char> <char> <num> <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
#> ---
#> 8281129: magl 880 <NA> NA O NA
#> 8281130: magl 880 <NA> NA O NA
#> 8281131: magl 880 <NA> NA O NA
#> 8281132: magl 880 <NA> NA O NA
#> 8281133: magl 880 <NA> NA O NA
#> time_interval u v value_unc
#> <int> <num> <num> <num>
#> 1: NA NA NA NA
#> 2: NA NA NA NA
#> 3: NA NA NA NA
#> 4: NA NA NA NA
#> 5: NA NA NA NA
#> ---
#> 8281129: NA NA NA NA
#> 8281130: NA NA NA NA
#> 8281131: NA NA NA NA
#> 8281132: NA NA NA NA
#> 8281133: NA NA 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 <- 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: 93 33 33 60 allvalid
#> 2: 93 33 33 60 allvalid
#> 3: 93 33 33 60 allvalid
#> 4: 93 33 33 60 allvalid
#> 5: 93 33 33 60 allvalid
#> ---
#> 8281129: 2669 3 3 2666 allvalid-3magl
#> 8281130: 2669 3 3 2666 allvalid-3magl
#> 8281131: 2669 3 3 2666 allvalid-3magl
#> 8281132: 2669 3 3 2666 allvalid-3magl
#> 8281133: 2669 3 3 2666 allvalid-3magl
#> site_name
#> <char>
#> 1: Abbotsford, British Columbia
#> 2: Abbotsford, British Columbia
#> 3: Abbotsford, British Columbia
#> 4: Abbotsford, British Columbia
#> 5: Abbotsford, British Columbia
#> ---
#> 8281129: Zugspitze / Schneefernerhaus
#> 8281130: Zugspitze / Schneefernerhaus
#> 8281131: Zugspitze / Schneefernerhaus
#> 8281132: Zugspitze / Schneefernerhaus
#> 8281133: Zugspitze / SchneefernerhausThe temporal range of data is
range(df$year)#> [1] 1983 2021We also check for dimensions of data
dim(df)#> [1] 8281133 54Filters
df <- df[year == yy]
df <- df[altitude_final < max_altitude &
latitude < north &
latitude > south &
longitude < east &
longitude > west]
dim(df)#> [1] 274587 54Towers 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: ABT 33.00
#> 2: BCK 60.00
#> 3: BRA 35.00
#> 4: BRW 16.46
#> 5: CARL 9.00
#> 6: CBY 12.00
#> 7: CHL 60.00
#> 8: CPS 40.00
#> 9: EGB 25.00
#> 10: ESP 40.00
#> 11: EST 50.00
#> 12: ETL 105.00
#> 13: FNE 15.00
#> 14: FORT 128.00
#> 15: FSD 40.00
#> 16: GHG06 100.00
#> 17: GHG09 100.00
#> 18: HNP 10.00
#> 19: HOBB 91.00
#> 20: INU 10.00
#> 21: INX01 121.00
#> 22: INX02 136.00
#> 23: INX07 58.00
#> 24: INX08 41.00
#> 25: INX09 130.00
#> 26: INX10 40.00
#> 27: INX13 87.00
#> 28: INX14 76.00
#> 29: LLB 50.00
#> 30: MALJ 134.00
#> 31: MLO 40.00
#> 32: OLI 0.00
#> 33: SGP 4.00
#> 34: THD 90.00
#> 35: TPD 35.00
#> 36: WSA 3.00
#> site_code max_altitudeKey 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: 2020-01-01 14:17:19 2020-01-01 14:00:00
#> 2: 2020-01-02 14:16:52 2020-01-02 14:00:00
#> 3: 2020-01-03 14:16:26 2020-01-03 14:00:00
#> 4: 2020-01-04 14:16:00 2020-01-04 14:00:00
#> 5: 2020-01-05 14:15:34 2020-01-05 14:00:00
#> ---
#> 11473: 2020-03-15 14:20:51 2020-03-15 14:00:00
#> 11474: 2020-03-16 14:21:08 2020-03-16 14:00:00
#> 11475: 2020-03-17 14:21:26 2020-03-17 14:00:00
#> 11476: 2020-03-18 14:21:44 2020-03-18 14:00:00
#> 11477: 2020-03-19 14:22:02 2020-03-19 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: ABT 33.00 33.00
#> 2: BCK 60.00 60.00
#> 3: BRA 35.00 35.00
#> 4: BRW 16.46 16.46
#> 5: CARL 9.00 9.00
#> 6: CBY 12.00 12.00
#> 7: CHL 60.00 60.00
#> 8: CPS 40.00 40.00
#> 9: EGB 25.00 25.00
#> 10: ESP 40.00 40.00
#> 11: EST 50.00 50.00
#> 12: ETL 105.00 105.00
#> 13: FNE 15.00 15.00
#> 14: FORT 128.00 128.00
#> 15: FSD 40.00 40.00
#> 16: GHG06 100.00 100.00
#> 17: GHG06 50.00 100.00
#> 18: GHG09 100.00 100.00
#> 19: GHG09 50.00 100.00
#> 20: HNP 10.00 10.00
#> 21: HOBB 91.00 91.00
#> 22: INU 10.00 10.00
#> 23: INX01 10.00 121.00
#> 24: INX01 121.00 121.00
#> 25: INX01 40.00 121.00
#> 26: INX02 136.00 136.00
#> 27: INX07 21.00 58.00
#> 28: INX07 58.00 58.00
#> 29: INX08 41.00 41.00
#> 30: INX09 130.00 130.00
#> 31: INX10 40.00 40.00
#> 32: INX13 87.00 87.00
#> 33: INX14 76.00 76.00
#> 34: LLB 50.00 50.00
#> 35: MALJ 134.00 134.00
#> 36: MLO 40.00 40.00
#> 37: OLI 0.00 0.00
#> 38: SGP 4.00 4.00
#> 39: THD 90.00 90.00
#> 40: TPD 35.00 35.00
#> 41: WSA 3.00 3.00
#> site_code altitude_final max_altitudeMaster
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\Rtmp4k67qL\file531463a43a74_surface-insitu.txtcsv
message(paste0(out,"_", datasetid, ".csv\n"))
fwrite(master,
paste0(out,"_", datasetid, ".csv"),
sep = ",")#> C:\Users\sibarrae\AppData\Local\Temp\Rtmp4k67qL\file531463a43a74_surface-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: surface-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\Rtmp4k67qL\file531463a43a74_surface-insitu.csvy
obs_read_csvy(paste0(out,"_", datasetid, ".csvy"))#> [1] "---"
#> [2] "name: Metadata "
#> [3] "sector: surface-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':\t11378 obs. of 20 variables:"
#> [12] " $ timeUTC : chr \"2020-01-01 14:00:00\" \".."
#> [13] " $ site_code : chr \"ABT\" \"ABT\" ..."
#> [14] " $ altitude_final : num 33 33 33 33 33 ..."
#> [15] " $ type_altitude : num NA NA NA NA NA ..."
#> [16] " $ lab_1_abbr : chr \"ECCC\" \"ECCC\" ..."
#> [17] " $ dataset_calibration_scale: chr \"WMO CH4 X2004A\" \"WMO \".."
#> [18] " $ value : num 1.96e-06 1.99e-06 ..."
#> [19] " $ latitude : num 49 49 ..."
#> [20] " $ longitude : num -122 -122 ..."
#> [21] " $ site_utc2lst : num -8 -8 -8 -8 -8 ..."
#> [22] " $ year : int 2020 2020 2020 2020 202.."
#> [23] " $ month : int 1 1 1 1 1 ..."
#> [24] " $ day : chr \"01\" \"02\" ..."
#> [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 33 33 33 33 33 ..."
#> [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: 2020-01-01 14:00:00 ABT 33 NA ECCC
#> 2: 2020-01-02 14:00:00 ABT 33 NA ECCC
#> 3: 2020-01-03 14:00:00 ABT 33 NA ECCC
#> 4: 2020-01-04 14:00:00 ABT 33 NA ECCC
#> 5: 2020-01-05 14:00:00 ABT 33 NA ECCC
#> ---
#> 11374: 2020-03-15 14:00:00 WSA 3 NA ECCC
#> 11375: 2020-03-16 14:00:00 WSA 3 NA ECCC
#> 11376: 2020-03-17 14:00:00 WSA 3 NA ECCC
#> 11377: 2020-03-18 14:00:00 WSA 3 NA ECCC
#> 11378: 2020-03-19 14:00:00 WSA 3 NA ECCC
#> dataset_calibration_scale value latitude longitude site_utc2lst
#> <char> <num> <num> <num> <int>
#> 1: WMO CH4 X2004A 1.9555e-06 49.0114 -122.3353 -8
#> 2: WMO CH4 X2004A 1.9929e-06 49.0114 -122.3353 -8
#> 3: WMO CH4 X2004A 1.9471e-06 49.0114 -122.3353 -8
#> 4: WMO CH4 X2004A 1.9763e-06 49.0114 -122.3353 -8
#> 5: WMO CH4 X2004A 1.9656e-06 49.0114 -122.3353 -8
#> ---
#> 11374: WMO CH4 X2004A 1.9610e-06 43.9322 -60.0093 -4
#> 11375: WMO CH4 X2004A 1.9614e-06 43.9322 -60.0093 -4
#> 11376: WMO CH4 X2004A 1.9560e-06 43.9322 -60.0093 -4
#> 11377: WMO CH4 X2004A 1.9663e-06 43.9322 -60.0093 -4
#> 11378: WMO CH4 X2004A 1.9711e-06 43.9322 -60.0093 -4
#> year month day hour minute second time time_decimal
#> <int> <int> <int> <int> <int> <int> <int> <num>
#> 1: 2020 1 1 14 0 0 1577887200 2020.002
#> 2: 2020 1 2 14 0 0 1577973600 2020.004
#> 3: 2020 1 3 14 0 0 1578060000 2020.007
#> 4: 2020 1 4 14 0 0 1578146400 2020.010
#> 5: 2020 1 5 14 0 0 1578232800 2020.013
#> ---
#> 11374: 2020 3 15 14 0 0 1584280800 2020.204
#> 11375: 2020 3 16 14 0 0 1584367200 2020.207
#> 11376: 2020 3 17 14 0 0 1584453600 2020.209
#> 11377: 2020 3 18 14 0 0 1584540000 2020.212
#> 11378: 2020 3 19 14 0 0 1584626400 2020.215
#> max_altitude local_time
#> <num> <lgcl>
#> 1: 33 NA
#> 2: 33 NA
#> 3: 33 NA
#> 4: 33 NA
#> 5: 33 NA
#> ---
#> 11374: 3 NA
#> 11375: 3 NA
#> 11376: 3 NA
#> 11377: 3 NA
#> 11378: 3 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\Rtmp4k67qL\file531463a43a74_surface-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] ABT BCK BRA BRW CARL CBY CHL CPS EGB ESP EST ETL
#> [13] FNE FORT FSD GHG06 GHG09 HNP HOBB INU INX01 INX02 INX07 INX08
#> [25] INX09 INX10 INX13 INX14 LLB MALJ MLO OLI SGP THD TPD WSA
#> Plotting the following sites:
#> [1] ABT BCK
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