The stationary from pavlovc2

Get hourly meteorological data from global met stations.

Examples

Get data from a station in Norway (with a USAF code of 13860, and a WBAN code of 99999). Specify the station_id as a string in the format [USAF]-[WBAN], and, provide beginning and ending years for data collection to startyear and endyear, respectively.

library(stationaRy)

met_data <- 
  get_isd_station_data(
    station_id = "13860-99999",
    startyear = 2010,
    endyear = 2011)
    
# Display part of the meteorological dataset
met_data
#> # A tibble: 16,474 × 18
#>      usaf  wban  year month   day  hour minute   lat   lon  elev    wd    ws
#>     <chr> <chr> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1  013860 99999  2010     1     1     0      0  60.8  11.1   132    NA    NA
#> 2  013860 99999  2010     1     1     1      0  60.8  11.1   132    NA    NA
#> 3  013860 99999  2010     1    21    15      0  60.8  11.1   132    NA    NA
#> 4  013860 99999  2010     1    21    16      0  60.8  11.1   132    NA    NA
#> 5  013860 99999  2010     1    21    17      0  60.8  11.1   132    NA    NA
#> 6  013860 99999  2010     1    21    18      0  60.8  11.1   132    NA    NA
#> 7  013860 99999  2010     1    21    19      0  60.8  11.1   132    NA    NA
#> 8  013860 99999  2010     1    21    20      0  60.8  11.1   132    NA    NA
#> 9  013860 99999  2010     1    21    21      0  60.8  11.1   132    NA    NA
#> 10 013860 99999  2010     1    21    22      0  60.8  11.1   132    NA    NA
#> # ... with 16,464 more rows, and 6 more variables: ceil_hgt <dbl>, temp <dbl>,
#> #   dew_point <dbl>, atmos_pres <dbl>, rh <dbl>, time <dttm>

This is useful if you know the USAF and WBAN numbers for a particular met station. Most of the time, however, you won't readily have this information. However, you can examine station metadata using the get_isd_stations() function (which has those ID values). Without providing any arguments, it provides a tibble for all available stations (with many variables to filter on). All stations in Norway, for example, can be isolated easily by using the filter() function from the dplyr package after the pipe (%>%).

library(stationaRy)
library(dplyr)

# Get a tibble with all stations in Norway
stations_norway <- 
  get_isd_stations() %>% 
  filter(country == "NO")
  
# Preview the dataset
stations_norway
#> # A tibble: 398 × 16
#>     usaf  wban                name country state    lat    lon  elev begin   end
#>    <dbl> <dbl>               <chr>   <chr> <chr>  <dbl>  <dbl> <dbl> <dbl> <dbl>
#> 1  10010 99999 JAN MAYEN(NOR-NAVY)      NO       70.933 -8.667   9.0  1931  2015
#> 2  10014 99999          SORSTOKKEN      NO       59.792  5.341  48.8  1986  2015
#> 3  10015 99999          BRINGELAND      NO       61.383  5.867 327.0  1987  2011
#> 4  10016 99999         RORVIK/RYUM      NO       64.850 11.233  14.0  1987  1991
#> 5  10017 99999               FRIGG      NO       59.980  2.250  48.0  1988  2005
#> 6  10020 99999       VERLEGENHUKEN      NO       80.050 16.250   8.0  1986  2015
#> 7  10030 99999            HORNSUND      NO       77.000 15.500  12.0  1985  2015
#> 8  10040 99999       NY-ALESUND II      NO       78.917 11.933   8.0  1973  2014
#> 9  10060 99999             EDGEOYA      NO       78.250 22.817  14.0  1973  2015
#> 10 10100 99999              ANDOYA      NO       69.293 16.144  13.1  1931  2015
#> # ... with 388 more rows, and 6 more variables: gmt_offset <dbl>, time_zone_id <chr>,
#> #   country_name <chr>, country_code <chr>, iso3166_2_subd <chr>, fips10_4_subd <chr>

This table can be greatly reduced to isolate the stations of interest. For example, another use of filter() could be used to get only high-altitude stations (above 1000 meters).

library(stationaRy)
library(dplyr)

# Filter the table with met stations
# in Norway to only those with elevation
# greater than 1000 m
norway_high_elev <-
  stations_norway %>% 
  filter(elev > 1000)

# Preview the dataset
norway_high_elev
#> # A tibble: 13 × 16
#>      usaf  wban               name country state     lat    lon   elev begin   end
#>     <dbl> <dbl>              <chr>   <chr> <chr>   <dbl>  <dbl>  <dbl> <dbl> <dbl>
#> 1   12200 99999             MANNEN      NO        62.450  7.767 1294.0  2010  2015
#> 2   12390 99999        HJERKINN II      NO        62.217  9.550 1012.0  2010  2015
#> 3   13460 99999          MIDTSTOVA      NO        60.650  7.267 1162.0  2011  2015
#> 4   13500 99999          FINSEVATN      NO        60.600  7.533 1208.0  2003  2015
#> 5   13510 99999          FINSE III      NO        60.600  7.500 1224.0  1973  2001
#> 6   13520 99999           SANDHAUG      NO        60.183  7.483 1250.0  2004  2015
#> 7   13620 99999         JUVVASSHOE      NO        61.667  8.367 1894.0  2009  2015
#> 8   13660 99999         SOGNEFJELL      NO        61.567  8.000 1413.0  1979  2015
#> 9   13750 99999          KVITFJELL      NO        61.467 10.133 1028.0  1973  2015
#> 10  14330 99999         MIDTLAEGER      NO        59.833  6.983 1081.0  1973  2015
#> 11  14400 99999             BLASJO      NO        59.333  6.867 1104.6  1973  2015
#> 12  14611 99999       GAUSTATOPPEN      NO        59.850  8.650 1803.7  2014  2015
#> 13 895040 99999 TROLL IN ANTARKTIS      NO       -72.017  2.383 1270.0  1994  2015
#> # ... with 6 more variables: gmt_offset <dbl>, time_zone_id <chr>, country_name <chr>,
#> #   country_code <chr>, iso3166_2_subd <chr>, fips10_4_subd <chr>

The station IDs from the tibble can be transformed into a vector with the get_station_ids() function.

library(stationaRy)
library(dplyr)

# With the filtered table of high-elevation
# sites in Norway, create a vector of
# station ID values
norway_high_elev_ids <-
  norway_high_elev %>% 
  get_station_ids

# Display the vector of station ID values
norway_high_elev_ids
#>  [1] "12200-99999"  "12390-99999"  "13460-99999"  "13500-99999"  "13510-99999" 
#>  [6] "13520-99999"  "13620-99999"  "13660-99999"  "13750-99999"  "14330-99999" 
#> [11] "14400-99999"  "14611-99999"  "895040-99999"

Suppose you'd like to collect several years of met data from a particular station and get only a listing of parameters that meet some criterion. Here's an example of obtaining temperatures above 37 degrees Celsius from the Bergen Point station:

library(stationaRy)
library(dplyr)

# Get high temperatures from 2006 to the end of 2015
# recorded at the Bergen Point met station
high_temps_at_bergen_point_stn <- 
  get_isd_stations() %>%
  filter(name == "BERGEN POINT") %>%
  get_station_ids %>%
  get_isd_station_data(startyear = 2006, endyear = 2015) %>%
  select(time, wd, ws, temp) %>% 
  filter(temp > 37) %>%
  mutate(temp_f = (temp * (9/5)) + 32)

# Preview the dataset
high_temps_at_bergen_point_stn
#> # A tibble: 3 × 5
#>                  time    wd    ws  temp temp_f
#>                <dttm> <dbl> <dbl> <dbl>  <dbl>
#> 1 2012-07-18 12:00:00   230   1.5  37.2  98.96
#> 2 2012-07-18 13:00:00   220   2.6  37.8 100.04
#> 3 2012-07-18 14:00:00   230   4.1  37.9 100.22

There can actually be a lot of additional met data beyond wind speed, temperatures, etc. It can vary greatly depending on the selected station. These additional categories are denoted 'two-letter + digit' identifiers (e.g., AA1, GA1, etc.). Within each category are numerous variables (coded as [identifer]_[index]). Here is the complete list of the different parameters:

Category	Identifier	Column Name
`AA1`	`aa1_1`	`aa1_liq_precip_period_quantity`
`AA1`	`aa1_2`	`aa1_liq_precip_depth_dimension`
`AA1`	`aa1_3`	`aa1_liq_precip_condition_code`
`AB1`	`ab1_1`	`aa1_liq_precip_quality_code`
`AB1`	`ab1_2`	`ab1_liq_precip_monthly_depth_dimension`
`AB1`	`ab1_3`	`ab1_liq_precip_monthly_condition_code`
`AB1`	`ab1_4`	`ab1_liq_precip_monthly_quality_code`
`AC1`	`ac1_1`	`ac1_precip_obs_history_duration_code`
`AC1`	`ac1_2`	`ac1_precip_obs_history_characteristic_code`
`AC1`	`ac1_3`	`ac1_precip_obs_history_quality_code`
`AD1`	`ad1_1`	`ad1_liq_precip_greatest_amt_24h_month_depth_dimension`
`AD1`	`ad1_2`	`ad1_liq_precip_greatest_amt_24h_month_condition_code`
`AD1`	`ad1_3`	`ad1_liq_precip_greatest_amt_24h_month_dates`
`AD1`	`ad1_4`	`ad1_liq_precip_greatest_amt_24h_month_quality_code`
`AE1`	`ae1_1`	`ae1_liq_precip_number_days_amt_month__01inch`
`AE1`	`ae1_2`	`ae1_liq_precip_number_days_amt_month__01inch_quality_code`
`AE1`	`ae1_3`	`ae1_liq_precip_number_days_amt_month__10inch`
`AE1`	`ae1_4`	`ae1_liq_precip_number_days_amt_month__10inch_quality_code`
`AE1`	`ae1_5`	`ae1_liq_precip_number_days_amt_month__50inch`
`AE1`	`ae1_6`	`ae1_liq_precip_number_days_amt_month__50inch_quality_code`
`AE1`	`ae1_7`	`ae1_liq_precip_number_days_amt_month_1_00inch`
`AE1`	`ae1_8`	`ae1_liq_precip_number_days_amt_month_1_00inch_quality_code`
`AG1`	`ag1_1`	`ag1_precip_est_obs_discrepancy_code`
`AG1`	`ag1_2`	`ag1_precip_est_obs_est_water_depth_dimension`
`AH1`	`ah1_3`	`ah1_liq_precip_max_short_dur_month_period_quantity`
`AH1`	`ah1_4`	`ah1_liq_precip_max_short_dur_month_depth_dimension`
`AH1`	`ah1_5`	`ah1_liq_precip_max_short_dur_month_condition_code`
`AH1`	`ah1_6`	`ah1_liq_precip_max_short_dur_month_end_date_time`
`AH1`	`ah1_7`	`ah1_liq_precip_max_short_dur_month_quality_code`
`AI1`	`ai1_1`	`ai1_liq_precip_max_short_dur_month_period_quantity`
`AI1`	`ai1_2`	`ai1_liq_precip_max_short_dur_month_depth_dimension`
`AI1`	`ai1_3`	`ai1_liq_precip_max_short_dur_month_condition_code`
`AI1`	`ai1_4`	`ai1_liq_precip_max_short_dur_month_end_date_time`
`AI1`	`ai1_5`	`ai1_liq_precip_max_short_dur_month_quality_code`
`AJ1`	`aj1_6`	`aj1_snow_depth_dimension`
`AJ1`	`aj1_7`	`aj1_snow_depth_condition_code`
`AJ1`	`aj1_8`	`aj1_snow_depth_quality_code`
`AJ1`	`aj1_9`	`aj1_snow_depth_equiv_water_depth_dimension`
`AJ1`	`aj1_10`	`aj1_snow_depth_equiv_water_condition_code`
`AJ1`	`aj1_11`	`aj1_snow_depth_equiv_water_quality_code`
`AK1`	`ad1_1`	`ak1_snow_depth_greatest_depth_month_depth_dimension`
`AK1`	`ad1_2`	`ak1_snow_depth_greatest_depth_month_condition_code`
`AK1`	`ak1_3`	`ak1_snow_depth_greatest_depth_month_dates_occurrence`
`AK1`	`ak1_4`	`ak1_snow_depth_greatest_depth_month_quality_code`
`AL1`	`al1_1`	`al1_snow_accumulation_period_quantity`
`AL1`	`al1_2`	`al1_snow_accumulation_depth_dimension`
`AL1`	`al1_3`	`al1_snow_accumulation_condition_code`
`AL1`	`al1_4`	`al1_snow_accumulation_quality_code`
`AM1`	`am1_1`	`am1_snow_accumulation_greatest_amt_24h_month_depth_dimension`
`AM1`	`am1_2`	`am1_snow_accumulation_greatest_amt_24h_month_condition_code`
`AM1`	`am1_3`	`am1_snow_accumulation_greatest_amt_24h_month_dates_occurrence_1`
`AM1`	`am1_4`	`am1_snow_accumulation_greatest_amt_24h_month_dates_occurrence_2`
`AM1`	`am1_5`	`am1_snow_accumulation_greatest_amt_24h_month_dates_occurrence_3`
`AM1`	`am1_6`	`am1_snow_accumulation_greatest_amt_24h_month_quality_code`
`AN1`	`an1_1`	`an1_snow_accumulation_month_period_quantity`
`AN1`	`an1_2`	`an1_snow_accumulation_month_depth_dimension`
`AN1`	`an1_3`	`an1_snow_accumulation_month_condition_code`
`AN1`	`an1_4`	`an1_snow_accumulation_month_quality_code`
`AO1`	`ao1_1`	`ao1_liq_precip_period_quantity_minutes`
`AO1`	`ao1_2`	`ao1_liq_precip_depth_dimension`
`AO1`	`ao1_3`	`ao1_liq_precip_condition_code`
`AO1`	`ao1_4`	`ao1_liq_precip_quality_code`
`AP1`	`ap1_1`	`ap1_15_min_liq_precip_hpd_gauge_value_45_min_prior`
`AP1`	`ap1_2`	`ap1_15_min_liq_precip_hpd_gauge_value_30_min_prior`
`AP1`	`ap1_3`	`ap1_15_min_liq_precip_hpd_gauge_value_15_min_prior`
`AP1`	`ap1_4`	`ap1_15_min_liq_precip_hpd_gauge_value_at_obs_time`
`AU1`	`au1_1`	`au1_present_weather_obs_intensity_code`
`AU1`	`au1_2`	`au1_present_weather_obs_descriptor_code`
`AU1`	`au1_3`	`au1_present_weather_obs_precipitation_code`
`AU1`	`au1_4`	`au1_present_weather_obs_obscuration_code`
`AU1`	`au1_5`	`au1_present_weather_obs_other_weather_phenomena_code`
`AU1`	`au1_6`	`au1_present_weather_obs_combination_indicator_code`
`AU1`	`au1_7`	`au1_present_weather_obs_quality_code`
`AW1`	`aw1_1`	`aw1_present_weather_obs_aut_weather_report_1`
`AW1`	`aw1_2`	`aw1_present_weather_obs_aut_weather_report_2`
`AW1`	`aw1_3`	`aw1_present_weather_obs_aut_weather_report_3`
`AW1`	`aw1_4`	`aw1_present_weather_obs_aut_weather_report_4`
`AX1`	`ax1_1`	`ax1_past_weather_obs_atmos_condition_code`
`AX1`	`ax1_2`	`ax1_past_weather_obs_quality_manual_atmos_condition_code`
`AX1`	`ax1_3`	`ax1_past_weather_obs_period_quantity`
`AX1`	`ax1_4`	`ax1_past_weather_obs_period_quality_code`
`AY1`	`ay1_1`	`ay1_past_weather_obs_manual_occurrence_identifier`
`AY1`	`ay1_2`	`ay1_past_weather_obs_quality_manual_atmos_condition_code`
`AY1`	`ay1_3`	`ay1_past_weather_obs_period_quantity`
`AY1`	`ay1_4`	`ay1_past_weather_obs_period_quality_code`
`AZ1`	`az1_1`	`az1_past_weather_obs_aut_occurrence_identifier`
`AZ1`	`az1_2`	`az1_past_weather_obs_quality_aut_atmos_condition_code`
`AZ1`	`az1_3`	`az1_past_weather_obs_period_quantity`
`AZ1`	`az1_4`	`az1_past_weather_obs_period_quality_code`
`CB1`	`cb1_1`	`cb1_subhrly_obs_liq_precip_2_sensor_period_quantity`
`CB1`	`cb1_2`	`cb1_subhrly_obs_liq_precip_2_sensor_precip_liq_depth`
`CB1`	`cb1_3`	`cb1_subhrly_obs_liq_precip_2_sensor_qc_quality_code`
`CB1`	`cb1_4`	`cb1_subhrly_obs_liq_precip_2_sensor_flag_quality_code`
`CF1`	`cf1_1`	`cf1_hrly_fan_speed_rate`
`CF1`	`cf1_2`	`cf1_hrly_fan_qc_quality_code`
`CF1`	`cf1_3`	`cf1_hrly_fan_flag_quality_code`
`CG1`	`cg1_1`	`cg1_subhrly_obs_liq_precip_1_sensor_precip_liq_depth`
`CG1`	`cg1_2`	`cg1_subhrly_obs_liq_precip_1_sensor_qc_quality_code`
`CG1`	`cg1_3`	`cg1_subhrly_obs_liq_precip_1_sensor_flag_quality_code`
`CH1`	`ch1_1`	`ch1_hrly_subhrly_rh_temp_period_quantity`
`CH1`	`ch1_2`	`ch1_hrly_subhrly_temp_avg_air_temp`
`CH1`	`ch1_3`	`ch1_hrly_subhrly_temp_qc_quality_code`
`CH1`	`ch1_4`	`ch1_hrly_subhrly_temp_flag_quality_code`
`CH1`	`ch1_5`	`ch1_hrly_subhrly_rh_avg_rh`
`CH1`	`ch1_6`	`ch1_hrly_subhrly_rh_qc_quality_code`
`CH1`	`ch1_7`	`ch1_hrly_subhrly_rh_flag_quality_code`
`CI1`	`ci1_1`	`ci1_hrly_rh_temp_min_hrly_temp`
`CI1`	`ci1_2`	`ci1_hrly_rh_temp_min_hrly_temp_qc_quality_code`
`CI1`	`ci1_3`	`ci1_hrly_rh_temp_min_hrly_temp_flag_quality_code`
`CI1`	`ci1_4`	`ci1_hrly_rh_temp_max_hrly_temp`
`CI1`	`ci1_5`	`ci1_hrly_rh_temp_max_hrly_temp_qc_quality_code`
`CI1`	`ci1_6`	`ci1_hrly_rh_temp_max_hrly_temp_flag_quality_code`
`CI1`	`ci1_7`	`ci1_hrly_rh_temp_std_dev_hrly_temp`
`CI1`	`ci1_8`	`ci1_hrly_rh_temp_std_dev_hrly_temp_qc_quality_code`
`CI1`	`ci1_9`	`ci1_hrly_rh_temp_std_dev_hrly_temp_flag_quality_code`
`CI1`	`ci1_10`	`ci1_hrly_rh_temp_std_dev_hrly_rh`
`CI1`	`ci1_11`	`ci1_hrly_rh_temp_std_dev_hrly_rh_qc_quality_code`
`CI1`	`ci1_12`	`ci1_hrly_rh_temp_std_dev_hrly_rh_flag_quality_code`
`CN1`	`cn1_1`	`cn1_hrly_batvol_sensors_transm_avg_voltage`
`CN1`	`cn1_2`	`cn1_hrly_batvol_sensors_transm_avg_voltage_qc_quality_code`
`CN1`	`cn1_3`	`cn1_hrly_batvol_sensors_transm_avg_voltage_flag_quality_code`
`CN1`	`cn1_4`	`cn1_hrly_batvol_full_load_avg_voltage`
`CN1`	`cn1_5`	`cn1_hrly_batvol_full_load_avg_voltage_qc_quality_code`
`CN1`	`cn1_6`	`cn1_hrly_batvol_full_load_avg_voltage_flag_quality_code`
`CN1`	`cn1_7`	`cn1_hrly_batvol_datalogger_avg_voltage`
`CN1`	`cn1_8`	`cn1_hrly_batvol_datalogger_avg_voltage_qc_quality_code`
`CN1`	`cn1_9`	`cn1_hrly_batvol_datalogger_avg_voltage_flag_quality_code`
`CN2`	`cn2_1`	`cn2_hrly_diagnostic_equipment_temp`
`CN2`	`cn2_2`	`cn2_hrly_diagnostic_equipment_temp_qc_quality_code`
`CN2`	`cn2_3`	`cn2_hrly_diagnostic_equipment_temp_flag_quality_code`
`CN2`	`cn2_4`	`cn2_hrly_diagnostic_geonor_inlet_temp`
`CN2`	`cn2_5`	`cn2_hrly_diagnostic_geonor_inlet_temp_qc_quality_code`
`CN2`	`cn2_6`	`cn2_hrly_diagnostic_geonor_inlet_temp_flag_quality_code`
`CN2`	`cn2_7`	`cn2_hrly_diagnostic_datalogger_opendoor_time`
`CN2`	`cn2_8`	`cn2_hrly_diagnostic_datalogger_opendoor_time_qc_quality_code`
`CN2`	`cn2_9`	`cn2_hrly_diagnostic_datalogger_opendoor_time_flag_quality_code`
`CN3`	`cn3_1`	`cn3_hrly_diagnostic_reference_resistor_avg_resistance`
`CN3`	`cn3_2`	`cn3_hrly_diagnostic_reference_resistor_avg_resistance_qc_quality_code`
`CN3`	`cn3_3`	`cn3_hrly_diagnostic_reference_resistor_avg_resistance_flag_quality_code`
`CN3`	`cn3_4`	`cn3_hrly_diagnostic_datalogger_signature_id`
`CN3`	`cn3_5`	`cn3_hrly_diagnostic_datalogger_signature_id_qc_quality_code`
`CN3`	`cn3_6`	`cn3_hrly_diagnostic_datalogger_signature_id_flag_quality_code`
`CN4`	`cn4_1`	`cn4_hrly_diagnostic_liq_precip_gauge_flag_bit`
`CN4`	`cn4_2`	`cn4_hrly_diagnostic_liq_precip_gauge_flag_bit_qc_quality_code`
`CN4`	`cn4_3`	`cn4_hrly_diagnostic_liq_precip_gauge_flag_bit_flag_quality_code`
`CN4`	`cn4_4`	`cn4_hrly_diagnostic_doorflag_field`
`CN4`	`cn4_5`	`cn4_hrly_diagnostic_doorflag_field_qc_quality_code`
`CN4`	`cn4_6`	`cn4_hrly_diagnostic_doorflag_field_flag_quality_code`
`CN4`	`cn4_7`	`cn4_hrly_diagnostic_forward_transmitter_rf_power`
`CN4`	`cn4_8`	`cn4_hrly_diagnostic_forward_transmitter_rf_power_qc_quality_code`
`CN4`	`cn4_9`	`cn4_hrly_diagnostic_forward_transmitter_rf_power_flag_quality_code`
`CN4`	`cn4_10`	`cn4_hrly_diagnostic_reflected_transmitter_rf_power`
`CN4`	`cn4_11`	`cn4_hrly_diagnostic_reflected_transmitter_rf_power_qc_quality_code`
`CN4`	`cn4_12`	`cn4_hrly_diagnostic_reflected_transmitter_rf_power_flag_quality_code`
`CR1`	`cr1_1`	`cr1_control_section_datalogger_version_number`
`CR1`	`cr1_2`	`cr1_control_section_datalogger_version_number_qc_quality_code`
`CR1`	`cr1_3`	`cr1_control_section_datalogger_version_number_flag_quality_code`
`CT1`	`ct1_1`	`ct1_subhrly_temp_avg_air_temp`
`CT1`	`ct1_2`	`ct1_subhrly_temp_avg_air_temp_qc_quality_code`
`CT1`	`ct1_3`	`ct1_subhrly_temp_avg_air_temp_flag_quality_code`
`CU1`	`cu1_1`	`cu1_hrly_temp_avg_air_temp`
`CU1`	`cu1_2`	`cu1_hrly_temp_avg_air_temp_qc_quality_code`
`CU1`	`cu1_3`	`cu1_hrly_temp_avg_air_temp_flag_quality_code`
`CU1`	`cu1_4`	`cu1_hrly_temp_avg_air_temp_st_dev`
`CU1`	`cu1_5`	`cu1_hrly_temp_avg_air_temp_st_dev_qc_quality_code`
`CU1`	`cu1_6`	`cu1_hrly_temp_avg_air_temp_st_dev_flag_quality_code`
`CV1`	`cv1_1`	`cv1_hrly_temp_min_air_temp`
`CV1`	`cv1_2`	`cv1_hrly_temp_min_air_temp_qc_quality_code`
`CV1`	`cv1_3`	`cv1_hrly_temp_min_air_temp_flag_quality_code`
`CV1`	`cv1_4`	`cv1_hrly_temp_min_air_temp_time`
`CV1`	`cv1_5`	`cv1_hrly_temp_min_air_temp_time_qc_quality_code`
`CV1`	`cv1_6`	`cv1_hrly_temp_min_air_temp_time_flag_quality_code`
`CV1`	`cv1_7`	`cv1_hrly_temp_max_air_temp`
`CV1`	`cv1_8`	`cv1_hrly_temp_max_air_temp_qc_quality_code`
`CV1`	`cv1_9`	`cv1_hrly_temp_max_air_temp_flag_quality_code`
`CV1`	`cv1_10`	`cv1_hrly_temp_max_air_temp_time`
`CV1`	`cv1_11`	`cv1_hrly_temp_max_air_temp_time_qc_quality_code`
`CV1`	`cv1_12`	`cv1_hrly_temp_max_air_temp_time_flag_quality_code`
`CW1`	`cw1_1`	`cw1_subhrly_wetness_wet1_indicator`
`CW1`	`cw1_2`	`cw1_subhrly_wetness_wet1_indicator_qc_quality_code`
`CW1`	`cw1_3`	`cw1_subhrly_wetness_wet1_indicator_flag_quality_code`
`CW1`	`cw1_4`	`cw1_subhrly_wetness_wet2_indicator`
`CW1`	`cw1_5`	`cw1_subhrly_wetness_wet2_indicator_qc_quality_code`
`CW1`	`cw1_6`	`cw1_subhrly_wetness_wet2_indicator_flag_quality_code`
`CX1`	`cx1_1`	`cx1_hourly_geonor_vib_wire_total_precip`
`CX1`	`cx1_2`	`cx1_hourly_geonor_vib_wire_total_precip_qc_quality_code`
`CX1`	`cx1_3`	`cx1_hourly_geonor_vib_wire_total_precip_flag_quality_code`
`CX1`	`cx1_4`	`cx1_hourly_geonor_vib_wire_freq_avg_precip`
`CX1`	`cx1_5`	`cx1_hourly_geonor_vib_wire_freq_avg_precip_qc_quality_code`
`CX1`	`cx1_6`	`cx1_hourly_geonor_vib_wire_freq_avg_precip_flag_quality_code`
`CX1`	`cx1_7`	`cx1_hourly_geonor_vib_wire_freq_min_precip`
`CX1`	`cx1_8`	`cx1_hourly_geonor_vib_wire_freq_min_precip_qc_quality_code`
`CX1`	`cx1_9`	`cx1_hourly_geonor_vib_wire_freq_min_precip_flag_quality_code`
`CX1`	`cx1_10`	`cx1_hourly_geonor_vib_wire_freq_max_precip`
`CX1`	`cx1_11`	`cx1_hourly_geonor_vib_wire_freq_max_precip_qc_quality_code`
`CX1`	`cx1_12`	`cx1_hourly_geonor_vib_wire_freq_max_precip_flag_quality_code`
`CO1`	`co1_1`	`co1_network_metadata_climate_division_number`
`CO1`	`co1_2`	`co1_network_metadata_utc_lst_time_conversion`
`CO1`	`co1_3`	`co2_us_network_cooperative_element_id`
`CO1`	`co1_4`	`co2_us_network_cooperative_time_offset`
`ED1`	`ed1_1`	`ed1_runway_vis_range_obs_direction_angle`
`ED1`	`ed1_2`	`ed1_runway_vis_range_obs_runway_designator_code`
`ED1`	`ed1_3`	`ed1_runway_vis_range_obs_vis_dimension`
`ED1`	`ed1_4`	`ed1_runway_vis_range_obs_quality_code`
`GA1`	`ga1_1`	`ga1_sky_cover_layer_coverage_code`
`GA1`	`ga1_2`	`ga1_sky_cover_layer_coverage_quality_code`
`GA1`	`ga1_3`	`ga1_sky_cover_layer_base_height`
`GA1`	`ga1_4`	`ga1_sky_cover_layer_base_height_quality_code`
`GA1`	`ga1_5`	`ga1_sky_cover_layer_cloud_type`
`GA1`	`ga1_6`	`ga1_sky_cover_layer_cloud_type_quality_code`
`GD1`	`gd1_1`	`gd1_sky_cover_summation_state_coverage_1`
`GD1`	`gd1_2`	`gd1_sky_cover_summation_state_coverage_2`
`GD1`	`gd1_3`	`gd1_sky_cover_summation_state_coverage_quality_code`
`GD1`	`gd1_4`	`gd1_sky_cover_summation_state_height`
`GD1`	`gd1_5`	`gd1_sky_cover_summation_state_height_quality_code`
`GD1`	`gd1_6`	`gd1_sky_cover_summation_state_characteristic_code`
`GF1`	`gf1_1`	`gf1_sky_condition_obs_total_coverage`
`GF1`	`gf1_2`	`gf1_sky_condition_obs_total_opaque_coverage`
`GF1`	`gf1_3`	`gf1_sky_condition_obs_total_coverage_quality_code`
`GF1`	`gf1_4`	`gf1_sky_condition_obs_total_lowest_cloud_cover`
`GF1`	`gf1_5`	`gf1_sky_condition_obs_total_lowest_cloud_cover_quality_code`
`GF1`	`gf1_6`	`gf1_sky_condition_obs_low_cloud_genus`
`GF1`	`gf1_7`	`gf1_sky_condition_obs_low_cloud_genus_quality_code`
`GF1`	`gf1_8`	`gf1_sky_condition_obs_lowest_cloud_base_height`
`GF1`	`gf1_9`	`gf1_sky_condition_obs_lowest_cloud_base_height_quality_code`
`GF1`	`gf1_10`	`gf1_sky_condition_obs_mid_cloud_genus`
`GF1`	`gf1_11`	`gf1_sky_condition_obs_mid_cloud_genus_quality_code`
`GF1`	`gf1_12`	`gf1_sky_condition_obs_high_cloud_genus`
`GF1`	`gf1_13`	`gf1_sky_condition_obs_high_cloud_genus_quality_code`
`GG1`	`gg1_1`	`gg1_below_stn_cloud_layer_coverage`
`GG1`	`gg1_2`	`gg1_below_stn_cloud_layer_coverage_quality_code`
`GG1`	`gg1_3`	`gg1_below_stn_cloud_layer_top_height`
`GG1`	`gg1_4`	`gg1_below_stn_cloud_layer_top_height_quality_code`
`GG1`	`gg1_5`	`gg1_below_stn_cloud_layer_type`
`GG1`	`gg1_6`	`gg1_below_stn_cloud_layer_type_quality_code`
`GG1`	`gg1_7`	`gg1_below_stn_cloud_layer_top`
`GG1`	`gg1_8`	`gg1_below_stn_cloud_layer_top_quality_code`
`GH1`	`gh1_1`	`gh1_hrly_solar_rad_hrly_avg_solarad`
`GH1`	`gh1_2`	`gh1_hrly_solar_rad_hrly_avg_solarad_qc_quality_code`
`GH1`	`gh1_3`	`gh1_hrly_solar_rad_hrly_avg_solarad_flag_quality_code`
`GH1`	`gh1_4`	`gh1_hrly_solar_rad_min_solarad`
`GH1`	`gh1_5`	`gh1_hrly_solar_rad_min_solarad_qc_quality_code`
`GH1`	`gh1_6`	`gh1_hrly_solar_rad_min_solarad_flag_quality_code`
`GH1`	`gh1_7`	`gh1_hrly_solar_rad_max_solarad`
`GH1`	`gh1_8`	`gh1_hrly_solar_rad_max_solarad_qc_quality_code`
`GH1`	`gh1_9`	`gh1_hrly_solar_rad_max_solarad_flag_quality_code`
`GH1`	`gh1_10`	`gh1_hrly_solar_rad_std_dev_solarad`
`GH1`	`gh1_11`	`gh1_hrly_solar_rad_std_dev_solarad_qc_quality_code`
`GH1`	`gh1_12`	`gh1_hrly_solar_rad_std_dev_solarad_flag_quality_code`
`GJ1`	`gj1_1`	`gj1_sunshine_obs_duration`
`GJ1`	`gj1_2`	`gj1_sunshine_obs_duration_quality_code`
`GK1`	`gk1_1`	`gk1_sunshine_obs_pct_possible_sunshine`
`GK1`	`gk1_2`	`gk1_sunshine_obs_pct_possible_quality_code`
`GL1`	`gl1_1`	`gl1_sunshine_obs_duration`
`GL1`	`gl1_2`	`gl1_sunshine_obs_duration_quality_code`
`GM1`	`gm1_1`	`gm1_solar_irradiance_time_period`
`GM1`	`gm1_2`	`gm1_solar_irradiance_global_irradiance`
`GM1`	`gm1_3`	`gm1_solar_irradiance_global_irradiance_data_flag`
`GM1`	`gm1_4`	`gm1_solar_irradiance_global_irradiance_quality_code`
`GM1`	`gm1_5`	`gm1_solar_irradiance_direct_beam_irradiance`
`GM1`	`gm1_6`	`gm1_solar_irradiance_direct_beam_irradiance_data_flag`
`GM1`	`gm1_7`	`gm1_solar_irradiance_direct_beam_irradiance_quality_code`
`GM1`	`gm1_8`	`gm1_solar_irradiance_diffuse_irradiance`
`GM1`	`gm1_9`	`gm1_solar_irradiance_diffuse_irradiance_data_flag`
`GM1`	`gm1_10`	`gm1_solar_irradiance_diffuse_irradiance_quality_code`
`GM1`	`gm1_11`	`gm1_solar_irradiance_uvb_global_irradiance`
`GM1`	`gm1_12`	`gm1_solar_irradiance_uvb_global_irradiance_data_flag`
`GM1`	`gm1_13`	`gm1_solar_irradiance_uvb_global_irradiance_quality_code`
`GN1`	`gn1_1`	`gn1_solar_rad_time_period`
`GN1`	`gn1_2`	`gn1_solar_rad_upwelling_global_solar_rad`
`GN1`	`gn1_3`	`gn1_solar_rad_upwelling_global_solar_rad_quality_code`
`GN1`	`gn1_4`	`gn1_solar_rad_downwelling_thermal_ir_rad`
`GN1`	`gn1_5`	`gn1_solar_rad_downwelling_thermal_ir_rad_quality_code`
`GN1`	`gn1_6`	`gn1_solar_rad_upwelling_thermal_ir_rad`
`GN1`	`gn1_7`	`gn1_solar_rad_upwelling_thermal_ir_rad_quality_code`
`GN1`	`gn1_8`	`gn1_solar_rad_par`
`GN1`	`gn1_9`	`gn1_solar_rad_par_quality_code`
`GN1`	`gn1_10`	`gn1_solar_rad_solar_zenith_angle`
`GN1`	`gn1_11`	`gn1_solar_rad_solar_zenith_angle_quality_code`
`GO1`	`go1_1`	`go1_net_solar_rad_time_period`
`GO1`	`go1_2`	`go1_net_solar_rad_net_solar_radiation`
`GO1`	`go1_3`	`go1_net_solar_rad_net_solar_radiation_quality_code`
`GO1`	`go1_4`	`go1_net_solar_rad_net_ir_radiation`
`GO1`	`go1_5`	`go1_net_solar_rad_net_ir_radiation_quality_code`
`GO1`	`go1_6`	`go1_net_solar_rad_net_radiation`
`GO1`	`go1_7`	`go1_net_solar_rad_net_radiation_quality_code`
`GP1`	`gp1_1`	`gp1_modeled_solar_irradiance_data_time_period`
`GP1`	`gp1_2`	`gp1_modeled_solar_irradiance_global_horizontal`
`GP1`	`gp1_3`	`gp1_modeled_solar_irradiance_global_horizontal_src_flag`
`GP1`	`gp1_4`	`gp1_modeled_solar_irradiance_global_horizontal_uncertainty`
`GP1`	`gp1_5`	`gp1_modeled_solar_irradiance_direct_normal`
`GP1`	`gp1_6`	`gp1_modeled_solar_irradiance_direct_normal_src_flag`
`GP1`	`gp1_7`	`gp1_modeled_solar_irradiance_direct_normal_uncertainty`
`GP1`	`gp1_8`	`gp1_modeled_solar_irradiance_diffuse_normal`
`GP1`	`gp1_9`	`gp1_modeled_solar_irradiance_diffuse_normal_src_flag`
`GP1`	`gp1_10`	`gp1_modeled_solar_irradiance_diffuse_normal_uncertainty`
`GP1`	`gp1_11`	`gp1_modeled_solar_irradiance_diffuse_horizontal`
`GP1`	`gp1_12`	`gp1_modeled_solar_irradiance_diffuse_horizontal_src_flag`
`GP1`	`gp1_13`	`gp1_modeled_solar_irradiance_diffuse_horizontal_uncertainty`
`GQ1`	`gq1_1`	`gq1_hrly_solar_angle_time_period`
`GQ1`	`gq1_2`	`gq1_hrly_solar_angle_mean_zenith_angle`
`GQ1`	`gq1_3`	`gq1_hrly_solar_angle_mean_zenith_angle_quality_code`
`GQ1`	`gq1_4`	`gq1_hrly_solar_angle_mean_azimuth_angle`
`GQ1`	`gq1_5`	`gq1_hrly_solar_angle_mean_azimuth_angle_quality_code`
`GR1`	`gr1_1`	`gr1_hrly_extraterrestrial_rad_time_period`
`GR1`	`gr1_2`	`gr1_hrly_extraterrestrial_rad_horizontal`
`GR1`	`gr1_3`	`gr1_hrly_extraterrestrial_rad_horizontal_quality_code`
`GR1`	`gr1_4`	`gr1_hrly_extraterrestrial_rad_normal`
`GR1`	`gr1_5`	`gr1_hrly_extraterrestrial_rad_normal_quality_code`
`HL1`	`hl1_1`	`hl1_hail_size`
`HL1`	`hl1_2`	`hl1_hail_size_quality_code`
`IA1`	`ia1_1`	`ia1_ground_surface_obs_code`
`IA1`	`ia1_2`	`ia1_ground_surface_obs_code_quality_code`
`IA2`	`ia1_3`	`ia2_ground_surface_obs_min_temp_time_period`
`IA2`	`ia1_4`	`ia2_ground_surface_obs_min_temp`
`IA2`	`ia1_5`	`ia2_ground_surface_obs_min_temp_quality_code`
`IB1`	`ib1_1`	`ib1_hrly_surface_temp`
`IB1`	`ib1_2`	`ib1_hrly_surface_temp_qc_quality_code`
`IB1`	`ib1_3`	`ib1_hrly_surface_temp_flag_quality_code`
`IB1`	`ib1_4`	`ib1_hrly_surface_min_temp`
`IB1`	`ib1_5`	`ib1_hrly_surface_min_temp_qc_quality_code`
`IB1`	`ib1_6`	`ib1_hrly_surface_min_temp_flag_quality_code`
`IB1`	`ib1_7`	`ib1_hrly_surface_max_temp`
`IB1`	`ib1_8`	`ib1_hrly_surface_max_temp_qc_quality_code`
`IB1`	`ib1_9`	`ib1_hrly_surface_max_temp_flag_quality_code`
`IB1`	`ib1_10`	`ib1_hrly_surface_std_temp`
`IB1`	`ib1_11`	`ib1_hrly_surface_std_temp_qc_quality_code`
`IB1`	`ib1_12`	`ib1_hrly_surface_std_temp_flag_quality_code`
`IB2`	`ib2_1`	`ib2_hrly_surface_temp_sb`
`IB2`	`ib2_2`	`ib2_hrly_surface_temp_sb_qc_quality_code`
`IB2`	`ib2_3`	`ib2_hrly_surface_temp_sb_flag_quality_code`
`IB2`	`ib2_4`	`ib2_hrly_surface_temp_sb_std`
`IB2`	`ib2_5`	`ib2_hrly_surface_temp_sb_std_qc_quality_code`
`IB2`	`ib2_6`	`ib2_hrly_surface_temp_sb_std_flag_quality_code`
`IC1`	`ic1_1`	`ic1_grnd_surface_obs_pan_evap_time_period`
`IC1`	`ic1_2`	`ic1_grnd_surface_obs_pan_evap_wind`
`IC1`	`ic1_3`	`ic1_grnd_surface_obs_pan_evap_wind_condition_code`
`IC1`	`ic1_4`	`ic1_grnd_surface_obs_pan_evap_wind_quality_code`
`IC1`	`ic1_5`	`ic1_grnd_surface_obs_pan_evap_data`
`IC1`	`ic1_6`	`ic1_grnd_surface_obs_pan_evap_data_condition_code`
`IC1`	`ic1_7`	`ic1_grnd_surface_obs_pan_evap_data_quality_code`
`IC1`	`ic1_8`	`ic1_grnd_surface_obs_pan_max_water_data`
`IC1`	`ic1_9`	`ic1_grnd_surface_obs_pan_max_water_data_condition_code`
`IC1`	`ic1_10`	`ic1_grnd_surface_obs_pan_max_water_data_quality_code`
`IC1`	`ic1_11`	`ic1_grnd_surface_obs_pan_min_water_data`
`IC1`	`ic1_12`	`ic1_grnd_surface_obs_pan_min_water_data_condition_code`
`IC1`	`ic1_13`	`ic1_grnd_surface_obs_pan_min_water_data_quality_code`
`KA1`	`ka1_1`	`ka1_extreme_air_temp_time_period`
`KA1`	`ka1_2`	`ka1_extreme_air_temp_code`
`KA1`	`ka1_3`	`ka1_extreme_air_temp_high_or_low`
`KA1`	`ka1_4`	`ka1_extreme_air_temp_high_or_low_quality_code`
`KB1`	`kb1_1`	`kb1_avg_air_temp_time_period`
`KB1`	`kb1_2`	`kb1_avg_air_temp_code`
`KB1`	`kb1_3`	`kb1_avg_air_temp_air_temp`
`KB1`	`kb1_4`	`kb1_avg_air_temp_air_temp_quality_code`
`KC1`	`kc1_1`	`kc1_extreme_air_temp_monthly_code`
`KC1`	`kc1_2`	`kc1_extreme_air_temp_monthly_condition_code`
`KC1`	`kc1_3`	`kc1_extreme_air_temp_monthly_temp`
`KC1`	`kc1_4`	`kc1_extreme_air_temp_monthly_date`
`KC1`	`kc1_5`	`kc1_extreme_air_temp_monthly_temp_quality_code`
`KD1`	`kd1_1`	`kd1_heat_cool_deg_days_time_period`
`KD1`	`kd1_2`	`kd1_heat_cool_deg_days_code`
`KD1`	`kd1_3`	`kd1_heat_cool_deg_days_value`
`KD1`	`kd1_4`	`kd1_heat_cool_deg_days_quality_code`
`KE1`	`ke1_1`	`ke1_extreme_temp_number_days_max_32f_or_lower`
`KE1`	`ke1_2`	`ke1_extreme_temp_number_days_max_32f_or_lower_quality_code`
`KE1`	`ke1_3`	`ke1_extreme_temp_number_days_max_90f_or_higher`
`KE1`	`ke1_4`	`ke1_extreme_temp_number_days_max_90f_or_higher_quality_code`
`KE1`	`ke1_5`	`ke1_extreme_temp_number_days_min_32f_or_lower`
`KE1`	`ke1_6`	`ke1_extreme_temp_number_days_min_32f_or_lower_quality_code`
`KE1`	`ke1_7`	`ke1_extreme_temp_number_days_min_0f_or_lower`
`KE1`	`ke1_8`	`ke1_extreme_temp_number_days_min_0f_or_lower_quality_code`
`KF1`	`kf1_1`	`kf1_hrly_calc_temp`
`KF1`	`kf1_2`	`kf1_hrly_calc_temp_quality_code`
`KG1`	`kg1_1`	`kg1_avg_dp_wb_temp_time_period`
`KG1`	`kg1_2`	`kg1_avg_dp_wb_temp_code`
`KG1`	`kg1_3`	`kg1_avg_dp_wb_temp`
`KG1`	`kg1_4`	`kg1_avg_dp_wb_temp_derived_code`
`KG1`	`kg1_5`	`kg1_avg_dp_wb_temp_quality_code`
`MA1`	`ma1_1`	`ma1_atmos_p_obs_altimeter_setting_rate`
`MA1`	`ma1_2`	`ma1_atmos_p_obs_altimeter_quality_code`
`MA1`	`ma1_3`	`ma1_atmos_p_obs_stn_pressure_rate`
`MA1`	`ma1_4`	`ma1_atmos_p_obs_stn_pressure_rate_quality_code`
`MD1`	`md1_1`	`md1_atmos_p_change_tendency_code`
`MD1`	`md1_2`	`md1_atmos_p_change_tendency_code_quality_code`
`MD1`	`md1_3`	`md1_atmos_p_change_3_hr_quantity`
`MD1`	`md1_4`	`md1_atmos_p_change_3_hr_quantity_quality_code`
`MD1`	`md1_5`	`md1_atmos_p_change_24_hr_quantity`
`MD1`	`md1_6`	`md1_atmos_p_change_24_hr_quantity_quality_code`
`ME1`	`me1_1`	`me1_geopotential_hgt_isobaric_lvl_code`
`ME1`	`me1_2`	`me1_geopotential_hgt_isobaric_lvl_height`
`ME1`	`me1_3`	`me1_geopotential_hgt_isobaric_lvl_height_quality_code`
`MF1`	`mf1_1`	`mf1_atmos_p_obs_stp_avg_stn_pressure_day`
`MF1`	`mf1_2`	`mf1_atmos_p_obs_stp_avg_stn_pressure_day_quality_code`
`MF1`	`mf1_3`	`mf1_atmos_p_obs_stp_avg_sea_lvl_pressure_day`
`MF1`	`mf1_4`	`mf1_atmos_p_obs_stp_avg_sea_lvl_pressure_day_quality_code`
`MG1`	`mg1_1`	`mg1_atmos_p_obs_avg_stn_pressure_day`
`MG1`	`mg1_2`	`mg1_atmos_p_obs_avg_stn_pressure_day_quality_code`
`MG1`	`mg1_3`	`mg1_atmos_p_obs_avg_sea_lvl_pressure_day`
`MG1`	`mg1_4`	`mg1_atmos_p_obs_avg_sea_lvl_pressure_day_quality_code`
`MH1`	`mh1_1`	`mh1_atmos_p_obs_avg_stn_pressure_month`
`MH1`	`mh1_2`	`mh1_atmos_p_obs_avg_stn_pressure_month_quality_code`
`MH1`	`mh1_3`	`mh1_atmos_p_obs_avg_sea_lvl_pressure_month`
`MH1`	`mh1_4`	`mh1_atmos_p_obs_avg_sea_lvl_pressure_month_quality_code`
`MK1`	`mk1_1`	`mk1_atmos_p_obs_max_sea_lvl_pressure_month`
`MK1`	`mk1_2`	`mk1_atmos_p_obs_max_sea_lvl_pressure_date_time`
`MK1`	`mk1_3`	`mk1_atmos_p_obs_max_sea_lvl_pressure_quality_code`
`MK1`	`mk1_4`	`mk1_atmos_p_obs_min_sea_lvl_pressure_month`
`MK1`	`mk1_5`	`mk1_atmos_p_obs_min_sea_lvl_pressure_date_time`
`MK1`	`mk1_6`	`mk1_atmos_p_obs_min_sea_lvl_pressure_quality_code`
`MV1`	`mv1_1`	`mv1_present_weather_obs_condition_code`
`MV1`	`mv1_2`	`mv1_present_weather_obs_condition_code_quality_code`
`MW1`	`mw1_1`	`mw1_present_weather_obs_manual_occurrence_condition_code`
`MW1`	`mw1_2`	`mw1_present_weather_obs_manual_occurrence_condition_code_quality_code`
`OA1`	`oa1_1`	`oa1_suppl_wind_obs_type`
`OA1`	`oa1_2`	`oa1_suppl_wind_obs_time_period`
`OA1`	`oa1_3`	`oa1_suppl_wind_obs_speed_rate`
`OA1`	`oa1_4`	`oa1_suppl_wind_obs_speed_rate_quality_code`
`OB1`	`ob1_1`	`ob1_hly_subhrly_wind_avg_time_period`
`OB1`	`ob1_2`	`ob1_hly_subhrly_wind_max_gust`
`OB1`	`ob1_3`	`ob1_hly_subhrly_wind_max_gust_quality_code`
`OB1`	`ob1_4`	`ob1_hly_subhrly_wind_max_gust_flag`
`OB1`	`ob1_5`	`ob1_hly_subhrly_wind_max_dir`
`OB1`	`ob1_6`	`ob1_hly_subhrly_wind_max_dir_quality_code`
`OB1`	`ob1_7`	`ob1_hly_subhrly_wind_max_dir_flag`
`OB1`	`ob1_8`	`ob1_hly_subhrly_wind_max_stdev`
`OB1`	`ob1_9`	`ob1_hly_subhrly_wind_max_stdev_quality_code`
`OB1`	`ob1_10`	`ob1_hly_subhrly_wind_max_stdev_flag`
`OB1`	`ob1_11`	`ob1_hly_subhrly_wind_max_dir_stdev`
`OB1`	`ob1_12`	`ob1_hly_subhrly_wind_max_dir_stdev_quality_code`
`OB1`	`ob1_13`	`ob1_hly_subhrly_wind_max_dir_stdev_flag`
`OC1`	`oc1_1`	`oc1_wind_gust_obs_speed_rate`
`OC1`	`oc1_2`	`oc1_wind_gust_obs_speed_rate_quality_code`
`OE1`	`oe1_1`	`oe1_summary_of_day_wind_obs_type`
`OE1`	`oe1_2`	`oe1_summary_of_day_wind_obs_time_period`
`OE1`	`oe1_3`	`oe1_summary_of_day_wind_obs_speed_rate`
`OE1`	`oe1_4`	`oe1_summary_of_day_wind_obs_dir`
`OE1`	`oe1_5`	`oe1_summary_of_day_wind_obs_time_occurrence`
`OE1`	`oe1_6`	`oe1_summary_of_day_wind_obs_quality_code`
`RH1`	`rh1_1`	`rh1_relative_humidity_time_period`
`RH1`	`rh1_2`	`rh1_relative_humidity_code`
`RH1`	`rh1_3`	`rh1_relative_humidity_percentage`
`RH1`	`rh1_4`	`rh1_relative_humidity_derived_code`
`RH1`	`rh1_5`	`rh1_relative_humidity_quality_code`
`SA1`	`sa1_1`	`sa1_sea_surf_temp`
`SA1`	`sa1_2`	`sa1_sea_surf_temp_quality_code`
`ST1`	`st1_1`	`st1_soil_temp_type`
`ST1`	`st1_2`	`st1_soil_temp_soil_temp`
`ST1`	`st1_3`	`st1_soil_temp_soil_temp_quality_code`
`ST1`	`st1_4`	`st1_soil_temp_depth`
`ST1`	`st1_5`	`st1_soil_temp_depth_quality_code`
`ST1`	`st1_6`	`st1_soil_temp_soil_cover`
`ST1`	`st1_7`	`st1_soil_temp_soil_cover_quality_code`
`ST1`	`st1_8`	`st1_soil_temp_sub_plot`
`ST1`	`st1_9`	`st1_soil_temp_sub_plot_quality_code`
`UA1`	`ua1_1`	`ua1_wave_meas_method_code`
`UA1`	`ua1_2`	`ua1_wave_meas_wave_period_quantity`
`UA1`	`ua1_3`	`ua1_wave_meas_wave_height_dimension`
`UA1`	`ua1_4`	`ua1_wave_meas_quality_code`
`UA1`	`ua1_5`	`ua1_wave_meas_sea_state_code`
`UA1`	`ua1_6`	`ua1_wave_meas_sea_state_code_quality_code`
`UG1`	`ug1_1`	`ug1_wave_meas_primary_swell_time_period`
`UG1`	`ug1_2`	`ug1_wave_meas_primary_swell_height_dimension`
`UG1`	`ug1_3`	`ug1_wave_meas_primary_swell_dir_angle`
`UG1`	`ug1_4`	`ug1_wave_meas_primary_swell_quality_code`
`UG2`	`ug2_1`	`ug2_wave_meas_secondary_swell_time_period`
`UG2`	`ug2_2`	`ug2_wave_meas_secondary_swell_height_dimension`
`UG2`	`ug2_3`	`ug2_wave_meas_secondary_swell_dir_angle`
`UG2`	`ug2_4`	`ug2_wave_meas_secondary_swell_quality_code`
`WA1`	`wa1_1`	`wa1_platform_ice_accr_source_code`
`WA1`	`wa1_2`	`wa1_platform_ice_accr_thickness_dimension`
`WA1`	`wa1_3`	`wa1_platform_ice_accr_tendency_code`
`WA1`	`wa1_4`	`wa1_platform_ice_accr_quality_code`
`WD1`	`wd1_1`	`wd1_water_surf_ice_obs_edge_bearing_code`
`WD1`	`wd1_2`	`wd1_water_surf_ice_obs_uniform_conc_rate`
`WD1`	`wd1_3`	`wd1_water_surf_ice_obs_non_uniform_conc_rate`
`WD1`	`wd1_4`	`wd1_water_surf_ice_obs_ship_rel_pos_code`
`WD1`	`wd1_5`	`wd1_water_surf_ice_obs_ship_penetrability_code`
`WD1`	`wd1_6`	`wd1_water_surf_ice_obs_ice_trend_code`
`WD1`	`wd1_7`	`wd1_water_surf_ice_obs_development_code`
`WD1`	`wd1_8`	`wd1_water_surf_ice_obs_growler_bergy_bit_pres_code`
`WD1`	`wd1_10`	`wd1_water_surf_ice_obs_growler_bergy_bit_quantity`
`WD1`	`wd1_11`	`wd1_water_surf_ice_obs_iceberg_quantity`
`WD1`	`wd1_12`	`wd1_water_surf_ice_obs_quality_code`
`WG1`	`wg1_1`	`wg1_water_surf_ice_hist_obs_edge_distance`
`WG1`	`wg1_2`	`wg1_water_surf_ice_hist_obs_edge_orient_code`
`WG1`	`wg1_3`	`wg1_water_surf_ice_hist_obs_form_type_code`
`WG1`	`wg1_4`	`wg1_water_surf_ice_hist_obs_nav_effect_code`
`WG1`	`wg1_5`	`wg1_water_surf_ice_hist_obs_quality_code`

More information about these variables can be found in this PDF document.

To find out which categories are available for a station, set the add_data_report argument of the get_isd_station_data() function to TRUE. This will provide a tibble with the available additional categories with their counts in the dataset.

library(stationaRy)
library(dplyr)

# Get information on which additional met data
# is available at the Bergen Point station
bergen_pt_add_data <-
  get_isd_stations() %>%
  filter(name == "BERGEN POINT") %>%
  get_station_ids %>%
  get_isd_station_data(
    startyear = 2015,
    endyear = 2015,
    add_data_report = TRUE)

# Preview the dataset
bergen_pt_add_data
#>   category total_count
#> 1      MD1       13170
#> 2      SA1       14479

The MD1 category deals with atmospheric pressure change and the SA1 category provides sea surface temperature. For SA1, the sa1_1 and sa1_2 variables represent the sea surface temperature and it's quality code (where 1 is the ideal quality code value). Using functions from dplyr (select(), filter(), group_by(), and summarize()) one can create a table of the mean ambient and sea-surface temperatures by month from the met data table. The additional data is included in the met data table by using the select_additional_data argument and specifying the SA1 category (multiple categories can be included).

library(stationaRy)
library(dplyr)

# Get the average ambient temperature and the
# average sea-surface temperatures (sst) from
# the Bergen Point station for every month
# during 2015
bergen_point_temps <- 
  get_isd_stations() %>%
  filter(name == "BERGEN POINT") %>%
  get_station_ids %>%
  get_isd_station_data(
    startyear = 2015,
    endyear = 2015,
    select_additional_data = "SA1") %>%
  select(month, temp, sa1_1, sa1_2) %>%
  filter(sa1_2 == 1) %>%
  group_by(month) %>%
  summarize(avg_temp = mean(temp, na.rm = TRUE),
            avg_sst = mean(sa1_1, na.rm = TRUE))

# Preview the dataset
bergen_point_temps
#> # A tibble: 12 × 3
#>    month    avg_temp   avg_sst
#>    <dbl>       <dbl>     <dbl>
#> 1      1 -0.05040928  4.192490
#> 2      2 -0.33406940  2.091724
#> 3      3  5.64884726  4.855947
#> 4      4 11.48056976  9.221271
#> 5      5 17.74683453 14.573022
#> 6      6 21.57782041 19.815111
#> 7      7 25.48305209 23.666417
#> 8      8 25.68534610 25.087288
#> 9      9 22.35404858 23.689652
#> 10    10 13.83142077 17.038115
#> 11    11 10.65389507 13.158805
#> 12    12 10.03515850 10.445389

Here's an example where rainfall amounts (over 6 hour periods) are summed by month for the year of 2015. The aa1_1 column is the duration in hours when the liquid precipitation was observed, and, the aa1_2 column is quantity of rain. With group_by() and summarize(), we can get the monthly total precipitation amounts in mm units.

library(stationaRy)
library(dplyr)

# Get the total monthly rainfall amounts
# by month for the Abbotsford station
# during 2015
monthly_rainfall <- 
  get_isd_stations() %>%
  filter(name == "ABBOTSFORD") %>%
  get_station_ids %>%
  get_isd_station_data(
    startyear = 2015,
    endyear = 2015,
    select_additional_data = "AA1") %>%
  filter(aa1_1 == 6, aa1_2 < 800) %>% 
  select(month, aa1_2) %>%
  group_by(month) %>%
  summarize(mm_per_month = sum(aa1_2))

# Preview the dataset
monthly_rainfall
#> # A tibble: 12 × 2
#>    month mm_per_month
#>    <dbl>        <dbl>
#> 1      1        317.8
#> 2      2        239.1
#> 3      3        304.5
#> 4      4         98.6
#> 5      5         41.9
#> 6      6         35.2
#> 7      7         56.9
#> 8      8         52.3
#> 9      9        115.4
#> 10    10         88.9
#> 11    11        175.7
#> 12    12        194.0

Installation

To install the development R package, use the following:

devtools::install_github("rich-iannone/stationaRy")

The package is also available in CRAN:

install.packages("stationaRy")

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