GitHub - bgolla/stationaRy: Get hourly meteorological data from one of thousands of global stations.

Get hourly meteorological data from a station located somewhere on Earth.

Examples

Get data from a station in Norway (with a USAF value of 13860, and a WBAN value 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 = 2009,
                                 endyear = 2010)

That's great if you know the USAF and WBAN numbers for a particular met station. Most of the time, however, you won't have this info. You can search for station metadata using the get_isd_stations function. Without providing any arguments, it gives you a data frame containing the entire dataset of stations. Currently, there are 27,446 rows in the dataset. Here are rows 250-255 from the dataset:

library(stationaRy)

get_isd_stations()[250:255,]

Source: local data frame [6 x 16]

   usaf  wban                 name country state    lat   lon elev begin  end
1 13220 99999          FORDE-TEFRE      NO       61.467 5.917   64  1973 2015
2 13230 99999           BRINGELAND      NO       61.393 5.764  327  1984 2015
3 13250 99999           MODALEN II      NO       60.833 5.950  114  1973 2008
4 13260 99999          MODALEN III      NO       60.850 5.983  125  2008 2015
5 13270 99999 KVAMSKOGEN-JONSHOGDI      NO       60.383 5.967  455  2006 2015
6 13280 99999           KVAMSKOGEN      NO       60.400 5.917  408  1973 2006
Variables not shown: gmt_offset (dbl), time_zone_id (chr), country_name (chr),
  country_code (chr), iso3166_2_subd (chr), fips10_4_subd (chr)

This list can be greatly reduced to isolate the stations of interest. One way to do this is to specify a geographic bounding box using lat/lon values to specify the bounds. Let's try a bounding box located in the west coast of Canada.

library(stationaRy)

get_isd_stations(lower_lat = 49.000,
                 upper_lat = 49.500,
                 lower_lon = -123.500,
                 upper_lon = -123.000)

Source: local data frame [20 x 16]

     usaf  wban                      name country state    lat      lon   elev begin  end
1  710040 99999    CYPRESS BOWL FREESTYLE      CA       49.400 -123.200  969.0  2007 2010
2  710370 99999            POINT ATKINSON      CA       49.330 -123.265   35.0  2003 2015
3  710420 99999           DELTA BURNS BOG      CA       49.133 -123.000    3.0  2001 2015
4  711120 99999 RICHMOND OPERATION CENTRE      CA       49.167 -123.067   16.0  1980 2015
5  712010 99999  VANCOUVER HARBOUR CS  BC      CA       49.283 -123.117    3.0  1980 2015
6  712013 99999            VANCOUVER INTL      CA       49.183 -123.167    3.0  1988 1988
7  712025 99999          WEST VAN CYPRESS      CA       49.350 -123.183  161.0  1993 1995
8  712045 99999           SAND HEADS (LS)      CA       49.100 -123.300    1.0  1992 1995
9  712090 99999          SANDHEADS CS  BC      CA       49.100 -123.300     NA  2001 2015
10 712110 99999      HOWE SOUND - PAM ROC      CA       49.483 -123.300    5.0  1996 2015
11 715620 99999    CYPRESS BOWL SNOWBOARD      CA       49.383 -123.200 1180.0  2010 2010
12 716080 99999  VANCOUVER SEA ISLAND CCG      CA       49.183 -123.183    2.1  1982 2015
13 716930 99999        CYPRESS BOWL SOUTH      CA       49.383 -123.200  886.0  2007 2014
14 717840 99999      WEST VANCOUVER (AUT)      CA       49.350 -123.200  168.0  1995 2015
15 718903 99999                 PAM ROCKS      CA       49.483 -123.283    0.0  1987 1995
16 718920 99999            VANCOUVER INTL      CA       49.194 -123.184    4.3  1955 2015
17 718925 99999         VANCOUVER HARBOUR      CA       49.300 -123.117    5.0  1977 1980
18 718926 99999       VANCOUVER HARBOUR &      CA       49.300 -123.117    5.0  1979 1979
19 728920 99999            VANCOUVER INTL      CA       49.183 -123.167    3.0  1973 1977
20 728925 99999       VANCOUVER HARBOUR &      CA       49.300 -123.117    5.0  1976 1977
Variables not shown: gmt_offset (dbl), time_zone_id (chr), country_name (chr),
  country_code (chr), iso3166_2_subd (chr), fips10_4_subd (chr)

To put these stations on a viewable map, use a magrittr or pipeR pipe, to send the output data frame as input to the map_isd_stations function. Pipes are great, amirite?

library(stationaRy)
library(magrittr)

get_isd_stations(lower_lat = 49.000,
                 upper_lat = 49.500,
                 lower_lon = -123.500,
                 upper_lon = -123.000) %>%
  map_isd_stations()

Upon inspecting the data frame, you can reduce it to a single station by specifying it's name (or part of its name). In this example, we wish to get data from the CYPRESS BOWL SNOWBOARD station. This can be done by extending with select_isd_station and using the name argument to supply part of the station name.

library(stationaRy)
library(magrittr)

get_isd_stations(lower_lat = 49.000,
                 upper_lat = 49.500,
                 lower_lon = -123.500,
                 upper_lon = -123.000) %>%
  select_isd_station(name = "cypress bowl")

Several stations matched. Provide a more specific search term.
Source: local data frame [3 x 16]

    usaf  wban                   name country state    lat    lon elev begin  end
1 710040 99999 CYPRESS BOWL FREESTYLE      CA       49.400 -123.2  969  2007 2010
2 715620 99999 CYPRESS BOWL SNOWBOARD      CA       49.383 -123.2 1180  2010 2010
3 716930 99999     CYPRESS BOWL SOUTH      CA       49.383 -123.2  886  2007 2014
Variables not shown: gmt_offset (dbl), time_zone_id (chr), country_name (chr),
  country_code (chr), iso3166_2_subd (chr), fips10_4_subd (chr)
[1] NA

As this function yielded a data frame with 3 stations (3 stations leading with CYPRESS BOWL in their station names), a set of strategies will be used to obtain single station. There are two ways to get a year of CYPRESS BOWL SNOWBOARD data for 2010: (1) provide the full name of the station, or (2) use the data frame with multiple stations and specify the row of the target station.

library(stationaRy)
library(magrittr)

cypress_bowl_snowboard_1 <- 
  get_isd_stations(lower_lat = 49.000,
                   upper_lat = 49.500,
                   lower_lon = -123.500,
                   upper_lon = -123.000) %>%
    select_isd_station(name = "cypress bowl snowboard") %>%
    get_isd_station_data(startyear = 2010, endyear = 2010)
    
cypress_bowl_snowboard_2 <- 
  get_isd_stations(lower_lat = 49.000,
                   upper_lat = 49.500,
                   lower_lon = -123.500,
                   upper_lon = -123.000) %>%
    select_isd_station(name = "cypress bowl", number = 2) %>%
    get_isd_station_data(startyear = 2010, endyear = 2010)

Both statements get the same met data (the select_isd_station function simply passes a USAF/WBAN string to get_isd_station_data via the %>% operator). Here's a bit of that met data:

Source: local data frame [711 x 18]

     usaf  wban year month day hour minute    lat      lon elev wd ws ceil_hgt temp
1  715620 99999 2010     1  28   16      0 50.633 -128.117  568 NA NA       NA  1.3
2  715620 99999 2010     1  28   22      0 50.633 -128.117  568 NA NA       NA  2.5
3  715620 99999 2010     1  29    4      0 50.633 -128.117  568 NA NA       NA  3.1
4  715620 99999 2010     1  29   10      0 50.633 -128.117  568 NA NA       NA  5.5
5  715620 99999 2010     1  29   16      0 50.633 -128.117  568 NA NA       NA  3.0
6  715620 99999 2010     1  29   22      0 50.633 -128.117  568 NA NA       NA  1.5
7  715620 99999 2010     1  30    4      0 50.633 -128.117  568 NA NA       NA  0.3
8  715620 99999 2010     1  30   10      0 50.633 -128.117  568 NA NA       NA  0.9
9  715620 99999 2010     1  30   16      0 50.633 -128.117  568 NA NA       NA  0.7
10 715620 99999 2010     1  30   22      0 50.633 -128.117  568 NA NA       NA -0.1
..    ...   ...  ...   ... ...  ...    ...    ...      ...  ... .. ..      ...  ...
Variables not shown: dew_point (dbl), atmos_pres (dbl), rh (dbl), time (time)

If you'd like to get weather data from a weather station at Tofino, BC, CA, it's possible to search using tofino as the value for the name argument in the select_isd_stations function.

library(stationaRy)
library(magrittr)

get_isd_stations() %>%
  select_isd_station(name = "tofino")

Several stations matched. Provide a more specific search term.
Source: local data frame [4 x 16]

    usaf  wban           name country state    lat      lon elev begin  end
1 711060 94234 TOFINO AIRPORT      CA    BC 49.083 -125.767 24.0  1958 2015
2 711060 99999         TOFINO      CA       49.082 -125.773 24.4  2000 2004
3 741060 94234         TOFINO      CA       49.083 -125.767 20.0  1973 1977
4 999999 94234         TOFINO      CA       49.083 -125.767 24.1  1964 1972
Variables not shown: gmt_offset (dbl), time_zone_id (chr), country_name (chr),
  country_code (chr), iso3166_2_subd (chr), fips10_4_subd (chr)
[1] NA

A number of stations with tofino in its name were returned. If the first station in the returned data frame is desired, it can be selected with a slight modification to the select_isd_station call (using the number argument). Then, pipe the output to the get_isd_station_data function and supply the desired period of retrieval.

library(stationaRy)
library(magrittr)

tofino_airport_2005_2010 <- 
  get_isd_stations() %>%
    select_isd_station(name = "tofino", number = 1) %>%
    get_isd_station_data(startyear = 2005, endyear = 2010)

That's gives you 34,877 rows of meteorological data from the Tofino Airport station:

Source: local data frame [34,877 x 18]

     usaf  wban year month day hour minute    lat      lon elev wd  ws ceil_hgt
1  711060 94234 2005     1   1    7      0 49.083 -125.767   24 NA 0.0     1500
2  711060 94234 2005     1   1    8      0 49.083 -125.767   24 NA 0.0     1500
3  711060 94234 2005     1   1    9      0 49.083 -125.767   24 NA 0.0     1500
4  711060 94234 2005     1   1   10      0 49.083 -125.767   24 NA 0.0     1200
5  711060 94234 2005     1   1   11      0 49.083 -125.767   24 80 1.5     1200
6  711060 94234 2005     1   1   12      0 49.083 -125.767   24 NA 0.0     1650
7  711060 94234 2005     1   1   13      0 49.083 -125.767   24 40 2.6     1650
8  711060 94234 2005     1   1   14      0 49.083 -125.767   24 50 3.6     1650
9  711060 94234 2005     1   1   15      0 49.083 -125.767   24 70 1.0      900
10 711060 94234 2005     1   1   16      0 49.083 -125.767   24 60 1.5     1650
..    ...   ...  ...   ... ...  ...    ...    ...      ...  ... .. ...      ...
Variables not shown: temp (dbl), dew_point (dbl), atmos_pres (dbl), rh (dbl), time
  (time)

Of course, dplyr works really well to work toward the data you need. 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 a particular station:

library(stationaRy)
library(magrittr)
library(dplyr)

high_temps_at_bergen_point_stn <- 
  get_isd_stations() %>%
  select_isd_station(name = "bergen point") %>%
  get_isd_station_data(startyear = 2006, endyear = 2015) %>%
  select(time, wd, ws, temp) %>% 
  filter(temp > 37) %>%
  mutate(temp_f = (temp * (9/5)) + 32)

#> Source: local data frame [3 x 5]
#> 
#>                  time  wd  ws temp temp_f
#> 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.). Here are the different parameters:

Category	Column Name
`AA1`	`aa1_liq_precip_period_quantity`
`AA1`	`aa1_liq_precip_depth_dimension`
`AA1`	`aa1_liq_precip_condition_code`
`AB1`	`aa1_liq_precip_quality_code`
`AB1`	`ab1_liq_precip_monthly_depth_dimension`
`AB1`	`ab1_liq_precip_monthly_condition_code`
`AB1`	`ab1_liq_precip_monthly_quality_code`
`AC1`	`ac1_precip_obs_history_duration_code`
`AC1`	`ac1_precip_obs_history_characteristic_code`
`AC1`	`ac1_precip_obs_history_quality_code`
`AD1`	`ad1_liq_precip_greatest_amt_24h_month_depth_dimension`
`AD1`	`ad1_liq_precip_greatest_amt_24h_month_condition_code`
`AD1`	`ad1_liq_precip_greatest_amt_24h_month_dates`
`AD1`	`ad1_liq_precip_greatest_amt_24h_month_quality_code`
`AE1`	`ae1_liq_precip_number_days_amt_month__01inch`
`AE1`	`ae1_liq_precip_number_days_amt_month__01inch_quality_code`
`AE1`	`ae1_liq_precip_number_days_amt_month__10inch`
`AE1`	`ae1_liq_precip_number_days_amt_month__10inch_quality_code`
`AE1`	`ae1_liq_precip_number_days_amt_month__50inch`
`AE1`	`ae1_liq_precip_number_days_amt_month__50inch_quality_code`
`AE1`	`ae1_liq_precip_number_days_amt_month_1_00inch`
`AE1`	`ae1_liq_precip_number_days_amt_month_1_00inch_quality_code`
`AG1`	`ag1_precip_est_obs_discrepancy_code`
`AG1`	`ag1_precip_est_obs_est_water_depth_dimension`
`AH1`	`ah1_liq_precip_max_short_dur_month_period_quantity`
`AH1`	`ah1_liq_precip_max_short_dur_month_depth_dimension`
`AH1`	`ah1_liq_precip_max_short_dur_month_condition_code`
`AH1`	`ah1_liq_precip_max_short_dur_month_end_date_time`
`AH1`	`ah1_liq_precip_max_short_dur_month_quality_code`
`AI1`	`ai1_liq_precip_max_short_dur_month_period_quantity`
`AI1`	`ai1_liq_precip_max_short_dur_month_depth_dimension`
`AI1`	`ai1_liq_precip_max_short_dur_month_condition_code`
`AI1`	`ai1_liq_precip_max_short_dur_month_end_date_time`
`AI1`	`ai1_liq_precip_max_short_dur_month_quality_code`
`AJ1`	`aj1_snow_depth_dimension`
`AJ1`	`aj1_snow_depth_condition_code`
`AJ1`	`aj1_snow_depth_quality_code`
`AJ1`	`aj1_snow_depth_equiv_water_depth_dimension`
`AJ1`	`aj1_snow_depth_equiv_water_condition_code`
`AJ1`	`aj1_snow_depth_equiv_water_quality_code`
`AK1`	`ak1_snow_depth_greatest_depth_month_depth_dimension`
`AK1`	`ak1_snow_depth_greatest_depth_month_condition_code`
`AK1`	`ak1_snow_depth_greatest_depth_month_dates_occurrence`
`AK1`	`ak1_snow_depth_greatest_depth_month_quality_code`
`AL1`	`al1_snow_accumulation_period_quantity`
`AL1`	`al1_snow_accumulation_depth_dimension`
`AL1`	`al1_snow_accumulation_condition_code`
`AL1`	`al1_snow_accumulation_quality_code`
`AM1`	`am1_snow_accumulation_greatest_amt_24h_month_depth_dimension`
`AM1`	`am1_snow_accumulation_greatest_amt_24h_month_condition_code`
`AM1`	`am1_snow_accumulation_greatest_amt_24h_month_dates_occurrence_1`
`AM1`	`am1_snow_accumulation_greatest_amt_24h_month_dates_occurrence_2`
`AM1`	`am1_snow_accumulation_greatest_amt_24h_month_dates_occurrence_3`
`AM1`	`am1_snow_accumulation_greatest_amt_24h_month_quality_code`
`AN1`	`an1_snow_accumulation_month_period_quantity`
`AN1`	`an1_snow_accumulation_month_depth_dimension`
`AN1`	`an1_snow_accumulation_month_condition_code`
`AN1`	`an1_snow_accumulation_month_quality_code`
`AO1`	`ao1_liq_precip_period_quantity_minutes`
`AO1`	`ao1_liq_precip_depth_dimension`
`AO1`	`ao1_liq_precip_condition_code`
`AO1`	`ao1_liq_precip_quality_code`
`AP1`	`ap1_15_min_liq_precip_hpd_gauge_value_45_min_prior`
`AP1`	`ap1_15_min_liq_precip_hpd_gauge_value_30_min_prior`
`AP1`	`ap1_15_min_liq_precip_hpd_gauge_value_15_min_prior`
`AP1`	`ap1_15_min_liq_precip_hpd_gauge_value_at_obs_time`
`AU1`	`au1_present_weather_obs_intensity_code`
`AU1`	`au1_present_weather_obs_descriptor_code`
`AU1`	`au1_present_weather_obs_precipitation_code`
`AU1`	`au1_present_weather_obs_obscuration_code`
`AU1`	`au1_present_weather_obs_other_weather_phenomena_code`
`AU1`	`au1_present_weather_obs_combination_indicator_code`
`AU1`	`au1_present_weather_obs_quality_code`
`AW1`	`aw1_present_weather_obs_aut_weather_report_1`
`AW1`	`aw1_present_weather_obs_aut_weather_report_2`
`AW1`	`aw1_present_weather_obs_aut_weather_report_3`
`AW1`	`aw1_present_weather_obs_aut_weather_report_4`
`AX1`	`ax1_past_weather_obs_atmos_condition_code`
`AX1`	`ax1_past_weather_obs_quality_manual_atmos_condition_code`
`AX1`	`ax1_past_weather_obs_period_quantity`
`AX1`	`ax1_past_weather_obs_period_quality_code`
`AY1`	`ay1_past_weather_obs_manual_occurrence_identifier`
`AY1`	`ay1_past_weather_obs_quality_manual_atmos_condition_code`
`AY1`	`ay1_past_weather_obs_period_quantity`
`AY1`	`ay1_past_weather_obs_period_quality_code`
`AZ1`	`az1_past_weather_obs_aut_occurrence_identifier`
`AZ1`	`az1_past_weather_obs_quality_aut_atmos_condition_code`
`AZ1`	`az1_past_weather_obs_period_quantity`
`AZ1`	`az1_past_weather_obs_period_quality_code`
`CB1`	`cb1_subhrly_obs_liq_precip_2_sensor_period_quantity`
`CB1`	`cb1_subhrly_obs_liq_precip_2_sensor_precip_liq_depth`
`CB1`	`cb1_subhrly_obs_liq_precip_2_sensor_qc_quality_code`
`CB1`	`cb1_subhrly_obs_liq_precip_2_sensor_flag_quality_code`
`CF1`	`cf1_hrly_fan_speed_rate`
`CF1`	`cf1_hrly_fan_qc_quality_code`
`CF1`	`cf1_hrly_fan_flag_quality_code`
`CG1`	`cg1_subhrly_obs_liq_precip_1_sensor_precip_liq_depth`
`CG1`	`cg1_subhrly_obs_liq_precip_1_sensor_qc_quality_code`
`CG1`	`cg1_subhrly_obs_liq_precip_1_sensor_flag_quality_code`
`CH1`	`ch1_hrly_subhrly_rh_temp_period_quantity`
`CH1`	`ch1_hrly_subhrly_temp_avg_air_temp`
`CH1`	`ch1_hrly_subhrly_temp_qc_quality_code`
`CH1`	`ch1_hrly_subhrly_temp_flag_quality_code`
`CH1`	`ch1_hrly_subhrly_rh_avg_rh`
`CH1`	`ch1_hrly_subhrly_rh_qc_quality_code`
`CH1`	`ch1_hrly_subhrly_rh_flag_quality_code`
`CI1`	`ci1_hrly_rh_temp_min_hrly_temp`
`CI1`	`ci1_hrly_rh_temp_min_hrly_temp_qc_quality_code`
`CI1`	`ci1_hrly_rh_temp_min_hrly_temp_flag_quality_code`
`CI1`	`ci1_hrly_rh_temp_max_hrly_temp`
`CI1`	`ci1_hrly_rh_temp_max_hrly_temp_qc_quality_code`
`CI1`	`ci1_hrly_rh_temp_max_hrly_temp_flag_quality_code`
`CI1`	`ci1_hrly_rh_temp_std_dev_hrly_temp`
`CI1`	`ci1_hrly_rh_temp_std_dev_hrly_temp_qc_quality_code`
`CI1`	`ci1_hrly_rh_temp_std_dev_hrly_temp_flag_quality_code`
`CI1`	`ci1_hrly_rh_temp_std_dev_hrly_rh`
`CI1`	`ci1_hrly_rh_temp_std_dev_hrly_rh_qc_quality_code`
`CI1`	`ci1_hrly_rh_temp_std_dev_hrly_rh_flag_quality_code`
`CN1`	`cn1_hrly_batvol_sensors_transm_avg_voltage`
`CN1`	`cn1_hrly_batvol_sensors_transm_avg_voltage_qc_quality_code`
`CN1`	`cn1_hrly_batvol_sensors_transm_avg_voltage_flag_quality_code`
`CN1`	`cn1_hrly_batvol_full_load_avg_voltage`
`CN1`	`cn1_hrly_batvol_full_load_avg_voltage_qc_quality_code`
`CN1`	`cn1_hrly_batvol_full_load_avg_voltage_flag_quality_code`
`CN1`	`cn1_hrly_batvol_datalogger_avg_voltage`
`CN1`	`cn1_hrly_batvol_datalogger_avg_voltage_qc_quality_code`
`CN1`	`cn1_hrly_batvol_datalogger_avg_voltage_flag_quality_code`
`CN2`	`cn2_hrly_diagnostic_equipment_temp`
`CN2`	`cn2_hrly_diagnostic_equipment_temp_qc_quality_code`
`CN2`	`cn2_hrly_diagnostic_equipment_temp_flag_quality_code`
`CN2`	`cn2_hrly_diagnostic_geonor_inlet_temp`
`CN2`	`cn2_hrly_diagnostic_geonor_inlet_temp_qc_quality_code`
`CN2`	`cn2_hrly_diagnostic_geonor_inlet_temp_flag_quality_code`
`CN2`	`cn2_hrly_diagnostic_datalogger_opendoor_time`
`CN2`	`cn2_hrly_diagnostic_datalogger_opendoor_time_qc_quality_code`
`CN2`	`cn2_hrly_diagnostic_datalogger_opendoor_time_flag_quality_code`
`CN3`	`cn3_hrly_diagnostic_reference_resistor_avg_resistance`
`CN3`	`cn3_hrly_diagnostic_reference_resistor_avg_resistance_qc_quality_code`
`CN3`	`cn3_hrly_diagnostic_reference_resistor_avg_resistance_flag_quality_code`
`CN3`	`cn3_hrly_diagnostic_datalogger_signature_id`
`CN3`	`cn3_hrly_diagnostic_datalogger_signature_id_qc_quality_code`
`CN3`	`cn3_hrly_diagnostic_datalogger_signature_id_flag_quality_code`
`CN4`	`cn4_hrly_diagnostic_liq_precip_gauge_flag_bit`
`CN4`	`cn4_hrly_diagnostic_liq_precip_gauge_flag_bit_qc_quality_code`
`CN4`	`cn4_hrly_diagnostic_liq_precip_gauge_flag_bit_flag_quality_code`
`CN4`	`cn4_hrly_diagnostic_doorflag_field`
`CN4`	`cn4_hrly_diagnostic_doorflag_field_qc_quality_code`
`CN4`	`cn4_hrly_diagnostic_doorflag_field_flag_quality_code`
`CN4`	`cn4_hrly_diagnostic_forward_transmitter_rf_power`
`CN4`	`cn4_hrly_diagnostic_forward_transmitter_rf_power_qc_quality_code`
`CN4`	`cn4_hrly_diagnostic_forward_transmitter_rf_power_flag_quality_code`
`CN4`	`cn4_hrly_diagnostic_reflected_transmitter_rf_power`
`CN4`	`cn4_hrly_diagnostic_reflected_transmitter_rf_power_qc_quality_code`
`CN4`	`cn4_hrly_diagnostic_reflected_transmitter_rf_power_flag_quality_code`
`CR1`	`cr1_control_section_datalogger_version_number`
`CR1`	`cr1_control_section_datalogger_version_number_qc_quality_code`
`CR1`	`cr1_control_section_datalogger_version_number_flag_quality_code`
`CT1`	`ct1_subhrly_temp_avg_air_temp`
`CT1`	`ct1_subhrly_temp_avg_air_temp_qc_quality_code`
`CT1`	`ct1_subhrly_temp_avg_air_temp_flag_quality_code`
`CU1`	`cu1_hrly_temp_avg_air_temp`
`CU1`	`cu1_hrly_temp_avg_air_temp_qc_quality_code`
`CU1`	`cu1_hrly_temp_avg_air_temp_flag_quality_code`
`CU1`	`cu1_hrly_temp_avg_air_temp_st_dev`
`CU1`	`cu1_hrly_temp_avg_air_temp_st_dev_qc_quality_code`
`CU1`	`cu1_hrly_temp_avg_air_temp_st_dev_flag_quality_code`
`CV1`	`cv1_hrly_temp_min_air_temp`
`CV1`	`cv1_hrly_temp_min_air_temp_qc_quality_code`
`CV1`	`cv1_hrly_temp_min_air_temp_flag_quality_code`
`CV1`	`cv1_hrly_temp_min_air_temp_time`
`CV1`	`cv1_hrly_temp_min_air_temp_time_qc_quality_code`
`CV1`	`cv1_hrly_temp_min_air_temp_time_flag_quality_code`
`CV1`	`cv1_hrly_temp_max_air_temp`
`CV1`	`cv1_hrly_temp_max_air_temp_qc_quality_code`
`CV1`	`cv1_hrly_temp_max_air_temp_flag_quality_code`
`CV1`	`cv1_hrly_temp_max_air_temp_time`
`CV1`	`cv1_hrly_temp_max_air_temp_time_qc_quality_code`
`CV1`	`cv1_hrly_temp_max_air_temp_time_flag_quality_code`
`CW1`	`cw1_subhrly_wetness_wet1_indicator`
`CW1`	`cw1_subhrly_wetness_wet1_indicator_qc_quality_code`
`CW1`	`cw1_subhrly_wetness_wet1_indicator_flag_quality_code`
`CW1`	`cw1_subhrly_wetness_wet2_indicator`
`CW1`	`cw1_subhrly_wetness_wet2_indicator_qc_quality_code`
`CW1`	`cw1_subhrly_wetness_wet2_indicator_flag_quality_code`
`CX1`	`cx1_hourly_geonor_vib_wire_total_precip`
`CX1`	`cx1_hourly_geonor_vib_wire_total_precip_qc_quality_code`
`CX1`	`cx1_hourly_geonor_vib_wire_total_precip_flag_quality_code`
`CX1`	`cx1_hourly_geonor_vib_wire_freq_avg_precip`
`CX1`	`cx1_hourly_geonor_vib_wire_freq_avg_precip_qc_quality_code`
`CX1`	`cx1_hourly_geonor_vib_wire_freq_avg_precip_flag_quality_code`
`CX1`	`cx1_hourly_geonor_vib_wire_freq_min_precip`
`CX1`	`cx1_hourly_geonor_vib_wire_freq_min_precip_qc_quality_code`
`CX1`	`cx1_hourly_geonor_vib_wire_freq_min_precip_flag_quality_code`
`CX1`	`cx1_hourly_geonor_vib_wire_freq_max_precip`
`CX1`	`cx1_hourly_geonor_vib_wire_freq_max_precip_qc_quality_code`
`CX1`	`cx1_hourly_geonor_vib_wire_freq_max_precip_flag_quality_code`
`CO1`	`co1_network_metadata_climate_division_number`
`CO1`	`co1_network_metadata_utc_lst_time_conversion`
`CO1`	`co2_us_network_cooperative_element_id`
`CO1`	`co2_us_network_cooperative_time_offset`
`ED1`	`ed1_runway_vis_range_obs_direction_angle`
`ED1`	`ed1_runway_vis_range_obs_runway_designator_code`
`ED1`	`ed1_runway_vis_range_obs_vis_dimension`
`ED1`	`ed1_runway_vis_range_obs_quality_code`
`GA1`	`ga1_sky_cover_layer_coverage_code`
`GA1`	`ga1_sky_cover_layer_coverage_quality_code`
`GA1`	`ga1_sky_cover_layer_base_height`
`GA1`	`ga1_sky_cover_layer_base_height_quality_code`
`GA1`	`ga1_sky_cover_layer_cloud_type`
`GA1`	`ga1_sky_cover_layer_cloud_type_quality_code`
`GD1`	`gd1_sky_cover_summation_state_coverage_1`
`GD1`	`gd1_sky_cover_summation_state_coverage_2`
`GD1`	`gd1_sky_cover_summation_state_coverage_quality_code`
`GD1`	`gd1_sky_cover_summation_state_height`
`GD1`	`gd1_sky_cover_summation_state_height_quality_code`
`GD1`	`gd1_sky_cover_summation_state_characteristic_code`
`GF1`	`gf1_sky_condition_obs_total_coverage`
`GF1`	`gf1_sky_condition_obs_total_opaque_coverage`
`GF1`	`gf1_sky_condition_obs_total_coverage_quality_code`
`GF1`	`gf1_sky_condition_obs_total_lowest_cloud_cover`
`GF1`	`gf1_sky_condition_obs_total_lowest_cloud_cover_quality_code`
`GF1`	`gf1_sky_condition_obs_low_cloud_genus`
`GF1`	`gf1_sky_condition_obs_low_cloud_genus_quality_code`
`GF1`	`gf1_sky_condition_obs_lowest_cloud_base_height`
`GF1`	`gf1_sky_condition_obs_lowest_cloud_base_height_quality_code`
`GF1`	`gf1_sky_condition_obs_mid_cloud_genus`
`GF1`	`gf1_sky_condition_obs_mid_cloud_genus_quality_code`
`GF1`	`gf1_sky_condition_obs_high_cloud_genus`
`GF1`	`gf1_sky_condition_obs_high_cloud_genus_quality_code`
`GG1`	`gg1_below_stn_cloud_layer_coverage`
`GG1`	`gg1_below_stn_cloud_layer_coverage_quality_code`
`GG1`	`gg1_below_stn_cloud_layer_top_height`
`GG1`	`gg1_below_stn_cloud_layer_top_height_quality_code`
`GG1`	`gg1_below_stn_cloud_layer_type`
`GG1`	`gg1_below_stn_cloud_layer_type_quality_code`
`GG1`	`gg1_below_stn_cloud_layer_top`
`GG1`	`gg1_below_stn_cloud_layer_top_quality_code`
`GH1`	`gh1_hrly_solar_rad_hrly_avg_solarad`
`GH1`	`gh1_hrly_solar_rad_hrly_avg_solarad_qc_quality_code`
`GH1`	`gh1_hrly_solar_rad_hrly_avg_solarad_flag_quality_code`
`GH1`	`gh1_hrly_solar_rad_min_solarad`
`GH1`	`gh1_hrly_solar_rad_min_solarad_qc_quality_code`
`GH1`	`gh1_hrly_solar_rad_min_solarad_flag_quality_code`
`GH1`	`gh1_hrly_solar_rad_max_solarad`
`GH1`	`gh1_hrly_solar_rad_max_solarad_qc_quality_code`
`GH1`	`gh1_hrly_solar_rad_max_solarad_flag_quality_code`
`GH1`	`gh1_hrly_solar_rad_std_dev_solarad`
`GH1`	`gh1_hrly_solar_rad_std_dev_solarad_qc_quality_code`
`GH1`	`gh1_hrly_solar_rad_std_dev_solarad_flag_quality_code`
`GJ1`	`gj1_sunshine_obs_duration`
`GJ1`	`gj1_sunshine_obs_duration_quality_code`
`GK1`	`gk1_sunshine_obs_pct_possible_sunshine`
`GK1`	`gk1_sunshine_obs_pct_possible_quality_code`
`GL1`	`gl1_sunshine_obs_duration`
`GL1`	`gl1_sunshine_obs_duration_quality_code`
`GM1`	`gm1_solar_irradiance_time_period`
`GM1`	`gm1_solar_irradiance_global_irradiance`
`GM1`	`gm1_solar_irradiance_global_irradiance_data_flag`
`GM1`	`gm1_solar_irradiance_global_irradiance_quality_code`
`GM1`	`gm1_solar_irradiance_direct_beam_irradiance`
`GM1`	`gm1_solar_irradiance_direct_beam_irradiance_data_flag`
`GM1`	`gm1_solar_irradiance_direct_beam_irradiance_quality_code`
`GM1`	`gm1_solar_irradiance_diffuse_irradiance`
`GM1`	`gm1_solar_irradiance_diffuse_irradiance_data_flag`
`GM1`	`gm1_solar_irradiance_diffuse_irradiance_quality_code`
`GM1`	`gm1_solar_irradiance_uvb_global_irradiance`
`GM1`	`gm1_solar_irradiance_uvb_global_irradiance_data_flag`
`GM1`	`gm1_solar_irradiance_uvb_global_irradiance_quality_code`
`GN1`	`gn1_solar_rad_time_period`
`GN1`	`gn1_solar_rad_upwelling_global_solar_rad`
`GN1`	`gn1_solar_rad_upwelling_global_solar_rad_quality_code`
`GN1`	`gn1_solar_rad_downwelling_thermal_ir_rad`
`GN1`	`gn1_solar_rad_downwelling_thermal_ir_rad_quality_code`
`GN1`	`gn1_solar_rad_upwelling_thermal_ir_rad`
`GN1`	`gn1_solar_rad_upwelling_thermal_ir_rad_quality_code`
`GN1`	`gn1_solar_rad_par`
`GN1`	`gn1_solar_rad_par_quality_code`
`GN1`	`gn1_solar_rad_solar_zenith_angle`
`GN1`	`gn1_solar_rad_solar_zenith_angle_quality_code`
`GO1`	`go1_net_solar_rad_time_period`
`GO1`	`go1_net_solar_rad_net_solar_radiation`
`GO1`	`go1_net_solar_rad_net_solar_radiation_quality_code`
`GO1`	`go1_net_solar_rad_net_ir_radiation`
`GO1`	`go1_net_solar_rad_net_ir_radiation_quality_code`
`GO1`	`go1_net_solar_rad_net_radiation`
`GO1`	`go1_net_solar_rad_net_radiation_quality_code`
`GP1`	`gp1_modeled_solar_irradiance_data_time_period`
`GP1`	`gp1_modeled_solar_irradiance_global_horizontal`
`GP1`	`gp1_modeled_solar_irradiance_global_horizontal_src_flag`
`GP1`	`gp1_modeled_solar_irradiance_global_horizontal_uncertainty`
`GP1`	`gp1_modeled_solar_irradiance_direct_normal`
`GP1`	`gp1_modeled_solar_irradiance_direct_normal_src_flag`
`GP1`	`gp1_modeled_solar_irradiance_direct_normal_uncertainty`
`GP1`	`gp1_modeled_solar_irradiance_diffuse_normal`
`GP1`	`gp1_modeled_solar_irradiance_diffuse_normal_src_flag`
`GP1`	`gp1_modeled_solar_irradiance_diffuse_normal_uncertainty`
`GP1`	`gp1_modeled_solar_irradiance_diffuse_horizontal`
`GP1`	`gp1_modeled_solar_irradiance_diffuse_horizontal_src_flag`
`GP1`	`gp1_modeled_solar_irradiance_diffuse_horizontal_uncertainty`
`GQ1`	`gq1_hrly_solar_angle_time_period`
`GQ1`	`gq1_hrly_solar_angle_mean_zenith_angle`
`GQ1`	`gq1_hrly_solar_angle_mean_zenith_angle_quality_code`
`GQ1`	`gq1_hrly_solar_angle_mean_azimuth_angle`
`GQ1`	`gq1_hrly_solar_angle_mean_azimuth_angle_quality_code`
`GR1`	`gr1_hrly_extraterrestrial_rad_time_period`
`GR1`	`gr1_hrly_extraterrestrial_rad_horizontal`
`GR1`	`gr1_hrly_extraterrestrial_rad_horizontal_quality_code`
`GR1`	`gr1_hrly_extraterrestrial_rad_normal`
`GR1`	`gr1_hrly_extraterrestrial_rad_normal_quality_code`
`HL1`	`hl1_hail_size`
`HL1`	`hl1_hail_size_quality_code`
`IA1`	`ia1_ground_surface_obs_code`
`IA1`	`ia1_ground_surface_obs_code_quality_code`
`IA2`	`ia2_ground_surface_obs_min_temp_time_period`
`IA2`	`ia2_ground_surface_obs_min_temp`
`IA2`	`ia2_ground_surface_obs_min_temp_quality_code`
`IB1`	`ib1_hrly_surface_temp`
`IB1`	`ib1_hrly_surface_temp_qc_quality_code`
`IB1`	`ib1_hrly_surface_temp_flag_quality_code`
`IB1`	`ib1_hrly_surface_min_temp`
`IB1`	`ib1_hrly_surface_min_temp_qc_quality_code`
`IB1`	`ib1_hrly_surface_min_temp_flag_quality_code`
`IB1`	`ib1_hrly_surface_max_temp`
`IB1`	`ib1_hrly_surface_max_temp_qc_quality_code`
`IB1`	`ib1_hrly_surface_max_temp_flag_quality_code`
`IB1`	`ib1_hrly_surface_std_temp`
`IB1`	`ib1_hrly_surface_std_temp_qc_quality_code`
`IB1`	`ib1_hrly_surface_std_temp_flag_quality_code`
`IB2`	`ib2_hrly_surface_temp_sb`
`IB2`	`ib2_hrly_surface_temp_sb_qc_quality_code`
`IB2`	`ib2_hrly_surface_temp_sb_flag_quality_code`
`IB2`	`ib2_hrly_surface_temp_sb_std`
`IB2`	`ib2_hrly_surface_temp_sb_std_qc_quality_code`
`IB2`	`ib2_hrly_surface_temp_sb_std_flag_quality_code`
`IC1`	`ic1_grnd_surface_obs_pan_evap_time_period`
`IC1`	`ic1_grnd_surface_obs_pan_evap_wind`
`IC1`	`ic1_grnd_surface_obs_pan_evap_wind_condition_code`
`IC1`	`ic1_grnd_surface_obs_pan_evap_wind_quality_code`
`IC1`	`ic1_grnd_surface_obs_pan_evap_data`
`IC1`	`ic1_grnd_surface_obs_pan_evap_data_condition_code`
`IC1`	`ic1_grnd_surface_obs_pan_evap_data_quality_code`
`IC1`	`ic1_grnd_surface_obs_pan_max_water_data`
`IC1`	`ic1_grnd_surface_obs_pan_max_water_data_condition_code`
`IC1`	`ic1_grnd_surface_obs_pan_max_water_data_quality_code`
`IC1`	`ic1_grnd_surface_obs_pan_min_water_data`
`IC1`	`ic1_grnd_surface_obs_pan_min_water_data_condition_code`
`IC1`	`ic1_grnd_surface_obs_pan_min_water_data_quality_code`
`KA1`	`ka1_extreme_air_temp_time_period`
`KA1`	`ka1_extreme_air_temp_code`
`KA1`	`ka1_extreme_air_temp_high_or_low`
`KA1`	`ka1_extreme_air_temp_high_or_low_quality_code`
`KB1`	`kb1_avg_air_temp_time_period`
`KB1`	`kb1_avg_air_temp_code`
`KB1`	`kb1_avg_air_temp_air_temp`
`KB1`	`kb1_avg_air_temp_air_temp_quality_code`
`KC1`	`kc1_extreme_air_temp_monthly_code`
`KC1`	`kc1_extreme_air_temp_monthly_condition_code`
`KC1`	`kc1_extreme_air_temp_monthly_temp`
`KC1`	`kc1_extreme_air_temp_monthly_date`
`KC1`	`kc1_extreme_air_temp_monthly_temp_quality_code`
`KD1`	`kd1_heat_cool_deg_days_time_period`
`KD1`	`kd1_heat_cool_deg_days_code`
`KD1`	`kd1_heat_cool_deg_days_value`
`KD1`	`kd1_heat_cool_deg_days_quality_code`
`KE1`	`ke1_extreme_temp_number_days_max_32f_or_lower`
`KE1`	`ke1_extreme_temp_number_days_max_32f_or_lower_quality_code`
`KE1`	`ke1_extreme_temp_number_days_max_90f_or_higher`
`KE1`	`ke1_extreme_temp_number_days_max_90f_or_higher_quality_code`
`KE1`	`ke1_extreme_temp_number_days_min_32f_or_lower`
`KE1`	`ke1_extreme_temp_number_days_min_32f_or_lower_quality_code`
`KE1`	`ke1_extreme_temp_number_days_min_0f_or_lower`
`KE1`	`ke1_extreme_temp_number_days_min_0f_or_lower_quality_code`
`KF1`	`kf1_hrly_calc_temp`
`KF1`	`kf1_hrly_calc_temp_quality_code`
`KG1`	`kg1_avg_dp_wb_temp_time_period`
`KG1`	`kg1_avg_dp_wb_temp_code`
`KG1`	`kg1_avg_dp_wb_temp`
`KG1`	`kg1_avg_dp_wb_temp_derived_code`
`KG1`	`kg1_avg_dp_wb_temp_quality_code`
`MA1`	`ma1_atmos_p_obs_altimeter_setting_rate`
`MA1`	`ma1_atmos_p_obs_altimeter_quality_code`
`MA1`	`ma1_atmos_p_obs_stn_pressure_rate`
`MA1`	`ma1_atmos_p_obs_stn_pressure_rate_quality_code`
`MD1`	`md1_atmos_p_change_tendency_code`
`MD1`	`md1_atmos_p_change_tendency_code_quality_code`
`MD1`	`md1_atmos_p_change_3_hr_quantity`
`MD1`	`md1_atmos_p_change_3_hr_quantity_quality_code`
`MD1`	`md1_atmos_p_change_24_hr_quantity`
`MD1`	`md1_atmos_p_change_24_hr_quantity_quality_code`
`ME1`	`me1_geopotential_hgt_isobaric_lvl_code`
`ME1`	`me1_geopotential_hgt_isobaric_lvl_height`
`ME1`	`me1_geopotential_hgt_isobaric_lvl_height_quality_code`
`MF1`	`mf1_atmos_p_obs_stp_avg_stn_pressure_day`
`MF1`	`mf1_atmos_p_obs_stp_avg_stn_pressure_day_quality_code`
`MF1`	`mf1_atmos_p_obs_stp_avg_sea_lvl_pressure_day`
`MF1`	`mf1_atmos_p_obs_stp_avg_sea_lvl_pressure_day_quality_code`
`MG1`	`mg1_atmos_p_obs_avg_stn_pressure_day`
`MG1`	`mg1_atmos_p_obs_avg_stn_pressure_day_quality_code`
`MG1`	`mg1_atmos_p_obs_avg_sea_lvl_pressure_day`
`MG1`	`mg1_atmos_p_obs_avg_sea_lvl_pressure_day_quality_code`
`MH1`	`mh1_atmos_p_obs_avg_stn_pressure_month`
`MH1`	`mh1_atmos_p_obs_avg_stn_pressure_month_quality_code`
`MH1`	`mh1_atmos_p_obs_avg_sea_lvl_pressure_month`
`MH1`	`mh1_atmos_p_obs_avg_sea_lvl_pressure_month_quality_code`
`MK1`	`mk1_atmos_p_obs_max_sea_lvl_pressure_month`
`MK1`	`mk1_atmos_p_obs_max_sea_lvl_pressure_date_time`
`MK1`	`mk1_atmos_p_obs_max_sea_lvl_pressure_quality_code`
`MK1`	`mk1_atmos_p_obs_min_sea_lvl_pressure_month`
`MK1`	`mk1_atmos_p_obs_min_sea_lvl_pressure_date_time`
`MK1`	`mk1_atmos_p_obs_min_sea_lvl_pressure_quality_code`
`MV1`	`mv1_present_weather_obs_condition_code`
`MV1`	`mv1_present_weather_obs_condition_code_quality_code`
`MW1`	`mw1_present_weather_obs_manual_occurrence_condition_code`
`MW1`	`mw1_present_weather_obs_manual_occurrence_condition_code_quality_code`
`OA1`	`oa1_suppl_wind_obs_type`
`OA1`	`oa1_suppl_wind_obs_time_period`
`OA1`	`oa1_suppl_wind_obs_speed_rate`
`OA1`	`oa1_suppl_wind_obs_speed_rate_quality_code`
`OB1`	`ob1_hly_subhrly_wind_avg_time_period`
`OB1`	`ob1_hly_subhrly_wind_max_gust`
`OB1`	`ob1_hly_subhrly_wind_max_gust_quality_code`
`OB1`	`ob1_hly_subhrly_wind_max_gust_flag`
`OB1`	`ob1_hly_subhrly_wind_max_dir`
`OB1`	`ob1_hly_subhrly_wind_max_dir_quality_code`
`OB1`	`ob1_hly_subhrly_wind_max_dir_flag`
`OB1`	`ob1_hly_subhrly_wind_max_stdev`
`OB1`	`ob1_hly_subhrly_wind_max_stdev_quality_code`
`OB1`	`ob1_hly_subhrly_wind_max_stdev_flag`
`OB1`	`ob1_hly_subhrly_wind_max_dir_stdev`
`OB1`	`ob1_hly_subhrly_wind_max_dir_stdev_quality_code`
`OB1`	`ob1_hly_subhrly_wind_max_dir_stdev_flag`
`OC1`	`oc1_wind_gust_obs_speed_rate`
`OC1`	`oc1_wind_gust_obs_speed_rate_quality_code`
`OE1`	`oe1_summary_of_day_wind_obs_type`
`OE1`	`oe1_summary_of_day_wind_obs_time_period`
`OE1`	`oe1_summary_of_day_wind_obs_speed_rate`
`OE1`	`oe1_summary_of_day_wind_obs_dir`
`OE1`	`oe1_summary_of_day_wind_obs_time_occurrence`
`OE1`	`oe1_summary_of_day_wind_obs_quality_code`
`RH1`	`rh1_relative_humidity_time_period`
`RH1`	`rh1_relative_humidity_code`
`RH1`	`rh1_relative_humidity_percentage`
`RH1`	`rh1_relative_humidity_derived_code`
`RH1`	`rh1_relative_humidity_quality_code`
`SA1`	`sa1_sea_surf_temp`
`SA1`	`sa1_sea_surf_temp_quality_code`
`ST1`	`st1_soil_temp_type`
`ST1`	`st1_soil_temp_soil_temp`
`ST1`	`st1_soil_temp_soil_temp_quality_code`
`ST1`	`st1_soil_temp_depth`
`ST1`	`st1_soil_temp_depth_quality_code`
`ST1`	`st1_soil_temp_soil_cover`
`ST1`	`st1_soil_temp_soil_cover_quality_code`
`ST1`	`st1_soil_temp_sub_plot`
`ST1`	`st1_soil_temp_sub_plot_quality_code`
`UA1`	`ua1_wave_meas_method_code`
`UA1`	`ua1_wave_meas_wave_period_quantity`
`UA1`	`ua1_wave_meas_wave_height_dimension`
`UA1`	`ua1_wave_meas_quality_code`
`UA1`	`ua1_wave_meas_sea_state_code`
`UA1`	`ua1_wave_meas_sea_state_code_quality_code`
`UG1`	`ug1_wave_meas_primary_swell_time_period`
`UG1`	`ug1_wave_meas_primary_swell_height_dimension`
`UG1`	`ug1_wave_meas_primary_swell_dir_angle`
`UG1`	`ug1_wave_meas_primary_swell_quality_code`
`UG2`	`ug2_wave_meas_secondary_swell_time_period`
`UG2`	`ug2_wave_meas_secondary_swell_height_dimension`
`UG2`	`ug2_wave_meas_secondary_swell_dir_angle`
`UG2`	`ug2_wave_meas_secondary_swell_quality_code`
`WA1`	`wa1_platform_ice_accr_source_code`
`WA1`	`wa1_platform_ice_accr_thickness_dimension`
`WA1`	`wa1_platform_ice_accr_tendency_code`
`WA1`	`wa1_platform_ice_accr_quality_code`
`WD1`	`wd1_water_surf_ice_obs_edge_bearing_code`
`WD1`	`wd1_water_surf_ice_obs_uniform_conc_rate`
`WD1`	`wd1_water_surf_ice_obs_non_uniform_conc_rate`
`WD1`	`wd1_water_surf_ice_obs_ship_rel_pos_code`
`WD1`	`wd1_water_surf_ice_obs_ship_penetrability_code`
`WD1`	`wd1_water_surf_ice_obs_ice_trend_code`
`WD1`	`wd1_water_surf_ice_obs_development_code`
`WD1`	`wd1_water_surf_ice_obs_growler_bergy_bit_pres_code`
`WD1`	`wd1_water_surf_ice_obs_growler_bergy_bit_quantity`
`WD1`	`wd1_water_surf_ice_obs_iceberg_quantity`
`WD1`	`wd1_water_surf_ice_obs_quality_code`
`WG1`	`wg1_water_surf_ice_hist_obs_edge_distance`
`WG1`	`wg1_water_surf_ice_hist_obs_edge_orient_code`
`WG1`	`wg1_water_surf_ice_hist_obs_form_type_code`
`WG1`	`wg1_water_surf_ice_hist_obs_nav_effect_code`
`WG1`	`wg1_water_surf_ice_hist_obs_quality_code`

More information about these observations 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 data frame with the available additional categories with their counts in the dataset.

library(stationaRy)
library(magrittr)
library(dplyr)

get_isd_stations(startyear = 1970, endyear = 2015,
                 lower_lat = 49, upper_lat = 58,
                 lower_lon = -125, upper_lon = -120) %>%
  select_isd_station(name = "abbotsford") %>%
  get_isd_station_data(startyear = 2015,
                       endyear = 2015,
                       add_data_report = TRUE)

#>    category total_count
#> 1       AA1         744
#> 2       AC1         817
#> 3       AJ1           5
#> 4       AL1           4
#> 5       AY1         248
#> 6       CB1          27
#> 7       CF1         125
#> 8       CI1         560
#> 9       CT1         406
#> 10      CU1         478
#> 11      ED1          27
#> 12      GA1         778
#> 13      GD1        4514
#> 14      GF1        5664
#> 15      IA1           5
#> 16      KA1         744
#> 17      MA1        5748
#> 18      MD1         736
#> 19      MW1        1609
#> 20      OC1         324
#> 21      ST1          38

Want the rainfall in mm units for a particular month? Here's an example where rainfall amounts (over 6 hour periods) are summed for the month of June in 2015 for Abbotsford, BC, Canada. The AA1 data category has to do with rainfall, and you can be include that data (where available) in the output data frame by using the select_additional_data argument and specifying which data categories you'd like. The AA1_1 column is the duration in hours when the liquid precipitation was observed, and, the AA1_2 column is quantity of rain in mm. The deft use of functions from the dplyr package makes this whole process less painful.

library(stationaRy)
library(magrittr)
library(dplyr)

rainfall_6h_june2015 <- 
  get_isd_stations(startyear = 1970, endyear = 2015,
                   lower_lat = 49, upper_lat = 58,
                   lower_lon = -125, upper_lon = -120) %>%
    select_isd_station(name = "abbotsford") %>%
    get_isd_station_data(startyear = 2015,
                         endyear = 2015,
                         select_additional_data = "AA1") %>%
    filter(month == 6, aa1_1 == 6) %>% 
        select(aa1_2) %>% sum()

[1] 12.5

Installation

Want to try this? Make sure you have R, then, use this:

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

or this:

install.packages("stationaRy")

Thanks for installing. :)

Name		Name	Last commit message	Last commit date
Latest commit History 682 Commits
R		R
inst		inst
man		man
tests		tests
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
.travis.yml		.travis.yml
DESCRIPTION		DESCRIPTION
LICENSE		LICENSE
NAMESPACE		NAMESPACE
README.md		README.md
stationaRy.Rproj		stationaRy.Rproj

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