Normal climatology values

Get normal climatology values for a station (or all the stations with aemet_normal_clim_all(). Standard climatology from 1981 to 2010.

Usage

aemet_normal_clim(
  station = NULL,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_normal_clim_all(
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

station: Character string with station identifier code(s) (see aemet_stations()) or "all" for all the stations.
verbose: Logical TRUE/FALSE. Provides information about the flow of information between the client and server.
return_sf: Logical TRUE or FALSE. Should the function return an sf spatial object? If FALSE (the default value) it returns a tibble. Note that you need to have the sf package installed.
extract_metadata: Logical TRUE/FALSE. On TRUE the output is a tibble with the description of the fields. See also get_metadata_aemet().
progress: Logical, display a cli::cli_progress_bar() object. If verbose = TRUE won't be displayed.

Value

A tibble or a sf object.

Note

Code modified from project https://github.com/SevillaR/aemet.

API Key

You need to set your API Key globally using aemet_api_key().

Examples


library(tibble)
obs <- aemet_normal_clim(c("9434", "3195"))
glimpse(obs)
#> Rows: 26
#> Columns: 475
#> $ indicativo  <chr> "9434", "9434", "9434", "9434", "9434", "9434", "9434", "9…
#> $ w_racha_max <dbl> 28.9, 27.5, 37.5, 28.9, 27.5, 30.3, 37.5, 25.8, 26.7, 30.8…
#> $ np_010_n    <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30…
#> $ np_010_s    <dbl> 2.31, 1.54, 8.80, 3.32, 3.38, 2.79, 1.61, 1.79, 2.43, 2.48…
#> $ q_max_s     <dbl> 3.69, 1.55, 2.77, 4.75, 3.65, 3.71, 2.09, 2.11, 3.08, 1.88…
#> $ n_tor_n     <dbl> 30, 30, 29, 30, 30, 30, 30, 30, 30, 30, 29, 30, 30, 30, 30…
#> $ n_tor_s     <dbl> 0.25, 2.20, 6.29, 0.00, 0.37, 0.82, 2.10, 1.73, 1.53, 2.33…
#> $ q_max_n     <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 29…
#> $ tm_min_q4   <dbl> 4.5, 19.5, 10.8, 4.2, 4.5, 6.7, 19.8, 16.5, 9.1, 17.2, 7.6…
#> $ tm_min_q1   <dbl> 2.6, 18.1, 9.8, 2.0, 2.0, 5.4, 17.7, 14.5, 7.7, 15.5, 5.1,…
#> $ tm_min_q3   <dbl> 4.1, 19.0, 10.4, 3.2, 4.0, 6.2, 19.1, 15.6, 8.7, 16.3, 6.8…
#> $ tm_min_q2   <dbl> 3.0, 18.7, 10.1, 2.6, 3.5, 5.6, 18.0, 15.1, 8.3, 15.9, 6.1…
#> $ q_mar_n     <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 29…
#> $ n_des_min   <dbl> 0, 5, 55, 0, 0, 0, 9, 3, 1, 2, 0, 0, 1, 0, 0, 76, 8, 2, 0,…
#> $ q_mar_s     <dbl> 4.79, 1.19, 1.25, 5.00, 5.41, 4.51, 1.11, 1.94, 2.57, 1.58…
#> $ q_med_n     <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 29…
#> $ ts_20_q1    <dbl> 7.7, 28.7, NA, 6.4, NA, 12.1, NA, 24.4, NA, 27.5, NA, NA, …
#> $ ts_20_q2    <dbl> 8.4, 30.7, NA, 7.3, NA, 13.1, NA, 25.3, NA, 28.4, NA, NA, …
#> $ e_cv        <dbl> 0.13, 0.10, 0.04, 0.11, 0.12, 0.10, 0.08, 0.10, 0.11, 0.08…
#> $ ts_20_q4    <dbl> 9.7, 32.0, NA, 8.1, NA, 14.9, NA, 26.8, NA, 30.6, NA, NA, …
#> $ e_min       <dbl> 48, 132, 105, 62, 60, 70, 132, 119, 75, 115, 69, 95, 110, …
#> $ np_300_q4   <dbl> 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0…
#> $ np_300_q1   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ np_300_q3   <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0…
#> $ hr_max      <dbl> 78, 57, 66, 83, 93, 70, 57, 65, 65, 57, 84, 64, 77, 81, 74…
#> $ np_300_cv   <dbl> NA, 3.05, 1.05, 5.48, NA, 3.81, 5.48, 2.59, 2.59, 2.59, 2.…
#> $ n_nub_max   <dbl> 22, 25, 255, 24, 24, 26, 21, 25, 24, 25, 26, 28, 27, 20, 2…
#> $ tm_min_cv   <dbl> 0.38, 0.05, 0.05, 0.50, 0.52, 0.17, 0.06, 0.08, 0.14, 0.08…
#> $ n_des_mn    <dbl> 5.5, 12.0, 80.5, 5.0, 4.0, 4.5, 14.0, 7.0, 4.5, 7.0, 3.0, …
#> $ n_des_md    <dbl> 5.9, 11.2, 80.7, 4.7, 4.0, 6.3, 13.7, 7.3, 4.9, 8.1, 3.7, …
#> $ ts_20_s     <dbl> 1.39, 2.11, NA, 1.29, NA, 1.54, NA, 1.91, NA, 2.38, NA, NA…
#> $ q_med_mn    <dbl> 988.9, 985.5, 986.9, 990.1, 990.1, 987.5, 985.7, 986.2, 98…
#> $ evap_cv     <dbl> 0.31, 0.18, 0.17, 0.37, 0.33, 0.23, 0.17, 0.22, 0.23, 0.21…
#> $ q_med_md    <dbl> 989.2, 985.5, 986.8, 990.5, 990.0, 986.8, 985.6, 986.2, 98…
#> $ nt_30_cv    <dbl> NA, 0.18, 0.16, NA, NA, NA, 0.20, 0.67, 2.14, 0.33, NA, 0.…
#> $ mes         <chr> "02", "08", "13", "01", "12", "03", "07", "09", "04", "06"…
#> $ ts_20_cv    <dbl> 0.16, 0.07, NA, 0.18, NA, 0.12, NA, 0.07, NA, 0.08, NA, NA…
#> $ inso_max    <dbl> 8.7, 11.8, 9.0, 6.9, 5.5, 10.1, 13.0, 10.0, 11.0, 12.0, 7.…
#> $ ts_20_max   <dbl> 11.8, 33.4, NA, 10.1, NA, 15.6, NA, 29.3, NA, 32.7, NA, NA…
#> $ np_001_max  <dbl> 14, 9, 108, 20, 20, 20, 8, 12, 16, 15, 17, 20, 16, 20, 21,…
#> $ n_llu_md    <dbl> 7.7, 5.4, 102.1, 9.3, 9.8, 8.9, 5.1, 7.6, 10.1, 7.9, 10.9,…
#> $ nv_1000_s   <dbl> 2.20, 0.00, 5.60, 2.97, 3.21, 0.46, 0.00, 0.31, 0.00, 0.00…
#> $ ta_min_mn   <dbl> -1.6, 14.2, -4.2, -2.9, -3.2, 0.8, 14.2, 9.9, 3.2, 11.1, -…
#> $ tm_mes_max  <dbl> 11.5, 27.9, 17.0, 9.7, 9.5, 14.6, 28.2, 24.0, 17.4, 26.6, …
#> $ ta_max_mn   <dbl> 19.8, 38.4, 39.8, 18.0, 17.7, 24.7, 39.0, 34.0, 28.0, 37.3…
#> $ nv_1000_n   <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 28, 28…
#> $ ta_max_md   <dbl> 19.6, 38.3, 40.1, 17.7, 18.1, 24.7, 39.2, 33.6, 28.1, 37.3…
#> $ nw_91_min   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, …
#> $ ta_max_n    <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30…
#> $ ta_max_s    <dbl> 2.16, 1.84, 1.89, 1.85, 1.69, 1.55, 2.10, 2.34, 2.06, 2.44…
#> $ ts_10_md    <dbl> 9.1, 31.8, NA, 7.2, NA, 14.0, NA, 26.3, NA, 30.4, NA, NA, …
#> $ nw_55_mn    <dbl> 9.0, 5.5, 75.0, 7.0, 6.0, 8.0, 6.5, 4.0, 7.0, 7.0, 6.0, 6.…
#> $ ta_min_s    <dbl> 1.89, 1.40, 1.86, 2.07, 2.23, 2.10, 1.78, 1.80, 1.85, 1.58…
#> $ nw_55_md    <dbl> 9.2, 5.8, 77.9, 8.0, 5.7, 8.9, 7.0, 4.1, 8.2, 6.6, 6.6, 7.…
#> $ np_300_mn   <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ evap_q4     <dbl> 1527, 3926, 27256, 1092, 1038, 2115, 4248, 2787, 2363, 359…
#> $ evap_q1     <dbl> 843, 2992, 22848, 628, 599, 1463, 3331, 1902, 1684, 2852, …
#> $ np_300_md   <dbl> 0.0, 0.1, 1.0, 0.0, 0.0, 0.1, 0.0, 0.1, 0.1, 0.1, 0.2, 0.1…
#> $ evap_q3     <dbl> 1307, 3714, 24968, 993, 846, 1931, 3828, 2569, 2068, 3281,…
#> $ evap_q2     <dbl> 1186, 3281, 24364, 797, 779, 1750, 3695, 2345, 1923, 3016,…
#> $ p_mes_min   <dbl> 0.0, 0.9, 182.9, 0.5, 0.6, 0.0, 0.3, 1.4, 3.6, 0.0, 0.4, 4…
#> $ ts_min_min  <dbl> 5.5, 20.8, 20.9, 5.2, 5.9, 8.5, 20.4, 17.1, 10.5, 18.7, 9.…
#> $ n_des_s     <dbl> 3.30, 3.14, 12.58, 3.32, 3.00, 5.08, 2.91, 2.28, 3.27, 3.9…
#> $ nv_0100_mn  <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ p_mes_md    <dbl> 19.8, 17.8, 328.9, 23.7, 19.1, 28.0, 16.5, 27.3, 40.0, 28.…
#> $ n_tor_cv    <dbl> 3.81, 0.56, 0.27, NA, 5.48, 1.44, 0.54, 0.58, 0.90, 0.50, …
#> $ nv_0100_md  <dbl> 0.2, 0.0, 1.1, 0.2, 0.4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0…
#> $ q_min_max   <dbl> 988.6, 979.0, 972.6, 986.3, 985.8, 980.9, 979.7, 980.6, 97…
#> $ n_des_n     <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 27, 28…
#> $ p_sol_s     <dbl> 10.09, 4.49, 3.64, 11.84, 9.64, 10.27, 5.04, 7.27, 7.68, 5…
#> $ ts_20_n     <dbl> 15, 16, 11, 15, 14, 15, 14, 15, 14, 15, 12, 14, 15, 0, 0, …
#> $ ts_10_cv    <dbl> 0.17, 0.07, NA, 0.17, NA, 0.10, NA, 0.07, NA, 0.08, NA, NA…
#> $ nw_91_s     <dbl> 0.51, 0.19, 1.17, 0.42, 0.45, 0.48, 0.48, 0.20, 0.26, 0.33…
#> $ nw_91_n     <dbl> 29, 28, 17, 28, 28, 30, 28, 26, 28, 25, 28, 27, 28, 27, 27…
#> $ nt_00_min   <dbl> 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0…
#> $ p_sol_n     <dbl> 30, 29, 29, 30, 30, 30, 30, 29, 30, 30, 30, 30, 30, 13, 15…
#> $ n_nie_mn    <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0…
#> $ n_nub_n     <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 27, 28…
#> $ n_nie_md    <dbl> 0.6, 0.0, 1.9, 0.5, 0.4, 0.3, 0.0, 0.0, 0.0, 0.0, 0.1, 0.0…
#> $ tm_mes_md   <dbl> 8.5, 25.6, 15.9, 7.0, 7.2, 11.8, 25.7, 21.4, 14.4, 23.1, 1…
#> $ p_sol_cv    <dbl> 0.17, 0.06, 0.06, 0.25, 0.22, 0.17, 0.06, 0.11, 0.12, 0.08…
#> $ n_nub_s     <dbl> 2.88, 2.88, 11.54, 3.58, 4.18, 3.58, 2.85, 2.43, 3.41, 3.5…
#> $ w_racha_min <dbl> 16.9, 14.4, 19.2, 15.8, 13.3, 15.0, 15.0, 16.1, 16.4, 12.5…
#> $ np_300_max  <dbl> 0, 1, 4, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 3, 0, 0, 1, 2…
#> $ nv_0050_md  <dbl> 0.1, 0.0, 0.3, 0.0, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0…
#> $ ta_min_min  <dbl> -7.2, 10.8, -9.5, -5.8, -9.5, -6.0, 10.8, 7.0, -0.8, 8.0, …
#> $ glo_mn      <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ inso_s      <dbl> 1.08, 0.61, 0.41, 1.12, 0.89, 1.21, 0.77, 0.90, 1.02, 0.91…
#> $ glo_md      <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ n_nie_q1    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0…
#> $ w_med_q4    <dbl> 22.0, 18.0, 17.2, 19.2, 16.2, 19.2, 19.2, 17.0, 19.0, 19.0…
#> $ n_tor_max   <dbl> 1, 9, 41, 0, 2, 3, 10, 8, 6, 9, 2, 10, 4, 1, 3, 23, 7, 1, …
#> $ w_med_q2    <dbl> 16.0, 15.0, 16.0, 14.0, 14.6, 16.6, 18.0, 14.0, 18.0, 16.6…
#> $ w_med_q3    <dbl> 18.4, 17.4, 17.0, 17.0, 15.0, 17.4, 19.0, 15.0, 18.4, 18.0…
#> $ n_gra_md    <dbl> 0.0, 0.1, 1.0, 0.1, 0.0, 0.1, 0.1, 0.1, 0.1, 0.1, 0.0, 0.1…
#> $ inso_q4     <dbl> 7.2, 11.1, 8.1, 5.7, 4.9, 8.2, 12.4, 9.4, 9.0, 11.4, 5.8, …
#> $ q_max_min   <dbl> 994.4, 989.8, 1000.3, 994.7, 995.1, 988.8, 991.2, 990.5, 9…
#> $ ts_10_max   <dbl> 12.7, 35.4, NA, 8.9, NA, 16.3, NA, 29.7, NA, 34.6, NA, NA,…
#> $ nt_30_min   <dbl> 0, 12, 49, 0, 0, 0, 14, 0, 0, 3, 0, 0, 0, 0, 0, 41, 17, 0,…
#> $ n_nub_cv    <dbl> 0.16, 0.16, 0.05, 0.20, 0.24, 0.19, 0.17, 0.12, 0.17, 0.18…
#> $ p_mes_n     <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30…
#> $ p_mes_q1    <dbl> 4.7, 3.3, 261.3, 7.2, 8.0, 9.2, 3.6, 7.5, 22.2, 7.4, 15.7,…
#> $ p_mes_q2    <dbl> 14.2, 8.1, 298.2, 14.6, 12.0, 15.5, 6.8, 14.7, 32.4, 13.3,…
#> $ p_mes_q3    <dbl> 21.8, 19.3, 336.7, 20.2, 16.6, 29.4, 16.3, 23.2, 41.4, 30.…
#> $ p_mes_q4    <dbl> 29.1, 29.4, 402.8, 36.0, 27.5, 49.4, 27.9, 42.1, 49.4, 47.…
#> $ hr_cv       <dbl> 0.09, 0.09, 0.04, 0.07, 0.07, 0.09, 0.08, 0.07, 0.08, 0.09…
#> $ n_tor_md    <dbl> 0.1, 3.9, 23.5, 0.0, 0.1, 0.6, 3.9, 3.0, 1.7, 4.6, 0.2, 4.…
#> $ p_mes_s     <dbl> 17.29, 17.29, 89.09, 21.10, 16.45, 21.13, 15.10, 26.53, 27…
#> $ nw_91_cv    <dbl> 2.12, 5.29, 1.04, 1.95, 3.14, 2.42, 2.66, 5.10, 3.67, 2.76…
#> $ n_tor_mn    <dbl> 0.0, 4.0, 23.0, 0.0, 0.0, 0.0, 3.0, 3.0, 1.0, 5.0, 0.0, 4.…
#> $ nv_1000_max <dbl> 9, 0, 22, 12, 13, 2, 0, 1, 0, 0, 9, 1, 4, 6, 2, 18, 0, 4, …
#> $ n_gra_cv    <dbl> NA, 3.26, 1.27, 3.74, NA, 3.26, 2.59, 2.59, 3.81, 2.59, 5.…
#> $ w_racha_n   <dbl> 29, 28, 19, 28, 28, 30, 28, 26, 28, 26, 29, 27, 28, 27, 27…
#> $ w_med_min   <dbl> 8, 9, 14, 9, 6, 11, 12, 11, 9, 13, 9, 13, 9, 4, 6, 6, 6, 5…
#> $ w_racha_s   <dbl> 3.01, 3.03, 3.90, 3.47, 3.20, 3.58, 4.55, 2.48, 2.67, 4.33…
#> $ np_100_max  <dbl> 3, 2, 17, 2, 1, 3, 2, 4, 4, 4, 3, 5, 4, 5, 6, 21, 2, 5, 7,…
#> $ e_s         <dbl> 9.93, 15.59, 4.49, 7.98, 9.46, 8.79, 12.98, 14.81, 10.04, …
#> $ w_med_s     <dbl> 4.83, 2.89, 1.22, 4.46, 3.41, 2.59, 2.77, 2.37, 3.44, 2.28…
#> $ p_sol_md    <dbl> 60, 77, 63, 48, 44, 61, 79, 69, 62, 71, 52, 65, 60, NA, 56…
#> $ n_cub_q4    <dbl> 7.0, 2.0, 63.2, 11.0, 13.0, 8.2, 1.0, 4.0, 8.0, 3.0, 10.0,…
#> $ n_cub_q3    <dbl> 5.4, 1.0, 60.0, 9.4, 8.4, 6.0, 1.0, 3.0, 6.0, 2.0, 8.4, 5.…
#> $ n_cub_q2    <dbl> 3.6, 1.0, 55.0, 7.0, 8.0, 4.6, 0.0, 2.0, 5.0, 2.0, 6.0, 4.…
#> $ n_cub_q1    <dbl> 2.8, 0.0, 48.8, 5.0, 6.8, 3.0, 0.0, 1.8, 3.8, 1.0, 3.0, 3.…
#> $ nw_55_max   <dbl> 19, 13, 120, 20, 16, 19, 14, 8, 15, 12, 20, 19, 11, 7, 7, …
#> $ p_sol_mn    <dbl> 59, 77, 62, 48, 45, 62, 79, 71, 63, 72, 53, 67, 61, NA, 58…
#> $ n_cub_s     <dbl> 2.61, 1.15, 9.01, 3.55, 4.26, 3.09, 0.73, 1.76, 2.46, 1.55…
#> $ hr_mn       <dbl> 65, 47, 60, 75, 77, 60, 46, 54, 56, 47, 73, 53, 65, 72, 57…
#> $ q_med_max   <dbl> 996.5, 988.5, 989.0, 998.9, 999.1, 993.4, 987.7, 989.2, 98…
#> $ hr_md       <dbl> 66, 48, 60, 74, 76, 59, 46, 55, 56, 48, 72, 52, 65, 72, 57…
#> $ np_010_md   <dbl> 3.7, 2.2, 52.1, 4.4, 4.5, 4.8, 2.6, 3.2, 5.6, 4.0, 5.6, 6.…
#> $ n_cub_n     <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 27, 28…
#> $ np_010_mn   <dbl> 4.0, 2.0, 50.0, 4.0, 4.0, 4.0, 2.0, 3.0, 6.0, 4.0, 5.5, 6.…
#> $ nw_91_q4    <dbl> 0.4, 0.0, 2.0, 0.6, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0…
#> $ p_max_n     <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30…
#> $ nw_91_q2    <dbl> 0.0, 0.0, 0.4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0…
#> $ nw_91_q3    <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, …
#> $ nw_91_q1    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, …
#> $ n_gra_q4    <dbl> 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 0, 0, 0, 0…
#> $ n_gra_q1    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ n_gra_q3    <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0…
#> $ nw_55_s     <dbl> 4.59, 3.69, 24.27, 4.98, 3.59, 4.49, 4.01, 1.99, 3.73, 2.9…
#> $ nw_55_n     <dbl> 29, 28, 17, 28, 28, 30, 28, 26, 28, 25, 28, 27, 28, 27, 27…
#> $ w_med_mn    <dbl> 17.5, 16.0, 17.0, 15.0, 15.0, 17.0, 18.0, 14.0, 18.0, 18.0…
#> $ n_nub_min   <dbl> 9, 13, 203, 11, 8, 11, 10, 14, 10, 10, 10, 15, 13, 9, 7, 1…
#> $ p_max_s     <dbl> 7.24, 12.03, 12.51, 9.06, 6.48, 9.79, 8.61, 15.83, 12.39, …
#> $ ts_20_min   <dbl> 6.8, 26.9, NA, 4.9, NA, 10.6, NA, 22.0, NA, 24.1, NA, NA, …
#> $ inso_min    <dbl> 4.2, 9.5, 6.9, 2.2, 2.2, 5.0, 9.0, 6.8, 6.2, 7.6, 3.2, 6.8…
#> $ n_gra_max   <dbl> 0, 2, 5, 1, 0, 2, 1, 1, 1, 1, 1, 2, 0, 1, 1, 6, 1, 0, 0, 2…
#> $ p_sol_q1    <dbl> 51, 72, 60, 38, 36, 52, 76, 63, 56, 68, 43, 61, 52, NA, 45…
#> $ p_sol_q3    <dbl> 62, 79, 63, 51, 48, 64, 81, 73, 64, 73, 54, 68, 64, NA, 59…
#> $ p_sol_q2    <dbl> 56, 76, 61, 44, 44, 56, 78, 67, 61, 71, 51, 64, 59, NA, 51…
#> $ p_sol_q4    <dbl> 68, 80, 65, 60, 53, 69, 83, 75, 67, 75, 59, 69, 67, NA, 64…
#> $ n_fog_q1    <dbl> 0.0, 0.0, 15.2, 2.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.6, 0.…
#> $ ta_min_md   <dbl> -2.0, 14.1, -4.3, -2.9, -2.9, 0.3, 14.2, 10.0, 3.0, 11.3, …
#> $ n_fog_q3    <dbl> 1.4, 0.0, 24.0, 6.4, 8.0, 0.4, 0.0, 0.0, 0.0, 0.0, 4.0, 0.…
#> $ n_fog_q2    <dbl> 1.0, 0.0, 18.2, 5.0, 5.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2, 0.…
#> $ n_fog_q4    <dbl> 5.0, 0.0, 29.0, 10.2, 10.0, 1.0, 0.0, 0.0, 0.0, 0.0, 5.0, …
#> $ w_med_cv    <dbl> 0.28, 0.18, 0.07, 0.28, 0.24, 0.15, 0.15, 0.16, 0.19, 0.13…
#> $ p_max_max   <dbl> 29.0, 51.9, 70.8, 41.0, 24.2, 32.5, 35.2, 70.8, 57.9, 41.1…
#> $ e_n         <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30…
#> $ q_min_md    <dbl> 971.0, 976.2, 960.5, 971.5, 970.7, 970.0, 976.6, 974.9, 96…
#> $ n_fog_cv    <dbl> 1.29, 5.48, 0.32, 0.69, 0.53, 1.35, 5.48, 2.77, 2.77, 3.05…
#> $ p_mes_cv    <dbl> 0.87, 0.97, 0.27, 0.89, 0.86, 0.75, 0.92, 0.97, 0.69, 0.89…
#> $ nv_0100_max <dbl> 2, 0, 6, 2, 3, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 2, 0, 1, 0, 0…
#> $ np_300_q2   <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ q_min_mn    <dbl> 970.4, 976.5, 959.9, 971.7, 969.9, 971.0, 976.6, 975.2, 97…
#> $ ti_max_min  <dbl> 0.8, 21.8, -3.0, -0.6, -3.0, 4.0, 19.6, 15.7, 6.6, 15.8, 3…
#> $ np_100_cv   <dbl> 1.82, 1.35, 0.41, 1.55, 1.69, 1.55, 1.26, 1.28, 0.89, 1.26…
#> $ hr_q3       <dbl> 67, 48, 61, 75, 78, 61, 46, 55, 56, 48, 75, 53, 68, 74, 58…
#> $ hr_q2       <dbl> 64, 47, 59, 74, 75, 59, 45, 54, 55, 46, 70, 51, 63, 71, 56…
#> $ hr_q1       <dbl> 61, 45, 58, 69, 71, 55, 43, 53, 53, 45, 68, 49, 61, 66, 51…
#> $ nv_0050_s   <dbl> 0.43, 0.00, 0.66, 0.18, 0.31, 0.00, 0.00, 0.00, 0.00, 0.00…
#> $ hr_q4       <dbl> 69, 52, 61, 79, 80, 63, 48, 58, 59, 51, 78, 55, 70, 77, 63…
#> $ evap_s      <dbl> 378.66, 620.40, 4111.15, 334.97, 263.16, 407.50, 633.10, 5…
#> $ w_med_q1    <dbl> 13.0, 14.0, 15.8, 12.8, 12.0, 15.8, 15.8, 12.0, 16.0, 16.0…
#> $ evap_n      <dbl> 30, 30, 26, 30, 28, 28, 29, 30, 29, 30, 30, 29, 30, 27, 27…
#> $ nv_0050_n   <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 28, 28…
#> $ nv_0050_q2  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ nv_0050_q3  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ q_max_md    <dbl> 1002.1, 993.7, 1006.3, 1003.5, 1002.5, 999.5, 994.3, 995.0…
#> $ nv_0050_q1  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ nv_0050_q4  <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ q_max_mn    <dbl> 1003.1, 993.3, 1006.5, 1003.6, 1002.5, 1000.6, 994.1, 994.…
#> $ n_llu_min   <dbl> 0, 1, 65, 0, 1, 0, 2, 3, 0, 3, 3, 3, 2, 0, 0, 55, 0, 1, 2,…
#> $ ts_min_max  <dbl> 11.6, 24.5, 24.7, 13.0, 13.6, 15.3, 23.8, 24.0, 15.6, 24.7…
#> $ evap_max    <dbl> 2140, 4664, 30688, 1860, 1304, 2555, 4766, 3071, 2978, 488…
#> $ ta_min_q1   <dbl> -3.5, 12.9, -5.6, -4.8, -4.2, -1.1, 12.8, 8.3, 1.8, 10.4, …
#> $ ta_min_q3   <dbl> -1.3, 14.3, -3.9, -2.6, -2.4, 1.4, 14.4, 10.3, 3.5, 11.4, …
#> $ n_cub_cv    <dbl> 0.56, 0.98, 0.16, 0.44, 0.45, 0.56, 1.28, 0.63, 0.44, 0.68…
#> $ ta_min_q4   <dbl> -0.7, 15.4, -3.0, -1.0, -0.6, 1.6, 15.6, 11.6, 3.9, 12.7, …
#> $ tm_mes_n    <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30…
#> $ tm_max_s    <dbl> 2.12, 1.50, 0.69, 1.71, 1.40, 1.58, 1.66, 1.56, 1.42, 2.10…
#> $ nt_30_md    <dbl> 0.0, 22.6, 75.4, 0.0, 0.0, 0.0, 23.4, 7.9, 0.4, 16.0, 0.0,…
#> $ tm_max_n    <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30…
#> $ tm_mes_s    <dbl> 1.59, 1.15, 0.59, 1.53, 1.46, 1.14, 1.40, 1.32, 1.22, 1.65…
#> $ nt_30_mn    <dbl> 0.0, 23.5, 77.0, 0.0, 0.0, 0.0, 23.0, 8.5, 0.0, 16.5, 0.0,…
#> $ ts_50_q4    <dbl> 9.1, 30.4, NA, 8.3, NA, 13.2, NA, NA, NA, 27.6, NA, NA, 21…
#> $ ts_50_q3    <dbl> 8.7, 29.2, NA, 7.9, NA, 12.3, NA, NA, NA, 26.8, NA, NA, 19…
#> $ ts_50_q2    <dbl> 8.1, 28.9, NA, 7.3, NA, 11.6, NA, NA, NA, 26.3, NA, NA, 18…
#> $ ts_50_q1    <dbl> 7.5, 27.8, NA, 6.7, NA, 11.0, NA, NA, NA, 25.9, NA, NA, 18…
#> $ q_mar_mn    <dbl> 1020.2, 1014.6, 1017.2, 1021.5, 1021.4, 1018.4, 1014.8, 10…
#> $ np_100_q4   <dbl> 1.0, 1.0, 12.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.…
#> $ np_100_q3   <dbl> 0.0, 0.4, 10.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.…
#> $ np_100_q2   <dbl> 0.0, 0.0, 8.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.6…
#> $ np_100_q1   <dbl> 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, …
#> $ n_des_cv    <dbl> 0.56, 0.28, 0.16, 0.70, 0.76, 0.81, 0.21, 0.31, 0.66, 0.49…
#> $ q_mar_md    <dbl> 1020.5, 1014.6, 1017.1, 1021.9, 1021.4, 1017.6, 1014.7, 10…
#> $ tm_mes_q4   <dbl> 10.0, 26.4, 16.4, 8.3, 8.3, 12.8, 27.1, 22.5, 15.2, 24.8, …
#> $ tm_mes_q2   <dbl> 7.7, 25.4, 15.7, 6.5, 7.1, 11.2, 25.1, 21.0, 14.2, 22.7, 1…
#> $ tm_mes_q3   <dbl> 9.3, 25.8, 16.1, 7.3, 7.8, 12.0, 26.4, 21.8, 14.6, 23.4, 1…
#> $ np_300_min  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ p_sol_min   <dbl> 40, 69, 55, 23, 23, 41, 61, 54, 46, 50, 32, 47, 42, NA, 41…
#> $ n_cub_min   <dbl> 0, 0, 37, 1, 5, 0, 0, 0, 1, 0, 2, 2, 1, 0, 1, 48, 0, 0, 3,…
#> $ n_gra_s     <dbl> 0.00, 0.43, 1.32, 0.26, 0.00, 0.43, 0.35, 0.35, 0.25, 0.35…
#> $ w_racha_mn  <dbl> 22.5, 19.6, 25.8, 21.3, 21.1, 21.6, 20.0, 18.9, 20.9, 20.8…
#> $ p_max_cv    <dbl> 0.83, 1.07, 0.35, 0.91, 0.81, 0.86, 0.92, 1.04, 0.73, 0.85…
#> $ n_des_q4    <dbl> 10.0, 14.0, 91.2, 8.0, 5.2, 11.0, 15.4, 9.0, 7.2, 11.2, 6.…
#> $ n_des_q1    <dbl> 3.0, 8.8, 73.0, 1.0, 1.0, 2.0, 10.8, 5.8, 2.0, 5.0, 1.0, 2…
#> $ n_des_q3    <dbl> 6.0, 12.0, 86.0, 6.0, 4.4, 7.0, 14.0, 8.0, 5.0, 8.0, 4.0, …
#> $ n_des_q2    <dbl> 5.0, 11.0, 78.0, 4.2, 3.0, 4.0, 13.0, 7.0, 4.0, 6.0, 2.0, …
#> $ ti_max_max  <dbl> 13.4, 28.3, 4.8, 8.6, 8.4, 16.5, 31.0, 23.6, 19.7, 26.4, 1…
#> $ n_gra_n     <dbl> 30, 30, 28, 29, 30, 30, 30, 30, 30, 30, 29, 30, 30, 30, 30…
#> $ tm_min_n    <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30…
#> $ nt_30_max   <dbl> 0, 29, 97, 0, 0, 0, 31, 20, 3, 24, 0, 12, 4, 0, 0, 93, 31,…
#> $ q_max_max   <dbl> 1007.3, 996.6, 1012.8, 1012.8, 1009.2, 1004.4, 998.0, 1000…
#> $ tm_min_s    <dbl> 1.30, 0.86, 0.55, 1.48, 1.71, 1.05, 1.18, 1.16, 1.14, 1.27…
#> $ nt_00_n     <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30…
#> $ nv_0050_min <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ ts_10_s     <dbl> 1.52, 2.18, NA, 1.19, NA, 1.41, NA, 1.85, NA, 2.40, NA, NA…
#> $ n_nub_q4    <dbl> 20.0, 21.0, 236.0, 21.2, 21.2, 21.2, 20.0, 22.0, 23.0, 22.…
#> $ n_nub_q1    <dbl> 16.0, 16.0, 220.8, 15.0, 14.0, 16.0, 14.8, 18.0, 16.8, 16.…
#> $ n_nub_q2    <dbl> 17.6, 18.0, 226.6, 18.0, 17.0, 18.0, 16.0, 19.0, 20.0, 20.…
#> $ n_nub_q3    <dbl> 19.0, 20.0, 231.0, 18.4, 19.0, 20.4, 17.0, 21.0, 21.0, 21.…
#> $ ts_50_s     <dbl> 1.03, 1.53, NA, 0.98, NA, 1.20, NA, NA, NA, 1.79, NA, NA, …
#> $ ts_10_n     <dbl> 15, 16, 11, 15, 14, 15, 14, 15, 14, 15, 12, 14, 15, 0, 0, …
#> $ nt_00_s     <dbl> 3.51, 0.00, 10.18, 4.96, 5.12, 1.53, 0.00, 0.00, 0.25, 0.0…
#> $ ts_10_min   <dbl> 7.3, 27.4, NA, 5.0, NA, 11.5, NA, 22.3, NA, 24.9, NA, NA, …
#> $ tm_mes_mn   <dbl> 8.4, 25.7, 15.9, 7.2, 7.7, 11.7, 25.5, 21.4, 14.4, 23.0, 1…
#> $ np_100_min  <dbl> 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0…
#> $ nv_1000_min <dbl> 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ nt_30_s     <dbl> 0.00, 4.11, 12.25, 0.00, 0.00, 0.00, 4.60, 5.34, 0.86, 5.3…
#> $ nt_00_q3    <dbl> 5.0, 0.0, 21.8, 7.0, 5.4, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 0.…
#> $ nt_00_q2    <dbl> 3.0, 0.0, 16.6, 5.6, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.…
#> $ nt_00_q1    <dbl> 1.8, 0.0, 10.4, 2.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.…
#> $ n_tor_min   <dbl> 0, 1, 13, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, …
#> $ nt_00_q4    <dbl> 8.0, 0.0, 26.0, 10.2, 10.2, 1.2, 0.0, 0.0, 0.0, 0.0, 3.0, …
#> $ ts_20_q3    <dbl> 8.8, 31.3, NA, 7.7, NA, 14.1, NA, 26.3, NA, 29.7, NA, NA, …
#> $ nt_00_max   <dbl> 13, 0, 49, 20, 21, 7, 0, 0, 1, 0, 5, 0, 0, 13, 7, 35, 0, 1…
#> $ nw_55_min   <dbl> 2, 0, 26, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0,…
#> $ n_llu_mn    <dbl> 9.0, 5.0, 102.0, 10.0, 9.5, 8.0, 4.0, 7.0, 10.0, 8.0, 11.0…
#> $ ts_50_mn    <dbl> 8.3, 29.0, NA, 7.5, NA, 11.9, NA, NA, NA, 26.7, NA, NA, 19…
#> $ glo_min     <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ ts_50_md    <dbl> 8.4, 29.1, NA, 7.5, NA, 12.0, NA, NA, NA, 26.5, NA, NA, 19…
#> $ q_med_min   <dbl> 977.3, 982.2, 983.8, 978.6, 979.2, 976.4, 983.7, 982.5, 97…
#> $ nv_0050_cv  <dbl> 3.26, NA, 1.98, 5.48, 3.05, NA, NA, NA, NA, NA, 5.48, NA, …
#> $ inso_q2     <dbl> 6.0, 10.6, 7.7, 4.1, 4.0, 6.7, 11.5, 8.3, 8.1, 10.8, 5.0, …
#> $ inso_q3     <dbl> 6.5, 10.9, 7.8, 4.9, 4.4, 7.6, 12.0, 9.0, 8.5, 11.1, 5.3, …
#> $ inso_q1     <dbl> 5.5, 10.0, 7.6, 3.6, 3.3, 6.2, 11.2, 7.8, 7.4, 10.3, 4.2, …
#> $ n_fog_max   <dbl> 12, 1, 36, 17, 17, 2, 1, 2, 2, 1, 12, 2, 5, 7, 2, 21, 0, 5…
#> $ np_100_s    <dbl> 0.97, 0.63, 3.52, 0.78, 0.45, 0.93, 0.63, 1.16, 1.01, 1.17…
#> $ np_300_s    <dbl> 0.00, 0.31, 1.05, 0.18, 0.00, 0.25, 0.18, 0.35, 0.35, 0.35…
#> $ ta_min_cv   <dbl> -0.96, 0.10, -0.44, -0.70, -0.78, 6.37, 0.13, 0.18, 0.61, …
#> $ tm_mes_cv   <dbl> 0.19, 0.05, 0.04, 0.22, 0.20, 0.10, 0.05, 0.06, 0.08, 0.07…
#> $ ts_min_md   <dbl> 9.3, 22.5, 23.0, 9.3, 9.7, 11.3, 22.4, 20.0, 13.3, 21.1, 1…
#> $ ts_min_mn   <dbl> 9.8, 22.6, 23.0, 9.3, 9.3, 11.2, 22.4, 19.9, 13.6, 21.1, 1…
#> $ inso_cv     <dbl> 0.17, 0.06, 0.05, 0.24, 0.22, 0.17, 0.07, 0.10, 0.12, 0.08…
#> $ np_300_n    <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30…
#> $ np_100_n    <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30…
#> $ ts_min_n    <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30…
#> $ tm_max_md   <dbl> 13.4, 32.4, 21.5, 11.0, 11.1, 17.5, 32.8, 27.4, 20.4, 30.0…
#> $ tm_max_mn   <dbl> 13.4, 32.3, 21.6, 11.1, 11.3, 17.4, 32.5, 27.4, 20.4, 29.8…
#> $ ts_min_s    <dbl> 1.68, 1.06, 1.02, 2.10, 1.91, 1.48, 0.91, 1.60, 1.35, 1.39…
#> $ tm_mes_q1   <dbl> 7.0, 24.9, 15.4, 6.1, 6.2, 11.0, 24.5, 20.3, 13.6, 22.1, 9…
#> $ p_max_q1    <dbl> 2.4, 3.2, 24.8, 3.0, 3.3, 4.0, 1.8, 4.3, 8.1, 3.9, 6.5, 6.…
#> $ p_max_q3    <dbl> 8.9, 9.4, 36.4, 8.4, 6.6, 8.8, 10.3, 12.6, 16.4, 13.8, 12.…
#> $ p_max_q2    <dbl> 5.7, 4.5, 30.6, 6.7, 4.8, 6.0, 4.5, 7.3, 12.2, 8.1, 8.8, 1…
#> $ p_max_q4    <dbl> 15.2, 17.2, 45.8, 13.8, 12.2, 19.9, 15.2, 24.0, 22.5, 21.3…
#> $ n_nie_s     <dbl> 0.96, 0.00, 2.06, 1.02, 0.97, 0.52, 0.00, 0.00, 0.00, 0.00…
#> $ n_gra_min   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ nt_30_n     <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30…
#> $ n_nie_n     <dbl> 30, 30, 28, 29, 30, 30, 30, 30, 30, 30, 29, 30, 30, 30, 30…
#> $ ts_50_n     <dbl> 15, 15, 9, 15, 14, 15, 13, 13, 14, 15, 12, 14, 15, 0, 0, 0…
#> $ nv_0100_min <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ q_mar_q1    <dbl> 1015.9, 1014.1, 1016.5, 1018.3, 1016.0, 1014.0, 1013.6, 10…
#> $ q_mar_q2    <dbl> 1019.1, 1014.3, 1017.1, 1020.9, 1020.6, 1017.3, 1014.6, 10…
#> $ q_mar_q3    <dbl> 1021.5, 1015.0, 1017.3, 1023.1, 1023.6, 1019.0, 1015.0, 10…
#> $ q_mar_q4    <dbl> 1025.6, 1015.5, 1018.1, 1026.8, 1026.3, 1021.2, 1015.4, 10…
#> $ p_max_min   <dbl> 0.0, 0.7, 17.2, 0.2, 0.3, 0.0, 0.2, 0.6, 1.4, 0.0, 0.4, 1.…
#> $ n_llu_cv    <dbl> 0.55, 0.47, 0.14, 0.52, 0.43, 0.55, 0.47, 0.35, 0.47, 0.42…
#> $ n_nub_mn    <dbl> 18.5, 19.0, 227.5, 18.0, 18.0, 19.0, 17.0, 19.0, 20.0, 21.…
#> $ w_racha_q1  <dbl> 20.6, 17.4, 24.7, 19.5, 19.0, 19.1, 18.4, 18.1, 18.7, 18.1…
#> $ w_racha_q3  <dbl> 23.2, 20.0, 27.1, 22.5, 22.3, 23.0, 21.2, 20.3, 21.7, 21.4…
#> $ w_racha_q2  <dbl> 22.2, 18.5, 25.8, 20.6, 20.2, 21.0, 19.4, 18.3, 20.0, 20.6…
#> $ n_nub_md    <dbl> 17.7, 18.6, 228.0, 18.1, 17.6, 19.2, 16.7, 19.9, 19.5, 19.…
#> $ w_racha_q4  <dbl> 25.3, 21.8, 28.5, 25.1, 23.2, 24.5, 23.0, 21.1, 23.0, 24.7…
#> $ nt_00_cv    <dbl> 0.75, NA, 0.53, 0.74, 0.94, 1.84, NA, NA, 3.81, NA, 1.17, …
#> $ np_010_q4   <dbl> 6.0, 3.2, 58.0, 6.2, 7.0, 7.0, 4.0, 4.2, 7.2, 6.0, 8.0, 8.…
#> $ np_010_q1   <dbl> 1.8, 1.0, 45.8, 2.0, 2.0, 3.0, 1.0, 2.0, 4.0, 1.8, 2.8, 4.…
#> $ np_010_q3   <dbl> 5.0, 2.0, 54.0, 4.4, 4.0, 5.0, 2.4, 4.0, 6.0, 5.0, 7.0, 7.…
#> $ np_010_q2   <dbl> 2.6, 1.6, 50.0, 3.0, 3.0, 4.0, 2.0, 3.0, 5.0, 3.0, 5.0, 5.…
#> $ nv_1000_q3  <dbl> 1.0, 0.0, 11.8, 4.4, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 0.…
#> $ ts_50_cv    <dbl> 0.12, 0.05, NA, 0.13, NA, 0.10, NA, NA, NA, 0.07, NA, NA, …
#> $ nv_0100_s   <dbl> 0.48, 0.00, 1.35, 0.57, 0.68, 0.00, 0.00, 0.00, 0.00, 0.00…
#> $ n_llu_max   <dbl> 15, 11, 137, 17, 19, 21, 10, 13, 20, 18, 21, 20, 21, 19, 2…
#> $ tm_max_min  <dbl> 10.0, 29.4, 20.1, 6.8, 8.5, 14.4, 29.0, 24.7, 17.5, 24.2, …
#> $ tm_min_md   <dbl> 3.5, 18.8, 10.3, 2.9, 3.3, 6.0, 18.6, 15.4, 8.5, 16.2, 6.5…
#> $ q_mar_cv    <dbl> 0.00, 0.00, 0.00, 0.00, 0.01, 0.00, 0.00, 0.00, 0.00, 0.00…
#> $ nv_0100_n   <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 28, 28…
#> $ tm_min_mn   <dbl> 3.5, 18.9, 10.2, 2.9, 3.8, 5.9, 18.6, 15.1, 8.4, 16.0, 6.5…
#> $ ts_10_mn    <dbl> 9.0, 32.1, NA, 7.5, NA, 14.1, NA, 26.2, NA, 30.8, NA, NA, …
#> $ w_racha_cv  <dbl> 0.13, 0.15, 0.15, 0.16, 0.15, 0.16, 0.22, 0.13, 0.13, 0.20…
#> $ evap_min    <dbl> 527, 2059, 14491, 358, 258, 947, 2318, 1146, 1004, 1374, 4…
#> $ ta_max_cv   <dbl> 0.11, 0.05, 0.05, 0.10, 0.09, 0.06, 0.05, 0.07, 0.07, 0.07…
#> $ n_llu_q4    <dbl> 11.0, 7.0, 112.4, 12.8, 13.0, 12.0, 8.0, 10.0, 14.0, 9.4, …
#> $ n_llu_q2    <dbl> 5.6, 5.0, 98.0, 9.0, 9.0, 8.0, 4.0, 7.0, 9.0, 7.6, 10.0, 1…
#> $ n_llu_q3    <dbl> 9.4, 6.0, 104.4, 10.0, 10.4, 9.4, 5.0, 8.0, 11.0, 8.0, 11.…
#> $ n_llu_q1    <dbl> 3.8, 3.0, 91.8, 4.8, 6.8, 5.0, 3.0, 5.0, 5.8, 5.8, 7.2, 7.…
#> $ q_min_q4    <dbl> 977.2, 978.2, 966.0, 980.0, 976.7, 976.6, 978.1, 978.6, 97…
#> $ hr_min      <dbl> 48, 41, 55, 63, 67, 48, 40, 49, 44, 38, 55, 44, 57, 59, 44…
#> $ w_med_max   <dbl> 27, 21, 18, 27, 24, 23, 25, 19, 26, 23, 26, 29, 19, 9, 12,…
#> $ nw_91_mn    <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, …
#> $ q_min_q1    <dbl> 963.6, 974.3, 955.8, 964.3, 965.3, 964.6, 975.4, 971.9, 96…
#> $ q_min_q2    <dbl> 968.9, 975.8, 959.3, 967.6, 968.0, 969.4, 976.0, 974.3, 96…
#> $ q_min_q3    <dbl> 972.4, 976.8, 960.4, 974.3, 971.3, 973.1, 977.0, 975.9, 97…
#> $ q_min_n     <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 29…
#> $ nw_91_md    <dbl> 0.2, 0.0, 1.1, 0.2, 0.1, 0.2, 0.2, 0.0, 0.1, 0.1, 0.1, 0.0…
#> $ q_min_s     <dbl> 7.78, 2.04, 5.25, 9.13, 7.84, 7.39, 1.86, 4.03, 4.78, 2.45…
#> $ n_tor_q4    <dbl> 0.0, 5.0, 27.4, 0.0, 0.0, 1.0, 6.0, 4.0, 3.0, 6.2, 0.0, 6.…
#> $ n_tor_q1    <dbl> 0.0, 1.8, 18.6, 0.0, 0.0, 0.0, 2.0, 1.8, 0.0, 3.0, 0.0, 2.…
#> $ n_tor_q2    <dbl> 0.0, 3.0, 21.0, 0.0, 0.0, 0.0, 3.0, 2.0, 1.0, 4.0, 0.0, 4.…
#> $ n_tor_q3    <dbl> 0.0, 5.0, 24.8, 0.0, 0.0, 0.4, 4.0, 3.0, 2.0, 5.0, 0.0, 4.…
#> $ tm_mes_min  <dbl> 5.5, 23.3, 14.9, 3.2, 3.2, 9.3, 22.7, 19.1, 11.7, 18.5, 8.…
#> $ n_cub_max   <dbl> 11, 4, 74, 15, 22, 12, 2, 8, 12, 6, 13, 10, 14, 16, 14, 83…
#> $ nw_55_cv    <dbl> 0.50, 0.64, 0.31, 0.62, 0.63, 0.50, 0.57, 0.48, 0.46, 0.45…
#> $ tm_min_min  <dbl> 0.8, 17.1, 9.5, -0.5, -2.1, 4.1, 16.4, 13.6, 5.8, 12.8, 4.…
#> $ q_mar_max   <dbl> 1027.7, 1017.7, 1019.3, 1030.7, 1030.8, 1024.3, 1017.0, 10…
#> $ nv_0100_q4  <dbl> 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ nv_0100_q1  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ nv_0100_q2  <dbl> 0.0, 0.0, 0.6, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0…
#> $ nv_0100_q3  <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ q_max_q2    <dbl> 1001.8, 993.0, 1005.5, 1002.7, 1001.4, 998.5, 993.5, 994.7…
#> $ q_max_q3    <dbl> 1004.0, 994.2, 1006.9, 1004.7, 1002.9, 1001.3, 994.5, 995.…
#> $ q_max_q1    <dbl> 998.8, 992.7, 1004.6, 1000.4, 1000.0, 996.5, 992.3, 993.7,…
#> $ q_max_q4    <dbl> 1005.2, 994.9, 1008.4, 1007.9, 1005.6, 1002.9, 996.7, 995.…
#> $ nv_0100_cv  <dbl> 2.42, NA, 1.23, 2.44, 1.57, NA, NA, NA, NA, NA, 2.27, NA, …
#> $ ts_10_q3    <dbl> 9.2, 32.1, NA, 7.5, NA, 14.7, NA, 26.4, NA, 30.9, NA, NA, …
#> $ ts_10_q2    <dbl> 8.3, 31.8, NA, 7.1, NA, 13.5, NA, 25.9, NA, 30.4, NA, NA, …
#> $ ts_10_q1    <dbl> 7.9, 30.1, NA, 6.2, NA, 12.8, NA, 25.0, NA, 28.9, NA, NA, …
#> $ ts_10_q4    <dbl> 9.9, 34.0, NA, 8.0, NA, 15.1, NA, 27.7, NA, 32.1, NA, NA, …
#> $ q_max_cv    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ ta_max_max  <dbl> 25.5, 42.8, 44.5, 20.6, 22.0, 27.3, 44.5, 38.4, 32.4, 43.2…
#> $ np_010_cv   <dbl> 0.62, 0.70, 0.17, 0.75, 0.75, 0.58, 0.63, 0.55, 0.43, 0.63…
#> $ ta_max_q4   <dbl> 21.1, 39.6, 42.1, 19.4, 19.2, 25.7, 40.7, 35.5, 30.0, 39.4…
#> $ ta_max_q3   <dbl> 20.4, 38.9, 40.1, 18.1, 18.0, 25.1, 39.2, 34.4, 28.4, 37.9…
#> $ ta_max_q2   <dbl> 19.2, 38.0, 39.5, 17.5, 17.2, 24.5, 38.4, 33.7, 27.3, 36.1…
#> $ ta_max_q1   <dbl> 17.8, 36.8, 38.4, 16.2, 16.9, 23.4, 37.7, 31.0, 26.1, 35.3…
#> $ np_010_min  <dbl> 0, 0, 36, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 35, 0, 0, 1,…
#> $ w_med_n     <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 28, 27…
#> $ nv_1000_q4  <dbl> 3.0, 0.0, 18.2, 5.2, 7.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0, 0.…
#> $ nv_1000_q2  <dbl> 0.0, 0.0, 9.0, 2.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0…
#> $ n_nie_max   <dbl> 3, 0, 7, 4, 4, 2, 0, 0, 0, 0, 1, 0, 0, 5, 2, 11, 0, 4, 1, …
#> $ nv_1000_q1  <dbl> 0, 0, 7, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0…
#> $ nv_1000_cv  <dbl> 1.69, NA, 0.47, 0.83, 0.74, 2.77, NA, 3.05, NA, NA, 1.03, …
#> $ glo_max     <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ n_fog_s     <dbl> 3.15, 0.18, 7.09, 4.37, 3.88, 0.63, 0.18, 0.46, 0.46, 0.31…
#> $ ti_max_q2   <dbl> 6.3, 24.5, 1.6, 4.1, 3.0, 9.0, 24.4, 19.3, 11.6, 20.9, 8.5…
#> $ n_fog_n     <dbl> 30, 30, 29, 30, 30, 30, 30, 30, 30, 30, 29, 30, 30, 30, 30…
#> $ n_nie_cv    <dbl> 1.52, NA, 1.09, 1.98, 2.42, 1.95, NA, NA, NA, NA, 3.74, NA…
#> $ np_001_min  <dbl> 0, 1, 61, 2, 2, 0, 1, 2, 2, 0, 1, 2, 2, 1, 0, 54, 0, 0, 2,…
#> $ nw_55_q4    <dbl> 12.4, 8.6, 95.0, 13.0, 8.6, 13.0, 11.0, 6.0, 11.6, 10.0, 1…
#> $ n_fog_min   <dbl> 0, 0, 12, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, …
#> $ nw_55_q2    <dbl> 8.0, 4.8, 72.8, 5.8, 5.0, 7.0, 5.0, 4.0, 7.0, 6.0, 5.0, 5.…
#> $ nw_55_q3    <dbl> 10.6, 7.0, 80.0, 8.4, 6.0, 8.8, 7.2, 5.0, 9.0, 7.0, 7.0, 7…
#> $ nw_55_q1    <dbl> 5.0, 2.0, 61.0, 4.0, 2.4, 5.0, 4.0, 2.0, 5.0, 4.8, 3.4, 4.…
#> $ hr_n        <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 23, 22…
#> $ hr_s        <dbl> 6.14, 4.28, 2.56, 5.35, 5.70, 5.07, 3.80, 4.00, 4.72, 4.44…
#> $ q_med_s     <dbl> 4.70, 1.17, 1.21, 4.77, 5.21, 4.39, 1.06, 1.94, 2.53, 1.53…
#> $ nv_0050_max <dbl> 2, 0, 2, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0…
#> $ ta_min_q2   <dbl> -2.4, 13.6, -4.7, -3.7, -3.6, 0.3, 13.7, 9.5, 3.0, 10.8, -…
#> $ n_des_max   <dbl> 13, 17, 107, 12, 12, 20, 21, 14, 16, 18, 16, 10, 16, 16, 2…
#> $ n_fog_mn    <dbl> 1, 0, 20, 5, 7, 0, 0, 0, 0, 0, 3, 0, 1, 2, 0, 6, 0, 0, 0, …
#> $ p_sol_max   <dbl> 82, 86, 73, 72, 59, 84, 87, 80, 83, 79, 79, 76, 79, NA, 75…
#> $ np_001_s    <dbl> 3.53, 1.90, 11.61, 4.80, 4.39, 4.11, 1.95, 2.24, 3.62, 2.7…
#> $ n_fog_md    <dbl> 2.4, 0.0, 21.8, 6.3, 7.3, 0.5, 0.0, 0.2, 0.2, 0.1, 3.6, 0.…
#> $ q_min_cv    <dbl> 0.01, 0.00, 0.01, 0.01, 0.01, 0.01, 0.00, 0.00, 0.00, 0.00…
#> $ np_001_n    <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30…
#> $ e_max       <dbl> 92, 219, 122, 97, 93, 104, 192, 180, 116, 164, 123, 143, 1…
#> $ nt_00_md    <dbl> 4.7, 0.0, 19.3, 6.7, 5.5, 0.8, 0.0, 0.0, 0.1, 0.0, 1.5, 0.…
#> $ np_100_mn   <dbl> 0.0, 0.0, 8.5, 0.0, 0.0, 0.0, 0.0, 0.5, 1.0, 0.5, 0.0, 1.0…
#> $ nt_30_q1    <dbl> 0.0, 19.8, 62.6, 0.0, 0.0, 0.0, 18.8, 3.6, 0.0, 11.0, 0.0,…
#> $ nt_00_mn    <dbl> 4.0, 0.0, 20.0, 6.0, 3.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.…
#> $ glo_q4      <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ glo_q2      <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ glo_q3      <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ np_100_md   <dbl> 0.5, 0.5, 8.7, 0.5, 0.3, 0.6, 0.5, 0.9, 1.1, 0.9, 0.8, 1.1…
#> $ inso_n      <dbl> 30, 29, 29, 30, 30, 30, 30, 29, 30, 30, 30, 30, 30, 13, 15…
#> $ q_med_q1    <dbl> 984.8, 984.9, 986.2, 987.1, 984.8, 983.5, 984.6, 984.8, 98…
#> $ np_001_md   <dbl> 6.7, 3.6, 85.1, 8.5, 8.5, 7.7, 4.2, 5.6, 8.7, 6.2, 8.9, 8.…
#> $ np_001_mn   <dbl> 6.5, 3.0, 84.5, 7.5, 8.0, 7.0, 4.0, 5.0, 9.5, 6.0, 9.0, 8.…
#> $ ti_max_cv   <dbl> 0.40, 0.07, 0.94, 0.60, 0.82, 0.32, 0.10, 0.11, 0.20, 0.14…
#> $ nv_1000_mn  <dbl> 0.0, 0.0, 10.5, 4.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.…
#> $ nv_1000_md  <dbl> 1.3, 0.0, 12.0, 3.6, 4.3, 0.2, 0.0, 0.1, 0.0, 0.0, 1.8, 0.…
#> $ evap_md     <dbl> 1233, 3430, 24276, 910, 795, 1799, 3712, 2357, 2005, 3176,…
#> $ ta_min_max  <dbl> 2.0, 16.5, -0.5, 1.8, 0.3, 4.8, 17.7, 12.7, 8.4, 15.1, 3.5…
#> $ e_md        <dbl> 74, 161, 112, 76, 80, 84, 155, 143, 96, 140, 96, 117, 125,…
#> $ e_mn        <dbl> 74, 161, 112, 75, 80, 84, 153, 140, 94, 141, 96, 117, 124,…
#> $ nt_30_q3    <dbl> 0.0, 24.4, 79.4, 0.0, 0.0, 0.0, 26.0, 10.0, 0.0, 18.4, 0.0…
#> $ ti_max_md   <dbl> 7.0, 25.0, 2.1, 4.2, 3.3, 9.7, 25.2, 19.7, 12.1, 21.2, 8.7…
#> $ n_llu_n     <dbl> 30, 30, 28, 29, 30, 30, 30, 30, 30, 30, 29, 30, 30, 30, 30…
#> $ ti_max_mn   <dbl> 7.1, 25.1, 2.1, 4.5, 3.5, 10.1, 24.9, 19.7, 11.8, 21.4, 8.…
#> $ glo_s       <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ glo_n       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ ts_min_cv   <dbl> 0.18, 0.05, 0.04, 0.23, 0.20, 0.13, 0.04, 0.08, 0.10, 0.07…
#> $ n_llu_s     <dbl> 4.24, 2.51, 14.79, 4.89, 4.24, 4.90, 2.36, 2.65, 4.75, 3.2…
#> $ nv_0050_mn  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ n_nie_q3    <dbl> 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 0, 1, 0, 0…
#> $ n_nie_q2    <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0…
#> $ n_nie_q4    <dbl> 1.2, 0.0, 3.0, 1.0, 0.2, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0…
#> $ glo_cv      <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ tm_max_max  <dbl> 17.4, 35.5, 22.6, 13.5, 13.1, 20.7, 35.8, 30.4, 23.7, 33.8…
#> $ ts_20_mn    <dbl> 8.6, 31.0, NA, 7.5, NA, 13.7, NA, 25.7, NA, 28.6, NA, NA, …
#> $ tm_max_cv   <dbl> 0.16, 0.05, 0.03, 0.16, 0.13, 0.09, 0.05, 0.06, 0.07, 0.07…
#> $ ts_20_md    <dbl> 8.8, 30.6, NA, 7.3, NA, 13.4, NA, 25.7, NA, 28.8, NA, NA, …
#> $ np_001_cv   <dbl> 0.52, 0.53, 0.14, 0.57, 0.52, 0.54, 0.47, 0.40, 0.41, 0.45…
#> $ p_max_mn    <dbl> 6.1, 7.0, 34.9, 7.5, 5.8, 7.0, 7.0, 9.4, 14.3, 10.7, 9.5, …
#> $ nw_91_max   <dbl> 2, 1, 3, 1, 2, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, NA, 1, 1, 0, …
#> $ p_max_md    <dbl> 8.7, 11.3, 35.8, 9.9, 8.0, 11.4, 9.4, 15.2, 17.0, 13.9, 14…
#> $ q_med_cv    <dbl> 0.00, 0.00, 0.00, 0.00, 0.01, 0.00, 0.00, 0.00, 0.00, 0.00…
#> $ ts_50_max   <dbl> 10.6, 31.5, NA, 9.0, NA, 13.7, NA, NA, NA, 29.4, NA, NA, 2…
#> $ ts_50_min   <dbl> 7.1, 26.4, NA, 5.6, NA, 10.0, NA, NA, NA, 23.0, NA, NA, 17…
#> $ ta_min_n    <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30…
#> $ e_q4        <dbl> 84, 169, 116, 82, 88, 91, 168, 155, 103, 147, 110, 126, 13…
#> $ e_q3        <dbl> 75, 161, 113, 78, 83, 85, 157, 145, 97, 142, 98, 117, 127,…
#> $ e_q2        <dbl> 72, 157, 111, 74, 79, 83, 150, 137, 92, 139, 94, 114, 120,…
#> $ e_q1        <dbl> 69, 150, 109, 70, 71, 76, 147, 131, 88, 133, 83, 109, 116,…
#> $ glo_q1      <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ w_racha_md  <dbl> 22.8, 19.7, 26.9, 21.9, 21.0, 22.2, 21.1, 19.7, 20.9, 21.4…
#> $ q_min_min   <dbl> 958.9, 971.1, 950.9, 950.9, 955.4, 953.7, 972.1, 962.1, 95…
#> $ ts_min_q3   <dbl> 10.0, 22.9, 23.4, 9.7, 10.5, 11.4, 22.5, 20.3, 13.8, 21.4,…
#> $ ts_min_q2   <dbl> 9.2, 22.3, 22.9, 8.6, 9.2, 11.0, 22.3, 19.5, 13.0, 20.6, 1…
#> $ ts_min_q1   <dbl> 8.0, 21.5, 22.4, 7.1, 8.1, 10.4, 21.5, 18.9, 12.1, 19.7, 1…
#> $ p_mes_max   <dbl> 70.5, 65.8, 541.6, 81.0, 77.1, 71.2, 50.6, 101.4, 126.6, 1…
#> $ ts_min_q4   <dbl> 10.7, 23.3, 23.8, 11.1, 11.2, 12.3, 23.2, 21.2, 14.4, 22.1…
#> $ q_mar_min   <dbl> 1008.3, 1011.2, 1014.0, 1009.4, 1010.2, 1006.8, 1012.6, 10…
#> $ tm_max_q3   <dbl> 14.3, 32.6, 21.7, 11.6, 11.5, 17.8, 33.5, 27.9, 20.7, 30.5…
#> $ tm_max_q2   <dbl> 12.3, 32.1, 21.3, 10.8, 11.1, 16.8, 32.3, 26.9, 20.1, 29.4…
#> $ tm_max_q1   <dbl> 11.5, 31.3, 20.9, 9.8, 9.9, 16.2, 31.3, 25.9, 19.1, 28.7, …
#> $ p_mes_mn    <dbl> 16.6, 9.5, 318.1, 17.2, 13.1, 21.3, 13.1, 20.1, 35.6, 22.7…
#> $ tm_max_q4   <dbl> 15.2, 33.6, 22.2, 12.7, 12.5, 18.8, 34.4, 28.5, 21.3, 32.0…
#> $ n_gra_mn    <dbl> 0.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0…
#> $ tm_min_max  <dbl> 5.6, 20.2, 11.5, 5.7, 5.9, 8.9, 20.8, 18.1, 11.2, 19.4, 9.…
#> $ np_001_q4   <dbl> 9.2, 5.0, 96.4, 13.0, 12.4, 10.2, 6.0, 7.0, 11.0, 8.2, 12.…
#> $ np_001_q3   <dbl> 8.0, 4.0, 88.0, 9.0, 9.0, 8.4, 4.0, 6.0, 10.0, 7.0, 10.0, …
#> $ np_001_q2   <dbl> 6.0, 3.0, 82.6, 7.0, 6.6, 6.6, 3.0, 5.0, 7.0, 5.0, 8.0, 8.…
#> $ np_001_q1   <dbl> 3.8, 2.0, 75.6, 4.0, 5.0, 5.0, 3.0, 4.0, 5.8, 4.0, 6.6, 6.…
#> $ ti_max_n    <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30…
#> $ w_med_md    <dbl> 17.5, 16.1, 16.4, 16.1, 14.5, 17.4, 18.0, 14.5, 18.1, 17.4…
#> $ ti_max_q4   <dbl> 8.5, 26.5, 4.0, 6.2, 5.4, 11.7, 27.2, 21.4, 13.5, 23.5, 10…
#> $ ti_max_q3   <dbl> 7.7, 25.5, 2.8, 4.8, 4.0, 10.4, 25.4, 20.2, 11.9, 21.9, 9.…
#> $ n_gra_q2    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0…
#> $ ti_max_q1   <dbl> 4.6, 23.2, 0.6, 1.8, 1.0, 6.5, 23.5, 17.4, 10.8, 19.0, 6.9…
#> $ ti_max_s    <dbl> 2.76, 1.71, 1.97, 2.50, 2.68, 3.08, 2.47, 2.10, 2.45, 2.90…
#> $ q_med_q2    <dbl> 987.9, 985.2, 986.8, 989.3, 989.2, 986.6, 985.5, 985.6, 98…
#> $ q_med_q3    <dbl> 990.1, 985.9, 987.0, 991.6, 992.0, 988.2, 985.8, 987.2, 98…
#> $ nt_30_q2    <dbl> 0.0, 22.6, 74.6, 0.0, 0.0, 0.0, 22.6, 6.6, 0.0, 14.0, 0.0,…
#> $ n_nie_min   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ nt_30_q4    <dbl> 0.0, 26.0, 85.2, 0.0, 0.0, 0.0, 28.0, 12.0, 1.0, 21.0, 0.0…
#> $ q_med_q4    <dbl> 994.4, 986.4, 987.7, 995.2, 994.8, 990.3, 986.4, 988.1, 98…
#> $ n_cub_md    <dbl> 4.6, 1.2, 56.6, 8.2, 9.4, 5.6, 0.6, 2.8, 5.5, 2.3, 6.9, 4.…
#> $ n_cub_mn    <dbl> 4.0, 1.0, 56.0, 8.0, 8.0, 6.0, 0.0, 2.5, 5.0, 2.0, 7.5, 4.…
#> $ ta_max_min  <dbl> 15.9, 34.9, 36.7, 13.4, 15.6, 21.0, 34.4, 29.6, 24.8, 32.3…
#> $ np_010_max  <dbl> 7, 5, 73, 15, 16, 13, 6, 7, 11, 10, 12, 16, 13, 14, 18, 86…
#> $ inso_md     <dbl> 6.4, 10.6, 7.8, 4.6, 4.1, 7.3, 11.7, 8.6, 8.2, 10.8, 5.2, …
#> $ inso_mn     <dbl> 6.2, 10.7, 7.7, 4.6, 4.1, 7.4, 11.8, 8.8, 8.4, 10.9, 5.2, …
#> $ evap_mn     <dbl> 1256, 3475, 24770, 876, 786, 1801, 3775, 2370, 1972, 3156,…