Introduction to the climate package

The goal of the climate R package is to automatize downloading of meteorological and hydrological data from publicly available repositories:

Functions

The climate package consists of ten main functions - three for meteorological data, one for hydrological data and six auxiliary functions and datasets:

Meteorological data

meteo_ogimet() - Downloading hourly and daily meteorological data from the SYNOP stations available in the ogimet.com collection. Any meteorological (aka SYNOP) station working under the World Meteorological Organizaton framework after year 2000 should be accessible.
meteo_imgw() - Downloading hourly, daily, and monthly meteorological data from the SYNOP/CLIMATE/PRECIP stations available in the danepubliczne.imgw.pl collection. It is a wrapper for meteo_monthly(), meteo_daily(), and meteo_hourly() from the imgw package.
meteo_noaa_hourly() - Downloading hourly NOAA Integrated Surface Hourly (ISH) meteorological data - Some stations have > 100 years long history of observations
sounding_wyoming() - Downloading measurements of the vertical profile of atmosphere (aka rawinsonde data)

Hydrological data

hydro_imgw() - Downloading hourly, daily, and monthly hydrological data from the SYNOP / CLIMATE / PRECIP stations available in the danepubliczne.imgw.pl collection. It is a wrapper for hydro_annual(), hydro_monthly(), and hydro_daily() from the imgw package.

Auxiliary functions and datasets

stations_ogimet() - Downloading information about all stations available in the selected country in the Ogimet repository
nearest_stations_ogimet() - Downloading information about nearest stations to the selected point available for the selected country in the Ogimet repository
imgw_meteo_stations - Built-in metadata from the IMGW-PIB repository for meteorological stations, their geographical coordinates, and ID numbers
imgw_hydro_stations - Built-in metadata from the IMGW-PIB repository for hydrological stations, their geographical coordinates, and ID numbers
imgw_meteo_abbrev - Dictionary explaining variables available for meteorological stations (from the IMGW-PIB repository)
imgw_hydro_abbrev - Dictionary explaining variables available for hydrological stations (from the IMGW-PIB repository)

Examples

Examples shows aplication of climate package with additional use of tools that help with processing the data to increase legible of downloaded data.

dplyr
tidyr

Example 1

Finding a 50 nearest meteorological stations in a given country:

library(climate)
ns = nearest_stations_ogimet(country ="United+Kingdom",point = c(-4, 56),
                             no_of_stations = 50, add_map = TRUE)
#> [1] "http://ogimet.com/cgi-bin/gsynres?lang=en&state=United+Kingdom&osum=no&fmt=html&ord=REV&ano=2020&mes=07&day=30&hora=06&ndays=1&Send=send"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file4631894cad6

head(ns)
#>    wmo_id        station_names       lon      lat alt distance [km]
#> 29  03144         Strathallan  -3.733348 56.31667  35      46.44794
#> 32  03155           Drumalbin  -3.733348 55.61668 245      52.38975
#> 30  03148           Glen Ogle  -4.316673 56.41667 564      58.71862
#> 27  03134   Glasgow Bishopton  -4.533344 55.90002  59      60.88179
#> 35  03166 Edinburgh Gogarbank  -3.350007 55.93335  57      73.30942
#> 28  03136      Prestwick RNAS  -4.583345 55.51668  26      84.99537
#>    wmo_id       station_names       lon       lat alt  distance [km]
#> 29  03144         Strathallan  -3.733348 56.31667  35      46.44794
#> 32  03155           Drumalbin  -3.733348 55.61668 245      52.38975
#> 30  03148           Glen Ogle  -4.316673 56.41667 564      58.71862
#> 27  03134   Glasgow Bishopton  -4.533344 55.90002  59      60.88179
#> 35  03166 Edinburgh Gogarbank  -3.350007 55.93335  57      73.30942
#> 28  03136      Prestwick RNAS  -4.583345 55.51668  26      84.99537

Example 2

Summary of stations available in Ogimet repository for a selected country:

library(climate)
PL = stations_ogimet(country ="Poland", add_map = TRUE)
#> [1] "http://ogimet.com/cgi-bin/gsynres?lang=en&state=Poland&osum=no&fmt=html&ord=REV&ano=2020&mes=07&day=30&hora=06&ndays=1&Send=send"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file4634c272ce5

head(PL)
#>   wmo_id     station_names      lon      lat alt
#> 1  12001 Petrobaltic Beta  18.16667 55.46668  46
#> 2  12100        Kolobrzeg  15.58334 54.18334   3
#> 3  12105         Koszalin  16.15000 54.20000  32
#> 4  12115            Ustka  16.86668 54.58335   6
#> 5  12120             Leba  17.53334 54.75001   2
#> 6  12124         Darlowek  16.40001 54.40001   2

Example 3

Downlading hourly meteorological data Svalbard in a random year from Ogimet

# downloading data with NOAA service:
df = meteo_noaa_hourly(station = "010080-99999", 
                       year = sample(2000:2020, 1))
#> [1] "https://www1.ncdc.noaa.gov/pub/data/noaa/2001/010080-99999-2001.gz"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file463727f7fc1

# downloading the same data with Ogimet.com (example for 2019):
# df <- meteo_ogimet(interval = "hourly", date = c("2019-01-01", "2019-12-31"),
#                   station = c("01008"))

Examplary data frame of meteorological data.
	date	lon	lat	alt	t2m	dpt2m	ws	wd	slp	visibility
1	2001-01-01 00:00:00	15.47	78.25	29	-7.0	-8.6	3.6	110	1009.1	20000
5	2001-01-01 03:00:00	15.47	78.25	29	-6.2	-8.9	3.6	110	1006.7	20000
9	2001-01-01 06:00:00	15.47	78.25	29	-5.0	-9.7	4.1	30	1005.8	20000
13	2001-01-01 09:00:00	15.47	78.25	29	-5.2	-9.1	5.1	60	1005.2	10000
17	2001-01-01 12:00:00	15.47	78.25	29	-5.6	-9.8	6.2	70	1004.5	15000
20	2001-01-01 15:00:00	15.47	78.25	29	-6.8	-12.0	5.1	80	1003.2	25000
24	2001-01-01 18:00:00	15.47	78.25	29	-8.0	-12.5	2.6	120	1001.8	30000
28	2001-01-01 21:00:00	15.47	78.25	29	-6.6	-7.2	2.1	100	999.9	3000
32	2001-01-02 00:00:00	15.47	78.25	29	-6.6	-7.2	1.5	110	997.9	9000
36	2001-01-02 03:00:00	15.47	78.25	29	-5.2	-5.7	1.0	120	995.4	12000

Example 4

Downloading atmospheric (sounding) profile for Łeba, PL rawinsonde station:

library(climate)
data("profile_demo")
# same as:
# profile_demo <- sounding_wyoming(wmo_id = 12120,
#                                  yy = 2000,
#                                  mm = 3,
#                                  dd = 23,
#                                  hh = 0)
df2 <- profile_demo[[1]] 
colnames(df2)[c(1, 3:4)] = c("PRESS", "TEMP", "DEWPT") # changing column names

Examplary data frame of sounding preprocessing
PRESS	HGHT	TEMP	DEWPT	RELH	MIXR	DRCT	SKNT	THTA	THTE	THTV
1013	6	4.2	3.8	97	4.98	270	8	276.3	290.0	277.2
1009	37	2.4	2.3	99	4.50	285	12	274.9	287.2	275.6
1000	107	2.2	1.9	98	4.41	295	17	275.4	287.5	276.1
976	303	0.8	-1.3	86	3.58	298	23	275.9	285.8	276.5
970	352	1.0	-6.0	60	2.53	299	25	276.6	283.8	277.0
959	444	1.0	-0.6	89	3.83	300	27	277.4	288.2	278.1
925	733	-1.1	-1.1	100	3.83	290	27	278.2	288.9	278.8
913	837	-1.5	-1.5	100	3.76	285	27	278.8	289.4	279.4
877	1157	-2.9	-2.9	100	3.54	288	29	280.6	290.6	281.2
850	1404	-4.1	-4.1	100	3.33	290	31	281.8	291.4	282.4

Example 5

Preparing an annual summary of air temperature and precipitation, processing with dplyr

library(climate)
library(dplyr)
#> Warning: package 'dplyr' was built under R version 3.6.2
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

df = meteo_imgw(interval = 'monthly', rank='synop', year = 1991:2019, station = "ŁEBA") 
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/s_m_d_format.txt"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file46339fe2e68
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/s_m_t_format.txt"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file4635e98cb96
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file46315e155f
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/1991_1995/1991_1995_m_s.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file46347cd10ac
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/1996_2000/1996_2000_m_s.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file463470d77e9
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/2001/2001_m_s.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file46346f74692
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/2002/2002_m_s.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file4637c28325b
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/2003/2003_m_s.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file46355a6d856
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/2004/2004_m_s.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file463323d5a63
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/2005/2005_m_s.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file463779a4634
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/2006/2006_m_s.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file463b5754bb
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/2007/2007_m_s.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file4637c3907dc
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/2008/2008_m_s.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file4633fb747a4
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/2009/2009_m_s.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file4632d3f261b
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/2010/2010_m_s.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file463779a9c97
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/2011/2011_m_s.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file4635fd214b
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/2012/2012_m_s.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file463252ffba
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/2013/2013_m_s.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file46363dd25e6
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/2014/2014_m_s.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file463392d91fa
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/2015/2015_m_s.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file463557a751d
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/2016/2016_m_s.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file463784d3108
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/2017/2017_m_s.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file4634a49f71c
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/2018/2018_m_s.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file463ccb8e1e
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_meteorologiczne/miesieczne/synop/2019/2019_m_s.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file46358967237
# please note that sometimes 2 names are used for the same station in different years

df2 = select(df, station:t2m_mean_mon, rr_monthly)

monthly_summary = df2 %>% 
  group_by(mm) %>% 
  summarise(tmax = mean(tmax_abs, na.rm = TRUE), 
            tmin = mean(tmin_abs, na.rm = TRUE),
            tavg = mean(t2m_mean_mon, na.rm = TRUE), 
            precip = sum(rr_monthly) / n_distinct(yy))            
#> `summarise()` ungrouping output (override with `.groups` argument)

monthly_summary = as.data.frame(t(monthly_summary[, c(5,2,3,4)])) 
monthly_summary = round(monthly_summary, 1)
colnames(monthly_summary) = month.abb

Examplary data frame of meteorological preprocessing.
	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
precip	42.0	32.9	37.7	28.7	49.0	54.3	73.0	81.5	76.0	77.6	59.5	54.1
tmax	7.5	8.9	14.5	22.1	26.2	28.5	29.6	29.0	24.5	18.8	12.2	8.7
tmin	-11.6	-10.0	-7.4	-3.7	-0.1	4.4	7.8	7.8	3.6	-0.8	-3.9	-8.8
tavg	0.1	0.5	2.7	6.9	11.3	14.9	17.5	17.5	13.9	9.2	4.7	1.6

Example 6

Calculate the mean maximum value of the flow on the stations in each year with dplyr’s summarise(), and spread data by year using tidyr’s spread() to get the annual means of maximum flow in the consecutive columns.

library(climate)
library(dplyr)
library(tidyr)
h = hydro_imgw(interval = "monthly", year = 2001:2005, coords = TRUE)
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_hydrologiczne/miesieczne/"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file463758dc9ef
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_hydrologiczne/miesieczne/mies_info.txt"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file4632fc4a634
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_hydrologiczne/miesieczne/2001/mies_2001.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file463177bb86c
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_hydrologiczne/miesieczne/2002/mies_2002.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file463109fda62
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_hydrologiczne/miesieczne/2003/mies_2003.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file463e129877
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_hydrologiczne/miesieczne/2004/mies_2004.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file4635b605ea7
#> [1] "https://dane.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_hydrologiczne/miesieczne/2005/mies_2005.zip"
#> /var/folders/lw/3fnnk87n4q1dkl35s2_p24h80000gn/T//RtmpnxbZW4/file46376c9ec3d
head(h)
#>              id       X        Y station riv_or_lake  hyy idhyy idex   H   Q  T
#> 47027 150210180 21.8335 50.88641 ANNOPOL   Wisła (2) 2001     1    1 214 172 NA
#> 47028 150210180 21.8335 50.88641 ANNOPOL   Wisła (2) 2001     1    2 228 207 NA
#> 47029 150210180 21.8335 50.88641 ANNOPOL   Wisła (2) 2001     1    3 250 272 NA
#> 47030 150210180 21.8335 50.88641 ANNOPOL   Wisła (2) 2001     2    1 215 174 NA
#> 47031 150210180 21.8335 50.88641 ANNOPOL   Wisła (2) 2001     2    2 225 201 NA
#> 47032 150210180 21.8335 50.88641 ANNOPOL   Wisła (2) 2001     2    3 258 297 NA
#>       mm
#> 47027 11
#> 47028 11
#> 47029 11
#> 47030 12
#> 47031 12
#> 47032 12

h2 = h %>%
  filter(idex == 3) %>%
  select(id, station, X, Y, hyy, Q) %>%
  group_by(hyy, id, station, X, Y) %>%
  summarise(annual_mean_Q = round(mean(Q, na.rm = TRUE), 1)) %>% 
  pivot_wider(names_from = hyy, values_from = annual_mean_Q)
#> `summarise()` regrouping output by 'hyy', 'id', 'station', 'X' (override with `.groups` argument)

Examplary data frame of hydrological preprocesssing.
id	station	X	Y	2001	2002	2003	2004	2005
149180010	KRZYŻANOWICE	18.28780	49.99301	200.5	147.4	87.9	109.2	170.6
149180020	CHAŁUPKI	18.32752	49.92127	174.7	96.7	57.6	91.8	146.9
149180040	GOŁKOWICE	18.49640	49.92579	4.5	2.0	1.7	1.7	2.5
149180050	ZEBRZYDOWICE	18.61326	49.88025	13.5	7.9	3.8	5.0	10.4
149180060	CIESZYN	18.62972	49.74616	57.2	57.7	29.8	26.8	65.4
149180070	CIESZYN	18.63137	49.74629	NaN	NaN	NaN	NaN	NaN