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.
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)

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 availible in Ogimet repositoy selected by county:

library(climate)
PL = stations_ogimet(country ="Poland", add_map = TRUE)

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 2018

# downloading data
df <- meteo_ogimet(interval = "hourly", date = c("2018-01-01", "2018-12-31"),station = c("01008"))
#> [1] "01008"
#> 
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Examplary data frame of meteorological data.
	station_ID	Date	TC	TdC	TmaxC	TminC	ddd	ffkmh	Gustkmh	P0hPa	PseahPa	PTnd
2	1008	2018-12-31 23:00:00	-14.3	-19.2	NA	NA	NNW	25.2	43.2	1000.5	1004.2	0.3
3	1008	2018-12-31 22:00:00	-13.7	-18.2	NA	NA	NW	21.6	32.4	1000.0	1003.8	-0.4
4	1008	2018-12-31 21:00:00	-15.9	-18.5	NA	NA	ESE	10.8	21.6	999.9	1003.7	-0.6
5	1008	2018-12-31 20:00:00	-16.8	-20.1	NA	NA	E	18.0	25.2	1000.1	1003.9	-0.9
6	1008	2018-12-31 19:00:00	-17.2	-21.7	NA	NA	ESE	21.6	28.8	1000.5	1004.3	-1.3
7	1008	2018-12-31 18:00:00	-18.3	-20.8	-15.5	-19.5	ESE	21.6	32.4	1000.7	1004.5	-1.9
8	1008	2018-12-31 17:00:00	-18.1	-23.1	NA	NA	ESE	25.2	32.4	1001.2	1005.0	-1.9
9	1008	2018-12-31 16:00:00	-18.1	-22.6	NA	NA	E	21.6	25.2	1001.9	1005.7	-1.6
10	1008	2018-12-31 15:00:00	-19.2	-24.1	NA	NA	ESE	21.6	25.2	1002.6	1006.5	-1.2
11	1008	2018-12-31 14:00:00	-18.9	-24.7	NA	NA	ESE	18.0	25.2	1003.2	1007.0	-0.7

Example 4

Downloading sounding data for Łeba (Poland) station

library(climate)
profile <- sounding_wyoming(wmo_id = 12120, yy = 2020, mm = 3, dd = 23, hh = 0)
df2 <- profile[[1]] 
colnames(df2)[c(1, 3:4)] = c("press", "temp", "dewpt") # changing column names

Examplary data frame of sounding preprocesssing.
press	HGHT	temp	dewpt	RELH	MIXR	DRCT	SKNT	THTA	THTE	THTV
1042	6	0.4	-6.6	59	2.25	70	2	270.4	276.6	270.7
1036	51	1.8	-8.2	47	2.00	71	3	272.2	277.9	272.5
1000	328	-0.7	-10.7	47	1.70	80	10	272.4	277.3	272.7
925	944	-6.7	-11.1	71	1.78	65	12	272.4	277.5	272.8
921	977	-7.0	-11.2	72	1.77	65	12	272.5	277.5	272.8
874	1381	-10.9	-12.3	89	1.71	80	12	272.6	277.5	272.9
850	1596	-12.9	-12.9	100	1.68	55	12	272.6	277.4	272.9
849	1605	-13.0	-13.0	100	1.67	55	12	272.6	277.4	272.9
832	1759	-14.2	-14.3	99	1.52	75	14	272.9	277.3	273.2
820	1869	-15.1	-15.3	98	1.43	66	17	273.1	277.2	273.3

Example 5

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

library(climate)
library(dplyr)
#> 
#> 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 = "POZNAŃ") 
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))            

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 preprocesssing.
	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
precip	37.0	30.9	38.2	30.5	54.5	60.2	93.2	60.7	42.3	38.0	35.6	38.6
tmax	8.6	11.1	17.3	23.8	28.2	31.5	32.3	31.8	26.7	21.4	14.4	9.7
tmin	-15.0	-12.2	-7.8	-3.3	0.9	5.6	8.9	7.4	2.7	-2.5	-5.2	-10.6
tavg	-1.0	0.3	3.7	9.4	14.3	17.3	19.4	19.0	14.2	9.2	4.6	0.8

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 = 2001:2010, coords = TRUE)
head(h)
#>              id       X        Y station riv_or_lake  hyy idhyy idex   H
#> 95158 150210180 21.8335 50.88641 ANNOPOL   Wisła (2) 2001     1    1 214
#> 95159 150210180 21.8335 50.88641 ANNOPOL   Wisła (2) 2001     1    2 228
#> 95160 150210180 21.8335 50.88641 ANNOPOL   Wisła (2) 2001     1    3 250
#> 95161 150210180 21.8335 50.88641 ANNOPOL   Wisła (2) 2001     2    1 215
#> 95162 150210180 21.8335 50.88641 ANNOPOL   Wisła (2) 2001     2    2 225
#> 95163 150210180 21.8335 50.88641 ANNOPOL   Wisła (2) 2001     2    3 258
#>         Q  T mm
#> 95158 172 NA 11
#> 95159 207 NA 11
#> 95160 272 NA 11
#> 95161 174 NA 12
#> 95162 201 NA 12
#> 95163 297 NA 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)

Examplary data frame of hydrological preprocesssing.
id	station	X	Y	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010
149180010	KRZYŻANOWICE	18.28780	49.99301	200.5	147.4	87.9	109.2	170.6	226.9	152.9	131.0	160.9	461.1
149180020	CHAŁUPKI	18.32752	49.92127	174.7	96.7	57.6	91.8	146.9	170.6	110.2	101.6	124.7	314.6
149180040	GOŁKOWICE	18.49640	49.92579	4.5	2.0	1.7	1.7	2.5	3.3	2.1	1.7	2.2	8.6
149180050	ZEBRZYDOWICE	18.61326	49.88025	13.5	7.9	3.8	5.0	10.4	6.5	5.8	2.8	4.5	23.6
149180060	CIESZYN	18.62972	49.74616	57.2	57.7	29.8	26.8	65.4	60.7	54.7	33.0	34.7	135.0
149180070	CIESZYN	18.63137	49.74629	NaN	NaN	NaN	NaN	NaN	NaN	0.6	0.5	0.6	0.6