climate
The goal of the climate R package is to automatize downloading of meteorological and hydrological data from publicly available repositories:
- OGIMET (ogimet.com)
- University of Wyoming - atmospheric vertical profiling data (http://weather.uwyo.edu/upperair/).
- Polish Institute of Meterology and Water Management - National Research Institute (IMGW-PIB)
Installation
The stable release of the climate package from the CRAN reposity can be installed with:
install.packages("climate")It is highly recommended to install the most up-to-date development version of climate from GitHub with:
library(remotes)
install_github("bczernecki/climate")Overview
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)
Example 1
Finding a nearest meteorological stations in a given country:
library(climate)
# find 100 nearest UK stations to longitude 1W and latitude 53N :
nearest_stations_ogimet(country = "United+Kingdom",
date = Sys.Date(),
add_map = TRUE,
point = c(-1, 53),
no_of_stations = 100
)
# wmo_id station_names lon lat alt distance [km]
# 66 03354 Nottingham Weather Centre -1.250005 53.00000 117 28.04973
# 69 03379 Cranwell -0.500010 53.03333 67 56.22175
# 68 03377 Waddington -0.516677 53.16667 68 57.36093
# 67 03373 Scampton -0.550011 53.30001 57 60.67897
# 78 03462 Wittering -0.466676 52.61668 84 73.68934
# 89 03544 Church Lawford -1.333340 52.36667 107 80.29844
# ...Example 2
Downloading daily (or hourly) data from a global repository knowing its ID (see Example 1):
library(climate)
o = meteo_ogimet(date = c(Sys.Date() - 5, Sys.Date() - 1),
interval = "daily",
coords = FALSE,
station = 12330)
head(o)
#> station_ID Date TemperatureCAvg TemperatureCMax TemperatureCMin TdAvgC HrAvg WindkmhDir
#> 3 12330 2019-12-21 8.8 13.2 4.9 5.3 79.3 SSE
#> 4 12330 2019-12-20 5.4 8.5 -1.2 4.5 92.4 ESE
#> 5 12330 2019-12-19 3.8 10.3 -3.0 1.9 89.6 SW
#> 6 12330 2019-12-18 6.3 9.0 2.2 4.1 84.8 S
#> 7 12330 2019-12-17 4.9 7.6 0.3 2.9 87.2 SSE
#> WindkmhInt WindkmhGust PresslevHp Precmm TotClOct lowClOct SunD1h VisKm SnowDepcm PreselevHp
#> 3 11.4 39.6 995.9 1.8 3.6 2.0 6.7 21.4 <NA> NA
#> 4 15.0 NA 1015.0 0.0 6.4 0.6 1.0 8.0 <NA> NA
#> 5 7.1 NA 1020.4 0.0 5.2 5.9 2.5 14.1 <NA> NA
#> 6 9.2 NA 1009.2 0.0 5.7 2.7 1.4 12.2 <NA> NA
#> 7 7.2 NA 1010.8 0.1 6.2 4.6 <NA> 13.0 <NA> NAExample 3
Downloading meteorological/hydrological data from the Polish (IMGW-PIB) repository:
m = meteo_imgw(interval = "monthly", rank = "synop", year = 2000, coords = TRUE)
head(m)
#> rank id X Y station yy mm tmax_abs
#> 575 SYNOPTYCZNA 353230295 23.16228 53.10726 BIAŁYSTOK 2000 1 5.3
#> 577 SYNOPTYCZNA 353230295 23.16228 53.10726 BIAŁYSTOK 2000 2 10.6
#> 578 SYNOPTYCZNA 353230295 23.16228 53.10726 BIAŁYSTOK 2000 3 14.8
#> 579 SYNOPTYCZNA 353230295 23.16228 53.10726 BIAŁYSTOK 2000 4 27.8
#> 580 SYNOPTYCZNA 353230295 23.16228 53.10726 BIAŁYSTOK 2000 5 29.3
#> 581 SYNOPTYCZNA 353230295 23.16228 53.10726 BIAŁYSTOK 2000 6 32.6
#> tmax_mean tmin_abs tmin_mean t2m_mean_mon t5cm_min rr_monthly
#> 575 0.4 -16.5 -4.5 -2.1 -23.5 34.2
#> 577 4.1 -10.4 -1.4 1.3 -12.9 25.4
#> 578 6.2 -6.4 -1.0 2.4 -9.4 45.5
#> 579 17.9 -4.6 4.7 11.5 -8.1 31.6
#> 580 21.3 -4.3 5.7 13.8 -8.3 9.4
#> 581 23.1 1.0 9.6 16.6 -1.8 36.4
h = hydro_imgw(interval = "semiannual_and_annual", year = 2010:2011)
head(h)
id station riv_or_lake hyy idyy Mesu idex H beyy bemm bedd behm
3223 150210180 ANNOPOL Wisła (2) 2010 13 H 1 227 2009 12 19 NA
3224 150210180 ANNOPOL Wisła (2) 2010 13 H 2 319 NA NA NA NA
3225 150210180 ANNOPOL Wisła (2) 2010 13 H 3 531 2010 3 3 18
3226 150210180 ANNOPOL Wisła (2) 2010 14 H 1 271 2010 8 29 NA
3227 150210180 ANNOPOL Wisła (2) 2010 14 H 1 271 2010 10 27 NA
3228 150210180 ANNOPOL Wisła (2) 2010 14 H 2 392 NA NA NA NAExample 4
Create Walter & Lieth climatic diagram based on downloaded data
library(climate)
library(dplyr)
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),
opad = 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
print(monthly_summary)
# Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
# opad 37.1 31.3 38.5 31.3 53.9 60.8 94.8 59.6 40.5 39.7 35.7 38.6
# tmax 8.7 11.2 17.2 23.8 28.3 31.6 32.3 31.8 26.9 21.3 14.3 9.8
# tmin -15.0 -11.9 -7.6 -3.3 1.0 5.8 8.9 7.5 2.7 -2.4 -5.2 -10.4
# tavg -1.0 0.5 3.7 9.4 14.4 17.4 19.4 19.0 14.3 9.1 4.5 0.8
# create plot with use of the "climatol" package:
climatol::diagwl(monthly_summary, mlab = "en", est = "POZNAŃ", alt = NA,
per = "1991-2019", p3line = FALSE)Acknowledgment
Ogimet.com, University of Wyoming, and Institute of Meteorology and Water Management - National Research Institute (IMGW-PIB) are the sources of the data.
Contribution
Contributions to this package are welcome. The preferred method of contribution is through a GitHub pull request. Feel also free to contact us by creating an issue.
Citation
To cite the climate package in publications, please use this paper:
Czernecki, B.; Głogowski, A.; Nowosad, J. Climate: An R Package to Access Free In-Situ Meteorological and Hydrological Datasets for Environmental Assessment. Sustainability 2020, 12, 394. https://doi.org/10.3390/su12010394"
LaTeX/BibTeX version can be obtained with:
library(climate)
citation("climate")