Climate
The R climate package: an interface for downloading in-situ meteorological (and hydrological) dataset
Install / Use
/learn @bczernecki/ClimateREADME
climate <img src="man/figures/logo.png" align="right" width="150" />
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The goal of the climate R package is to automatize downloading of in-situ meteorological and hydrological data from publicly available repositories:
- OGIMET (ogimet.com) - up-to-date collection of SYNOP dataset
- University of Wyoming - atmospheric vertical profiling data (http://weather.uwyo.edu/upperair/)
- National Oceanic & Atmospheric Administration - Earth System Research Laboratories - Global Monitoring Laboratory (NOAA)
- Polish Institute of Meteorology and Water Management - National Research Institute (IMGW-PIB)
- National Centers for Environmental Information, National Oceanic & Atmospheric Administration - National Climatic Data Center - Integrated Surface Hourly (ISH) (NOAA)
Installation
The stable release of the climate package from the CRAN repository 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(), andmeteo_hourly(). If 10-min dataset is needed then consider usingmeteo_imgw_datastore() -
meteo_noaa_hourly() - Downloading hourly NCEI/NOAA Integrated Surface Hourly (ISH) meteorological data - Some stations have > 100 years long history of observations
-
meteo_noaa_co2() - Downloading monthly CO2 measurements from Mauna Loa Observatory
-
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 stations available in the
danepubliczne.imgw.pl collection.
It is a wrapper for previously developed set of functions such as:
hydro_monthly(), andhydro_daily() - hydro_imgw_datastore() - Downloading hourly and subhourly hydrological data from the IMGW-PIB hydro telemetry stations.
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 using Ogimet repository
- nearest_stations_noaa() - Downloading information about nearest stations to the selected point available for the selected country in the NOAA ISH meteorological repository
- nearest_stations_imgw() - List of nearby meteorological or hydrological IMGW-PIB stations in Poland
- 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
- stations_meteo_imgw_telemetry - Downloading complete and up-to-date information about coordinates for IMGW-PIB telemetry meteorological stations
- stations_hydro_imgw_telemetry - Downloading complete and up-to-date information about coordinates for IMGW-PIB telemetry hydrological stations
Example 1
Download hourly dataset from NCEI/NOAA ISH meteorological repository:
library(climate)
noaa <- meteo_noaa_hourly(station = "123300-99999", year = 2018:2019) # station ID: Poznan, Poland
head(noaa)
# year month day hour lon lat alt t2m dpt2m ws wd slp visibility
# 2019 1 1 0 16.85 52.417 84 3.3 2.3 5 220 1025.0 6000
# 2019 1 1 1 16.85 52.417 84 3.7 3.0 4 220 1024.2 1500
# 2019 1 1 2 16.85 52.417 84 4.2 3.6 4 220 1022.5 1300
# 2019 1 1 3 16.85 52.417 84 5.2 4.6 5 240 1021.2 1900
Example 2
Finding a nearest meteorological stations in a given country using NCEI/NOAA ISH data source:
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]
# 03354 Nottingham Weather Centre -1.250005 53.00000 117 28.04973
# 03379 Cranwell -0.500010 53.03333 67 56.22175
# 03377 Waddington -0.516677 53.16667 68 57.36093
# 03373 Scampton -0.550011 53.30001 57 60.67897
# 03462 Wittering -0.466676 52.61668 84 73.68934
# 03544 Church Lawford -1.333340 52.36667 107 80.29844
# ...
Example 3
Downloading daily (or hourly) data from a global (OGIMET) repository knowing its ID (see also nearest_stations_ogimet()):
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> NA
Example 4
Downloading monthly/daily/hourly 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 = "daily", year = 2010:2011)
head(h)
id station riv_or_lake date hyy idhyy dd H Q T mm thick
1 150210180 ANNOPOL Wisła (2) 2009-11-01 2010 1 1 287 436 NA 11 NA
2 150210180 ANNOPOL Wisła (2) 2009-11-02 2010 1 2 282 412 NA 11 NA
3 150210180 ANNOPOL Wisła (2) 2009-11-03 2010 1 3 272 368 NA 11 NA
4 150210180 ANNOPOL Wisła (2) 2009-11-04 2010 1 4 268 352 NA 11 NA
5 150210180 ANNOPOL Wisła (2) 2009-11-05 2010 1 5 264 336 NA 11 NA
6 150210180 ANNOPOL Wisła (2) 2009-11-06 2010 1 6 260 320 NA 11 NA
Example 5
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, s
