The goal of the climate R package is to automatize downloading of in-situ meteorological and hydrological data from publicly available repositories:
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")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(). If 10-min dataset is needed then consider
using meteo_imgw_datastore()
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)
meteo_noaa_co2() - Downloading monthly CO2 measurements from Mauna Loa Observatory
hydro_monthly(), and hydro_daily()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 1900library(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
# ...
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> NAm = 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 NAlibrary(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),
prec = 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
# prec 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)
library(climate)
library(ggplot2)
library(ggthemes)
co2 = meteo_noaa_co2()
head(co2)
co2$date = ISOdate(co2$yy, co2$mm, 1)
ggplot(co2, aes(date, co2_avg)) +
geom_line()+ geom_smooth()+
theme_bw()+
labs(
title = "Carbon Dioxide (CO2)",
subtitle = paste0("Mauna Loa Observatory "),
caption = "data source: NOAA
visualization: Bartosz Czernecki / R climate package",
x = "",
y = "ppm"
)
# load required packages
from rpy2.robjects.packages import importr
import rpy2.robjects as robjects
import pandas as pd
import datetime as dt
# load climate package (make sure that it was installed in R before)
importr("climate")
# test functionality e.g. with meteo_ogimet function for New York - La Guardia:
df = robjects.r["meteo_ogimet"](interval = "daily", station = 72503,
date = robjects.StrVector(["2022-05-01", "2022-06-15"]))
# optionally - transform object to pandas data frame and rename columns + fix datetime:
res = pd.DataFrame(df).transpose()
res.columns = df.colnames
res["Date"] = pd.TimedeltaIndex(res["Date"], unit="d") + dt.datetime(1970,1,1)
res.head
>>> res[res.columns[0:7]].head()
# station_ID Date TemperatureCAvg ... TemperatureCMin TdAvgC HrAvg
#0 72503.0 2022-06-15 23.5 ... 19.4 10.9 45.2
#1 72503.0 2022-06-14 25.0 ... 20.6 16.1 59.0
#2 72503.0 2022-06-13 20.4 ... 17.8 16.0 74.8
#3 72503.0 2022-06-12 21.3 ... 18.3 12.0 57.1
#4 72503.0 2022-06-11 22.6 ... 17.8 8.1 40.1Ogimet.com, University of Wyoming, and Institute of Meteorology and Water Management - National Research Institute (IMGW-PIB), National Oceanic & Atmospheric Administration (NOAA) - Earth System Research Laboratory, Global Monitoring Division and Integrated Surface Hourly (NOAA ISH) are the sources of the data.
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.
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")