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The R climate package: an interface for downloading in-situ meteorological (and hydrological) dataset
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bczernecki/climate
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The goal of theclimate R package is to automatize downloading ofin-situ meteorologicaland 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 Oceanic & Atmospheric Administration - National Climatic Data Center - Integrated Surface Hourly (ISH)(NOAA)
The stable release of theclimate package from theCRAN repository can be installed with:
install.packages("climate")It is highly recommended to install the most up-to-date development version ofclimate fromGitHub 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(), andmeteo_hourly(). If 10-min dataset is needed then consider usingmeteo_imgw_datastore()meteo_noaa_hourly() - Downloading hourly 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)
- hydro_imgw() - Downloading hourly, daily, and monthly hydrological data from stations available in thedanepubliczne.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.
- stations_ogimet() - Downloading information about all stations available in the selectedcountry 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
library(climate)noaa <- meteo_noaa_hourly(station = "123300-99999", year = 2018:2019) # station ID: Poznan, Polandhead(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# ...
Downloading daily (or hourly) data from a global (OGIMET) repository knowing its ID (see alsonearest_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
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 SYNOPTYCZNA35323029523.1622853.10726 BIAŁYSTOK200015.3#>577 SYNOPTYCZNA35323029523.1622853.10726 BIAŁYSTOK2000210.6#>578 SYNOPTYCZNA35323029523.1622853.10726 BIAŁYSTOK2000314.8#>579 SYNOPTYCZNA35323029523.1622853.10726 BIAŁYSTOK2000427.8#>580 SYNOPTYCZNA35323029523.1622853.10726 BIAŁYSTOK2000529.3#>581 SYNOPTYCZNA35323029523.1622853.10726 BIAŁYSTOK2000632.6#> tmax_mean tmin_abs tmin_mean t2m_mean_mon t5cm_min rr_monthly#>5750.4-16.5-4.5-2.1-23.534.2#>5774.1-10.4-1.41.3-12.925.4#>5786.2-6.4-1.02.4-9.445.5#>57917.9-4.64.711.5-8.131.6#>58021.3-4.35.713.8-8.39.4#>58123.11.09.616.6-1.836.4h= hydro_imgw(interval="daily", year=2010:2011)head(h) id station riv_or_lake date hyy idhyy dd H Q T mm thick1150210180 ANNOPOL Wisła (2)2009-11-01201011287436 NA11 NA2150210180 ANNOPOL Wisła (2)2009-11-02201012282412 NA11 NA3150210180 ANNOPOL Wisła (2)2009-11-03201013272368 NA11 NA4150210180 ANNOPOL Wisła (2)2009-11-04201014268352 NA11 NA5150210180 ANNOPOL Wisła (2)2009-11-05201015264336 NA11 NA6150210180 ANNOPOL Wisła (2)2009-11-06201016260320 NA11 NA
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), 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.abbprint(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 packagesfromrpy2.robjects.packagesimportimportrimportrpy2.robjectsasrobjectsimportpandasaspdimportdatetimeasdt# 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.colnamesres["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 40.1
Ogimet.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 creatingan issue.
To cite theclimate package in publications, please usethis 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")About
The R climate package: an interface for downloading in-situ meteorological (and hydrological) dataset
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