climate

July 10, 2026 · View on GitHub

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 also possible 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 Organization framework after year 2000 should be accessible. Two backends are available and selected automatically: raw SYNOP decoding (source = "synop", default for interval = "hourly") and HTML scraping (source = "html", default for interval = "daily"). Country-level bulk downloads are supported via the country_name argument (SYNOP backend only).
🌍 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)
🇵🇱 meteo_imgw - Downloading hourly, daily, and monthly meteorological data from the Polish met service across all types of stations (i.e. SYNOP/CLIMATE/PRECIP ) available in the danepubliczne.imgw.pl collection. It is a wrapper for meteo_monthly(), meteo_daily(), meteo_hourly() and meteo_imgw_datastore() which gives access from montly to even to 10-min dataset.

(Polish) 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(), and hydro_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 - as above, for hydrological stations
🇵🇱 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
🌍 synop_parser - Decoding raw SYNOP meteorological messages into structured R lists or data frames. For a full walkthrough see the SYNOP Messages vignette.

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 (used in Ex. 1):

# 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
...	...	...	...	...	...

100 nearest stations to given coordinates in UK

Example 3

Downloading daily (or hourly) data from a global (OGIMET) repository knowing its ID (see also `nearest_stations_ogimet()`):

# Daily summary — uses HTML backend by default
o = meteo_ogimet(date = c(Sys.Date() - 5, Sys.Date() - 1), 
                 interval = "daily",
                 station = 12330)
head(o)

station_ID	Date	TempCAvg	TempCMax	TempCMin	TdAvgC	HrAvg	WindDir	WindInt	WindGust	PressHp	Precmm	TotClOct	lowClOct	SunD1h	VisKm
12330	2019-12-21	8.8	13.2	4.9	5.3	79.3	SSE	11.4	39.6	995.9	1.8	3.6	2.0	6.7	21.4
12330	2019-12-20	5.4	8.5	-1.2	4.5	92.4	ESE	15.0	NA	1015.0	0.0	6.4	0.6	1.0	8.0
12330	2019-12-19	3.8	10.3	-3.0	1.9	89.6	SW	7.1	NA	1020.4	0.0	5.2	5.9	2.5	14.1
12330	2019-12-18	6.3	9.0	2.2	4.1	84.8	S	9.2	NA	1009.2	0.0	5.7	2.7	1.4	12.2
12330	2019-12-17	4.9	7.6	0.3	2.9	87.2	SSE	7.2	NA	1010.8	0.1	6.2	4.6	NA	13.0

# Hourly observations — decoded from raw SYNOP messages by default
h = meteo_ogimet(date = c("2009-12-01", "2009-12-04"),
                 interval = "hourly",
                 station  = 12330)
head(h)

date	station	t2m	dpt2m	rel_hum	tmax	tmin	wd	ws	gust	press	slp	precip	Nt	snow
2009-12-01 00:00:00	12330	2.0	0.0	93	NA	NA	210	5	NA	1007.4	1016.3	NA	8	NA
2009-12-01 06:00:00	12330	1.0	-1.0	92	NA	NA	200	3	NA	1009.8	1018.8	NA	8	NA
2009-12-01 12:00:00	12330	3.0	1.0	93	5.8	2.9	230	4	NA	1011.5	1020.4	NA	8	NA
2009-12-01 18:00:00	12330	2.0	0.0	93	NA	NA	240	3	NA	1013.3	1022.1	NA	7	NA

# Country-level bulk download — all stations in a country for a given day
poland = meteo_ogimet(interval      = "hourly",
                      country_name  = "Poland",
                      date          = c("2009-12-15", "2009-12-15"))
nrow(poland)  #> several hundred rows (one per observation per station)

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	tmax_mean	tmin_abs	tmin_mean	t2m_mean_mon	t5cm_min	rr_monthly
SYNOP	353230295	23.16228	53.10726	BIAŁYSTOK	2000	1	5.3	0.4	-16.5	-4.5	-2.1	-23.5	34.2
SYNOP	353230295	23.16228	53.10726	BIAŁYSTOK	2000	2	10.6	4.1	-10.4	-1.4	1.3	-12.9	25.4
SYNOP	353230295	23.16228	53.10726	BIAŁYSTOK	2000	3	14.8	6.2	-6.4	-1.0	2.4	-9.4	45.5
SYNOP	353230295	23.16228	53.10726	BIAŁYSTOK	2000	4	27.8	17.9	-4.6	4.7	11.5	-8.1	31.6
SYNOP	353230295	23.16228	53.10726	BIAŁYSTOK	2000	5	29.3	21.3	-4.3	5.7	13.8	-8.3	9.4
SYNOP	353230295	23.16228	53.10726	BIAŁYSTOK	2000	6	32.6	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
150210180	ANNOPOL	Wisła (2)	2009-11-01	2010	1	1	287	436	NA	11	NA
150210180	ANNOPOL	Wisła (2)	2009-11-02	2010	1	2	282	412	NA	11	NA
150210180	ANNOPOL	Wisła (2)	2009-11-03	2010	1	3	272	368	NA	11	NA
150210180	ANNOPOL	Wisła (2)	2009-11-04	2010	1	4	268	352	NA	11	NA
150210180	ANNOPOL	Wisła (2)	2009-11-05	2010	1	5	264	336	NA	11	NA
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, 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)

Walter and Lieth climatic diagram for Poznan, Poland

Example 6

Download monthly CO2 dataset from Mauna Loa observatory

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"
)

CO2 monthly concentration, Mauna Loa observatory

Example 7

Use "climate" inside python environment via rpy2

# 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
72503.0	2022-06-15	23.5	19.4	10.9	45.2
72503.0	2022-06-14	25.0	20.6	16.1	59.0
72503.0	2022-06-13	20.4	17.8	16.0	74.8
72503.0	2022-06-12	21.3	18.3	12.0	57.1
72503.0	2022-06-11	22.6	17.8	8.1	40.1

Example 8

Decode raw SYNOP messages with `synop_parser()`

The synop_parser() function decodes FM-12 SYNOP meteorological messages into structured R objects. For a detailed guide including all parameters and output formats, see the SYNOP Messages vignette.

library(climate)

synop_code = "AAXX 01004 88889 12782 61506 10094 20047 30111 40197 53007 60001 81541"

# Decode a single message — returns a named list
result = parser(synop_code)
result$station_id$value        #> "88889"
result$air_temperature$value   #> 9.4
result$wind_speed$value        #> 6
result$visibility$value        #> 40000
result$sea_level_pressure$value #> 1019.7

# Return a tidy data frame with one row per message
df = parser(synop_code, as_data_frame = TRUE)
df

station_type	station_id	region	obs_day	obs_hour	wind_unit	wind_estimated	visibility	cloud_cover	wind_direction	wind_speed	air_temperature	dewpoint_temperature
AAXX	88889	III	1	0	KT	FALSE	40000	6	150	6	9.4	4.7

station_pressure	sea_level_pressure	pressure_tendency	pressure_change	precipitation_amount	precipitation_time	cloud_base_min	cloud_base_max	low_cloud_type	middle_cloud_type	high_cloud_type	low_cloud_amount	source
1011.1	1019.7	0	7	0	6	1500	2000	5	4	1	1	AAXX 01004 88889 12782…

# Decode multiple SYNOP messages at once
msgs = c(
  "AAXX 01004 88889 12782 61506 10094 20047 30111 40197 53007 60001 81541",
  "AAXX 10124 26477 32560 83102 10156 20106 38528 40128 52003 60001 333 56017"
)
df2 = parser(msgs, as_data_frame = TRUE)
nrow(df2)       #> 2
df2$station_id  #> c("88889", "26477")
df2$source      # original SYNOP strings preserved in last column

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.

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")