Introduction to the climate package

In climate: Interface to Download Meteorological (and Hydrological) Datasets

knitr::opts_chunk$set(
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options(scipen = 999)

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)
• National Oceanic & Atmospheric Administration - Earth System Research Laboratory - Global Monitoring Division (NOAA)
• National Oceanic & Atmospheric Administration - National Climatic Data Center - Integrated Surface Hourly (ISH) (NOAA)

Functions

The climate package consists of ten main functions - three for meteorological data, one for hydrological data and six auxiliary functions and datasets:

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 dane.imgw.pl collection. It is a wrapper for meteo_monthly(), meteo_daily(), and meteo_hourly() from the imgw package.

• 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)

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)

Examples

Examples shows aplication of climate package with additional use of tools that help with processing the data to increase legible of downloaded data.

• dplyr
• tidyr

Example 1

Finding a 50 nearest meteorological stations in a given country:

``` {r stations , eval=T, fig.width=7,fig.height=7, fig.fullwidth=TRUE} library(climate) ns = nearest_stations_ogimet(country ="United+Kingdom", point = c(-4, 56), no_of_stations = 50, add_map = TRUE) head(ns)

> wmo_id station_names lon lat alt distance [km]

> 29 03144 Strathallan -3.733348 56.31667 35 46.44794

> 32 03155 Drumalbin -3.733348 55.61668 245 52.38975

> 30 03148 Glen Ogle -4.316673 56.41667 564 58.71862

> 27 03134 Glasgow Bishopton -4.533344 55.90002 59 60.88179

> 35 03166 Edinburgh Gogarbank -3.350007 55.93335 57 73.30942

> 28 03136 Prestwick RNAS -4.583345 55.51668 26 84.99537

### Example 2

Summary of stations available in Ogimet repository for a selected country:

``` {r stations, eval=T, fig.width=7, fig.height=7, fig.fullwidth=T}
library(climate)
PL = stations_ogimet(country = "Poland", add_map = TRUE)
head(PL)

Example 3

Downlading hourly meteorological data Svalbard in a random year from Ogimet

``` {r windrose,eval=T}

downloading data with NOAA service:

df = meteo_noaa_hourly(station = "010080-99999", year = sample(2000:2020, 1))

downloading the same data with Ogimet.com (example for 2019):

df <- meteo_ogimet(interval = "hourly", date = c("2019-01-01", "2019-12-31"),

station = c("01008"))

```r
library(knitr)
kable(head(df[,c(-2:-5)], 10), caption = "Examplary data frame of meteorological data.")

Example 4

Downloading atmospheric (sounding) profile for Łeba, PL rawinsonde station:

library(climate)
data("profile_demo")
# same as:
# profile_demo <- sounding_wyoming(wmo_id = 12120,
#                                  yy = 2000,
#                                  mm = 3,
#                                  dd = 23,
#                                  hh = 0)
df2 <- profile_demo[[1]] 
colnames(df2)[c(1, 3:4)] = c("PRESS", "TEMP", "DEWPT") # changing column names

library(knitr)
kable(head(df2,10), caption = "Examplary data frame of sounding preprocessing")

Example 5

Preparing an annual summary of air temperature and precipitation, processing with dplyr

library(climate)
library(dplyr)

df = meteo_imgw(interval = "monthly", rank = "synop", year = 1991:2019, station = "ŁEBA") 
# please note that sometimes 2 names are used for the same station in different years

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), 
            precip = 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

library(knitr)
kable(head(monthly_summary), caption = "Examplary data frame of meteorological preprocessing.")

Example 6

Calculate the mean maximum value of the flow on the stations in each year with dplyr's summarise(), and spread data by year using tidyr's spread() to get the annual means of maximum flow in the consecutive columns.

library(climate)
library(dplyr)
library(tidyr)
h = hydro_imgw(interval = "monthly", year = 2001:2005, coords = TRUE)
head(h)

h2 = h %>%
  filter(idex == 3) %>%
  select(id, station, X, Y, hyy, Q) %>%
  group_by(hyy, id, station, X, Y) %>%
  summarise(annual_mean_Q = round(mean(Q, na.rm = TRUE), 1)) %>% 
  pivot_wider(names_from = hyy, values_from = annual_mean_Q)

library(knitr)
kable(head(h2), caption = "Examplary data frame of hydrological preprocesssing.")

Acknowledgment

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 Laboratories - Global Monitoring 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 version can be obtained with:

library(climate)
citation("climate")

climate documentation built on Aug. 9, 2022, 5:08 p.m.