The rp5pik package provides a set of functions to download and
preprocess meteorological data from http://www.pogodaiklimat.ru/
You can install the development version of rp5pik from GitHub with:
# install.packages("devtools")
devtools::install_github("atsyplenkov/rp5pik")
# OR
# install.packages("remotes")
remotes::install_github("atsyplenkov/rp5pik")Below is an example for rp_parse_pik functions. It allows you to
download meteo data at 3-hour temporal resolution for various
stations using their WMO ID from http://www.pogodaiklimat.ru/:
library(rp5pik)
example <-
rp_parse_pik(
wmo_id = c("20069", "27524"),
start_date = "2022-05-01",
end_date = "2022-05-31"
)
#> ⠙ 1/2 ETA: 4s | Downloading data Parsing data
example
#> # A tibble: 496 × 11
#> wmo datetime_utc ta td rh ps psl prec windd
#> <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 20069 2022-05-01 00:00:00 -7.9 -9.1 91 993 996. 0.8 90
#> 2 20069 2022-05-01 03:00:00 -7 -8.2 91 994. 997. NA 90
#> 3 20069 2022-05-01 06:00:00 -7.2 -8.4 91 994. 997. NA 90
#> 4 20069 2022-05-01 09:00:00 -7.7 -8.8 92 994. 998. NA 90
#> 5 20069 2022-05-01 12:00:00 -8.4 -9.5 92 995. 998. 0.6 90
#> 6 20069 2022-05-01 15:00:00 -9.8 -11 91 995 998. NA 45
#> 7 20069 2022-05-01 18:00:00 -11.3 -12.7 89 995. 998. NA 45
#> 8 20069 2022-05-01 21:00:00 -14.2 -16 86 995. 999. NA 45
#> 9 20069 2022-05-02 00:00:00 -14.3 -15.9 88 995. 998. NA 45
#> 10 20069 2022-05-02 03:00:00 -11.8 -14.4 81 994. 997. NA 0
#> # ℹ 486 more rows
#> # ℹ 2 more variables: winds_mean <dbl>, winds_max <dbl>List of available variables:
-
wmocharacter. WMO index of the meteostation -
datetime_utcPOSIXct. Date and Time of the measurement at UTC -
tanumeric. Air temperature at 2m above the surface, °C -
tdnumeric. Dew point, °C -
rhnumeric. Relative humidity at 2m above the surface, % -
psnumeric. Atmosphere pressure at meteostation, hPa -
pslnumeric. Atmosphere pressure adjusted to the height of mean sea level, hPa -
precnumeric. Cumulative precipitation for the last 12 hours, mm -
winddinteger. Wind direction, deg -
winds_meannumeric. Average 10-min wind speed, m/s -
winds_maxnumeric. Maximum wind speed, m/s
We can visualize the example dataset using ggplot2 as follows:
library(ggplot2)
example |>
ggplot(
aes(
x = datetime_utc,
y = ta,
group = wmo
)
) +
geom_line(aes(color = wmo)) +
labs(
x = "",
y = "Average Temperature, °C"
) +
theme_minimal()
Since the downloaded with rp_parse_pik data contains raw data, it
requires additional checking and cleaning. We suggest to explore the raw
dataset by yourselves before any further manipulations.
However, the rp5pik package has a function to aggregate raw data on
daily (24h) or semi-daily (12h) periods. The rp_aggregate_pik
function removes known error codes from precipitation data (699
values). Additionally, it calculates daily precipitation sums based on
measured precipitation at 06 UTC and 18 UTC in European part of Russia
(see meteostation manuals for more
info).
⚠ As of 2023-06-28 this function works only with Moscow timezone.
This is how you can aggregate data daily:
library(dplyr)
example_daily <-
example |>
rp_aggregate_pik(.period = "24h") |>
group_split(wmo)
example_daily
#> <list_of<
#> tbl_df<
#> wmo : character
#> date : date
#> p : double
#> ta : double
#> td : double
#> rh : double
#> ps : double
#> psl : double
#> windd : double
#> winds_mean: double
#> winds_max : double
#> >
#> >[2]>
#> [[1]]
#> # A tibble: 32 × 11
#> wmo date p ta td rh ps psl windd winds_mean
#> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 20069 2022-05-01 NA -8.47 -9.67 91 994. 998. 77.1 9.14
#> 2 20069 2022-05-02 NA -12.9 -14.9 85 992. 995. 129. 6.12
#> 3 20069 2022-05-03 NA -16.0 -19.0 77.8 988. 992. 152. 2.25
#> 4 20069 2022-05-04 NA -14.3 -17.4 77.2 996. 999. 248. 5.38
#> 5 20069 2022-05-05 NA -14.2 -16.0 86.6 998. 1002. 67.5 3.38
#> 6 20069 2022-05-06 NA -12.6 -14.3 87.2 994. 997. 253. 4.12
#> 7 20069 2022-05-07 NA -9.31 -11.1 86.9 999. 1002. 231. 3.12
#> 8 20069 2022-05-08 NA -6.88 -8.25 90.1 999. 1003. 152. 9.25
#> 9 20069 2022-05-09 NA -7.19 -9.02 86.6 991. 995. 202. 9.62
#> 10 20069 2022-05-10 NA -7.25 -8.89 88 987. 990. 197. 7.75
#> # ℹ 22 more rows
#> # ℹ 1 more variable: winds_max <dbl>
#>
#> [[2]]
#> # A tibble: 32 × 11
#> wmo date p ta td rh ps psl windd winds_mean
#> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 27524 2022-05-01 NA 7.21 -5.59 43 996. 1023. 238. 3.71
#> 2 27524 2022-05-02 0 9.85 -2.4 44.1 991. 1018. 199. 3
#> 3 27524 2022-05-03 NA 11.6 1.54 53.9 982. 1008. 231. 5.88
#> 4 27524 2022-05-04 0.6 6 -1.14 60.5 985. 1012. 298. 5.25
#> 5 27524 2022-05-05 NA 5.24 -6.68 45.5 997. 1025. 248. 3.75
#> 6 27524 2022-05-06 NA 9.59 -5.91 34.2 1001. 1028. 191. 3.5
#> 7 27524 2022-05-07 NA 13.1 -2.59 34.2 999. 1025. 180 3.62
#> 8 27524 2022-05-08 1 13.6 2.17 47.8 992. 1019. 208. 4.75
#> 9 27524 2022-05-09 2.6 6.99 0.575 64.2 994. 1021. 158. 6.25
#> 10 27524 2022-05-10 0 6.62 -3.5 50.6 992. 1019. 158. 7.38
#> # ℹ 22 more rows
#> # ℹ 1 more variable: winds_max <dbl>Or semi-daily:
library(dplyr)
example_12h <-
example |>
rp_aggregate_pik(.period = "12h", .tz = "Europe/Moscow") |>
group_split(wmo)
example_12h
#> <list_of<
#> tbl_df<
#> wmo : character
#> datetime_tz: datetime<Europe/Moscow>
#> p : double
#> ta : double
#> td : double
#> rh : double
#> ps : double
#> psl : double
#> windd : double
#> winds_mean : double
#> winds_max : double
#> >
#> >[2]>
#> [[1]]
#> # A tibble: 63 × 11
#> wmo datetime_tz p ta td rh ps psl windd
#> <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 20069 2022-05-01 09:00:00 NA -7.37 -8.57 91 993. 997. 90
#> 2 20069 2022-05-01 21:00:00 NA -9.3 -10.5 91 995. 998. 67.5
#> 3 20069 2022-05-02 09:00:00 NA -13.0 -15.0 84.8 994. 997. 22.5
#> 4 20069 2022-05-02 21:00:00 NA -12.8 -14.8 85.2 989. 993. 236.
#> 5 20069 2022-05-03 09:00:00 NA -17.0 -19.7 79.8 987. 990. 90
#> 6 20069 2022-05-03 21:00:00 NA -15 -18.4 75.8 990. 993. 214.
#> 7 20069 2022-05-04 09:00:00 NA -15.0 -18.6 74.5 993. 997. 248.
#> 8 20069 2022-05-04 21:00:00 NA -13.5 -16.2 80 998. 1001. 248.
#> 9 20069 2022-05-05 09:00:00 NA -14.9 -16.6 86.8 1000. 1003. 124.
#> 10 20069 2022-05-05 21:00:00 NA -13.4 -15.3 86.5 997. 1000. 11.2
#> # ℹ 53 more rows
#> # ℹ 2 more variables: winds_mean <dbl>, winds_max <dbl>
#>
#> [[2]]
#> # A tibble: 63 × 11
#> wmo datetime_tz p ta td rh ps psl windd
#> <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 27524 2022-05-01 09:00:00 NA 2.47 -5.27 59.3 998. 1026. 225
#> 2 27524 2022-05-01 21:00:00 NA 10.8 -5.82 30.8 995. 1022. 248.
#> 3 27524 2022-05-02 09:00:00 NA 7.55 -1.58 53 993. 1020. 195
#> 4 27524 2022-05-02 21:00:00 0 12.2 -3.22 35.2 990. 1016. 202.
#> 5 27524 2022-05-03 09:00:00 NA 7.8 3.22 73.2 985. 1011. 214.
#> 6 27524 2022-05-03 21:00:00 NA 15.4 -0.15 34.5 980. 1006. 248.
#> 7 27524 2022-05-04 09:00:00 0.3 7 0.55 63.5 982. 1009. 292.
#> 8 27524 2022-05-04 21:00:00 0.3 5 -2.82 57.5 988. 1015. 304.
#> 9 27524 2022-05-05 09:00:00 NA 1.9 -4.85 62 996. 1023. 315
#> 10 27524 2022-05-05 21:00:00 NA 8.58 -8.5 29 999. 1026. 180
#> # ℹ 53 more rows
#> # ℹ 2 more variables: winds_mean <dbl>, winds_max <dbl>rp5pik 📦
├── Parser functions for
│ ├── pogodaiklimat
│ │ ├── rp5pik::rp_parse_pik ✅
│ │ └── rp5pik::rp_aggregate_pik ✅
│ ├── rp5 🔲
│ └── gmvo.skniivh 🔲
├── WMO stations coordinates 🔲
└── Rain/Snow guessing
└── rp5pik::rp_get_temp50 ✅