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Repository
Download And Preprocess Russian Meteorological Data
Basic Info
- Host: GitHub
- Owner: atsyplenkov
- License: other
- Language: R
- Default Branch: master
- Size: 2.46 MB
Statistics
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 2
Created about 3 years ago
· Last pushed over 2 years ago
Metadata Files
Readme
Changelog
License
Codemeta
README.Rmd
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output: github_document
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chunk_output_type: inline
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# rp5pik 
[](https://doi.org/10.5281/zenodo.8090329)
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[](https://github.com/atsyplenkov/rp5pik/actions?query=workflow%3Apkgcheck)
[](https://CRAN.R-project.org/package=rp5pik)
The `rp5pik` package provides a set of functions to download and preprocess meteorological data from http://www.pogodaiklimat.ru/
## Installation
You can install the development version of rp5pik from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("atsyplenkov/rp5pik")
# OR
# install.packages("remotes")
remotes::install_github("atsyplenkov/rp5pik")
```
## Examples
### 1. Data download
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/](http://www.pogodaiklimat.ru/):
```{r example}
library(rp5pik)
example <-
rp_parse_pik(
wmo_id = c("20069", "27524"),
start_date = "2022-05-01",
end_date = "2022-05-31"
)
example
```
List of available variables:
- `wmo`
character. WMO index of the meteostation
- `datetime_utc`
POSIXct. Date and Time of the measurement at UTC
- `ta`
numeric. Air temperature at 2m above the surface, °C
- `td`
numeric. Dew point, °C
- `rh`
numeric. Relative humidity at 2m above the surface, %
- `ps`
numeric. Atmosphere pressure at meteostation, hPa
- `psl`
numeric. Atmosphere pressure adjusted to the height of mean sea level, hPa
- `prec`
numeric. Cumulative precipitation for the last 12 hours, mm
- `windd`
integer. Wind direction, deg
- `winds_mean`
numeric. Average 10-min wind speed, m/s
- `winds_max`
numeric. Maximum wind speed, m/s
We can visualize the `example` dataset using `ggplot2` as follows:
```{r plot, fig.align='center', dpi=1000, fig.width=7, fig.height=5, out.width='80%'}
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()
```
### 2. Data preprocessing
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](https://method.meteorf.ru/ansambl/pojasnenijaansambl.html)).
⚠ As of `r Sys.Date()` this function works only with Moscow timezone.
This is how you can aggregate data daily:
```{r aggregate, message=FALSE, warning=FALSE}
library(dplyr)
example_daily <-
example |>
rp_aggregate_pik(.period = "24h") |>
group_split(wmo)
example_daily
```
Or semi-daily:
```{r aggregate_12, message=FALSE, warning=FALSE}
library(dplyr)
example_12h <-
example |>
rp_aggregate_pik(.period = "12h", .tz = "Europe/Moscow") |>
group_split(wmo)
example_12h
```
## Roadmap
```
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 ✅
```
Owner
- Name: Anatolii Tsyplenkov
- Login: atsyplenkov
- Kind: user
- Location: New Zealand
- Company: @manaakiwhenua
- Website: anatolii.nz
- Repositories: 79
- Profile: https://github.com/atsyplenkov
Scientist-Geomorphologist and Research Software Engineer, fond of all things geospatial
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DESCRIPTION
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