Reshaping meteorological data for meteoland

In meteoland: Landscape Meteorology Tools

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(meteoland)
library(stars)
library(dplyr)

unformatted_meteo <- tibble(
  date = rep(seq(from = as.Date("2022-12-01"), to = as.Date("2022-12-05"), by = 1), 3),
  station = rep(letters[1:3], each = 5),
  latitude = rep(c(41.35, 40.11, 42.00), each = 5),
  longitude = rep(c(-0.33, 0.12, 1.12), each = 5),
  min_temp = rnorm(15, 10, 6),
  max_temp = min_temp + rnorm(15, 10, 3),
  rh = rnorm(15, 50, 25)
) |>
  dplyr::mutate(
    rh = dplyr::if_else(rh > 100, 100, rh),
    rh = dplyr::if_else(rh < 20, 20, rh)
  )

Meteorological data format

For the interpolation of meteorological variables on our target locations, we need meteorological data for a reference set of locations. In meteoland this is a sf object with the spatial coordinates of our reference locations (usually meteorological stations) and daily values of the meteorological variables needed to perform the interpolation, i.e.:

meteoland_meteo_example

meteoland expects names to be as in the example:

names(meteoland_meteo_example)

The only mandatory variables are MinTemperature and MaxTemperature. Other variables (Precipitation, WindSpeed...), when present, allow for a more complete interpolation.

Converting meteorological data to meteoland format

Meteorological data can come in many formats and with different names for the same variables. As we saw above, we need to convert it to a meteoland compliant format (sf object with correct names).

Converting from data frames

If we have our meteorological data in a data.frame (i.e. we obtained it from our own weather stations, or download it from other source in this format) we can just simply transform it to the desired format:

unformatted_meteo

ready_meteo <- unformatted_meteo |>
  # convert names to correct ones
  dplyr::mutate(
    MinTemperature = min_temp,
    MaxTemperature = max_temp,
    MeanRelativeHumidity = rh
  ) |>
  # transform to sf (WGS84)
  sf::st_as_sf(
    coords = c("longitude", "latitude"),
    crs = sf::st_crs(4326)
  )

ready_meteo

And voilà, we have our meteo data in the correct format

Meteorological data from other R packages

meteoland offers transformation functions for meteorological data downloaded from the meteospain and worldmet R packages.

meteospain data

For data coming from meteospain package we have the meteospain2meteoland function that transforming the data for us:

library(meteospain)
get_meteo_from(
  "meteogalicia",
  meteogalicia_options('daily', as.Date("2022-12-01"), as.Date("2022-12-05"))
) |>
  meteospain2meteoland()

worldmet data

For data coming from worldmet package we have the worldmet2meteoland function that do the reshaping for us:

library(worldmet)
worldmet::importNOAA("081120-99999", year = 2022) |>
  worldmet2meteoland()

Meteorological data from raster sources

As we have seen, we need the meteorological reference data in a sf object (points). To be able to create an interpolator from a raster, we need to transform the cell values to points (i.e. using the cell center coordinates) for each variable and day and use it to create the interpolator.

So if we have a multi-layered raster with several dates of meteorological data:

> Remember that the raster, besides the meteo data, needs to contain also the topographic
(elevation, aspect and slope) data for the interpolator to work.

raster_meteo_reference <- interpolate_data(
  raster_to_interpolate_example, meteoland_interpolator_example, verbose = FALSE
)

raster_meteo_reference

we need to convert it to points:

points_meteo_reference <- names(raster_meteo_reference) |>
  # for each variable
  purrr::map(
    # take the variable raster
    ~ raster_meteo_reference[.x] |>
      # convert to sf
      sf::st_as_sf(as_points = TRUE, na.rm = FALSE) |>
      # pivot the data for dates to be in one column
      tidyr::pivot_longer(cols = -geometry, names_to = "dates", values_to = .x) |>
      # convert to tibble to fasten the process
      dplyr::as_tibble() |>
      # convert to date and create stationID
      dplyr::mutate(
        dates = as.Date(dates),
        stationID = as.character(geometry)
      )
  ) |>
  # join all variables
  purrr::reduce(dplyr::left_join) |>
  # create the points sf object
  sf::st_as_sf()

points_meteo_reference

And now we can use it to build an interpolator object:

with_meteo(points_meteo_reference) |>
  create_meteo_interpolator()

meteoland documentation built on Aug. 21, 2023, 5:10 p.m.