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ropenmeteo

Wrappers for application programming interfaces on the Open-Meteo project. Open-Meteo is an amazing project that streamlines access to a range of publicly available historical and forecast meteorology data from agencies across the world. The free access tier allows for 10,000 API calls per day. The paid tiers increase the number of daily API calls. Learn more about the Open-Meteo project at their website (https://open-meteo.com) and consider supporting their efforts.

Open-Meteo citation: Zippenfenig, Patrick. (2023). Open-Meteo.com Weather API (0.2.69). Zenodo. https://doi.org/10.5281/zenodo.8112599

The package includes additional functionality to facilitate use in mechanistic environmental/ecological models. This includes the calculation of longwave radiation (not provided through the API) from air temperature and cloud cover, the writing of output to the format required by the General Lake Model (GLM), and the conversion to the standard used in the NEON Ecological Forecasting Challenge that is run by the Ecological Forecasting Initiative Research Coordination Network.

The package uses a long format standard with the following columns:

  • datetime = date and time of forecasted value
  • reference_datetime = the date and time of the beginning of the forecast (horizon = 0). Does not apply to historical weather.
  • site_id = column to identify site location. If null in function call it defaults to latitude_longitude
  • model_id = id of model that generated the forecast
  • ensemble = ensemble member number (only for ensemble weather and seasonal forecasts)
  • variable = forecasted variable
  • prediction = forecasted value
  • unit = units of the variable

Install

Install the development version from GitHub with:

devtools::install_github("FLARE-forecast/ropenmeteo")

Quick Start

The simplest call returns a best-match single forecast for any location on Earth using get_forecast() (max 16 days, all variables returned in m/s, °C, mm, etc.):

df <- get_forecast(
  latitude = 37.30,
  longitude = -79.83,
  forecast_days = 7,
  past_days = 2,
  model = "generic",
  variables = c("temperature_2m", "wind_speed_10m"))
head(df)
## # A tibble: 6 × 7
##   datetime            reference_datetime  site_id      model_id variable
##   <dttm>              <dttm>              <chr>        <chr>    <chr>
## 1 2024-08-25 00:00:00 2024-08-27 00:00:00 37.3_-79.83 generic  temperature_2m
## 2 2024-08-25 01:00:00 2024-08-27 00:00:00 37.3_-79.83 generic  temperature_2m
## 3 2024-08-25 02:00:00 2024-08-27 00:00:00 37.3_-79.83 generic  temperature_2m
## 4 2024-08-25 03:00:00 2024-08-27 00:00:00 37.3_-79.83 generic  temperature_2m
## 5 2024-08-25 04:00:00 2024-08-27 00:00:00 37.3_-79.83 generic  temperature_2m
## 6 2024-08-25 05:00:00 2024-08-27 00:00:00 37.3_-79.83 generic  temperature_2m
## # ℹ 2 more variables: prediction <dbl>, unit <chr>

For probabilistic forecasts use get_ensemble_forecast() (max 35 days, 30–51 ensemble members depending on model):

df <- get_ensemble_forecast(
  latitude = 37.30,
  longitude = -79.83,
  forecast_days = 7,
  past_days = 2,
  model = "gfs_seamless",
  variables = c("temperature_2m"))

The long format output is straightforward to visualize with ggplot2:

df |>
  mutate(variable = paste(variable, unit)) |>
  ggplot(aes(x = datetime, y = prediction, color = ensemble)) +
  geom_line() +
  geom_vline(aes(xintercept = reference_datetime)) +
  facet_wrap(~variable, scale = "free", ncol = 2)

Examples

See the vignette for full examples covering ensemble forecasts, historical weather, seasonal forecasts, climate projections, GLM integration, and the EFI standard.