R wrappers for APIs on Open-Meteo project. The Open-Meteo is a amazing project that streamlines the access to a range of publicly historical and forecasted 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 (support for paid APIs in this package is pending). 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 functionally to facilitate the 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 Initiative Research Coordination Network ([https://neon4cast.org]). Future functionally includes the temporal downscaling of the daily climate projection output and the 6-hourly seasonal forecast to the hourly time step.
The package uses a long format standard with the following columns
datetime= date and time of forecasted valuereference_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_longitudemodel_id= id of model that generated the forecastensemble= ensemble member number (only for ensemble weather and seasonal forecasts)variable= forecasted variableprediction= forecasted valueunit= units of the variable
remotes::install_github("FLARE-forecast/RopenMeteo")library(tidyverse)The open-meteo project combines the the best models for each location
across the globe to provide the best possible forecast. open-meteo
defines this as model = "generic".
[https://open-meteo.com/en/docs]
df <- RopenMeteo::get_forecast(latitude = 37.30,
longitude = -79.83,
forecast_days = 7,
past_days = 2,
model = "generic",
variables = c("temperature_2m"))
head(df)## # A tibble: 6 × 7
## datetime reference_datetime site_id model_id variable prediction
## <dttm> <dttm> <chr> <chr> <chr> <dbl>
## 1 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79… generic tempera… 16.6
## 2 2023-09-23 01:00:00 2023-09-25 00:00:00 37.3_-79… generic tempera… 16
## 3 2023-09-23 02:00:00 2023-09-25 00:00:00 37.3_-79… generic tempera… 15.4
## 4 2023-09-23 03:00:00 2023-09-25 00:00:00 37.3_-79… generic tempera… 15.7
## 5 2023-09-23 04:00:00 2023-09-25 00:00:00 37.3_-79… generic tempera… 14.7
## 6 2023-09-23 05:00:00 2023-09-25 00:00:00 37.3_-79… generic tempera… 14
## # ℹ 1 more variable: unit <chr>
df |>
mutate(variable = paste(variable, unit)) |>
ggplot(aes(x = datetime, y = prediction)) +
geom_line(color = "#F8766D") +
geom_vline(aes(xintercept = reference_datetime)) +
facet_wrap(~variable, scale = "free")
Ensemble forecasts from individual models are available.
[https://open-meteo.com/en/docs/ensemble-api]
df <- RopenMeteo::get_ensemble_forecast(
latitude = 37.30,
longitude = -79.83,
forecast_days = 7,
past_days = 2,
model = "gfs_seamless",
variables = c("temperature_2m"))
head(df)## # A tibble: 6 × 8
## datetime reference_datetime site_id model_id ensemble variable
## <dttm> <dttm> <chr> <chr> <chr> <chr>
## 1 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… 00 tempera…
## 2 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… 01 tempera…
## 3 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… 02 tempera…
## 4 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… 03 tempera…
## 5 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… 04 tempera…
## 6 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… 05 tempera…
## # ℹ 2 more variables: prediction <dbl>, unit <chr>
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)
Options for models and variables are at https://open-meteo.com/en/docs/ensemble-api
Note that ecmwf_ifs04 does not include solar radiation.
List of global model ids:
icon_seamless, icon_global, gfs_seamless, gfs025, gfs05, ecmwf_ifs04, gem_global
We have included functions that allow the output to be used with the General Lake Model ([https://doi.org/10.5194/gmd-12-473-2019]). Since the open-meteo models do not include longwave radiation, the package provides a function to calculate it from the cloud cover and air temperature.
GLM requires a set of variables that are provided
df <- RopenMeteo::get_ensemble_forecast(
latitude = 37.30,
longitude = -79.83,
forecast_days = 7,
past_days = 2,
model = "gfs_seamless",
variables = RopenMeteo::glm_variables(product = "ensemble_forecast",
time_step = "hourly"))
head(df)## # A tibble: 6 × 8
## datetime reference_datetime site_id model_id ensemble variable
## <dttm> <dttm> <chr> <chr> <chr> <chr>
## 1 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… 00 relativ…
## 2 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… 01 relativ…
## 3 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… 02 relativ…
## 4 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… 03 relativ…
## 5 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… 04 relativ…
## 6 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… 05 relativ…
## # ℹ 2 more variables: prediction <dbl>, unit <chr>
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)
The following converts to GLM format
path <- tempdir()
df |>
RopenMeteo::add_longwave() |>
RopenMeteo::write_glm_format(path = path)
head(read.csv(list.files(path = path, full.names = TRUE, pattern = ".csv")[1]))## time AirTemp ShortWave LongWave RelHum WindSpeed Rain
## 1 2023-09-23 00:00 13.7 0 359.60 67 2.41 0
## 2 2023-09-23 01:00 12.7 0 357.53 73 2.33 0
## 3 2023-09-23 02:00 12.3 0 356.12 77 2.38 0
## 4 2023-09-23 03:00 12.1 0 356.12 79 2.53 0
## 5 2023-09-23 04:00 12.1 0 356.83 78 2.72 0
## 6 2023-09-23 05:00 12.2 0 356.83 76 3.07 0
The standard used in the NEON Ecological Forecasting Challenge is
slightly different from the standard in this package. It uses the column
parameter for ensemble because the Challenge standard allows the
flexibility to use parametric distributions (i.e., normal distribution
mean and sd) in the same standard as a ensemble (or sample)
forecast. The family column defines the distribution (here family =
ensemble).
The EFI standard also follows CF-conventions so the variable names are converted to be CF compliant.
The output from RopenMeteo::convert_to_efi_standard() is the same as
the output from neon4cast::stage2()
Learn more about neon4cast::stage2() here:
[https://projects.ecoforecast.org/neon4cast-docs/Shared-Forecast-Drivers.html]
df |>
RopenMeteo::add_longwave() |>
RopenMeteo::convert_to_efi_standard()## # A tibble: 53,568 × 8
## datetime reference_datetime site_id model_id family parameter
## <dttm> <dttm> <chr> <chr> <chr> <chr>
## 1 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… ensem… 00
## 2 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… ensem… 00
## 3 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… ensem… 00
## 4 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… ensem… 00
## 5 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… ensem… 00
## 6 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… ensem… 00
## 7 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… ensem… 00
## 8 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… ensem… 00
## 9 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… ensem… 01
## 10 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… ensem… 01
## # ℹ 53,558 more rows
## # ℹ 2 more variables: variable <chr>, prediction <dbl>
Note that neon4cast::stage3() is similar to
df |>
RopenMeteo::add_longwave() |>
RopenMeteo::convert_to_efi_standard() |>
filter(datetime < reference_datetime)## # A tibble: 11,904 × 8
## datetime reference_datetime site_id model_id family parameter
## <dttm> <dttm> <chr> <chr> <chr> <chr>
## 1 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… ensem… 00
## 2 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… ensem… 00
## 3 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… ensem… 00
## 4 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… ensem… 00
## 5 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… ensem… 00
## 6 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… ensem… 00
## 7 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… ensem… 00
## 8 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… ensem… 00
## 9 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… ensem… 01
## 10 2023-09-23 00:00:00 2023-09-25 00:00:00 37.3_-79.83 gfs_sea… ensem… 01
## # ℹ 11,894 more rows
## # ℹ 2 more variables: variable <chr>, prediction <dbl>
With the only difference that the number of days is equal to the
past_days in the call to RopenMeteo::get_ensemble_forecast(). The
max past_days from open-meteo is ~60 days.
If you need more historical days for model calibration and testing, historical data are available through open-meteo’s historical weather API.
[https://open-meteo.com/en/docs/historical-weather-api]
df <- RopenMeteo::get_historical_weather(
latitude = 37.30,
longitude = -79.83,
start_date = "2023-01-01",
end_date = Sys.Date(),
variables = c("temperature_2m"))
tail(df |> na.omit())## # A tibble: 6 × 6
## datetime site_id model_id variable prediction unit
## <dttm> <chr> <chr> <chr> <dbl> <chr>
## 1 2023-09-18 18:00:00 37.3_-79.83 ERA5 temperature_2m 21.3 °C
## 2 2023-09-18 19:00:00 37.3_-79.83 ERA5 temperature_2m 21.5 °C
## 3 2023-09-18 20:00:00 37.3_-79.83 ERA5 temperature_2m 21.5 °C
## 4 2023-09-18 21:00:00 37.3_-79.83 ERA5 temperature_2m 21.5 °C
## 5 2023-09-18 22:00:00 37.3_-79.83 ERA5 temperature_2m 19.5 °C
## 6 2023-09-18 23:00:00 37.3_-79.83 ERA5 temperature_2m 18.6 °C
Notice the delay of ~7 days.
df |>
mutate(variable = paste(variable, unit)) |>
ggplot(aes(x = datetime, y = prediction)) +
geom_line(color = "#F8766D") +
geom_vline(aes(xintercept = lubridate::with_tz(Sys.time(), tzone = "UTC"))) +
facet_wrap(~variable, scale = "free")## Warning: Removed 168 rows containing missing values (`geom_line()`).
Weather forecasts for up to 9 months in the future are available from the NOAA Climate Forecasting System
[https://open-meteo.com/en/docs/seasonal-forecast-api]
df <- RopenMeteo::get_seasonal_forecast(
latitude = 37.30,
longitude = -79.83,
forecast_days = 274,
past_days = 5,
variables = c("temperature_2m"))
head(df)## # A tibble: 6 × 8
## datetime reference_datetime site_id model_id ensemble variable
## <dttm> <dttm> <chr> <chr> <chr> <chr>
## 1 2023-09-20 00:00:00 2023-09-25 00:00:00 37.3_-79.83 cfs 01 tempera…
## 2 2023-09-20 00:00:00 2023-09-25 00:00:00 37.3_-79.83 cfs 02 tempera…
## 3 2023-09-20 00:00:00 2023-09-25 00:00:00 37.3_-79.83 cfs 03 tempera…
## 4 2023-09-20 00:00:00 2023-09-25 00:00:00 37.3_-79.83 cfs 04 tempera…
## 5 2023-09-20 06:00:00 2023-09-25 00:00:00 37.3_-79.83 cfs 01 tempera…
## 6 2023-09-20 06:00:00 2023-09-25 00:00:00 37.3_-79.83 cfs 02 tempera…
## # ℹ 2 more variables: prediction <dbl>, unit <chr>
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")## Warning: Removed 2204 rows containing missing values (`geom_line()`).
The downscaling uses the GLM variables
df <- RopenMeteo::get_seasonal_forecast(
latitude = 37.30,
longitude = -79.83,
forecast_days = 30,
past_days = 5,
variables = RopenMeteo::glm_variables(product = "seasonal_forecast",
time_step = "6hourly"))df |>
RopenMeteo::six_hourly_to_hourly(latitude = 37.30, longitude = -79.83, use_solar_geom = TRUE) |>
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)## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
Climate projections from different models are available through 2050. The output is a daily time-step.
Note the units for shortwave radiation are different for the climate projection.
[https://open-meteo.com/en/docs/climate-api]
df <- RopenMeteo::get_climate_projections(
latitude = 37.30,
longitude = -79.83,
start_date = Sys.Date(),
end_date = Sys.Date() + lubridate::years(1),
model = "EC_Earth3P_HR",
variables = c("temperature_2m_mean"))
head(df)## # A tibble: 6 × 6
## datetime site_id model_id variable prediction unit
## <date> <chr> <chr> <chr> <dbl> <chr>
## 1 2023-09-25 37.3_-79.83 EC_Earth3P_HR temperature_2m_mean 15.4 °C
## 2 2023-09-26 37.3_-79.83 EC_Earth3P_HR temperature_2m_mean 16.2 °C
## 3 2023-09-27 37.3_-79.83 EC_Earth3P_HR temperature_2m_mean 14.5 °C
## 4 2023-09-28 37.3_-79.83 EC_Earth3P_HR temperature_2m_mean 12.4 °C
## 5 2023-09-29 37.3_-79.83 EC_Earth3P_HR temperature_2m_mean 12.6 °C
## 6 2023-09-30 37.3_-79.83 EC_Earth3P_HR temperature_2m_mean 13.2 °C
df |>
mutate(variable = paste(variable, unit)) |>
ggplot(aes(x = datetime, y = prediction)) +
geom_line(color = "#F8766D") +
facet_wrap(~variable, scale = "free")
models <- c("CMCC_CM2_VHR4","FGOALS_f3_H","HiRAM_SIT_HR","MRI_AGCM3_2_S","EC_Earth3P_HR","MPI_ESM1_2_XR","NICAM16_8S")
df <- purrr::map_df(models, function(model){
RopenMeteo::get_climate_projections(
latitude = 37.30,
longitude = -79.83,
start_date = Sys.Date(),
end_date = Sys.Date() + lubridate::years(1),
model = model,
variables = c("temperature_2m_mean"))
})df |>
mutate(variable = paste(variable, unit)) |>
ggplot(aes(x = datetime, y = prediction, color = model_id)) +
geom_line() +
facet_wrap(~variable, scale = "free")
The download of multiple sites uses the optional site_id to add column
that denotes the different sites.
sites <- tibble::tibble(site_id = c("fcre", "sunp"),
latitude = c(37.30, 43.39),
longitude = c(-79.83, -72.05))
df <- purrr::map_df(1:nrow(sites), function(i, sites){
RopenMeteo::get_climate_projections(
latitude = sites$latitude[i],
longitude = sites$longitude[i],
site_id = sites$site_id[i],
start_date = Sys.Date(),
end_date = Sys.Date() + lubridate::years(1),
model = "MPI_ESM1_2_XR",
variables = c("temperature_2m_mean"))
},
sites)
head(df)## # A tibble: 6 × 6
## datetime site_id model_id variable prediction unit
## <date> <chr> <chr> <chr> <dbl> <chr>
## 1 2023-09-25 fcre MPI_ESM1_2_XR temperature_2m_mean 14.7 °C
## 2 2023-09-26 fcre MPI_ESM1_2_XR temperature_2m_mean 17.8 °C
## 3 2023-09-27 fcre MPI_ESM1_2_XR temperature_2m_mean 19.3 °C
## 4 2023-09-28 fcre MPI_ESM1_2_XR temperature_2m_mean 21.6 °C
## 5 2023-09-29 fcre MPI_ESM1_2_XR temperature_2m_mean 15.9 °C
## 6 2023-09-30 fcre MPI_ESM1_2_XR temperature_2m_mean 11 °C
df |>
mutate(variable = paste(variable, unit)) |>
ggplot(aes(x = datetime, y = prediction, color = site_id)) +
geom_line() +
facet_wrap(~variable, scale = "free")
Photosynthesis is non-linearly sensitive to shortwave radiation. Therefore, the photosynthesis response to hourly radiation is different than the response to the aggregated daily mean radiation. To address this issue, we provide a function to convert the daily sum of shortwave radiation to hourly values that uses solar geometry to impute. Additionally, the sum of precipitation is divided by 24 hours to convert to an hourly time-step. All other variables have their daily mean applied to each hour.
df <- RopenMeteo::get_climate_projections(
latitude = 37.30,
longitude = -79.83,
start_date = Sys.Date(),
end_date = Sys.Date() + lubridate::years(1),
model = "EC_Earth3P_HR",
variables = RopenMeteo::glm_variables(product = "climate_projection", time_step = "daily"))df |>
RopenMeteo::daily_to_hourly(latitude = 37.30, longitude = -79.83) |>
mutate(variable = paste(variable, unit)) |>
ggplot(aes(x = datetime, y = prediction)) +
geom_line(color = "#F8766D") +
facet_wrap(~variable, scale = "free", ncol = 2)