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| #' | ||
| #' @param df dataframe of stage1 NEON GEFS forecasts for sites to forecast at | ||
| #' @param site_list a dataframe of the latitude and longitude for all site_ids in df | ||
| #' @param use_solar_geom logical for using solar geometry for daily SW calculation | ||
| #' @param psuedo logical for something... | ||
| to_hourly <- function(df, | ||
| site_list, | ||
| use_solar_geom = TRUE, | ||
| psuedo = FALSE){ | ||
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| if(!psuedo){ | ||
| reference_datetime <- lubridate::as_datetime(df$reference_datetime)[1] | ||
| }else{ | ||
| reference_datetime <- NA | ||
| } | ||
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| var_order <- names(df) | ||
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| ensemble_maxtime <- df |> | ||
| dplyr::group_by(site_id, family, ensemble) |> | ||
| dplyr::summarise(max_time = max(datetime), .groups = "drop") | ||
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| ensembles <- unique(df$ensemble) | ||
| datetime <- seq(min(df$datetime), max(df$datetime), by = "1 hour") | ||
| variables <- unique(df$variable) | ||
| sites <- unique(df$site_id) | ||
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| full_time <- expand.grid(sites, ensembles, datetime, variables) |> | ||
| dplyr::rename(site_id = Var1, | ||
| ensemble = Var2, | ||
| datetime = Var3, | ||
| variable = Var4) |> | ||
| dplyr::mutate(datetime = lubridate::as_datetime(datetime)) |> | ||
| dplyr::arrange(site_id, ensemble, variable, datetime) |> | ||
| dplyr::left_join(ensemble_maxtime, by = c("site_id","ensemble")) |> | ||
| dplyr::filter(datetime <= max_time) |> | ||
| dplyr::select(-c("max_time")) |> | ||
| dplyr::distinct() | ||
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| states <- df |> | ||
| dplyr::select(site_id, family, horizon, ensemble, datetime, variable, prediction) |> | ||
| dplyr::filter(!psuedo | (psuedo & horizon != "006") | (psuedo & datetime == max(df$datetime))) |> | ||
| dplyr::select(-horizon) |> | ||
| dplyr::group_by(site_id, family, ensemble, variable) |> | ||
| dplyr::right_join(full_time, by = c("site_id", "ensemble", "datetime", "family", "variable")) |> | ||
| dplyr::filter(variable %in% c("PRES", "RH", "TMP", "UGRD", "VGRD")) |> | ||
| dplyr::arrange(site_id, family, ensemble, datetime) |> | ||
| dplyr::mutate(prediction = imputeTS::na_interpolation(prediction, option = "linear")) |> | ||
| dplyr::mutate(prediction = ifelse(variable == "TMP", prediction + 273, prediction)) |> | ||
| dplyr::mutate(prediction = ifelse(variable == "RH", prediction/100, prediction)) |> | ||
| dplyr::ungroup() | ||
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| fluxes <- df |> | ||
| dplyr::select(site_id, family, horizon, ensemble, datetime, variable, prediction) |> | ||
| dplyr::filter(horizon != "003") |> | ||
| dplyr::select(-horizon) |> | ||
| dplyr::group_by(site_id, family, ensemble, variable) |> | ||
| dplyr::right_join(full_time, by = c("site_id", "ensemble", "datetime", "family", "variable")) |> | ||
| dplyr::filter(variable %in% c("APCP","DSWRF","DLWRF")) |> | ||
| dplyr::arrange(site_id, family, ensemble, datetime) |> | ||
| tidyr::fill(prediction, .direction = "up") |> | ||
| dplyr::mutate(prediction = ifelse(variable == "APCP", prediction / (6 * 60 * 60), prediction), | ||
| variable = ifelse(variable == "APCP", "PRATE", variable)) |> | ||
| dplyr::ungroup() | ||
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| if(use_solar_geom){ | ||
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| fluxes <- fluxes |> | ||
| dplyr::left_join(site_list, by = "site_id") |> | ||
| dplyr::mutate(hour = lubridate::hour(datetime), | ||
| date = lubridate::as_date(datetime), | ||
| doy = lubridate::yday(datetime) + hour/24, | ||
| longitude = ifelse(longitude < 0, 360 + longitude, longitude), | ||
| rpot = downscale_solar_geom(doy, longitude, latitude)) |> # hourly sw flux calculated using solar geometry | ||
| dplyr::group_by(site_id, family, ensemble, date, variable) |> | ||
| dplyr::mutate(avg.rpot = mean(rpot, na.rm = TRUE), | ||
| avg.SW = mean(prediction, na.rm = TRUE))|> # daily sw mean from solar geometry | ||
| dplyr::ungroup() |> | ||
| dplyr::mutate(prediction = ifelse(variable == "DSWRF" & avg.rpot > 0.0, rpot * (avg.SW/avg.rpot),prediction)) |> | ||
| dplyr::select(any_of(var_order)) | ||
| } | ||
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| hourly_df <- dplyr::bind_rows(states, fluxes) |> | ||
| dplyr::arrange(site_id, family, ensemble, datetime) |> | ||
| dplyr::mutate(variable = ifelse(variable == "TMP", "air_temperature", variable), | ||
| variable = ifelse(variable == "PRES", "air_pressure", variable), | ||
| variable = ifelse(variable == "RH", "relative_humidity", variable), | ||
| variable = ifelse(variable == "DLWRF", "surface_downwelling_longwave_flux_in_air", variable), | ||
| variable = ifelse(variable == "DSWRF", "surface_downwelling_shortwave_flux_in_air", variable), | ||
| variable = ifelse(variable == "PRATE", "precipitation_flux", variable), | ||
| variable = ifelse(variable == "VGRD", "eastward_wind", variable), | ||
| variable = ifelse(variable == "UGRD", "northward_wind", variable), | ||
| variable = ifelse(variable == "APCP", "precipitation_amount", variable), | ||
| reference_datetime = reference_datetime) |> | ||
| dplyr::select(any_of(var_order)) | ||
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| return(hourly_df) | ||
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| } | ||
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| cos_solar_zenith_angle <- function(doy, lat, lon, dt, hr) { | ||
| et <- equation_of_time(doy) | ||
| merid <- floor(lon / 15) * 15 | ||
| merid[merid < 0] <- merid[merid < 0] + 15 | ||
| lc <- (lon - merid) * -4/60 ## longitude correction | ||
| tz <- merid / 360 * 24 ## time zone | ||
| midbin <- 0.5 * dt / 86400 * 24 ## shift calc to middle of bin | ||
| t0 <- 12 + lc - et - tz - midbin ## solar time | ||
| h <- pi/12 * (hr - t0) ## solar hour | ||
| dec <- -23.45 * pi / 180 * cos(2 * pi * (doy + 10) / 365) ## declination | ||
| cosz <- sin(lat * pi / 180) * sin(dec) + cos(lat * pi / 180) * cos(dec) * cos(h) | ||
| cosz[cosz < 0] <- 0 | ||
| return(cosz) | ||
| } | ||
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| equation_of_time <- function(doy) { | ||
| stopifnot(doy <= 367) | ||
| f <- pi / 180 * (279.5 + 0.9856 * doy) | ||
| et <- (-104.7 * sin(f) + 596.2 * sin(2 * f) + 4.3 * | ||
| sin(4 * f) - 429.3 * cos(f) - 2 * | ||
| cos(2 * f) + 19.3 * cos(3 * f)) / 3600 # equation of time -> eccentricity and obliquity | ||
| return(et) | ||
| } | ||
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| downscale_solar_geom <- function(doy, lon, lat) { | ||
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| dt <- median(diff(doy)) * 86400 # average number of seconds in time interval | ||
| hr <- (doy - floor(doy)) * 24 # hour of day for each element of doy | ||
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| ## calculate potential radiation | ||
| cosz <- cos_solar_zenith_angle(doy, lat, lon, dt, hr) | ||
| rpot <- 1366 * cosz | ||
| return(rpot) | ||
| } | ||
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