03_terrestrial_flux_daily_climatology.R

#'# Ecological Forecasting Initiative Null Model 

#'## Set-up

print(paste0("Running Creating Daily Terrestrial Forecasts at ", Sys.time()))

#'Load renv.lock file that includes the versions of all the packages used
#'You can generate using the command renv::snapshot()

#' Required packages.  
#' EFIstandards is at remotes::install_github("eco4cast/EFIstandards")
library(tidyverse)
library(lubridate)
library(aws.s3)
library(prov)
library(EFIstandards)
library(EML)
library(jsonlite)
library(imputeTS)


#' set the random number for reproducible MCMC runs
set.seed(329)

#'Generate plot to visualized forecast
generate_plots <- TRUE
#'Is the forecast run on the Ecological Forecasting Initiative Server?
#'Setting to TRUE published the forecast on the server.
efi_server <- TRUE

#' List of team members. Used in the generation of the metadata
#team_list <- list(list(individualName = list(givenName = "Quinn", surName = "Thomas"), 
#                       id = "https://orcid.org/0000-0003-1282-7825"),
#                  list(individualName = list(givenName = "Others",  surName ="Pending")),
#)

#'Team name code
team_name <- "climatology"

#'Read in target file.  The guess_max is specified because there could be a lot of
#'NA values at the beginning of the file
targets <- read_csv("https://data.ecoforecast.org/targets/terrestrial_daily/terrestrial_daily-targets.csv.gz", guess_max = 10000)

sites <- read_csv("https://raw.githubusercontent.com/eco4cast/neon4cast-terrestrial/master/Terrestrial_NEON_Field_Site_Metadata_20210928.csv")

site_names <- sites$field_site_id

target_clim <- targets %>%  
  mutate(doy = yday(time)) %>% 
  group_by(doy, siteID) %>% 
  summarise(nee_clim = mean(nee, na.rm = TRUE),
            le_clim = mean(le, na.rm = TRUE),
            nee_sd = sd(nee, na.rm = TRUE),
            le_sd = sd(le, na.rm = TRUE),
            .groups = "drop") %>% 
  mutate(nee_clim = ifelse(is.nan(nee_clim), NA, nee_clim),
         le_clim = ifelse(is.nan(le_clim), NA, le_clim))

#curr_month <- month(Sys.Date())
curr_month <- month(Sys.Date())
if(curr_month < 10){
  curr_month <- paste0("0", curr_month)
}


curr_year <- year(Sys.Date())
start_date <- Sys.Date() + days(1)

forecast_dates <- seq(start_date, as_date(start_date + days(35)), "1 day")
forecast_doy <- yday(forecast_dates)

forecast <- target_clim %>%
  mutate(doy = as.integer(doy)) %>% 
  filter(doy %in% forecast_doy) %>% 
  mutate(time = as_date(ifelse(doy > last(doy),
                               as_date((doy-1), origin = paste(year(Sys.Date())+1, "01", "01", sep = "-")),
                               as_date((doy-1), origin = paste(year(Sys.Date()), "01", "01", sep = "-")))))

subseted_site_names <- unique(forecast$siteID)
site_vector <- NULL
for(i in 1:length(subseted_site_names)){
  site_vector <- c(site_vector, rep(subseted_site_names[i], length(forecast_dates)))
}

forecast_tibble <- tibble(time = rep(forecast_dates, length(subseted_site_names)),
                          siteID = site_vector)

nee <- forecast %>% 
  select(time, siteID, nee_clim, nee_sd) %>% 
  rename(mean = nee_clim,
         sd = nee_sd) %>% 
  group_by(siteID) %>% 
  mutate(mean = imputeTS::na_interpolation(x = mean, maxgap = 3),
         sd = median(sd, na.rm = TRUE)) %>%
  pivot_longer(c("mean", "sd"),names_to = "statistic", values_to = "nee")

le <- forecast %>% 
  select(time, siteID, le_clim, le_sd) %>% 
  rename(mean = le_clim,
         sd = le_sd) %>% 
  group_by(siteID) %>% 
  mutate(mean = imputeTS::na_interpolation(mean, maxgap = 3),
         sd = median(sd, na.rm = TRUE)) %>%
  pivot_longer(c("mean", "sd"),names_to = "statistic", values_to = "le")

combined <- full_join(nee, le) %>%  
  mutate(data_assimilation = 0,
         forecast = 1) %>% 
  select(time, siteID, statistic, forecast, nee, le) %>% 
  arrange(siteID, time, statistic) 

combined %>% 
  select(time, nee ,statistic, siteID) %>% 
  pivot_wider(names_from = statistic, values_from = nee) %>% 
  ggplot(aes(x = time)) +
  geom_ribbon(aes(ymin=mean - sd*1.96, ymax=mean + sd*1.96), alpha = 0.1) + 
  geom_point(aes(y = mean)) +
  facet_wrap(~siteID)

forecast_file <- paste("terrestrial_daily", min(combined$time), "climatology.csv.gz", sep = "-")

write_csv(combined, forecast_file)

neon4cast::submit(forecast_file = forecast_file, 
                  metadata = NULL, 
                  ask = FALSE)

unlink(forecast_file)