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Copy path03_terrestrial_flux_30min_clim.R
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03_terrestrial_flux_30min_clim.R
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print(paste0("Running Creating 30-minute Terrestrial Historical Means Forecasts at ", Sys.time()))
pecan_flux_uncertainty <- "../pecan/modules/uncertainty/R/flux_uncertainty.R"
source(pecan_flux_uncertainty)
if(!require(prov)){
devtools::install_github("https://github.com/cboettig/prov")
}
library(ncdf4)
library(tidyverse)
library(lubridate)
library(rjags)
library(tidybayes)
library(modelr)
library(aws.s3)
library(EFIstandards)
library(EML)
library(jsonlite)
set.seed(42)
ne = 200 ## number of ensemble members
team_list <- list(list(individualName = list(givenName = "Mike", surName ="Dietze"),
id = "https://orcid.org/0000-0002-2324-2518"),
list(individualName = list(givenName = "Quinn", surName = "Thomas"),
id = "https://orcid.org/0000-0003-1282-7825")
)
team_name <- "climatology"
forecast_project_id <- "efi_hist"
download.file("https://data.ecoforecast.org/neon4cast-targets/terrestrial_30min/terrestrial_30min-targets.csv.gz",
"terrestrial_30min-targets.csv.gz")
terrestrial_targets <- read_csv("terrestrial_30min-targets.csv.gz", guess_max = 10000, show_col_types = FALSE)
site_names <- read_csv("https://raw.githubusercontent.com/eco4cast/neon4cast-targets/main/NEON_Field_Site_Metadata_20220412.csv") |>
dplyr::filter(terrestrial == 1) |>
dplyr::pull(field_site_id)
start_forecast <- as.POSIXct(Sys.Date(),tz="UTC")
fx_time <- seq(start_forecast, start_forecast + days(35), by = "30 min")
nee_fx = le_fx = array(NA, dim=c(length(fx_time),length(site_names),ne)) ## dimensions: time, space, ensemble
## Forecast by site
for(s in 1:length(site_names)){
site_data_var <- terrestrial_targets %>%
filter(site_id == site_names[s])
max_time <- max(site_data_var$time) + days(1)
min_time <- min(which(!is.na(site_data_var$observed)))
# This is key here - I added 16 days on the end of the data for the forecast period
full_time1 <- tibble(time = seq(site_data_var$time[min_time], max(site_data_var$time), by = "30 min"),
variable = "nee")
full_time2 = tibble(time = seq(site_data_var$time[min_time], max(site_data_var$time), by = "30 min"),
variable = "le")
full_time <- bind_rows(full_time1, full_time2)
site_data_var <- left_join(full_time, site_data_var) %>%
filter(month(time) == month(max_time)) ## grab all historical data for this month
############ NEE #############
#Full time series with gaps
y_wgaps <- site_data_var$observed[which(site_data_var$variable == "nee")]
time <- c(site_data_var$time[which(site_data_var$variable == "nee")])
#Remove gaps
t_nogaps <- time[!is.na(y_wgaps)]
y_nogaps <- y_wgaps[!is.na(y_wgaps)]
## check that we can calculate mean diurnal cycle
htod = hour(t_nogaps) + minute(t_nogaps)/60 ## historical time of day
nee_diurnal = tapply(y_nogaps,htod,mean)
n_diurnal = tapply(y_nogaps,htod,length)
# plot(nee_diurnal,main=site_names[s])
## forecast
ftod = hour(fx_time) + minute(fx_time)/60
for(i in 1:48){
tod = (i-1)/2
window = 0.25
hindex = which(abs(htod - tod) < window) ## indices in historical data to resample
while(length(hindex) < 10){
window = window + 0.5
hindex = which(abs(htod - tod) < window) ## if no data at that time, look at adjacent times
#print(window)
}
findex = which(ftod == tod) ## columns in fx to fill in
for(j in findex){
nee_fx[j,s,] = y_nogaps[sample(hindex,ne,replace=TRUE)]
}
}
############ Latent heat ############
#Full time series with gaps
y_wgaps <- site_data_var$observed[which(site_data_var$variable == "le")]
time <- c(site_data_var$time[which(site_data_var$variable == "le")])
#Remove gaps
t_nogaps <- time[!is.na(y_wgaps)]
y_nogaps <- y_wgaps[!is.na(y_wgaps)]
htod = hour(t_nogaps) + minute(t_nogaps)/60 ## historical time of day
for(i in 1:48){
tod = (i-1)/2
window = 0.25
hindex = which(abs(htod - tod) < window) ## indices in historical data to resample
while(length(hindex) < 10){
window = window + 0.5
hindex = which(abs(htod - tod) < window) ## if no data at that time, look at adjacent times
print(window)
}
findex = which(ftod == tod) ## columns in fx to fill in
for(j in findex){
le_fx[j,s,] = y_nogaps[sample(hindex,ne,replace=TRUE)]
}
}
} ## end loop over sites
## Set dimensions
timedim <- ncdim_def("time", ## dimension name
units = paste('seconds since',fx_time[1]),
## size of timestep, with units and start date
vals = as.numeric(fx_time - fx_time[1]),
## sequence of values that defines the dimension.
longname = 'time')
## descriptive name
sitedim <- ncdim_def("site",
units="",
vals=1:length(site_names),
longname = "NEON siteID")
ensdim <- ncdim_def("ensemble",
units = "",
vals = seq_len(ne),
longname = 'ensemble member')
nchardim <- ncdim_def("nchar",units="",1:4,create_dimvar=FALSE)
## quick check that units are valid
udunits2::ud.is.parseable(timedim$units)
udunits2::ud.is.parseable(sitedim$units)
udunits2::ud.is.parseable(ensdim$units)
fillvalue <- 1e32 ## missing data value
#Define variables
def_list <- list()
def_list[[1]] <- ncvar_def(name = "nee",
units = "umol CO2 m-2 s-1",
dim = list(timedim, sitedim, ensdim),
missval = fillvalue,
longname = 'net ecosystem exchange of CO2',
prec="double")
def_list[[2]] <- ncvar_def(name = "le",
units = "W m-2",
dim = list(timedim, sitedim, ensdim),
missval = fillvalue,
longname = 'latent heat flux',
prec="double")
def_list[[3]] <- ncvar_def(name = "site_id",
units="",
dim = list(nchardim,sitedim),
longname = "NEON site codes",
prec="char")
ncfname <- paste0("terrestrial_30min-",as_date(start_forecast),"-",team_name,".nc")
ncout <- nc_create(ncfname,def_list,force_v4=T)
ncvar_put(ncout,def_list[[1]] , nee_fx)
ncvar_put(ncout,def_list[[2]] , le_fx)
ncvar_put(ncout,def_list[[3]] , site_names)
## Global attributes (metadata)
curr_time <- with_tz(Sys.time(), "UTC")
ncatt_put(ncout,0,"start_time", as.character(fx_time[1]),
prec = "text")
nc_close(ncout) ## make sure to close the file
# Generate metadata
forecast_issue_time <- as_date(curr_time)
forecast_iteration_id <- start_forecast
forecast_model_id <- team_name
source("generate_metadata.R")
meta_data_filename <- generate_metadata(forecast_file = ncfname,
metadata_yaml = "metadata_30min.yml",
forecast_issue_time = as_date(with_tz(Sys.time(), "UTC")),
forecast_iteration_id = start_forecast,
forecast_file_name_base = strsplit(ncfname,".",fixed=TRUE)[[1]][1],
start_time = fx_time[1],
stop_time = last(fx_time))
neon4cast::submit(forecast_file = ncfname,
metadata = meta_data_filename,
ask = FALSE)
unlink(ncfname)
unlink(meta_data_filename)