rm(list = ls())
library(rrtm) # remotes::install_github("ashiklom/rrtm")
example <- edr_r(
# Cohort state variables, in order from tallest to shortest
pft = c(1, 1, 2),
lai = c(2, 1, 0.8),
wai = c(0.01, 0.01, 0.01),
cai = c(1, 1, 1),
# PFT-level parameters -- indexed by `pft` above
N = c(1.4, 1.5),
Cab = c(40, 30),
Car = c(8, 8),
Cw = c(0.01, 0.01),
Cm = c(0.008, 0.01),
orient_factor = c(0.5, 0.5),
clumping_factor = c(0.84, 0.735),
# Global parameters
soil_moisture = 0.5,
direct_sky_frac = 0.9,
czen = 1)
# Inspect the `example` object:
str(example)
# Result is a list containing the top-of-canopy albedo, and additional
# information about the light levels by cohort layer. `2101` is the number of
# wavelengths -- 400-2500nm at 1 nm resolution. There are 3 cohorts in this
# simulation, so there are 4 `up` and `down` fluxes -- one for each cohort
# layer, plus the ground (soil) layer.
# Below, we do a simple plot of the albedo.
plot(400:2500, example$albedo, type = "l",
xlab = "Wavelength (nm)",
ylab = "Albedo [0, 1]")
# Liana-infested
LI <- edr_r(
# Cohort state variables, in order from tallest to shortest
pft = c(1, 2, 2),
lai = c(0.5, 1, 0.8),
wai = c(0.01, 0.01, 0.01),
cai = c(1, 1, 1),
# PFT-level parameters -- indexed by `pft` above
N = c(1.4, 1.5),
Cab = c(40, 30),
Car = c(8, 8),
Cw = c(0.01, 0.01),
Cm = c(0.008, 0.01),
orient_factor = c(0.5, 0.5),
clumping_factor = c(0.84, 0.735),
# Global parameters
soil_moisture = 0.5,
direct_sky_frac = 0.9,
czen = 1)
# Liana-free
LF <- edr_r(
# Cohort state variables, in order from tallest to shortest
pft = c(2, 2),
lai = c(1, 0.8),
wai = c(0.01, 0.01),
cai = c(1, 1),
# PFT-level parameters -- indexed by `pft` above
N = c(1.4, 1.5),
Cab = c(40, 30),
Car = c(8, 8),
Cw = c(0.01, 0.01),
Cm = c(0.008, 0.01),
orient_factor = c(0.5, 0.5),
clumping_factor = c(0.84, 0.735),
# Global parameters
soil_moisture = 0.5,
direct_sky_frac = 0.9,
czen = 1)
plot(LF$albedo - LI$albedo,type = 'l')
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