end_date -> stop_date; hide MODIS path (#1344)

Author: kmdeck

docs/src/getting_started.md

@@ -49,7 +49,7 @@ surface_space = domain.space.surface;
 We choose the initial and final simulation times as `DateTime`s, and a timestep in seconds.
 ```julia
 start_date = DateTime(2008);
-end_date = start_date + Second(60 * 60 * 72);
+stop_date = start_date + Second(60 * 60 * 72);
 dt = 1000.0;
 ```
 
@@ -103,7 +103,7 @@ end
 Now construct the `LandSimulation` object, which contains the model
 and additional timestepping information.
 ```julia
-simulation = LandSimulation(start_date, end_date, dt, model; set_ic!, user_callbacks = ());
+simulation = LandSimulation(start_date, stop_date, dt, model; set_ic!, user_callbacks = ());
 ```
 
 Now we can run the simulation!

docs/src/tutorials/calibration/obs_site_level_calibration.jl

@@ -104,15 +104,8 @@ forcing = FluxnetSimulations.prescribed_forcing_fluxnet(
 );
 
 # Get Leaf Area Index (LAI) data from MODIS satellite observations.
-modis_lai_ncdata_path = ClimaLand.Artifacts.modis_lai_multiyear_paths(;
-    start_date,
-    end_date = stop_date,
-)
-LAI = ClimaLand.prescribed_lai_modis(
-    modis_lai_ncdata_path,
-    domain.space.surface,
-    start_date,
-);
+LAI =
+    ClimaLand.prescribed_lai_modis(domain.space.surface, start_date, stop_date);
 
 # ## Model Setup
 # 

docs/src/tutorials/calibration/perfect_model_site_level_calibration.jl

@@ -107,15 +107,8 @@ forcing = FluxnetSimulations.prescribed_forcing_fluxnet(
 );
 
 # Get Leaf Area Index (LAI) data from MODIS satellite observations.
-modis_lai_ncdata_path = ClimaLand.Artifacts.modis_lai_multiyear_paths(;
-    start_date,
-    end_date = stop_date,
-)
-LAI = ClimaLand.prescribed_lai_modis(
-    modis_lai_ncdata_path,
-    domain.space.surface,
-    start_date,
-);
+LAI =
+    ClimaLand.prescribed_lai_modis(domain.space.surface, start_date, stop_date);
 
 # ## Model Setup
 # 

docs/src/tutorials/global/snowy_land.jl

@@ -60,17 +60,8 @@ forcing = ClimaLand.prescribed_forcing_era5(
 );
 
 # MODIS LAI is prescribed for the canopy model:
-modis_lai_ncdata_path = ClimaLand.Artifacts.modis_lai_multiyear_paths(;
-    context,
-    start_date,
-    end_date = stop_date,
-)
-LAI = ClimaLand.prescribed_lai_modis(
-    modis_lai_ncdata_path,
-    domain.space.surface,
-    start_date;
-    time_interpolation_method = LinearInterpolation(),
-);
+LAI =
+    ClimaLand.prescribed_lai_modis(domain.space.surface, start_date, stop_date);
 # Make the model:
 model = ClimaLand.LandModel{FT}(forcing, LAI, earth_param_set, domain, Δt);
 simulation = ClimaLand.Simulations.LandSimulation(

docs/src/tutorials/integrated/changing_snowy_land_parameterizations.jl

@@ -84,15 +84,8 @@ forcing = FluxnetSimulations.prescribed_forcing_fluxnet(
     FT,
 );
 # LAI for the site - this uses our interface for working with MODIS data.
-modis_lai_ncdata_path = ClimaLand.Artifacts.modis_lai_multiyear_paths(;
-    start_date,
-    end_date = stop_date,
-);
-LAI = ClimaLand.prescribed_lai_modis(
-    modis_lai_ncdata_path,
-    domain.space.surface,
-    start_date,
-);
+LAI =
+    ClimaLand.prescribed_lai_modis(domain.space.surface, start_date, stop_date);
 
 # # Setup the integrated model
 

docs/src/tutorials/integrated/fluxnet_data.jl

@@ -117,23 +117,14 @@ domain =
     Column(; zlim = (FT(-3.0), FT(0.0)), nelements = 10, longlat = (long, lat))
 surface_space = domain.space.surface;
 # Get the paths to each year of MODIS data within the start and stop dates.
-modis_lai_ncdata_path = ClimaLand.Artifacts.modis_lai_multiyear_paths(;
-    start_date,
-    end_date = stop_date,
-)
-LAI = ClimaLand.prescribed_lai_modis(
-    modis_lai_ncdata_path,
-    surface_space,
-    start_date,
-);
+LAI = ClimaLand.prescribed_lai_modis(surface_space, start_date, stop_date);
 
 # Just like with the air temperature, the LAI is an object that we can use
 # to linearly interpolate observed LAI to any simulation time.
 
 # It can also be useful to know the maximum LAI at a site. To do so, we
-# can call, for the first year of data (first element of `modis_lai_ncdata_path`):
-maxLAI =
-    FluxnetSimulations.get_maxLAI_at_site(modis_lai_ncdata_path[1], lat, long)
+# can call, for the first year of data:
+maxLAI = FluxnetSimulations.get_maxLAI_at_site(start_date, lat, long)
 
 # # Fluxnet comparison data
 

docs/src/tutorials/integrated/snowy_land_fluxnet_tutorial.jl

@@ -76,15 +76,8 @@ forcing = FluxnetSimulations.prescribed_forcing_fluxnet(
     FT,
 );
 # LAI for the site - this uses our interface for working with MODIS data.
-modis_lai_ncdata_path = ClimaLand.Artifacts.modis_lai_multiyear_paths(;
-    start_date,
-    end_date = stop_date,
-)
-LAI = ClimaLand.prescribed_lai_modis(
-    modis_lai_ncdata_path,
-    domain.space.surface,
-    start_date,
-);
+LAI =
+    ClimaLand.prescribed_lai_modis(domain.space.surface, start_date, stop_date);
 
 # # Setup the integrated model
 

docs/src/tutorials/integrated/soil_canopy_fluxnet_tutorial.jl

@@ -80,15 +80,8 @@ forcing = FluxnetSimulations.prescribed_forcing_fluxnet(
     FT,
 );
 # LAI for the site - this uses our interface for working with MODIS data.
-modis_lai_ncdata_path = ClimaLand.Artifacts.modis_lai_multiyear_paths(;
-    start_date,
-    end_date = stop_date,
-)
-LAI = ClimaLand.prescribed_lai_modis(
-    modis_lai_ncdata_path,
-    domain.space.surface,
-    start_date,
-);
+LAI =
+    ClimaLand.prescribed_lai_modis(domain.space.surface, start_date, stop_date);
 
 # # Setup the integrated model
 

docs/src/tutorials/standalone/Bucket/bucket_tutorial.jl

@@ -214,7 +214,7 @@ bucket_parameters = BucketModelParameters(FT; albedo, z_0m, z_0b, τc);
 start_date = DateTime(2005);
 # Simulation start date, end time, and timestep
 
-end_date = start_date + Second(7 * 86400);
+stop_date = start_date + Second(7 * 86400);
 Δt = 3600.0;
 
 # To drive the system in standalone mode,
@@ -302,7 +302,7 @@ ode_algo = CTS.ExplicitAlgorithm(timestepper)
 # We need a callback to get and store the auxiliary fields, as they
 # are not stored by default. We also need a callback to update the
 # drivers (atmos and radiation)
-saveat = collect(start_date:Second(Δt):end_date);
+saveat = collect(start_date:Second(Δt):stop_date);
 saved_values = (;
     t = Array{DateTime}(undef, length(saveat)),
     saveval = Array{NamedTuple}(undef, length(saveat)),
@@ -314,7 +314,7 @@ updateat = deepcopy(saveat);
 # the cache, the driver callbacks, and set the initial conditions.
 simulation = LandSimulation(
     start_date,
-    end_date,
+    stop_date,
     Δt,
     model;
     set_ic! = set_ic!,

docs/src/tutorials/standalone/Canopy/canopy_tutorial.jl

@@ -74,7 +74,7 @@ start_date = DateTime(2010) + Hour(time_offset)
 # the timestep depends on the problem you are solving, the accuracy of the
 # solution required, and the timestepping algorithm you are using.
 N_days = 364
-end_date = start_date + Day(N_days)
+stop_date = start_date + Day(N_days)
 dt = 225.0
 
 # Site latitude and longitude
@@ -136,16 +136,9 @@ forcing = (; atmos, radiation, ground);
 
 # Now we read in time-varying LAI from a global MODIS dataset.
 surface_space = domain.space.surface;
-modis_lai_ncdata_path =
-    ClimaLand.Artifacts.modis_lai_multiyear_paths(; start_date, end_date)
-LAI = ClimaLand.prescribed_lai_modis(
-    modis_lai_ncdata_path,
-    surface_space,
-    start_date,
-);
+LAI = ClimaLand.prescribed_lai_modis(surface_space, start_date, stop_date);
 # Get the maximum LAI at this site over the first year of the simulation
-maxLAI =
-    FluxnetSimulations.get_maxLAI_at_site(modis_lai_ncdata_path[1], lat, long);
+maxLAI = FluxnetSimulations.get_maxLAI_at_site(start_date, lat, long);
 
 # Construct radiative transfer model, overwriting some default parameters.
 radiation_parameters = (;
@@ -244,7 +237,7 @@ diagnostics = ClimaLand.Diagnostics.default_diagnostics(
 
 # Create the callback function which updates the forcing variables,
 # or drivers.
-updateat = Array(start_date:Second(1800):end_date);
+updateat = Array(start_date:Second(1800):stop_date);
 
 
 # Select a timestepping algorithm and setup the ODE problem.
@@ -261,7 +254,7 @@ ode_algo = CTS.IMEXAlgorithm(
 # the cache, the driver callbacks, and set the initial conditions.
 simulation = LandSimulation(
     start_date,
-    end_date,
+    stop_date,
     dt,
     canopy;
     set_ic!,