Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

updating metadata for stream forecast #13

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

updating metadata for stream forecast #13

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
96 changes: 48 additions & 48 deletions metadata/aquatics-2021-05-01-BTW.yml
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -63,16 +63,16 @@ metadata:
#species in a community model, number of age/size classes in a population model,
#number of pools in a biogeochemical model.
initial_conditions:
status: absent #options: absent, present, data_driven, propagates, assimilates
complexity: 0 #How many models states need initial conditions
status: assimilates #options: absent, present, data_driven, propagates, assimilates
Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

If running this model with EnKF, then this should be assimilates

complexity: 1 #How many models states need initial conditions
Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

we have one state variable (temperature) with initial conditions for this model

propagation:
type: ensemble #How does your model propogate initial conditions (ensemble or MCMC is most common)
size: 2000. #number of ensemble or MCMC members
size: 31 #number of ensemble or MCMC members
Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Based on the model output, there are 31 ensemble members, not 2000

#Leave everything below blank UNLESS status = assimilates
assimilation:
type: refit #description of assimilation method
reference: "NA" #reference for assimilation method
complexity: 4 #number of states that are updated with assimilation
type: EnKF #description of assimilation method
reference: https://aslopubs.onlinelibrary.wiley.com/doi/full/10.1002/lom3.10302 #reference for assimilation method
Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

If using EnKF to run the model, then the url is an appropriate citation

complexity: 1 #number of states that are updated with assimilation
#
#DRIVERS
#uncertainty in model drivers, covariates, and exogenous scenarios (X).
Expand All @@ -85,17 +85,17 @@ metadata:
#model, this would be the number of climate inputs (temperature, precip, solar
#radiation, etc.).
drivers:
status: present #options: absent, present, data_driven, propagates, assimilates
status: propogates #options: absent, present, data_driven, propagates, assimilates
Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

This is changed to propagates because the model incorporates driver uncertainty (the ensembles) into the model predictions of stream temperature

complexity: 1 #How many drivers are used?
Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

only using air temperature, correct?

#Leave everything below blank if status = absent, present, or data_driven
propagation:
type: ensemble #How does your model propogate driver (ensemble or MCMC is most common)
size: 31 #number of ensemble or MCMC members
#Leave everything below blank UNLESS status = assimilates
assimilation:
type: refit #description of assimilation method
reference: "none" #reference for assimilation method
complexity: 4 #number of states that are updated with assimilation
# assimilation:
# type: EnKF #description of assimilation method
# reference: https://aslopubs.onlinelibrary.wiley.com/doi/full/10.1002/lom3.10302 #reference for assimilation method
# complexity: #number of states that are updated with assimilation
#
#PARAMETERS
#Uncertainty in model parameters (). For most ecological processes the parameters
Expand All @@ -108,14 +108,14 @@ metadata:
status: present #options: absent, present, data_driven, propagates, assimilates
complexity: 2
#Leave everything below blank if status = absent, present, or data_driven
propagation:
type: ensemble #how does your model propogate parameter uncertainity?
size: 2000
#Leave everything below blank UNLESS status = assimilates
assimilation:
type: refit
reference: "none"
complexity: 4
# propagation:
# type: ensemble #how does your model propogate parameter uncertainity?
# size: 2000
# #Leave everything below blank UNLESS status = assimilates
# assimilation:
# type: refit
# reference: "none"
# complexity: 4
Comment on lines +111 to +118
Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

don't need these lines since the parameters are present but not drawn from a distribution / propogated into the forecasts. The may be data driven , but I'm not sure if the model was fit to the POSE data before running the forecast. If so, change this to data_driven

#
#RANDOM EFFECTS
#Unexplained variability and heterogeneity in model parameters (). Hierarchical
Expand All @@ -135,16 +135,16 @@ metadata:
#
random_effects:
status: absent #options: absent, present, data_driven, propagates, assimilates
complexity: 2
#Leave everything below blank if status = absent, present, or data_driven
propagation:
type: ensemble #How does your model propogate random effects (ensemble or MCMC is most common)
size: 2000 #number of ensemble or MCMC members
#Leave everything below blank UNLESS status = assimilates
assimilation:
type: refit #description of assimilation method
reference: "none" #reference for assimilation method
complexity: 4 #number of states that are updated with assimilation
# complexity: 2
# #Leave everything below blank if status = absent, present, or data_driven
# propagation:
# type: ensemble #How does your model propogate random effects (ensemble or MCMC is most common)
# size: 2000 #number of ensemble or MCMC members
# #Leave everything below blank UNLESS status = assimilates
# assimilation:
# type: refit #description of assimilation method
# reference: "none" #reference for assimilation method
# complexity: 4 #number of states that are updated with assimilation
#
#PROCESS ERROR
#Dynamic uncertainty in the process model () attributable to both model
Expand All @@ -157,18 +157,18 @@ metadata:
#match the dimensionality of the initial_conditions unless there are state
#variables where process error is not being estimated or propagated
process_error:
status: absent #options: absent, present, data_driven, propagates, assimilates
complexity: 2 #Leave blank if status = absent
status: assimilates #options: absent, present, data_driven, propagates, assimilates
complexity: 1 #Leave blank if status = absent
#Leave everything below blank if status = absent, present, or data_driven
propagation:
type: ensemble #How does your model propogate random effects uncertainty (ensemble or MCMC is most common)
size: 2000
size: 31
#Leave everything below blank UNLESS status = assimilates
assimilation:
type: refit
reference: "none"
complexity: 4
covariance: FALSE #TRUE OR FALSE
type: EnKF
reference: https://aslopubs.onlinelibrary.wiley.com/doi/full/10.1002/lom3.10302
complexity: 1
Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

should be same size as the number of states we're updating, which is just water temperature

covariance: TRUE #TRUE OR FALSE
Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

if using the EnKF, then this is TRUE

localization: FALSE
#
#OBSERVATION ERROR
Expand All @@ -185,16 +185,16 @@ metadata:
#match the dimensionality of the initial_conditions unless there are state
#variables where process error is not being estimated or propagated
obs_error:
status: absent #options: absent, present, data_driven, propagates, assimilates
complexity: 2 #Leave blank if status = absent
status: data_driven #options: absent, present, data_driven, propagates, assimilates
Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

will either be present or data_driven if using the EnKF. I set to data_driven because I think the observation uncertainty was guided by reported thermistor uncertainty

complexity: 1 #Leave blank if status = absent
#Leave everything below blank if status = absent, present, or data_driven
propagation:
type: ensemble #How does your model propogate observation error (ensemble or MCMC is most common)
size: 31. #number of ensemble or MCMC members
#Leave everything below blank UNLESS status = assimilates
assimilation:
type: refit #description of assimilation method
reference: "none" #reference for assimilation method
complexity: 4 #number of states that are updated with assimilation
covariance: FALSE #TRUE OR FALSE
localization: FALSE
# propagation:
# type: ensemble #How does your model propogate observation error (ensemble or MCMC is most common)
# size: 31. #number of ensemble or MCMC members
# #Leave everything below blank UNLESS status = assimilates
# assimilation:
# type: refit #description of assimilation method
# reference: "none" #reference for assimilation method
# complexity: 4 #number of states that are updated with assimilation
# covariance: FALSE #TRUE OR FALSE
# localization: FALSE