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updating metadata for stream forecast #13

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updating metadata for stream forecast #13

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96 changes: 48 additions & 48 deletions metadata/aquatics-2021-05-01-BTW.yml
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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
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If running this model with EnKF, then this should be assimilates

complexity: 1 #How many models states need initial conditions
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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
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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
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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
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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?
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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
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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
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should be same size as the number of states we're updating, which is just water temperature

covariance: TRUE #TRUE OR FALSE
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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
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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