Configuring GARD
After building GARD, it is run on the command line following this syntax:
./gard downscale_options.txt
where downscale_options.txt
is a Fortran style namelist (see example in GARD/run).
The namelist options are described in the tables below and a sample file is available online. :
Parameters
Name | Type | Required? | Default | Description |
---|---|---|---|---|
training_file | string | No | Options File Name | Used to specify a separate namelist file for the training section shown below |
observation_file | string | No | Options File Name | Used to specify a separate namelist file for the observation section shown below |
prediction_file | string | No | Options File Name | Used to specify a separate namelist file for the prediction section shown below |
output_file | string | No | downscaled_output.nc | Downscaled output filename. |
pass_through_var | integer | No | 1 | Select the variable to be passed through as the output if pass_through=True |
n_analogs | integer | No (1) | -1 | The number of analogs to include |
n_log_analogs | integer | No (1) | -1 | The number of analogs to include when computing threshold exceedence from analogs |
analog_threshold | real | No (1) | -1 | If specified, GARD will compute the probability of exceeding this threshold |
start_date | string | Yes | n/a | Start date for prediction period |
end_date | string | Yes | n/a | End date for prediction period |
start_train | string | Yes | n/a | Start date for training period |
end_train | string | Yes | n/a | End date for training period |
start_transform | string | Yes | n/a | Start date for transform period |
end_transform | string | Yes | n/a | End date for transform period |
pure_regression | logical | No | FALSE | Use pure regression downscaling approach |
pure_analog | logical | No | FALSE | Use pure analog downscaling approach |
analog_regression | logical | No | TRUE | Use analog regression downscaling approach |
pass_through | logical | No | False | Pass a given predictor variable through as the output without downscaling |
sample_analog | logical | No | FALSE | |
logistic_from_analog_exceedance | logical | No | FALSE | |
logistic_threshold | logical | No | -9999 | |
weight_analogs | logical | No | TRUE | |
debug | logical | No | TRUE | prints more output at runtime and outputs files including the coefficients used in each analog regression (or analog values) as well as the predictor data |
interactive | logical | No | TRUE | Print downscaling status as a percentage on the command line |
Notes:
- Must specify one of n_analogs, n_log_analogs, or analog_threshold
Training Parameters
See description of training data suggestions.
Name | Type | Required? | Default | Description |
---|---|---|---|---|
name | string | Yes | n/a | Name of the training parameters dataset |
preloaded | string | No | n/a | filepath of the preloaded training parameters dataset |
interpolation_method | integer | No | 1 | nearest neighbor= 1, bilenear =2 |
normalization_method | integer | No | 0 | no normalization = 0, mean/stddev from: training data = 1 |
time_indices | integer | Yes | -1 | list of timesteps in file to aggregate over (GEFS only) |
time_weights | real | No | 1 | list of averaging weights for individual time indices |
agg_method | integer | No | 0 | per variable aggregation method when aggregating over time_indices: mean = 0, minimum = 1, maximum = 2, sum = 3 (GEFS only) |
nvars | integer | Yes | -1 | number of variables to be used in training |
data_type | string | Yes | n/a | dataset type: GEFS or GCM |
lat_name | string | Yes | n/a | netCDF variable name for latitude |
lon_name | string | Yes | n/a | netCDF variable name for longitude |
time_name | string | Yes | n/a | netCDF variable name for time |
nfiles | integer | Yes | -1 | number of files in each file list |
input_transformations | integer | No | 0 | no transform = 0, quantile mapping = 1, log transform = 2, cube root = 3, fifth root = 4 |
var_names | string | Yes | n/a | variables names to use in training (one for each variable) |
file_list | string | Yes | n/a | path to file containing a list of training files (one for each variable) |
selected_time | integer | No | -1 | if set, only this time step will be read from each input file (GEFS only) |
calendar | string | Yes | standard | |
calendar_start_year | integer | No | 1900 | |
timezone_offset | real | No | 0 | hours to offset time variable (e.g. time_name ) to account for timezone. |
Prediction Parameters
See description of predictor data suggestions.
Name | Type | Required? | Default | Description |
---|---|---|---|---|
name | string | Yes | n/a | Name of the prediction parameters dataset |
preloaded | string | No | n/a | filepath of the preloaded prediction parameters dataset |
interpolation_method | integer | No | 1 | nearest neighbor= 1, bilenear =2 |
normalization_method | integer | No | 0 | no normalization = 0, mean/stddev from: prediction data = 1, training data = 2 |
time_indices | integer | Yes | -1 | list of timesteps in file to aggregate over (GEFS only) |
time_weights | real | No | 1 | list of averaging weights for individual time indices |
agg_method | integer | No | 0 | per variable aggregation method when aggregating over time_indices: mean = 0, minimum = 1, maximum = 2, sum = 3 (GEFS only) |
nvars | integer | Yes | -1 | number of prediction parameters to use in downscaling |
data_type | string | Yes | n/a | dataset type: GEFS or GCM |
lat_name | string | Yes | n/a | netCDF variable name for latitude |
lon_name | string | Yes | n/a | netCDF variable name for longitude |
time_name | string | Yes | n/a | netCDF variable name for time |
nfiles | integer | Yes | -1 | number of files in each file list |
transformations | integer | No | 0 | no transform = 0, quantile mapping = 1, log transform = 2, cube root = 3, fifth root = 4 |
input_transformations | integer | No | 0 | no transform = 0, quantile mapping = 1, log transform = 2, cube root = 3, fifth root = 4 |
var_names | string | Yes | n/a | variables names to use in prediction (one for each variable) |
file_list | string | Yes | n/a | path to file containing a list of prediction filepaths (one for each variable) |
selected_time | integer | No | -1 | if set, only this time step will be read from each input file (GEFS only) |
calendar | string | Yes | standard | |
calendar_start_year | integer | No | 1900 | |
timezone_offset | real | No | 0 | hours to offset time variable (e.g. time_name ) to account for timezone. |
Observation Parameters
See description of observational data suggestions.
Name | Type | Required? | Default | Description |
---|---|---|---|---|
name | string | Yes | n/a | Name of the observation parameters dataset |
preloaded | string | No | n/a | filepath of the preloaded observation parameters dataset |
nvars | integer | Yes | -1 | number of observation variables to downscale (currently must be 1) |
nfiles | integer | Yes | -1 | number of files in each file list |
data_type | string | Yes | n/a | dataset type, typically "obs" |
lat_name | string | Yes | n/a | netCDF variable name for latitude |
lon_name | string | Yes | n/a | netCDF variable name for longitude |
time_name | string | Yes | n/a | netCDF variable name for time |
input_transformations | integer | No | 0 | no transform = 0, quantile mapping = 1, log transform = 2, cube root = 3, fifth root = 4 |
var_names | string | Yes | n/a | variables names to use in obs dataset |
file_list | string | Yes | n/a | path to file containing a list of obs filepaths |
calendar | string | Yes | standard | |
calendar_start_year | integer | No | 1900 |