All python code are written using a maximum line length of 120 characters to improve readability particularly with respect to longer equations that were difficult to read with the PEP-8 suggestion of 80 characters.
(Add model user manual or link to model user manual here) Things to include in user manual:
- Prior to running the script, need to know the keys associated with the GCM netcdf files Therefore, it is recommended to open the netcdf file in your own python environment and determine the names that are used for the (1) variable of interest, (2) latitude, (3) longitude, (4) time
- NetCDF4 automatically "unpacks" the netcdf file, i.e., it automatically applies the scaling_factor and add_offset such that the user is left with the raw product. It's good practice to double check that this has been done properly by comparing the two when you first download the data such that you know you are working with the correct data.
========== MODEL RUN DETAILS ================================================ The model is run through a series of steps:
Step 01: Region/Glaciers Selection The user needs to define the region/glaciers that will be used in the model run. The user has the option of choosing the standard RGI regions or defining their own. Step 02: Model Time Frame The user should consider the time step and duration of the model run along with any calibration product and/or model spinup that may be included as well. Step 03: Climate Data The user has the option to choose the type of climate data being used in the model run, and how that data will be downscaled to the glacier and bins. Step 04: Glacier Evolution The heart of the model is the glacier evolution, which includes calculating the specific mass balance, the surface type, and any changes to the size of the glacier (glacier dynamics). The user has many options for how this aspect of the model is run. Others: model output? model input?
========== LIST OF MODEL VARIABLES (alphabetical) =========================== Prefixes and Suffixes: annual: dataframe containing information with an annual time step as opposed to the time step specified by the model (daily or monthly). bin: dataframe containing information related to each elevation bin/band on the glacier. These dataframes are indexed such that the main index (rows) are elevation bins and the columns are the time series. glac: dataframe containing information related to a glacier. When used by itself (e.g., main_glac_rgi or gcm_glac_temp) it refers to each row being a specific glacier. When used as a prefix followed by a descriptor (e.g., glac_bin_temp), the entire dataframe is for one glacier and the descriptor provides information as to the rows gcm: meteorological data from the global climate model or reanalysis dataset main_: dataframe containing important information related to all the glaciers in the study, where each row represents a glacier (ex. main_glac_rgi). series_: series containing information for a given time step with respect to all the glacier bins. This is needed when cycling through each time step to calculate the mass balance since snow accumulates and alters the surface type.
Variables: dates_table: dataframe of the dates, month, year, and number of days in the month for each date. Rows = dates, Cols = attributes
end_date: end date of model run (MAY NOT BE NEEDED - CHECK WHERE IT'S USED) gcm_glac_elev: Series of elevation data associated with the global climate model temperature data gcm_glac_prec: dataframe of the global climate model precipitation data, typically based on the nearest neighbor. gcm_glac_temp: dataframe of the global climate model temperature data, typically based on the nearest neighbor. glac_bin_temp: dataframe of temperature for each bin for each time step on the glacier glac_bin_prec: dataframe of precipitation (liquid) for each bin for each time step on the glacier glac_bin_precsnow: dataframe of the total precipitation (liquid and solid) for each bin for each time step on the glacier glac_bin_snow: dataframe of snow for each bin for each time step on the glacier glac_bin_snowonsurface: dataframe of the snow that has accumulated on the surface of the glacier for each bin for each time step glac_bin_surftype: datframe of the surface type for each bin for each time step on the glacier main_glac_rgi: dataframe containing generic attributes (RGIId, Area, etc.) from the Randolph Glacier Inventory for each glacier. Rows = glaciers, Cols = attributes main_glac_parameters: dataframe containing the calibrated parameters for each glacier main_glac_surftypeinit: dataframe containing in the initial surface type for each glacier start_date: start date of model run (MAY NOT BE NEEDED - CHECK WHERE IT'S USED)