credit.nwp#

Attributes#

`gfs_map`
`level_map`
`upper_air`
`surface`

Functions#

`build_GFS_init`(output_grid, date, variables[, ...])	Download GFS model level initial conditions, regrid to CREDIT model's grid, and vertically interpolate
`_get_gfs_maps`(variable_mapping_type)
`_add_pressure_and_geopotential`(data, temperature_var, ...)	Derive pressure and geopotential fields from model level data and to dataset
`_build_file_path`(date, base_path[, file_type, step])	Create NOMADS filepaths for etiher upper air or surface data
`_load_gfs_variable`(variable[, full_file_path])
`_load_gfs_data`(full_file_path, variables[, pool])	Load GFS data directly from Nomads or Google Cloud server
`_combine_data`(atm_data, sfc_data, temperature_var, ...)	Merge upper air and surface data
`_regrid_variable`(variable_data, regridder)
`_regrid`(nwp_data, output_grid[, method, pool])	Spatially regrid (interpolate) from GFS grid to CREDIT grid
`_vertical_interpolation`(state_dataset, ...)
`_format_data`(data_dict, regridded_data, model_levels)	Format data for CREDIT model ingestion
`_format_datetime`(init_time)	Format datetime string from CREDIT configuration file

Module Contents#

credit.nwp.gfs_map#

credit.nwp.level_map#

credit.nwp.upper_air#

credit.nwp.surface#

credit.nwp.build_GFS_init(output_grid: xarray.Dataset, date: pandas.Timestamp, variables: list, model_level_file: str = '', model_levels: numpy.ndarray = None, gdas_base_path: str = 'gs://global-forecast-system/', variable_mapping: str = 'wchapmanera5', n_procs: int = 1)#

Download GFS model level initial conditions, regrid to CREDIT model’s grid, and vertically interpolate 3D variables to CREDIT model levels.

Parameters:

output_grid (xr.Dataset) – netCDF file containing latitude and longitude coordinates of CREDIT model grid.
date (pd.Timestamp) – date of GFS initialization
variables (list) – list of variable names.
model_level_file (str) – Path to file containing output model level a and b coefficients on full and half levels.
model_levels (np.ndarray) – Subset of model levels to interpolate to.
gdas_base_path (str) – Path to GFS base directory on NOMADS (archives last 10 days) or Google Cloud (since 2021)
variable_mapping (str)
n_procs (int) – Number of processors to use in pool.

Returns:

(xr.Dataset) Interpolated GFS initial conditions

credit.nwp._get_gfs_maps(variable_mapping_type: str)#

credit.nwp._add_pressure_and_geopotential(data, temperature_var, specific_humidity_var)#

Derive pressure and geopotential fields from model level data and to dataset :param data: (xr.Dataset) GFS model level data

Returns:: xr.Dataset

credit.nwp._build_file_path(date, base_path, file_type='atm', step='f000')#

Create NOMADS filepaths for etiher upper air or surface data :param date: (pd.Timestamp) date of GFS initialization :param base_path: (str) NOMADS base directory (archives last 10 days) :param file_type: (str) Type of analysis data (supports ‘atm’ or ‘sfc’) :param step: (str) “anl” or “f000” to “f009”. f times have additional diagnostics

like ugrd10 and vgrd10 not found in the analysis files.

Returns:: (str) NOMADS or Google Cloud filepaths that can be read in xarray with the h5netcdf engine

credit.nwp._load_gfs_variable(variable, full_file_path=None)#

credit.nwp._load_gfs_data(full_file_path, variables, pool=None)#

Load GFS data directly from Nomads or Google Cloud server :param full_file_path: (str) NOMADS filepath :param variables: (list) list of variable names

Returns:: xr.Dataset

credit.nwp._combine_data(atm_data, sfc_data, temperature_var, specific_humidity_var)#

Merge upper air and surface data :param atm_data: (xr.Dataset) GFS upper air data :param sfc_data: (xr.Dataset) GFS surface data

Returns:: xr.Dataset

credit.nwp._regrid_variable(variable_data, regridder)#

credit.nwp._regrid(nwp_data, output_grid, method='bilinear', pool=None)#

Spatially regrid (interpolate) from GFS grid to CREDIT grid :param nwp_data: GFS initial conditions :type nwp_data: xr.Dataset :param output_grid: CREDIT grid :type output_grid: xr.Dataset :param method: conservative or bilinear :type method: str

Returns:: Regridded GFS initial conditions
Return type:: (xr.Dataset)

credit.nwp._vertical_interpolation(state_dataset, model_level_file, model_levels, variable_mapping, variables, surface_pressure_var)#

credit.nwp._format_data(data_dict, regridded_data, model_levels)#

Format data for CREDIT model ingestion :param data_dict: (dict) Dictionary of xr.DataArrays of interpolated GFS model level data :param regridded_data: (xr.Dataset) GFS initial conditions on CREDIT grid :param model_levels: (list) list of model level indices to extract from L137 model levels

Returns:: xr.Dataset of GFS initial conditions interpolated to CREDIT grid and model levels

credit.nwp._format_datetime(init_time)#

Format datetime string from CREDIT configuration file :param init_time: (dict) Dictionary of Forecast times from configuration file

Returns:: pd.Timestamp of initialization time