Download and install miniconda: https://conda.io/miniconda.html
Make sure you are using the conda-forge channel:
$ conda config --add channels conda-forge
$ conda update --yes conda python
Install gsshapy:
$ conda create -n gssha python=2
$ source activate gssha
(gssha)$ conda install --yes gsshapy pynio jupyter
Install GSSHA: http://www.gsshawiki.com/GSSHA_Download
In [17]:
import os
from datetime import datetime, timedelta
from gsshapy.modeling import GSSHAFramework
from gsshapy.grid import HRRRtoGSSHA
from gsshapy.grid.hrrr_to_gssha import download_hrrr_for_gssha
from gsshapy.lib import db_tools as dbt
import pangaea as pa
Setup environment:
In [2]:
# assuming notebook is run from examples folder
# DONT FORGET dos2unix or unix2dos
base_dir = '/Users/rdchlads/GSSHA_INPUT/'
gssha_model_name = '2017_08_16_270m'
gssha_model_directory = os.path.join(base_dir, gssha_model_name)
hrrr_output_directory = os.path.join(gssha_model_directory, 'hrrr_data')
try:
os.mkdir(hrrr_output_directory)
except OSError:
pass
Get GSSHA model bounds:
In [3]:
# load in GSSHA model files
project_manager, db_sessionmaker = \
dbt.get_project_session(gssha_model_name,
gssha_model_directory)
db_session = db_sessionmaker()
project_manager.read(directory=gssha_model_directory,
filename="{0}.prj".format(gssha_model_name),
session=db_session)
gssha_grid = project_manager.getGrid()
# reproject GSSHA grid and get bounds
min_x, max_x, min_y, max_y = gssha_grid.bounds(as_geographic=True)
min_x, max_x, min_y, max_y
Out[3]:
Download HRRR Data:
In [4]:
downloaded_files = download_hrrr_for_gssha(main_directory=hrrr_output_directory,
forecast_start_date_string='20170913',
forecast_start_hour_string='00',
leftlon=min_x,
rightlon=max_x,
toplat=max_y,
bottomlat=min_y)
downloaded_files
Out[4]:
Inspect the grid data:
In [19]:
with pa.open_mfdataset(downloaded_files,
lat_var='gridlat_0',
lon_var='gridlon_0',
time_var='time',
lat_dim='ygrid_0',
lon_dim='xgrid_0',
time_dim='time',
loader='hrrr') as hrd:
print(hrd)
print(hrd.PRATE_P0_L1_GLC0)
Map the variable in the GRIB files to the conversion function:
In [7]:
hrrr_forecast_dir = os.path.dirname(downloaded_files[0])
data_var_map_array = [
['precipitation_rate', 'PRATE_P0_L1_GLC0'],
['pressure', 'PRES_P0_L1_GLC0'],
['relative_humidity', 'RH_P0_L103_GLC0'],
['wind_speed', ['UGRD_P0_L103_GLC0', 'VGRD_P0_L103_GLC0']],
['direct_radiation_cc', ['DSWRF_P0_L1_GLC0', 'TCDC_P0_L10_GLC0']],
['diffusive_radiation_cc', ['DSWRF_P0_L1_GLC0', 'TCDC_P0_L10_GLC0']],
['temperature', 'TMP_P0_L1_GLC0'],
['cloud_cover_pc', 'TCDC_P0_L10_GLC0'],
]
Option 1. Convert Data:
In [18]:
h2g = HRRRtoGSSHA(gssha_project_folder=gssha_model_directory,
gssha_project_file_name="{0}.prj".format(gssha_model_name),
lsm_input_folder_path=hrrr_forecast_dir,
lsm_search_card="hrrr.*.grib2")
# hmet
h2g.lsm_data_to_arc_ascii(data_var_map_array)
# gag
out_gage_file = os.path.join(gssha_model_directory,
'gage_hrrr.gag')
h2g.lsm_precip_to_gssha_precip_gage(out_gage_file,
lsm_data_var='PRATE_P0_L1_GLC0',
precip_type='RADAR')
Option 2. Convert Data & Run the model:
In [ ]:
grf = GSSHAFramework("gssha",
gssha_model_directory,
"{0}.prj".format(gssha_model_name),
lsm_folder=hrrr_forecast_dir,
lsm_data_var_map_array=data_var_map_array,
lsm_precip_data_var='PRATE_P0_L1_GLC0',
lsm_precip_type='RADAR',
lsm_search_card="hrrr.*.grib2",
lsm_lat_var='gridlat_0',
lsm_lon_var='gridlon_0',
lsm_time_var='time',
lsm_lat_dim='ygrid_0',
lsm_lon_dim='xgrid_0',
lsm_time_dim='time',
grid_module='hrrr')
gssha_event_directory = gr.run_forecast()
gssha_event_directory
The gssha_event_directory is where the simulation output is stored.
In [ ]: