Skip to content
master
Switch branches/tags
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
old
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Stories in Ready Build Status

[ DOCUMENTATION UPDATE IN EARLY 2019 ]

downscale

Python Package for Simple Delta-Downscaling of Climatic Research Unit's TS 3.x OR PCMDI's CMIP5 data to a set of baseline monthly climatologies. Focus of this work is for regional downscaling of data over Alaska region, but we do aim to make it flexible enough to do this very simple downscaling over any area of interest.

Note:

This package developing rapidly and is expected to be somewhat problematic until full release. It is also geared specifically for the needs of Scenarios Network for Alaska + Arctic Planning (SNAP).

AR5 Example:
import glob, os
import downscale

# SETUP BASELINE
clim_path = './climatology'
filelist = glob.glob( os.path.join( clim_path, '*.tif' ) )
baseline = downscale.Baseline( filelist )

# SETUP DATASET
future_fn = './hur_Amon_IPSL-CM5A-LR_rcp26_r1i1p1_200601_210012.nc'
historical_fn = './hur_Amon_IPSL-CM5A-LR_historical_r1i1p1_185001_200512.nc'
variable = 'hur'
model = 'IPSL-CM5A-LR'
scenario = 'rcp26'
historical = downscale.Dataset( historical_fn, variable, model, scenario, units=None )
future = downscale.Dataset( future_fn, variable, model, scenario, units=None )

# DOWNSCALE
output_dir = './outputs'
clim_begin = '1961'
clim_end = '1990'
ar5 = downscale.DeltaDownscale( baseline, clim_begin, clim_end, historical, future, \
		metric='mean', ds_type='absolute', level=1000, level_name='plev' )
ar5.downscale( output_dir=output_dir )
CRU Example:
import glob, os
import downscale

# SETUP BASELINE
clim_path = './climatology'
filelist = glob.glob( os.path.join( clim_path, '*.tif' ) )
baseline = downscale.Baseline( filelist )

# SETUP DATASET
historical_fn = './cru_ts3.23.1901.2014.cld.dat.nc'
variable = 'cld'
model = 'cru_ts31'
scenario = 'observed'

# this read in will interpolate across NAs to fill in around the coastlines for later masking
historical = downscale.Dataset( historical_fn, variable, model, scenario, units=None, interp=True )

# DOWNSCALE
output_dir = './outputs'
clim_begin = '1961'
clim_end = '1990'
cru = downscale.DeltaDownscale( baseline, clim_begin, clim_end, historical, \
						metric='mean', ds_type='relative' )
cru.downscale( output_dir=output_dir )

About

Simple Delta-Downscaling for SNAP Climate Data

Resources

License

Packages

No packages published

Languages