Source code for SMBcorr.racmo_extrap_daily
#!/usr/bin/env python
u"""
racmo_extrap_daily.py
Written by Tyler Sutterley (09/2024)
Interpolates and extrapolates daily RACMO products to times and coordinates
Uses fast nearest-neighbor search algorithms
https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.BallTree.html
https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html
and inverse distance weighted interpolation to extrapolate spatially
INPUTS:
base_dir: working data directory
EPSG: projection of input spatial coordinates
MODEL: daily model outputs to interpolate
FGRN055: 5.5km Greenland RACMO2.3p2
FGRN11: 11km Greenland RACMO2.3p2
XANT27: 27km Antarctic RACMO2.3p2
ASE055: 5.5km Amundsen Sea Embayment RACMO2.3p2
XPEN055: 5.5km Antarctic Peninsula RACMO2.3p2
tdec: dates to interpolate in year-decimal
X: x-coordinates to interpolate in projection EPSG
Y: y-coordinates to interpolate in projection EPSG
OPTIONS:
VARIABLE: RACMO product to interpolate
smb: Surface Mass Balance
hgtsrf: Change of Surface Height
SEARCH: nearest-neighbor search algorithm (BallTree or KDTree)
NN: number of nearest-neighbor points to use
POWER: inverse distance weighting power
SIGMA: Standard deviation for Gaussian kernel
FILL_VALUE: output fill_value for invalid points
EXTRAPOLATE: create a regression model to extrapolate out in time
PYTHON DEPENDENCIES:
numpy: Scientific Computing Tools For Python
https://numpy.org
https://numpy.org/doc/stable/user/numpy-for-matlab-users.html
scipy: Scientific Tools for Python
https://docs.scipy.org/doc/
netCDF4: Python interface to the netCDF C library
https://unidata.github.io/netcdf4-python/netCDF4/index.html
pyproj: Python interface to PROJ library
https://pypi.org/project/pyproj/
PROGRAM DEPENDENCIES:
regress_model.py: models a time series using least-squares regression
time.py: utilities for calculating time operations
UPDATE HISTORY:
Updated 09/2024: use wrapper to importlib for optional dependencies
Updated 02/2023: don't recompute min and max time cutoffs for cases
Updated 08/2022: updated docstrings to numpy documentation format
Updated 01/2021: using conversion protocols following pyproj-2 updates
https://pyproj4.github.io/pyproj/stable/gotchas.html
using utilities from time module for conversions
Updated 06/2020: set all values initially to fill_value
Updated 05/2020: Gaussian average model fields before interpolation
accumulate variable over all available dates
Written 04/2020
"""
from __future__ import print_function
import sys
import os
import re
import warnings
import numpy as np
import scipy.spatial
import scipy.ndimage
import scipy.interpolate
from SMBcorr.regress_model import regress_model
import SMBcorr.time
import SMBcorr.spatial
import SMBcorr.utilities
# attempt imports
netCDF4 = SMBcorr.utilities.import_dependency('netCDF4')
pyproj = SMBcorr.utilities.import_dependency('pyproj')
# PURPOSE: read and interpolate daily RACMO2.3 outputs
[docs]def extrapolate_racmo_daily(base_dir, EPSG, MODEL, tdec, X, Y,
VARIABLE='smb', SEARCH='BallTree', NN=10, POWER=2.0,
SIGMA=1.5, FILL_VALUE=None, EXTRAPOLATE=False):
"""
Spatially extrapolates daily RACMO products
Parameters
----------
base_dir: str
Working data directory
EPSG: str or int
input coordinate reference system
MODEL: str
Daily model outputs to interpolate
- ``FGRN055``: 5.5km Greenland RACMO2.3p2
tdec: float
time coordinates to interpolate in year-decimal
X: float
x-coordinates to interpolate
Y: float
y-coordinates to interpolate
VARIABLE: str, default 'smb'
RACMO product to interpolate
- ``smb``: Surface Mass Balance
- ``hgtsrf``: Change of Surface Height
SEARCH: str, default 'BallTree'
nearest-neighbor search algorithm
NN: int, default 10
number of nearest-neighbor points to use
POWER: int or float, default 2.0
Inverse distance weighting power
SIGMA: float, default 1.5
Standard deviation for Gaussian kernel
FILL_VALUE: float or NoneType, default None
Output fill_value for invalid points
Default will use fill values from data file
EXTRAPOLATE: bool, default False
Create a regression model to extrapolate in time
"""
# start and end years to read
SY = np.nanmin(np.floor(tdec)).astype(np.int64)
EY = np.nanmax(np.floor(tdec)).astype(np.int64)
YRS = '|'.join(['{0:4d}'.format(Y) for Y in range(SY,EY+1)])
# input list of files
if (MODEL == 'FGRN055'):
# filename and directory for input FGRN055 files
file_pattern = 'RACMO2.3p2_FGRN055_{0}_daily_(\d+).nc'
DIRECTORY = os.path.join(base_dir,'RACMO','GL','RACMO2.3p2_FGRN055')
# create list of files to read
rx = re.compile(file_pattern.format(VARIABLE,YRS),re.VERBOSE)
input_files=sorted([f for f in os.listdir(DIRECTORY) if rx.match(f)])
# calculate number of time steps to read
nt = 0
for f,FILE in enumerate(input_files):
# Open the RACMO NetCDF file for reading
with netCDF4.Dataset(os.path.join(DIRECTORY,FILE), 'r') as fileID:
nx = len(fileID.variables['rlon'][:])
ny = len(fileID.variables['rlat'][:])
nt += len(fileID.variables['time'][:])
# invalid data value
fv = np.float64(fileID.variables[VARIABLE]._FillValue)
# scaling factor for converting units
if (VARIABLE == 'hgtsrf'):
scale_factor = 86400.0
elif (VARIABLE == 'smb'):
scale_factor = 1.0
# python dictionary with file variables
fd = {}
fd['time'] = np.zeros((nt))
# python dictionary with gaussian filtered variables
gs = {}
# calculate cumulative sum of gaussian filtered values
cumulative = np.zeros((ny,nx))
gs['cumulative'] = np.ma.zeros((nt,ny,nx), fill_value=fv)
gs['cumulative'].mask = np.zeros((nt,ny,nx), dtype=bool)
# create a counter variable for filling variables
c = 0
# for each file in the list
for f,FILE in enumerate(input_files):
# Open the RACMO NetCDF file for reading
with netCDF4.Dataset(os.path.join(DIRECTORY,FILE), 'r') as fileID:
# number of time variables within file
t=len(fileID.variables['time'][:])
fd[VARIABLE] = np.ma.zeros((t,ny,nx),fill_value=fv)
fd[VARIABLE].mask = np.ones((t,ny,nx),dtype=bool)
# Get data from netCDF variable and remove singleton dimensions
tmp=np.squeeze(fileID.variables[VARIABLE][:])
fd[VARIABLE][:] = scale_factor*tmp
# indices of specified ice mask
i,j = np.nonzero(tmp[0,:,:] != fv)
fd[VARIABLE].mask[:,i,j] = False
# combine mask object through time to create a single mask
fd['mask']=1.0-np.any(fd[VARIABLE].mask,axis=0).astype(np.float64)
# racmo coordinates
fd['lon']=fileID.variables['lon'][:,:].copy()
fd['lat']=fileID.variables['lat'][:,:].copy()
fd['x']=fileID.variables['rlon'][:].copy()
fd['y']=fileID.variables['rlat'][:].copy()
# rotated pole parameters
proj4_params=fileID.variables['rotated_pole'].proj4_params
# extract delta time and epoch of time
delta_time=fileID.variables['time'][:].astype(np.float64)
date_string=fileID.variables['time'].units
# extract epoch and units
epoch,to_secs = SMBcorr.time.parse_date_string(date_string)
# calculate time array in Julian days
JD = SMBcorr.time.convert_delta_time(delta_time*to_secs, epoch1=epoch,
epoch2=(1858,11,17,0,0,0), scale=1.0/86400.0) + 2400000.5
# convert from Julian days to calendar dates
YY,MM,DD,hh,mm,ss = SMBcorr.time.convert_julian(JD)
# calculate time in year-decimal
fd['time'][c:c+t] = SMBcorr.time.convert_calendar_decimal(YY,MM,
day=DD,hour=hh,minute=mm,second=ss)
# use a gaussian filter to smooth mask
gs['mask'] = scipy.ndimage.gaussian_filter(fd['mask'],SIGMA,
mode='constant',cval=0)
# indices of smoothed ice mask
ii,jj = np.nonzero(np.ceil(gs['mask']) == 1.0)
# use a gaussian filter to smooth each model field
gs[VARIABLE] = np.ma.zeros((t,ny,nx), fill_value=fv)
gs[VARIABLE].mask = np.ones((t,ny,nx), dtype=bool)
# for each time
for tt in range(t):
# replace fill values before smoothing data
temp1 = np.zeros((ny,nx))
i,j = np.nonzero(~fd[VARIABLE].mask[tt,:,:])
temp1[i,j] = fd[VARIABLE][tt,i,j].copy()
# smooth spatial field
temp2 = scipy.ndimage.gaussian_filter(temp1, SIGMA,
mode='constant', cval=0)
# scale output smoothed field
gs[VARIABLE][tt,ii,jj] = temp2[ii,jj]/gs['mask'][ii,jj]
# replace valid values with original
gs[VARIABLE][tt,i,j] = temp1[i,j]
# set mask variables for time
gs[VARIABLE].mask[tt,ii,jj] = False
# calculate cumulative
cumulative[ii,jj] += gs[VARIABLE][tt,ii,jj]
gs['cumulative'].data[c+tt,ii,jj] = np.copy(cumulative[ii,jj])
gs['cumulative'].mask[c+tt,ii,jj] = False
# add to counter
c += t
# convert RACMO latitude and longitude to input coordinates (EPSG)
crs1 = pyproj.CRS.from_string(EPSG)
crs2 = pyproj.CRS.from_epsg(4326)
transformer = pyproj.Transformer.from_crs(crs1, crs2, always_xy=True)
direction = pyproj.enums.TransformDirection.INVERSE
# convert projection from model coordinates
xg,yg = transformer.transform(fd['lon'], fd['lat'], direction=direction)
# construct search tree from original points
# can use either BallTree or KDTree algorithms
xy1 = np.concatenate((xg[i,j,None],yg[i,j,None]),axis=1)
tree = SMBcorr.spatial.build_tree(xy1, SEARCH=SEARCH)
# output interpolated arrays of variable
npts = len(tdec)
extrap = np.ma.zeros((npts),fill_value=fv,dtype=np.float64)
extrap.mask = np.ones((npts),dtype=bool)
# initially set all values to fill value
extrap.data[:] = extrap.fill_value
# type designating algorithm used (1:interpolate, 2:backward, 3:forward)
extrap.interpolation = np.zeros((npts),dtype=np.uint8)
# time cutoff without close time interpolation
time_cutoff = (fd['time'].min(), fd['time'].max())
# find days that can be interpolated
if np.any((tdec >= time_cutoff[0]) & (tdec < time_cutoff[1])):
# indices of dates for interpolated days
ind,=np.nonzero((tdec >= time_cutoff[0]) & (tdec < time_cutoff[1]))
# reduce x, y and t coordinates
xind,yind,tind = (X[ind],Y[ind],tdec[ind])
# find indices for linearly interpolating in time
f = scipy.interpolate.interp1d(fd['time'], np.arange(nt), kind='linear')
date_indice = f(tind).astype(np.int64)
# for each unique racmo date
# linearly interpolate in time between two racmo maps
# then then inverse distance weighting to extrapolate in space
for k in np.unique(date_indice):
kk, = np.nonzero(date_indice==k)
count = np.count_nonzero(date_indice==k)
# query the search tree to find the NN closest points
xy2 = np.concatenate((xind[kk,None],yind[kk,None]),axis=1)
dist,indices = tree.query(xy2, k=NN, return_distance=True)
# normalized weights if POWER > 0 (typically between 1 and 3)
# in the inverse distance weighting
power_inverse_distance = dist**(-POWER)
s = np.sum(power_inverse_distance, axis=1)
w = power_inverse_distance/np.broadcast_to(s[:,None],(count,NN))
# variable for times before and after tdec
var1 = gs['cumulative'][k,i,j]
var2 = gs['cumulative'][k+1,i,j]
# linearly interpolate to date
dt = (tind[kk] - fd['time'][k])/(fd['time'][k+1] - fd['time'][k])
# spatially extrapolate using inverse distance weighting
extrap[kk] = (1.0-dt)*np.sum(w*var1[indices],axis=1) + \
dt*np.sum(w*var2[indices], axis=1)
# set interpolation type (1: interpolated in time)
extrap.interpolation[ind] = 1
# check if needing to extrapolate backwards in time
count = np.count_nonzero(tdec < time_cutoff[0])
if (count > 0) and EXTRAPOLATE:
# indices of dates before model
ind, = np.nonzero(tdec < time_cutoff[0])
# query the search tree to find the NN closest points
xy2 = np.concatenate((X[ind,None],Y[ind,None]),axis=1)
dist,indices = tree.query(xy2, k=NN, return_distance=True)
# normalized weights if POWER > 0 (typically between 1 and 3)
# in the inverse distance weighting
power_inverse_distance = dist**(-POWER)
s = np.sum(power_inverse_distance, axis=1)
w = power_inverse_distance/np.broadcast_to(s[:,None],(count,NN))
# read the first year of data to create regression model
N = 365
# calculate a regression model for calculating values
# spatially interpolate variable to coordinates
DATA = np.zeros((count,N))
TIME = np.zeros((N))
# create interpolated time series for calculating regression model
for k in range(N):
# time at k
TIME[k] = fd['time'][k]
# spatially extrapolate variable
tmp = gs['cumulative'][k,i,j]
DATA[:,k] = np.sum(w*tmp[indices],axis=1)
# calculate regression model
for n,v in enumerate(ind):
extrap[v] = regress_model(TIME, DATA[n,:], tdec[v],
ORDER=2, CYCLES=[0.25,0.5,1.0], RELATIVE=TIME[0])
# set interpolation type (2: extrapolated backwards in time)
extrap.interpolation[ind] = 2
# check if needing to extrapolate forward in time
count = np.count_nonzero(tdec >= time_cutoff[1])
if (count > 0) and EXTRAPOLATE:
# indices of dates after racmo model
ind, = np.nonzero(tdec >= time_cutoff[1])
# query the search tree to find the NN closest points
xy2 = np.concatenate((X[ind,None],Y[ind,None]),axis=1)
dist,indices = tree.query(xy2, k=NN, return_distance=True)
# normalized weights if POWER > 0 (typically between 1 and 3)
# in the inverse distance weighting
power_inverse_distance = dist**(-POWER)
s = np.sum(power_inverse_distance, axis=1)
w = power_inverse_distance/np.broadcast_to(s[:,None],(count,NN))
# read the last year of data to create regression model
N = 365
# calculate a regression model for calculating values
# spatially interpolate variable to coordinates
DATA = np.zeros((count,N))
TIME = np.zeros((N))
# create interpolated time series for calculating regression model
for k in range(N):
kk = nt - N + k
# time at kk
TIME[k] = fd['time'][kk]
# spatially extrapolate variable
tmp = gs['cumulative'][kk,i,j]
DATA[:,k] = np.sum(w*tmp[indices],axis=1)
# calculate regression model
for n,v in enumerate(ind):
extrap[v] = regress_model(TIME, DATA[n,:], tdec[v],
ORDER=2, CYCLES=[0.25,0.5,1.0], RELATIVE=TIME[-1])
# set interpolation type (3: extrapolated forward in time)
extrap.interpolation[ind] = 3
# complete mask if any invalid in data
invalid, = np.nonzero((extrap.data == extrap.fill_value) |
np.isnan(extrap.data))
extrap.mask[invalid] = True
# replace fill value if specified
if FILL_VALUE:
extrap.fill_value = FILL_VALUE
extrap.data[extrap.mask] = extrap.fill_value
# return the interpolated values
return extrap