Regrid SSH from Aviso

Regrid SSH from Aviso#

This notebook documents the steps needed to regrid Aviso datasets into a nominal 1/4 deg MOM grid (tx1_4).

%matplotlib inline

import xarray as xr
import xesmf
import numpy as np
from datetime import datetime
import matplotlib.pyplot as plt

Mean sea level anomaly#

infile = '/glade/campaign/cgd/oce/datasets/obs/aviso/mean_sea_level_anomaly/dt_global_allsat_msla_h_*'
ds_in = xr.open_mfdataset(infile)

ds_in

<xarray.Dataset>
Dimensions:           (time: 312, nv: 2, lat: 720, lon: 1440)
Coordinates:
  * time              (time) datetime64[ns] 1993-01-15 1993-02-15 ... 2018-12-15
  * lat               (lat) float32 -89.88 -89.62 -89.38 ... 89.38 89.62 89.88
  * lon               (lon) float32 0.125 0.375 0.625 ... 359.4 359.6 359.9
  * nv                (nv) int32 0 1
Data variables:
    climatology_bnds  (time, nv) datetime64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>
    lat_bnds          (time, lat, nv) float32 dask.array<chunksize=(1, 720, 2), meta=np.ndarray>
    lon_bnds          (time, lon, nv) float32 dask.array<chunksize=(1, 1440, 2), meta=np.ndarray>
    crs               (time) int32 -2147483647 -2147483647 ... -2147483647
    sla               (time, lat, lon) float64 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
Attributes: (12/39)
    cdm_data_type:                   Grid
    comment:                         Monthly Mean of Sea Level Anomalies refe...
    date_issued:                     2018-02-13 16:54:24Z
    time_coverage_resolution:        P1M
    creator_email:                   aviso@altimetry.fr
    product_version:                 5.10
    ...                              ...
    geospatial_vertical_min:         0.0
    geospatial_vertical_max:         0.0
    geospatial_lat_units:            degrees_north
    geospatial_lon_units:            degrees_east
    geospatial_lat_resolution:       0.25
    geospatial_lon_resolution:       0.25

sla_std = ds_in.sla.std('time').load()

/glade/work/gmarques/miniconda/envs/omwg_dev/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide
  x = np.divide(x1, x2, out)

sla_std.plot()

<matplotlib.collections.QuadMesh at 0x2afd91159880>

../_images/0133a91873325f65387efc2d55ab32a46aff5c8f7ae52eb06978cc31dcf2472d.png

def regrid_tracer(ds_in, ds_out, method='bilinear'):

    regrid = xesmf.Regridder(
        ds_in,
        ds_out,
        method=method,
        periodic=True,
    )
    fld_out = regrid(ds_in)
    return fld_out

sla_std_regrid = regrid_tracer(sla_std, ds_out).rename('ssh_std')

/glade/work/gmarques/miniconda/envs/omwg_dev/lib/python3.8/site-packages/xarray/core/dataarray.py:780: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  return key in self.data

# visual inspection. Make sure original and remapped plots look similar
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(18,4))
sla_std_regrid.where(ds_out.tmask==1.).plot.pcolormesh(ax=axes[0], vmin=0,vmax=0.5)
sla_std.plot.pcolormesh(ax=axes[1], vmin=0,vmax=0.5)
axes[0].set_title('Remapped')
axes[1].set_title('Original grid')

Text(0.5, 1.0, 'Original grid')

../_images/931680be672d22a90a7302340f40b33175e7d8f32c52c7023a373e99b836e1e6.png

# Global attrs
sla_std_regrid.attrs['title'] = 'Sea surface high standard deviation calculated from monthly means sla from AVISO and remapped to tx1_4.'
sla_std_regrid.attrs['start_time'] = str(ds_in.time[0].values)
sla_std_regrid.attrs['end_time'] = str(ds_in.time[-1].values)
sla_std_regrid.attrs['author'] = 'Gustavo Marques (gmarques@ucar.edu)'
sla_std_regrid.attrs['date'] = datetime.now().isoformat()
sla_std_regrid.attrs['infile'] = infile
sla_std_regrid.attrs['url'] = 'https://github.com/NCAR/tx1_4/ssh/'
# save
fname = 'ssh_std_aviso_to_tx1_4.nc'
sla_std_regrid.to_netcdf(fname)

Mean sea surface height above geoid#

The absolute dynamic topography is the sea surface height above geoid; the adt is obtained as follows: adt=sla+mdt where mdt is the mean dynamic topography; see the product user manual for details

infile = '/glade/campaign/cgd/oce/datasets/obs/aviso/mean_sea_level/dt_global_allsat_phy_l4_*.nc'
ds_in = xr.open_mfdataset(infile)

adt_mean = ds_in.adt.mean('time').load()

adt_mean_regrid = regrid_tracer(adt_mean, ds_out).rename('adt_mean')

/glade/work/gmarques/miniconda/envs/omwg_dev/lib/python3.8/site-packages/xarray/core/dataarray.py:780: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  return key in self.data

# visual inspection. Make sure original and remapped plots look similar
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(18,4))
adt_mean_regrid.where(ds_out.tmask==1.).plot.pcolormesh(ax=axes[0], vmin=-1,vmax=1)
adt_mean.plot.pcolormesh(ax=axes[1], vmin=-1,vmax=1)
axes[0].set_title('Remapped')
axes[1].set_title('Original grid');

../_images/d0fe0d63c405d6b63bbe366b60e48b5e9dc45319096321e2e472497701418391.png

# Global attrs
adt_mean_regrid.attrs['title'] = 'Mean sea surface height above geoid from AVISO remapped to tx1_4.'
adt_mean_regrid.attrs['start_time'] = str(ds_in.time[0].values)
adt_mean_regrid.attrs['end_time'] = str(ds_in.time[-1].values)
adt_mean_regrid.attrs['author'] = 'Gustavo Marques (gmarques@ucar.edu)'
adt_mean_regrid.attrs['date'] = datetime.now().isoformat()
adt_mean_regrid.attrs['infile'] = infile
adt_mean_regrid.attrs['url'] = 'https://github.com/NCAR/tx1_4/ssh/'
# save
fname = 'adt_mean_aviso_to_tx1_4.nc'
adt_mean_regrid.to_netcdf(fname)

Regrid SSH from Aviso

Contents

Regrid SSH from Aviso#

Mean sea level anomaly#

Mean sea surface height above geoid#