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            A subset of CREATE-IP variables can be visualized with CREATE-V.




         Regridding in cdo.


         Regridding in nco


         Regridding in NCL


         Regridding in Opengrads


    Methodology Notes on Regridding the 8 Ocean Reanalyses used to Generate the ORA Ensemble

CREATE-IP recently completed a project to develop an eight-member ensemble of ocean reanalyses. The first step in developing these ensembles and preparing the data for CMOR2 processing involved standardizing the various curvilinear and rectilinear grids to the standard World Ocean Atlas 2009 (WOA09) 1x1 grid. Here is a list of tools -- some standard, some more specialized -- that we used for each reanalysis.

NCEP Climate Forecast System Reanalysis (CFSR): We used CDO remapnn (remap nearest neighbor) function with a 1x1 defined grid template. CFSR's salinity was corrected from kg kg to psu using CDO ncap2.

Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) Global Ocean Physical Reanalysis System (C-GLORSv5): CMCC's C GLORSv5 uses an ArakawaC-Grid staggering. We used the same tool CMCC uses, Only a Surface Interpolation Environment (SOSIE), to 'unrotate' uo and vo and horizontally regrid to 1x1. The C-GLORSv5 NetCDF required some dimension, variable and attribute adjustments, in order to be recognized by our NetCDF tools and by CMOR2: we used CDO's settaxis and NCO's ncatted and ncrename for these edits.

ECMWF Ocean Reanalysis and System 4 (ORAS4): ORAS4 was provided to us in 1x1 grid, with longitude at 0.5 to 359.5 by 1 degrees_east circular and latitude at -89.5 to 89.5 by 1 degrees_north; no horizontal regridding or NetCDF formatting was required.

German Estimating the Circulation and Climate of the Ocean (GECCO2): We received GECCO2 in 1x1 resolution but without the standard lon/lat starting points and with longitude in degrees west. We used CDO setgrid to format the grid to be recognized as a lat/lon grid by NetCDF utilities, then CDO remapnn with a defined grid template to regrid.

Geophysical Fluid Dynamics Laboratory (GFDL): We regridded the GFDL ECDAv31 curvilinear grid using Ferret's CURVE_TO_RECT function, which implements the spherical interpolation code written at GFDL. CDO splityear and ncrename was used to create monthly files and rename variables and dimensions to the NetCDF standard.

NCEP Global Ocean Data Assimilation (GODAS): GODAS was provided in GRIB format. We used CDO copy, chvar and remapnn to convert the GRIB to NetCDF, rename the variables and regrid to 1x1. The GODAS latitude is -74.5 to 63.5, we used CDO enlargegrid with a 1x1 180x360 template to fill the missing data.

Japan Meteorological Agency (JMA) Meteorological Research Institute (MRI) MOVE-G2i: The MRI data was provided in binary format. We used Grid Analysis and Display System (GrADS) to convert to NetCDF, CDO setzaxis to define depth_below_sea levels and remapnn to regrid. The MRI latitude is -78 to 90, we used CDO enlargegrid with a 1x1 180x360 template to fill the missing data.

ECMWF Ocean ReAnalysis Pilot 5.0 (ORAP5): ORAP5 was regridded with CDO remapnn. NCO ncatted and ncrename were required to edit variables and attributes; CDO copy was required to convert the NetCDF type from 64-bit offset to classic (for our CMOR2 processing).



The uv-cdat utility, cdscan, can be used to aggregate files. 


Instructions can be found in the CDO manual distributed with the code. Generally, to concatenate 3 datasets with different time steps of the same variables use:

cdo copy ifile1 ifile2 ifile3 ofile


ncrcat --ovr -h 

--ovr = -O, --ovr, --overwrite    Overwrite existing output file, if any 

-h = -h, --hst, --history    Do not append to "history" global attribute 


Vertical Interpolation Code *

A python script is provided to vertically harmonize various oceanic reanalysis data along World Ocean Atlas 2009 (WOA09) standard levels.  The center depths of target levels range from 5-meter to 5750-meter, 33 levels in total. Linear interpolation is applied for re-mapping physical variables from source levels to target levels. To run the script, it requires Python 2.6 or later, numpy array module, and unidata netCDF4 module.


The script retrieves physical variables from netCDF files. For each target level, all source levels that have overlaps with the target levels are taken into consideration.  A weighted average is calculated across these source levels, and the weight coefficients are inversely related to the distance between the centers of source and target levels. Interpolated results and associated attributes are stored in netCDF4 format with default compression. Vertical dimension information is updated accordingly. Other depth-irrelevant variables and attributes are identical with those in the input files.


*For Ocean Models Only 


Downscaling – coming soon

GIS – coming soon

Last Update: June 16, 2020, 10 a.m. by Vincent Wild
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