Display and Analysis tools that read PSL's netCDF files


There are multiple plotting/analysis packages that can read and manipulate our netCDF files. Select a program to see links and examples of how to use them. We do not warranty any of this information, nor do we generally offer help with using any of these tools.

FERRET GrADS ncBrowse
NCL: NCAR Command Language IDL NCO: netCDF Operator
CDAT: Climate Data Analysis Tools Panoply IDV: Integrated Data Viewer
MATLABExcel ArcGIS
RPython OriginPro

There is also a contributed filter for the ncdump utility that understands the UDUNITS dates in our files and translates them to a human readable output.  See this page for documentation.

List of packages:

 

FERRET

FERRET is a freely available visualization and analysis tool from the Pacific Marine Environment Laboratory. FERRET commands similar to these should read the file and produce a plot:
yes? use "uwnd.mean.nc"
yes? !Use only the first time step for this plot
yes? set region/l=1
yes? contour uwnd
 

GrADS

The Graphic Analysis and Display System (GrADS), developed/supported at COLA/George Mason U, is also freely available. GrADS has a SDF (Self Describing File) interface that can read a netCDF or HDF (Scientific Data Sets) file that is compatible with the CF netCDF conventions.
To create a plot in GrADS use commands similar to these:
ga-> sdfopen uwnd.mean.nc
Scanning self-describing file:  uwnd.mean.nc
Found displayable variable uwnd with 0 levels in SDF file.
Data file uwnd.mean.nc is open as file 1
LON set to 0 360 
LAT set to -89 89 
LEV set to 0 0 
Time values set: 1980:1:1:0 1980:1:1:0 
ga-> d uwnd
Note: NARR data cannot be read directly with sdfopen because it is stored geographically differently than most other files. There is a standard GrADS script "narropen.gs" that will read the data or you can write a DDF.
 

IDL

IDL is a commercial software package from ITT Visual Information Solutions which can also read our netCDF files. In IDL 4.0 (or later), commands similar to these should work:
IDL> id=ncdf_open("/Datasets/coads1a.enh/sst.mean.nc")
IDL> sstid=ncdf_varid(id,"sst")
IDL> ncdf_varget,id,sstid,sst
IDL> contour,sst(*,*,1)

IDV (Integrated Data Viewer) 

The IDV is a meteorologically oriented, platform-independent application for visualization and analysis, developed using Java, VisAD and other component libraries, that emphasizes interactive 3D visualization and integration of diverse data types, including CF netCDF files from PSL. IDV is developed at UCAR's Unidata Program Center. See the IDV homepage for information on how to install IDV on your platform. If you have Java WebStart installed you can launch IDV here. In IDV:
Publicly available PSL datasets are available through the PSL THREDDS catalog which is linked from the main IDV catalog in the Catalog Chooser under Community Data Servers. Select the dataset you want to load. Once it is loaded the data source appears in the IDV's Field Selector window. You can subset the times for the entire dataset or an individual display. To select times for the entire dataset, right-click on the data source name; click on Properties in the pop-up menu that appears. In the Times tab in the Properties dialog, click off the checkbox "Use Default". Then select a range of times to use. You can right click on the times to get some predefined options, or select the starting time and Shift-click on the end time of the range you want. (Many PSL data sets have thousands of time steps and are too large to load all at once.) After you select the time, choose the field and display type you want by clicking on them. Alternatively, you can select the times for a particular variable in the Times tab of the Data Subset panel in the lower right portion of the Field Selector. Then click the Create Display button. For more information, see the IDV Users Guide.
 

ncBrowse

ncBrowse is a pure Java desktop application that can create graphical displays and view the attributes of our netCDF files.
 

Panoply

Panoply is a GUI netCDF data viewer from NASA/GISS. It plots maps and zonal averages and is easy to use. It's free and available for Mac, Windows or Linux.

NCL 

NCL, the NCAR Command Language, is a freely available software package from NCAR which can read our NetCDF files as well. Here is a sequence of commands that should work:
f = addfile("slp.2002.nc", "r")
slp =short2flt(f->slp) ; convert to float [contributed.ncl] wks =gsn_open_wks("ps", "slp_ce") ; open a "x11", "ps" or "ncgm" fil plot=gsn_csm_contour_map_ce(wks, slp(0, :, :), False) ; create a default ce plot
For unpacked files in our collection, the line with "short2flt" in it should be changed to "slp=f->slp".
Further examples of NCL use are available.
 

NCO

NCO is a (free) suite of programs that operate on netCDF files. Each operator is a standalone, command line program which is executed at the UNIX (or NT) shell-level like, e.g., ls or mkdir. The operators take netCDF or HDF4 files as input, then perform a set of operations (e.g., deriving new data, averaging, hyperslabbing, or metadata manipulation) and produce a netCDF file as output. The operators are primarily designed to aid manipulation and analysis of gridded scientific data. These tools are a powerful and easy way to perform simple manipulations on netCDF files without a major programming effort.

For example: The command:

ncrcat 85.nc 86.nc 87.nc 88.nc 89.nc 8589.nc
concatenates the 4 files along the record (time in this case) dimension and creates one single file
 

CDAT 

CDAT Climate Data Analysis Tools (CDAT) is a software system designed to provide access to and management of gridded climate data. It uses an object-oriented scripting language to link together separate software subsystems and packages thus forming an integrated environment for solving model diagnosis problems. The power of the system comes from Python and its ability to seamlessly interconnect software. Python provides a general purpose and full-featured scripting language with a variety of user interfaces including command-line interaction, stand-alone scripts (applications) and graphical user interfaces (GUI). The modular CDAT subsystems provide access to the data, to large-array numerical operations (via Numerical Python), and visualization.

 MATLAB

MATLAB is a high-level language and interactive environment w/extensive plotting and numerical processing available. NetCDF support is built in for versions MATLAB 7.7 and beyond. For versions prior to that one, you can use http://mexcdf.sourceforge.net/ for the MEXNC interface to netCDF files. NetCDF-4 support starts with version R2010b.

Higher Level NetCDF Code

  %  MATLAB script  to inquire about variable names and read in all variables
%  from a netcdf file.  Uses native netcdf support in MATLAB 
%
%  Usage:  
%  oid='myfile.nc';
%  nc_readall
%
%  Warning: this reads in variables and assigns them to the same variable name in MATLAB as in the netcdf file
%           does not handle udunits, so time coordinate typically needs to be modified for use in MATLAB. 
%
% oid  file name or URL to a netcdf file

f = ncinfo(oid);
nvars = length(f.Variables);
for k = 1:nvars
   varname=f.Variables(k).Name;
   disp(['Reading:  ' varname]);
   eval([varname ' = ncread(''' oid ''',''' varname ''');']);
end

Native NetCDF code

oid='https://psl.noaa.gov/thredds/dodsC/Datasets/noaa.ersst/sst.mnmean.nc'
sst=double(ncread(oid,'sst'));
nlat=double(ncread(oid,'lat'));
nlon=double(ncread(oid,'lon'));

mv=ncreadatt(oid,'/sst','missing_value');
sst(find(sst==mv))=NaN;

[Nlt,Nlg]=meshgrid(nlat,nlon);

%Plot the SST data without using the MATLAB Mapping Toolbox

figure
pcolor(Nlg,Nlt,sst(:,:,1));shading interp;
load coast;hold on;plot(long,lat);plot(long+360,lat);hold off
colorbar

%Plot the SST data using the MATLAB Mapping Toolbox

figure
axesm('eqdcylin','maplatlimit',[-80 80],'maplonlimit',[0 360]);  % Create a cylindrical equidistant map
pcolorm(Nlt,Nlg,sst(:,:,1))           % pseudocolor plot "stretched" to the grid
load coast                                 % add continental outlines
plotm(lat,long)
colorbar

oid = 'https://psl.noaa.gov/thredds/dodsC/Datasets/NARR/Monthlies/monolevel/prate.mon.mean.nc'
prate=ncread(oid,'prate');
nlat=double(ncread(oid,'lat'));
nlon=double(ncread(oid,'lon'));

scale = ncreadatt(oid,'/prate','scale_factor');
offset = ncreadatt(oid,'/prate','add_offset');

mv=double(ncreadatt(oid,'/prate','missing_value'));
mv = double(mv*scale+offset);
prate(find(prate>=mv))=NaN;

%Plot the NARR precipitation data using the MATLAB Mapping Toolbox
figure
axesm('robinson','Frame','on','Grid','on','maplatlimit',[0 88],'maplonlimit',[150 0],'MeridianLabel','on','ParallelLabel','on');
pcolorm(nlat,nlon,log(prate(:,:,1)))           % pseudocolor plot "stretched" to the grid
load coast                                 % add continental outlines
plotm(lat,long)
colorbar

figure
axesm('ortho','Grid','on','Frame','on','Origin',[40,250,14],'maplatlimit',[0 88],'maplonlimit',[150 0]); % Create a cylindrical equidistant map
pcolorm(nlat,nlon,log(prate(:,:,1)))           % pseudocolor plot "stretched" to the grid
load coast                                 % add continental outlines
plotm(lat,long)
colorbar

Excel 

Reading NetCDF in Excel

ArcGIS 

Reading NetCDF in ArcGIS. NetCDF ArcGIS Tutorial.

R 

Reading NetCDF in R
library(ncdf4)
name="/Datasets/noaa.ersst.v5/sst.mnmean.nc"  
ncin <- nc_open(name)
lon <- ncvar_get(ncin,"lon")
head(lon)
sst <- ncvar_get(ncin,dname)
dname <- "sst" 	      

Python 

Reading NetCDF in Python
import netCDF4
import numpy as np
f = netCDF4.Dataset('/Datasets/noaa.ersst.v5/sst.mnmean.nc')
print(f.variables.keys()) # get all variable names
sst = f.variables['sst'] # sst variable
time = f.variables['time']
print(time)

OriginPro 

The OriginPro Application is a commercial graphing and analysis software package from OriginLab. Version 2021b (9.85) and later supports import, graphing and analysis of NetCDF files in a point-and-click GUI environment. Users can easily import and reduce NetCDF files by partial import or averaging, and perform matrix stats, subtraction, simple math, and linear fit along the time axis. Shapefile-based ROI averaging is supported to create time axis profiles. Contour and image plots can be created to explore the data.