Postprecessing tools of model data
NCL (NCAR Command Language)
The NCAR Command Language (NCL) is a free interpreted language designed specifically for scientific data processing and visualization. NCL has robust file input and output. It can read in netCDF, HDF4, HDF4-EOS, GRIB1, GRIB2 (as of version 4.2.0.a035), binary and ASCII data. The graphics are world class and highly customizable.
It runs on many different operating systems including Solaris, AIX, IRIX, Linux, MacOSX, Dec Alpha, and Cygwin/X running on Windows. It’s available for free in binary format.
NCL can be run ininteractive mode, where each line is interpreted as it is entered at your workstation, or it can be run in batch mode as an interpreter of complete scripts. You can also use command line options to set options or variables on the NCL command line.
NCL supports calling C and Fortran external routines, which makes NCL infinitely configurable.
netCDF, HDF4, HDF4-EOS, GRIB1, GRIB2(到4.2.0.a035版本为止)，二进制和ASCII数据。NCL可输出各种图形,且图形可根据需要随意设置。
NCL可以运行在不同的操作系统下，比方说Solaris, AIX, IRIX, Linux, MacOSX, Dec Alpha以及运行在Windows上的Cygwin/X。
Codes, Documentation and Examples of NCL:
NCO (netCDF operator)
NCO is a suite of programs known as operators. Each operator is a standalone, command line program executed at the shell-level like, e.g., ls or mkdir. The operators take netCDF files (including HDF5 files constructed using the netCDF API) as input, perform an operation (e.g., averaging or hyperslabbing), and produce a netCDF file as output. The operators are primarily designed to aid manipulation and analysis of data.
The Grid Analysis and Display System (GrADS) is an interactive desktop tool that is currently in use worldwide for the analysis and display of earth science data. GrADS is implemented on all commonly available UNIX workstations and DOS based PCs, and is freely distributed over the Internet. GrADS provides an integrated environment for access, manipulation, and display of earth science data.
Useful Linkages for GrADS:
GrADS Homepage :
GrADS Script Library:
http://grads.iges.org/grads/gadoc/library.html http://web.lasg.ac.cn/grads/library.html (本地)
Tools of time series analysis
Redfit and Red2con
Redfit: Red-noise spectra directly from unevenly spaced time series
Paleoclimatic time series are commonly unevenly spaced in time, making it difficult to obtain an accurate estimate of their typical red-noise spectrum. This Fortran 90 program overcomes this problem by fitting a first-order autoregressive (AR1) process, being characteristic for many climatic processes, directly to unevenly spaced time series. Hence, interpolation in the time domain and its inevitable bias can be avoided. The program can be used to test if peaks in the spectrum of a time series are significant against the red-noise background from an AR1 process.
Download: Version 3.8e, Binaries for Win2000/XP/Win7, Fortran 90 source code and documentation
Red2con is a graphical user interface for REDFIT. It was developed by Boris Priehs (Univ. Bremen) and uses the commercial software MATLAB (Version 7 or above) to easily control the settings of REDFIT and to visualize the results: MATLAB script files and documentation.
Matlab code to plot phase wheel
(phaseanglemap.m links )
Time series analysis tool for Macintosh operating system, can be downloaded from following linkage:
Originally, AnalySeries was designed (by Didier Paillard) specially to facilitate the study of paleoclimatic records using the approach and some of the methods defined by the SPECMAP group (Martinson et al., 1987; Imbrie et al., 1984, 1989). It addressed two main problems: transforming “data versus depth” records into “data versus age” records; and spectral analysis of the paleoclimatic records for studying their relationships with insolation, ice volume, and other climatic parameters in the frequency domain. A fully redesigned Java version of AnalySeries is given by following linkage: http://code.google.com/p/analyseries
Many time series in geophysics exhibit non-stationarity in their statistics. While the series may contain dominant periodic signals, these signals can vary in both amplitude and frequency over long periods of time. Wavelet analysis attempts to get information on both the amplitude of any “periodic” signals within the series, and how this amplitude varies with time, by decomposing a time series into time/frequency space simultaneously. Several Wavelet analysis tools can be found in following linkages: