Metadata-Version: 2.1
Name: tnflux
Version: 0.9.2
Summary: Caculating the T-N Wave Activity Flux derived by Takaya and Nakamura (JAS,2001).
Home-page: https://github.com/jokervTv/T-N_Wave-Activity-Flux
Author: Lai Sheng<laish12@lzu.edu.cn>, Yongpeng Zhang<zhangyp6603@outlook.com>
Author-email: laish12@lzu.edu.cn
Maintainer: Yongpeng Zhang
Maintainer-email: zhangyp6603@outlook.com
License: MIT License
Description: # T-N Wave-Activity Flux
        
        Python scripts for caculating the `T-N Wave-Activity Flux` derived by `Takaya and Nakamura (JAS,2001)`.
        
        ## Introduction 
        
        Takaya and Nakamura generalize the Plumb Wave-Activity Flux(Plumb,1985) so as to be applicable to small-amplitude Quasi-Geostrophic(QG) disturbances, either stationary or migratory, that are superimposed on a zonally varying basic flow, and introduced the `T-N Wave-Activity Flux` ('TN01' for short).
        
        TN01 is of great advantage in climate monitoring and diagnosis.
        
        >TN01 with improved meridional component based on Plumb Wave-Activity Flux is appropriate for analyzing Rossby waves in the zonally asymmetric westerly. And it reflect the evolution of long-waves which the E-P Flux can't. 
        >(Shi Chunhua,2017)
        
        ## Formulation
        
        These Python scripts use the TN01 diagnostic formula in Spherical coordinates, which is the Eq.38 of Takaya's paper published in 2001:
        
        ![eq38](img/eq38.jpg)
        
        The first two terms in Eq.38 are taken into account while computing on the horizontal direction.
        And assuming the wave is stationary, so the Cu in Eq.38 would be zero.
        So the formula of horizontal T-N Wave-Activity Flux could yield as followed:
        
        ![eq38_hor](img/eq38_hor.jpg)
        
        ## Programing
        
        We modified the GRADS script by Kazuaki Nishii into a Python3 version  
        (http://www.atmos.rcast.u-tokyo.ac.jp/nishii/programs/index.html)  
        
        * Python version
            * Python 3
        * Computation
            * numpy
        
        All computations are based on `numpy` arrays, which are very efficient.  
        Partial differential terms in the formula are calculated by `numpy.gradient` in the central difference method.  
        
        ### Horizontal
        
        #### Data & Process
        
        Horizontal TN01 caltulation require the datas below:
        
        * Climatology average background of wind `U_c` and `V_c` and geopotential `phi_c`.
        * Geopotential in the analysis period `phi`.
        
        Geopotential anomalies will be used to compute pertubation stream-function `psi_p` in Quasi-Geostrophic(QG) assumption:
        
        * `psi_p` = (`pi` - `pi_c`) / `f`  
        `f` is the Coriolis parameter: `f` = 2 \* omega \* sin(`lat`)
        
        **Input Data is Geopotential, NOT Geopotential Height!!!**  
        The Re-analysis from NCEP/NCAR(NCEP1) is Geopotential Height, Geopotential Height multiplied by gravity `g` makes Geopotential.
        
        #### Output
        
        - `px` for longitude direction
        - `py` for latitude direction
        
        ## Reliability
        
        The output figures sample(Datas from `ECMWF ERA-Interim`)
        
        ![jan1981](img/jan1981.jpg)
        
        Results are compatible with the Wave-Activity Flux figures(JRA-55) made by JMA-TCC.  
        (http://ds.data.jma.go.jp/tcc/tcc/products/clisys/figures/db_hist_pen_tcc.html)
        
        ![psnh_mon_hist_waf300_198101](img/psnh_mon_hist_waf300_198101.jpg)
        
        and also the programs by Kazuaki Nishii @ University of Tokyo.  
        (http://www.atmos.rcast.u-tokyo.ac.jp/nishii/programs/index.html)
        
        ## Authors
        
        Lai Sheng: laish12@lzu.edu.cn .
        You can also visit his site for more detail: http://500hpa.cn/pyinmet/tnflux/
        
        Yongpeng Zhang: zhangyp6603@outlook.com
        
Keywords: tn wave activity flux
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Atmospheric Science
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
