Convectively Coupled Equatorial Waves (CCEWs)

•October 20, 2012 • 1 Comment

Convectively Coupled Tropical Waves (CCEWs) 

Codes by  Sandro W. Lubis
(Former) Master Student at the Institute of Meteorology, University of Leipzig, Leipzig, Germany, 2015
and (Former) Ph.D. Student at the GEOMAR – Helmholtz Centre for Ocean Research Kiel, Kiel, Germany, 2018

Updated: May, 2022

   Related Works !!:

  • Lubis, S. W., and Respati, M. R. (2020). Impacts of Convectively Coupled Equatorial Waves on Rainfall Extremes in Java, Indonesia. International Journal of Climatology 41, 4, 2418-2440. Link.
  • Lubis, S. W., and Jacobi, C. (2015). The Modulating Influence of Convectively Coupled Equatorial Waves (CCEWs) on the Variability of Tropical Precipitation. International Journal of Climatology 35, 7, 1465-1483. Link.

  Attention !!:

This blog was designed under Linux operating system thus I apologize this page will not look nice if you are using another operating system.

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“Forced Stationary Waves”

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 Theoretical structure of atmospheric forced stationary waves in the tropics. The heating response Q is the forcing term which is proportional to the vertical derivative of the diabatic heating and the dissipation rate (representing Rayleigh friction), and Newtonian cooling is assumed to 0.2 (nondim). The background atmosphere was assumed to be at restOnce the set of forcing functions for u, v and \Phi, namely (0, 0, Q), are given by a linear combination of the eigenfunctions , it is straightforward to calculate the responses of u, v and \Phi to the forcing.

thick

forced

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“Free Equatorial Waves”

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 Theoretical structure of equatorially trapped waves of Matsuno’s normal modes superimposed with divergence and convergence of wind fields. All fields have been nondimensionalized by using the equatorial radius of deformation.  Any further queries regarding the calculation can be found in this manuscript (part of my bachelor thesis): Sandro_Manuscript — [Click the button below to see my calculations related to equatorial waves and also the figure to see an animation related to cloud convective regions associated with Convergence Fields].

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wig1

wig1

xxxx

“2D Theoretical Structures”

 >>ERn=1    >> MRG n=0      >> EIG=0     >>  EIGn=1    >> WIGn=1   >> Kelvin n=-1     
   >>EIGn=2         >> WIGn=2     >> ERn=2    >>  EIGn=3    >> WIGn=3    >> ERn=3

xxx

“2D Theoretical Animations”

Animations of the Equatorially Trapped Waves of Matsuno’s  normal modes superimposed with divergence and convergence of wind fields. The black solid lines indicate the convergence, while the divergence fields are represented by the dashed lines. Theoretical geopotential height structures are shown in color contours. 

 >>Kelvin n=-1       >> ERn=1     >> EIGn=0      >> MRG n=0     >>  EIGn=1         >> WIGn=1    
   >>EIGn=2         >> WIGn=2     >> ERn=2    >>  EIGn=3    >> WIGn=3    >> ERn=3

Equatorial Planetary Waves Amplitudes associated with Height perturbations for (a) Kelvin (Yellow), (b) MRG (red), (c) EIG n=0 (green), (d) Rossby waves n=1 (purple), (d) WIG n=1 (blue) and (e) WIG n=2 (white). All scales and fields have been nondimensionalized. X is nondimensionalized latitude and Y is amplitude.

==xxx==

“Observational Evidences”

“Convectively Coupled Equatorial Waves (CCEWs)”

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CCEWs play a crucial role in controlling the tropical weather system, as they modulate or organize the convection and a substantial portion of tropical rainfall and reinforce the tropical cyclone activities (e.g. Kiladis et al. 2009; Schreck et al. 2013, Kim and Alexander 2013 etc).

sym_er_n1

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“2D Observational Variances of CCEWs”

Spatial distributions of the Filtered OLR  variance 1981-2010 (30 years) for various parts of the wavenumber-frequency domain of interest. [All Season Variance]

 ERn=1                              MRG n=0                           Kelvin                              EIGn=0
 WIGn=1       WIGn=2     TDD   MJO                         

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“2D Observational Snapshots in NCEPR2 & ERA”

Snapshots and animations of the space-time filtered OLR assoaciated with CCEWs.  Please kindly contact me for any further queries regarding the isolation technique. Reference vector is 1 m/s

NCEP-R2 [2.5 deg]

     ER Waves n=1                 Kelvin Waves n=-1   
      TDD like-waves  
       MRG waves n=0  

ERA Interim – ECMWF [1.5 deg]

    ER Waves n=1             Kelvin Waves n=-1      
    TDD like-waves      
    MRG waves n=0     

XXX

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“2D Animations of CCEWs in OLR, U, V,  and Z”

**Filtered OLR Anomalies ** 

    Animations   Animations   Animations
               MJO                       Total Kelvin Waves n=-1              Total ER Waves n=1    
   Total MRG Waves n=0         TDD/ Easterly Waves        Sym_Kelvin_Waves 
      Sym_ER_Waves             Anti_Sym_ER_Waves      Anti_Sym_MRG_Waves

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**Filtered OLR & 850 hPa Wind Perturbations**

 Animations   Animations   Animations
            MJO                Total Kelvin Waves n=-1            Total ER Waves n=1    
      Total MRG Waves n=0    TDD/ Easterly Waves        Sym_Kelvin_Waves  
      Sym_ER_Waves    Anti_Sym_ER_Waves    Anti_Sym_MRG_Waves 

***

**Filtered Geopotential Height & 850 hPa Wind Perturbations**

Animations   Animations   Animations
                MJO                        Total Kelvin Waves n=-1           Total ER Waves n=1       
      Total MRG Waves n=0        TDD/ Easterly Waves       Sym_Kelvin_Waves
      Sym_ER_Waves        Anti_Sym_ER_Waves    Anti_Sym_MRG_Waves

***

**Filtered Geopotential Height & 200 hPa Wind Perturbations**

 Animations   Animations   Animations
 MJO
    Total Kelvin Waves n=-1           Total ER Waves n=1      
  Total MRG Waves n=0        TDD/ Easterly Waves       Sym_Kelvin_Waves
      Sym_ER_Waves Anti_Sym_ER_Waves     Anti_Sym_MRG_Waves

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===== Spaca-Time Spectra Diagram (STSD) =====

****[Through Decomposition]****

spectra

****[Without Decomposition] ****

A. The Entire Calendar Years 

spectra_lubis_total

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B. Extended Summer and Winter

To find the dominant wave modes, the spectrum from each season is divided by the
annual mean of red noise (background).

spectra_season2

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C. Seasonal Spectra

spectra_season4

Note: Contour begins at a value of 2.1 for which the spectral signatures are statistically significantly above the background at the 95% level (based on 500 dof)– WK99.

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****[With Doppler Shifting Effect]****

spectra_season_shift2

XXX

Composite Analysis of CCEWs in OLR

Hovmueller diagram of CCEWs. The OLR anomalies are regressed onto a time series of wave-filtered OLR anomalies (in this example: Kelvin, MRG, and ER waves).

hov_Kelvin hov_ER hov_MRG

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===== Spaca-Time Cross-Spectra Diagram (STCSD) =====

Space Time Cross Spectra OLR and Zonal Wind at 850 hPa level with a period of 96 days was chosen for the Fast Fourier Transform (FFT) with 65-day overlap and taper. 

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“Theory: Phase Angles of Kelvin and MRG Waves”

Constant Phase Angle of Kelvin Waves (n=-1) and MRG Waves (n=0)

Solving for particular “n” meridional wave mode number, we will find a set of frequency-vertical wavenumber relations for specific “n” that can reveal the slope of constant wave phase angle at the (Z,X) plane and at the (Z,t) plane in which Z is denoted as altitude, X is longitude, and t is time. The following figure is a vertical structure of free Kelvin waves and MRG n=0 in Z,X plane and Z,t plane in a constant N troposphere respectively, with Lz=6 km, he=10, H=7.3 km and dt/dz=-7 K/km. All units have been nondimensionalized using m* ≡ |m|β/Nk^2, ω* ≡ ωk/β, z*=z(Nk^2/β), t*=t(β/k), and x*=x/√(N/|m|β). The contours indicate the geopotential perturbations associated with the wave amplitude.

Kelvin_XZ

Kelvin_XT

MRG_XT

MRG_XZ

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Vertical Group Velocity of Kelvin Waves

Theoretical structure of vertical group velocity values (Cgz) for dry Kelvin waves in a constant N (Brunt–Väisälä frequency) troposphere and lower stratosphere with doppler shifting effect. Related articles: Kiladis et al. 2009 and Yang et al. 2011

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The equatorial planetary waves (hereafter, EPW) are generated by diabatic heating due to organized tropical large-scale convective heating in the equatorial belt. Although they do not contain as much energy as other typical tropospheric weather disturbances, EPW causes predominant disturbances in the equatorial atmosphere, e.g.,  induces mean-meridional circulation, modulates the climatological cold tropopause, affects the patterns of low-level moisture convergence, controls the distribution of convective heating and convective storms in large longitudinal distances and also plays important role in the heat balance of the equatorial belt.  Matsuno (1966) developed a model of quasi-geostrophic motions in the equatorial area to explain the characteristic of EPW by applying a single layer of a homogeneous incompressible fluid with free surface and assuming that Coriolis parameter to be proportional to the latitude. His work concerned with mathematical analyses of the simplified hydrodynamical equations, meanwhile Lindzen (1967)  used the β-planes approximation and Laplace’s Tidal Equation to describe planetary waves in the equator and mid-latitudes. Both of these works are very fundamental to explain the main characteristic of equatorial planetary waves in the atmosphere.

A frequency-wavenumber spectral analysis technique has been performed to identify convectively coupled equatorial planetary waves (CCEWs) signals in the atmosphere using one of the proxies of tropical convection (in this case OLR) and other related variables such as geopotential height and wind velocity at 850 hPa and 200 hPa. The dispersion diagram was obtained by performing a two-dimensional space and time spectral analysis for 31 years. The wavenumber–frequency spectrum for unfiltered/filtered daily anomalies are divided by an estimate of the red background in order to normalize the data following Wheeler & Kiladis (1999, JAS) and Wheeler & Weickmann (2001, Monthly Weather Review) and then separated into a symmetric and anti-symmetric component. Data starts to cluster into different categories Kelvin waves, Mixed Rossby Gravity waves, Equatorial Rossby waves, TD-type waves, etc. It’s shown by spectral peaks tending to lie along the dispersion curves for shallow water equatorial waves (black lines) with flow background U=0. Should be noted in this case, anomalies are calculated from Smooth Daily Climatology by following this common procedure: (1) perform an FFT to get real and imaginary coefficients. (2) arbitrarily retain the 1st ‘nHarm’ coefficients. No change in coefficients. (3) set the (nHarm+1) real and imaginary coefficients to 0.5 of the original values. Set all others to 0.0. Presumably these are high-frequency noise. (4) Do a back transform with coefficients as described in (3). The procedure of filtering process for each wave meets the following criteria:  MJO: Wave numbers 1to 5; period of 30-96 days, Kelvin waves: Wave numbers 1 to 14; period of 2.5-30 days; equivalent depth of 8-90m. ER waves: Wave numbers -1 to -10; period of 5-48 days; equivalent depth of 0-90m. MRG waves: Wave numbers -1 to -10; period of 3-10 days; equivalent depth of 8-90m. For each case, missing values are filled using linear interpolation in space and time.  Contours are in a space-time filtered of the shaded field for each mode of the waves.

Horizontal Structure of CCEWs

Experiment 1. Equatorial Kelvin Waves period 2.5–17 days, wavenumber 1-14 and equivalent depth 8-90 m. Remarks: Propagation Direction is Eastward.

Experiment 2. Equatorially trapped Rossby waves period 9–72 days, wavenumber 1-10, and equivalent depth 0-90 m. Remarks: Propagation Direction is westward.

Experiment 3. Mixed Rossby-Gravity waves (MRG) period 3–10 days, wavenumber 1-10 and equivalent depth8-90 m. Remarks: Propagation Direction is westward.

==Quartet Filter in Hovmöller Diagram==

Hovmöller Diagram of CCEWs using 2D FFT filters in space and time related to the time evolution of the waves in the average latitude. Contours are filtered in space-time. Data are first detrended and tapered in time, and filtered using a 2-dimensional fast Fourier transform (FFT).  The procedure of filtering process for each wave follows these criteria: Kelvin Filtering: k= 1 to 14; period of 2.5-30 days; equivalent depth of 8-90m. MJO Filtering: k= 1 to 5; period of 30-96 days ER Filtering: k= -1 to -10; period of 5-48 days; equivalent depth of 8-90m. Black dashed lines indicate convectively active phases of MJO associated with negative anomalies of OLR.

quartet_filter

==Dispersion Curves==

Theoretical dispersion curves (solid lines) are depicted for U=0 and separated into two components about the equator: symmetric and asymmetric

Figure 1. Wavenumber–frequency spectrum for unfiltered daily OLR and filtered anomalies of OLR [1980-2011, divided by an estimate of the red background following Wheeler & Kiladis (1999, JAS). The panel on the left shows signals that are anti-symmetric about the equator, while the panel on the right shows those that are symmetric. Spectral peaks tend to lie along the dispersion curves for shallow water equatorial waves (black lines). The unfiltered Omega shown above just to verify the constructed diagram that  has been created.

Figure 2. Zonal wavenumber-frequency power spectra of the (a) antisymmetric component and (b) symmetric component of  unfiltered daily OLR 1980-2011.

Figure 3.  Zonal wavenumber-frequency spectrum of the base-10 logarithm of the ‘‘background’’ power calculated by averaging the individual power spectra of unfiltered daily OLR 1980-2011 in Figs. 2a and 2b, and smoothing many times with a 1-2-1.

Comparation to the Theoretical Structure of Tropical Waves 

In this section, the two-dimensional structure of EPW based on the linear wave theory has experimented with specific frequency and modes. For details explanation please go directly to this article: Free Atmospheric Planetary Waves on an Equatorial β-plane Part I: Theoretical Background and Simulation. Simulation of horizontal wind and geopotential field perturbations corresponding to equatorial Rossby waves mode n= 1, which satisfies this general analytic solution:

Simulation of geopotential field and horizontal wind perturbations corresponding to Kelvin waves mode n= -1 with zonal wavenumber (k) =1.0 and  Yanai waves mode n= 0 with zonal wavenumber (k)=1.0.

The theoretical dispersion relation for free equatorial planetary waves (EPW) in nondimensionalized wavenumber and frequency (Kelvin waves, Yanai waves, Rossby waves, East Poincare ́ waves (EIG) and West Poincare ́ waves (WIG).

–Sandro Lubis–

My special acknowledgment to Dr. Carl J. Schreck, III 

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The Storm Tracks in ERA

•October 5, 2014 • Leave a Comment

The Storm Track

By Sandro Wellyanto Lubis

Former Graduate Student of Leipzig Institute for Meteorology, University of Leipzig, Germany, PhD Candidate in GEOMAR, Kiel, Germany

Storm tracks are the atmospheric zones where extra-tropical cyclones grow and decay. A measure of strength and direction of the storm tracks can be quantified in a number of ways. One such way is track the lows as they move across the oceans and compile statistics about the tracks over a long period of time. Following figures are one of methods to indetify the storm tracks using transient poleward temperature flux and transient geopotential height covariance flux.

validation_storm_track_6day

The 700mb transient poleward temperature flux from observations for 55 Northern Hemisphere winters (1958-2012). The measure reaches a maximum over the North Atlantic and North Pacific Oceans, the storm track regions where extra-tropical cyclones are most frequent and most intense.

validation_storm_track_6day_phi

 

Transient 250mb covariance of geopotential height flux for 55 Northern Hemisphere winters DJF (1958-2012), on a pressure level which is at a height of a few kilometres up into the atmosphere

Planetary Waves

•September 19, 2012 • Leave a Comment

Planetary Waves

By Sandro Lubis

updated: January 2017

Planetary waves can be defined as large-scale atmospheric perturbations or disturbances having zonal wavelengths in the scale of the earth’s radius. Planetary waves have significant influence on the circulation, temperature, and distribution of ozone. Planetary waves are responsible for the prevailing variability in the stratosphere, such as QBO, SSW, and the vacillation of the mean flow at the extratropical latitudes, and also play an important role for the MLT dynamics (Salby 1984, Pogoreltsev et al., 2007, Forbes et al. 2004). Planetary waves are generated by orographic and diabatic heating in the troposphere and also possible forced by irregular thermal or mechanical forcing in the lower atmosphere (Holton 2004, Andrews et al., 1987, Fedulina 2004).

zwn1_sandro

Planetary waves also can be distinguished into two types.  (1) Stationary Planetary Waves (SPWs) are waves whose surfaces of constant phase and amplitude are fixed with respect to the equator and (2) Travelling Planetary Waves (TPWs) are waves that propagate in time and space having incoherent progressive and retrogressive components. The progressive and retrogressive terms arise from the separation technique in space time-frequency domain analysis purposed by Hayashi (1971, 1979). The other types of planetary waves such as quasi-two-day wave and quasi-stationary wave are also exist in the atmosphere. These waves are commonly generated by baroclinic instability or by deposition of zonally asymmetric momentum onto the mean flow.

[CLICK THE ANIMATIONS BELLOW!!]

Figure 1.  Polar stereographic projection of geopotential heights associated with the a westward propagating of 16 -day wave, k=1 for (left) SPWs and right (TPWs) [Part of my master report for the  “middle atmosphere” course from Prof. Ch. Jacobi].

SPWs with zonal wave number 1 and 2 are striking features of the winter middle atmosphere dynamics (Fedulina 2004, Fröhlich 2005, Pancheva et al., 2007). The most prominent traveling waves observed in the atmosphere are traveling Rossby waves or global normal mode with s=1 which is 5-day wave (Andrews, 1987). Traveling planetary waves predominantly propagates westwards. In the middle atmosphere they have zonal wave number between 1 and 4 with periods between 2 days and 16 days (Fröhlich 2005). Many of them are able to propagate upwards through the winter hemisphere into the MLT region. The 5-day waves with zonal wave number 1appears also in the summer hemisphere (Fröhlich 2005, Pogoreltsev et al., 2007).

Madden – Julian Oscillation (MJO) Part-II

•September 17, 2012 • Leave a Comment

Madden–Julian Oscillation Life Cycle and Phase Diagram

Analysis and Reconstructed Codes by Sandro Lubis  Graduate Student of Leipzig Institute for Meteorology, University of Leipzig, Germany

All computations are made using[ NCAR Command Language (NCL)] 

September, 2012

The Madden-Julian Oscillation (MJO) is the leading mode of intra-seasonal variability in the tropical troposphere, characterized by an eastward moving ‘pulse’ of cloud and rainfall near the equator that persists from weekly to monthly timescales. The following analysis follows the standard procedures of investigation MJO defined by US MJO Clivar WG. Generally, there are two main  diagnostics steps pointed out by MJO Clivar WG, Waliser et al., 2009:  Level 1: These diagnostics are meant to provide a basic indication of the spatial and temporal intraseasonal variability that can be easily understood and/or calculated by the non-MJO expert. These diagnostics include assessing variance in preferred frequency bands, spectral analysis over key domains, univariate empirical orthogonal function (EOF) analysis of bandpass filtered data, statistical significance assessment of the EOFs, and lead-lag assessment of the dominant intraseasonal principal component (PC) time series. Level 2: Diagnostics that provide a more comprehensive diagnosis of the MJO through multivariate EOF analysis and frequency wave-number decomposition.

Data used in this analysis was retrieved from NOAA Reanalysis-II datasets consist of three main components: OLR, U200, and U850. V850 is also required for composite analysis of MJO. The daily anomalies are calculated using the first 3 harmonics of the seasonal cycle for 1980–2011.  Afterwards,  the band pass filter has been applied to these original anomalies. Compute the zonal mean of the temporal variance in order to normalize the variances. Then, compute combined EOF and normalize the multivariate EOF 1&2 component time series. Indices (multivariate MJO index)  are obtained by computing (PC1^2+PC2^2).

–Sandro–