15:45 〜 16:00
[PPS05-02] Growing modes of Venus's atmosphere using Bred Vectors
キーワード：Bred Vector, data assimilation, Venus's atmosphere
Numerical simulation of Venus's atmosphere is useful to understand the dynamics of the system. The Venus atmospheric data assimilation system "ALEDAS-V" (Sugimoto et al. 2017) based on the Venus atmospheric general circulation model "AFES-Venus" (Sugimoto et al. 2014) has been used to simulate Venus's atmosphere and generated some key phenomena such as the super-rotation and thermal tides. To further understand the dynamics of Venus's atmosphere, Bred Vector (BV) analysis is applied to the AFES-Venus model in this study. BV analysis identifies the growing modes of the system and therefore the unstable regions (Toth and Kalnay 1993, 1997). It has been used in the studies of the earth and Martian atmospheres (Greybush et al. 2013) to investigate the dynamics of the atmosphere in different seasons and regions and design a better data assimilation system. However, to our knowledge, there has been no similar study on Venus's atmosphere. To conduct the BV analysis, we first produced a 5-year free run of the AFES-Venus model initialized from an idealized super-rotation zonal wind profile at 0 year. The states on January 01 and September 01 in the 5th year were used as the initial conditions for the control run and the perturbed run, respectively. The norm of the BV is defined by the temperature norm from 60 km to 80 km altitude of the atmosphere with more active dynamics. At every rescaling interval, if the norm is bigger than the prescribed rescaling norm, the BV is rescaled to the rescaling norm. The rescaling interval and norm are the two parameters of the breeding cycle.
Different combinations of the parameters were tested. The norm decayed at the beginning, then it started capturing the growing modes after 6~15 Earth days. The growth rate remains stable throughout the following Venus year without significant seasonal variability. In addition, the growth rate is higher when the rescaling norm is smaller, which indicates that the faster growing mode of the system is dominant in this case. In comparison, there are seasonal variabilities of the growth and decay modes in the Martian atmosphere. Further BV analysis will be conducted to study the dynamics of the thermal tide, super-rotation, and other important features of Venus's atmosphere.
Reference:
Greybush, S. J., E. Kalnay, M. J. Hoffman, and R. J. Wilson, 2013: Identifying Martian atmospheric instabilities and their physical origins using bred vectors. Q. J. R. Meteorol. Soc., 139, 639–653, https://doi.org/10.1002/qj.1990.
Sugimoto, N., M. Takagi, and Y. Matsuda, 2014: Baroclinic instability in the Venus atmosphere simulated by GCM. J. Geophys. Res. Planets, 119, 1950–1968, https://doi.org/10.1002/2014JE004624.
——, A. Yamazaki, T. Kouyama, H. Kashimura, T. Enomoto, and M. Takagi, 2017: Development of an ensemble Kalman filter data assimilation system for the Venusian atmosphere. Sci. Rep., 7, 9321, https://doi.org/10.1038/s41598-017-09461-1.
Toth, Z., and E. Kalnay, 1993: Ensemble Forecasting at NMC: The Generation of Perturbations. Bull. Am. Meteorol. Soc., 74, 2317–2330, https://doi.org/10.1175/1520-0477(1993)074<2317:EFANTG>2.0.CO;2.
——, and ——, 1997: Ensemble Forecasting at NCEP and the Breeding Method. Mon. Weather Rev., 125, 3297–3319, https://doi.org/10.1175/1520-0493(1997)125<3297:EFANAT>2.0.CO;2.
Different combinations of the parameters were tested. The norm decayed at the beginning, then it started capturing the growing modes after 6~15 Earth days. The growth rate remains stable throughout the following Venus year without significant seasonal variability. In addition, the growth rate is higher when the rescaling norm is smaller, which indicates that the faster growing mode of the system is dominant in this case. In comparison, there are seasonal variabilities of the growth and decay modes in the Martian atmosphere. Further BV analysis will be conducted to study the dynamics of the thermal tide, super-rotation, and other important features of Venus's atmosphere.
Reference:
Greybush, S. J., E. Kalnay, M. J. Hoffman, and R. J. Wilson, 2013: Identifying Martian atmospheric instabilities and their physical origins using bred vectors. Q. J. R. Meteorol. Soc., 139, 639–653, https://doi.org/10.1002/qj.1990.
Sugimoto, N., M. Takagi, and Y. Matsuda, 2014: Baroclinic instability in the Venus atmosphere simulated by GCM. J. Geophys. Res. Planets, 119, 1950–1968, https://doi.org/10.1002/2014JE004624.
——, A. Yamazaki, T. Kouyama, H. Kashimura, T. Enomoto, and M. Takagi, 2017: Development of an ensemble Kalman filter data assimilation system for the Venusian atmosphere. Sci. Rep., 7, 9321, https://doi.org/10.1038/s41598-017-09461-1.
Toth, Z., and E. Kalnay, 1993: Ensemble Forecasting at NMC: The Generation of Perturbations. Bull. Am. Meteorol. Soc., 74, 2317–2330, https://doi.org/10.1175/1520-0477(1993)074<2317:EFANTG>2.0.CO;2.
——, and ——, 1997: Ensemble Forecasting at NCEP and the Breeding Method. Mon. Weather Rev., 125, 3297–3319, https://doi.org/10.1175/1520-0493(1997)125<3297:EFANAT>2.0.CO;2.