Planetary-scale 4-day Kelvin-type waves at the cloud top of the Venus atmosphere have been reported from the 1980s, and their significance for atmospheric dynamics has been pointed out. However, these waves have not been reproduced in Venus atmospheric general circulation models (VGCMs). Recently, horizontal winds associated with the planetary-scale waves at the cloud top have been obtained from cloud images taken by cameras onboard Venus orbiters, which could enable us to clarify the structure and roles of Kelvin-type waves. In order to examine this possibility, our team carried out an idealized observing system simulation experiment (OSSE) with a data assimilation system which we developed. The wind velocity data provided by a CCSR/NIES (Center for Climate System Research/National Institute for Environmental Studies) VGCM where equatorial Kelvin-type waves were assumed below the cloud bottom was used as idealized observations. Results show that 4-day planetary-scale Kelvin-type waves are successfully reproduced if the wind velocity between 15° S and 15° N latitudes is assimilated every 6 h at 70 km altitude. It is strongly suggested that the Kelvin-type waves could be reproduced and investigated by the data assimilation with the horizontal wind data derived from Akatsuki ultraviolet images. The present results also contribute to planning future missions for understanding planetary atmospheres.
At the cloud top of the Venus atmosphere, equatorial Kelvin waves have been observed and are considered to play an important role in the super-rotation. We were able to reproduce the wave in a general circulation model (GCM) by conducting an observing system simulation experiment (OSSE) with the help of a data assimilation system. The synthetic horizontal winds of the Kelvin wave produced by the linear wave propagating model are assimilated at the cloud top (~70 km) in realistic conditions, assuming they are obtained from cloud tracking of ultra-violet images (UVI) taken by the Venus orbiters. It is demonstrated using Eliassen–Palm (EP) fluxes that the reproduced Kelvin wave transports angular momentum and plays an important role in the magnitude and structure of the super-rotation, causing the acceleration and deceleration of zonal wind of ~0.1 m/s day−1. The conditions required in order to reproduce the Kelvin wave have also been investigated. It is desirable to have 24 hourly dayside satellite observations in an equatorial orbit, such as the Akatsuki Venus climate orbiter. The results of this type of data assimilation study will be useful in the planning of future observation missions to the atmospheres of planets.
<div class="co_mto_htmlabstract mt-3"> <div class="co_mto_htmlabstract-affilitions"> <p>&#160;</p> </div> <div class="co_mto_htmlabstract-content mt-3"> <div class="co_mto_htmlabstract mt-3"> <div class="co_mto_htmlabstract-content mt-3"><!-- COMO-HTML-CONTENT-START --> <p><strong>Abstract</strong></p> <p><strong>&#160;</strong></p> <p>Structure of the planetary scale wave, which has been studied for over decades since its discovery in 1980s, is yet to be shrouded in mystery. To clarify this, images by cameras would definitely be necessary. Our team has been assimilating data with AFES LETKF Data Assimilation System for Venus (ALEDAS-V): the first data assimilation system for the Venusian atmosphere, as a pre-experiment before executing the mission. Results show that you can successfully reproduce 4-day planetary scale wave when assimilating data of wind velocity of latitude S15&#176;- N15&#176; every 6 hours at an altitude of 70 km. This discovery will contribute not only to a mission to observe wind velocity of Venus, but also to proposal regarding missions for further understanding of atmospheric structure on other planets, in the future.</p> <p><strong>&#160;</strong></p> <ol> <li><strong> Introduction</strong></li> </ol> <p><strong>&#160;</strong></p> <p>The planetary scale wave, which is considered as 4-day &#8220;equatorial Kelvin wave&#8221; is existing at the cloud top in the equatorial region on the Venus atmosphere [1,2]. The equatorial Kelvin wave is pointed out to have a possibility of contributing to generation and maintenance of what is called &#8220;Super Rotation&#8221;: the wind circulating Venus 60 times faster than the speed of Venusian rotation [3]. However, the equatorial Kelvin wave has not been simulated in any Venusian atmospheric General Circulation Model (VGCM) in the world. We have so far developed AFES-Venus; Atmospheric GCM for the Earth Simulator for Venus, and ALEDAS-V; AFES LETKF Data Assimilation System for Venus, using LETKF; Local Ensemble Transform Kalman Filter, for the first time in the world [4,5]. In this study, we made an observation data regarding the speed of wind, by varying conditions such as altitude, latitudinal range, and frequency of observations, assuming observations with various wavelength cameras. We created the idealized observation data from CCSR/NIES Venus AGCM [6], in which the equatorial Kelvin wave is reproduced at ~70 km by 5.5-day wave forcing from the lowest level (~30 km).</p> <p><strong>&#160;</strong></p> <ol start="2"> <li><strong> Experimental setting</strong></li> </ol> <p><strong>&#160;</strong></p> <p>AFES-Venus solves dry 3-D Primitive equation on sphere. The physical parameters are based on Venus. The latitude-longitude grids (128 times 64) with 60 vertical layers are used. Solar heating is based on Tomasko (1980) and Crisp (1989), and radiative process is simplified by Newtonian cooling. The simulation starts from zonal wind assuming super rotation and spin up for 4 Earth years. ALEDAS-V uses the LETKF [7] to produce an improved estimate (called analysis) by combining observations and short time ensemble forecasts of AFES-Venus. The number of ensemble members is 31. Assimilation cycle is 6 hourly interval. Observation period and error are set to 1 month and 3 m/s, respectively. We first assimilated the wave observation at several altitudes and different frequency, however, the 4-day wave was not perfectly reproduced. Therefore, we conducted sensitivity experiments of observational domain at an altitude of 70 km, where the wave was mostly reproduced. In this experiment, observation with ultraviolet wavelength camera on Akatsuki for example, is assumed to be used for observations.</p> <p><strong>&#160;</strong></p> <ol start="3"> <li><strong> Results</strong></li> </ol> <p><strong>&#160;</strong></p> <p>We have been conducting various cases of experiments, though, we will show the results partially here.</p> <p>&#160;</p> <p><img src="data:image/png;base64, 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