Mathematics – Logic
Scientific paper
Dec 2001
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001agufmsa51a0780m&link_type=abstract
American Geophysical Union, Fall Meeting 2001, abstract #SA51A-0780
Mathematics
Logic
0343 Planetary Atmospheres (5405, 5407, 5409, 5704, 5705, 5707), 5700 Planetology: Fluid Planets, 5707 Atmospheres: Structure And Dynamics
Scientific paper
The primary goal of Japan's Venus meteorological satellite mission (Planet-C), which will be launched in 2007, is to reveal the formation and maintenance mechanism of high speed westward winds in the Venusian atmosphere called super-rotation (approximately 100 m/s at the altitude of 70 km). The mechanism to keep on transporting momentum from the surface to the cloud-top has been wrapped in puzzle. In order to elucidate this mechanism it is necessary to obtain the information of atmosphere under the cloud-top. The Planet-C has some imaging cameras of UV (290,380nm) disperses by Venus cloud-top and of Near-IR (1.0,1.7,2.3,2.4um) which is the heat radiation from the lower atmosphere and the surface, visualizes the relatively thick clouds as the dark shadows. The Planet-C's cameras take cloud motion images continually, and we track the clouds to derive the Atmospheric Motion Vectors (AMVs) using 2 pictures. As it is shown by previous researches that the observations at the different altitude derived from the pictures of the different wavelength make us to obtain the 3-D information of AMVs inside cloud layer. It is important for the elucidation of super-rotation. In addition, it is necessary for the elucidation of super-rotation to derive AMVs in high quality and large quantities. The previous Venus inquiries did not reach to the elucidation of super-rotation due to poor spacial resolution of observations and data insufficiency. In the Planet-C mission the higher spacial resolution than previous observations and the approximately 2-year observation period improves these problems. We improved these problems of observational techniques, but became aware that there was a problem even in the analytical technique used in previous researches, which was in error estimation method of AMVs. The previous error estimation of AMVs is as follows: The method of deriving the AMVs from the successive cloud images is already established in terrestrial atmosphere. In the case of Earth, the error of AMVs is defined as a difference between AMVs and wind vectors observed by radiosondes simultaneously. But in the case of other planets such as Venus, as the in-situ observations by the radiosondes and the probes simultaneously are difficult, the error of AMVs cannot be evaluated with this method. In previous studies the error of AMVs was the dispersion of neighboring AMVs. Therefore the disturbance, which exists in the adjacent territory, was included in error. (Here, as regional variation of AMVs, disturbance is the important signal. And error is the difference between the derived AMVs and the actual cloud motions, which is caused by the restriction of spacial resolution, the change of the cloud shape and so on. Therefore error and disturbance are the completely different things.) Mechanism of the global atmospheric circulation, which is seen in the cloudy planets, hasn't been elucidated yet. One of keys, which solve this mechanism, is disturbance. Therefore it is a serious problem that the disturbance is included in error, so we devised the method of improving this problem. In this presentation we propose the method of error estimation that removes disturbance from error. In addition, in order to suite the efficiency of the cameras for Venus clouds, it is necessary to grasp the feature of Venus clouds. Therefore we report a connection between the cloud shapes and the error sizes.
Higuchi Takeo
Imamura Takashi
Murachi Tetsunori
Nakamura Maho
No associations
LandOfFree
Error Estimation in deriving the Atmospheric Motion Vectors from cloud images does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Error Estimation in deriving the Atmospheric Motion Vectors from cloud images, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Error Estimation in deriving the Atmospheric Motion Vectors from cloud images will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-1379464