Method for Calculating Uncertainty in Automated Cloud-tracking Wind Measurements

Abstract:

We present a method to estimate the uncertainties in remote-sensing wind measurements of planetary atmospheres. Many planetary missions have measured atmospheric wind speeds by tracking the movements of the clouds. One of the common cloud-tracking methods used today is the two-dimensional Correlation Imaging Velocimetry (CIV) technique, which returns a two-dimensional wind vector field from a pair of images taken at two different times. The CIV algorithm computes a wind vector by calculating the two-dimensional correlation between the pair of maps; a peak in the correlation is judged to be a match. For zonal wind measurements, error bars can be represented by the zonal standard deviation of the wind vectors; while this serves as an indicator of the overall quality of the wind field, it does not address the quality of the individual vectors. Furthermore, the zonal standard deviation contains contributions from both real spatial variations in the wind speed as well as uncertainty in the measurements. This raises a difficulty in distinguishing small, real changes in the wind field from the uncertainties in the measurement. We have developed a technique which isolates real spatial variations from measurement uncertainties by analyzing the correlation fields produced in the CIV algorithm. We determine the size, shape, and orientation of the peak by fitting an ellipse to the peak and calculating the area, eccentricity, and orientation angle of the ellipse. Combining these metrics provides a measure of the uncertainty associated with individual wind vectors. Vectors with smaller, sharper, more circular peaks will have a smaller uncertainty than vectors with larger, flatter, more elliptical peaks. We use our new technique to make zonal wind measurements of Saturn using Cassini ISS images taken between 2004 and 2014. Our measurements reveal small temporal changes in the zonal wind profiles. Our work has been supported by NASA PATM NNX14AK07G and NSF AAG 1212216.