New Methods toward Minimizing the Slow Speed Bias Associated with Atmospheric Motion Vectors

Abstract

Comparisons between satellite-derived winds and collocated rawinsonde observations often show a pronounced slow speed bias at mid-and upper levels of the atmosphere. A leading cause of the slow speed bias is the improper assignment of the tracer to a height that is too high in the atmosphere. Height errors alone cannot fully explain the slow bias, however. Another factor influencing the speed bias is the size of the target window used in the tracking step. Tracking with a large target window can cause excessive averaging to occur and a smoothing of the instantaneous wind field. Conversely, if too small a window is specified, there is an increased risk of finding a false match. The authors have developed a new ''nested tracking'' approach that isolates the dominant local motion within a cloud scene and minimizes the smoothing of the motion estimate. A major advantage of the new approach is the ability to identify which pixels within the cloud scene are contributing to the tracking solution. Knowing which pixels contribute to the dominant motion allows for a more representative height to be derived, thereby directly linking the height assignment to the tracking process, which is an important goal for producers of global atmospheric motion vector (AMV) data. When compared with equivalent rawinsondes, the AMVs derived with the new approach show a considerable improvement in the speed bias and root-mean-square error over a control set of AMVs derived with moreconventional methods.

Impact of Wind Guess on the Tracking of Atmospheric Motion Vectors

“…Note that the mean AMV speeds are generally faster using small target boxes for both the WG and NWG configurations. This is in good agreement with the results presented in Bresky et al (2012) and Sohn and Borde (2008). Mean AMV speeds are also generally faster for small temporal gaps, except when using small target boxes.…”

“…The motion is derived by matching the position that best corresponds to this target in the later image. However, it is a common process to use wind guess (WG) information to locate the search area in the later image before the matching (Velden et al 1997;Bedka and Mecikalski 2005;Bresky et al 2012). This allows the use of smaller search areas, which speeds up the process and reduces the computing time necessary to derive operationally the AMVs.…”

Assimilation Experiments of MTSAT Rapid Scan Atmospheric Motion Vectors on a Heavy Rainfall Event

“…In ME, RTN-AMV IR1 winds at all levels showed significant negative biases, and these biases were alleviated in RS-AMV IR1 winds. This bias toward wind speeds lower than the observed is a well-known problem that has been attributed to either height-assignment or tracking errors (Bresky et al 2012). The results of this comparison with NHM forecast winds show that the quality of RS-AMVs is as high as that of RTN-AMVs, and that they are equally suitable for use in the operational 4D-Var assimilation system.…”

A novel approach for the extraction of cloud motion vectors using airglow imager measurements

“…While the large target window allows other unwanted noisy structures enter into the search domain, too small window increases false alarms (Bresky et al, 2012). Conventionally, wind guess (WG) information supplements this exercise and to set the coordinates of smaller windows in the latter image before matching (Velden et al, 1997;Bedka and Mecikalski, 2005;Bresky et al, 2012). In the present study, the horizontal wind climatology discussed in Sect.…”

“…To reduce the computational load and to avoid other clouds or noisy structures, if any remain, entering into the area of interest; it is a common practice to track the target cloud in a smaller search domain in successive images. While the large target window allows other unwanted noisy structures enter into the search domain, too small window increases false alarms (Bresky et al, 2012). Conventionally, wind guess (WG) information supplements this exercise and to set the coordinates of smaller windows in the latter image before matching (Velden et al, 1997;Bedka and Mecikalski, 2005;Bresky et al, 2012).…”