Data from several satellite passes can be combined to produce surface vector wind fields for the bulk of the storm. A wide range of gridding techniques and assumptions can be used to produce these gridded wind fields, each with substantial strengths and weaknesses. For example, numerical weather prediction (NWP) tools can be used to assimilate the satellite data, and to advect information to a common time. However, such models tend to have rather poor boundary-layer physics, and imposed physical assumptions rarely work well for severe weather. These problems are much worse in the tropics than in mid-latitudes; however, they remain a serious problem for warm core seclusions (mid-latitude storms with a core that is warm relative to its surroundings), which are extremely powerful storms. Another problem with these storms is that they tend to translate very rapidly and change their structure on short time scales compared to NWP output. These rapid changes hamper traditional objective analysis techniques, greatly limiting the time window for which data can be usefully assimilated. Data from adjacent scatterometer passes (less than two hours difference) have been used with some success in the representation of patterns for wind speed and direction. Spatial derivative of wind vectors are often of more interest for ocean and atmospheric dynamics. In this study, the vorticity field (the curl of the winds) is investigated in the context of warm core seclusions. The vorticity is calculated in the individual swaths (prior to regridding), and in the gridded product.
[1]
Mark A. Bourassa,et al.
Remotely sensed winds for episodic forcing of ocean models
,
2005
.
[2]
Mark A. Bourassa,et al.
Objectively Derived Daily “Winds” from Satellite Scatterometer Data
,
2000
.
[3]
Philip Cunningham,et al.
Vorticity-Based Detection of Tropical Cyclogenesis
,
2006
.
[4]
J. O'Brien,et al.
Modeling studies of the upper ocean response to a tropical cyclone
,
2006
.
[5]
Michael H. Freilich,et al.
Sampling Errors in Wind Fields Constructed from Single and Tandem Scatterometer Datasets
,
2001
.