Mixing in Haro Strait
Abstract : The long-term goal is to better understand mixing processes in estuaries. Of particular interest are the processes at work in the Haro Strait region between Washington State and British Columbia, which are modulated by a spring/neap cycle in tidal velocities. The field phase of the Haro Strait experiment took place in the summer of 1996 and involved a collaborative effort between MIT, WHOI and IOS. Our objectives are to understand the dynamics associated with intense coastal mixing processes in which separation of tidal flows past topography leads to formation of fronts accompanied by instability and mixing. During this year, we have begun to analyze this data in two different areas. First, we wanted to better quantify the large-scale dynamics. This means estimating horizontal layer transports, rates and amounts of mixing that take place. Second, we want to use these results to understand the importance of smaller-scale mixing that occurs at convergence fronts within this region.
Real-time frontal mapping with AUVs in a coastal environment
A new oceanographic measurement concept is presented, combining a tomographic network with small autonomous underwater vehicles. Using wireless local area network technology, the acoustic tomography data are recorded and processed in real time for achieving a low-resolution estimate oceanographic parameters. The results are used to focus direct measurements by a network of small AUVs in areas of high spatial and temporal variability. By combining the high coverage but low resolution of the tomography with the high resolution capabilities of the AUVs, oceanographic phenomena with small scale dynamics controlled by large scale environmental forcing can be mapped. The feasibility of the new measurement concept has been demonstrated in connection with a June 96 experiment in Haro Strait, BC, aimed at mapping the properties coastal fronts driven by combined estuarine and tidal forcing.
The parallel waveform relaxation stochastic Runge–Kutta method for stochastic differential equations
For large-scale non-autonomous Stratonovich stochastic differential equations, we study a very general parallel waveform relaxation process which is on the basis of stochastic Runge–Kutta (SRK) method of mean-square order 1.0 in this literature. The convergence of the whole parallel numerical iterative scheme can be guaranteed and the scheme provides better properties in terms of decreasing the load of the computation and operating speed. At the same time, the related limit method is also introduced as the continuous approximation derived from the iterative scheme. In the approximation interval, it is worth noting that the mean-square order of the parallel numerical iterative scheme can be kept consistent with the previous SRK method at any arbitrary time point, not just at discrete points. Some numerical simulations are presented to elaborate the computing efficiency of the parallel numerical iterative scheme.