A Simple Technique to Remove Tidal Influence from ADCP Measurements
Justin Gilchrist, Alex Davies and Ajoy Kumar.
Millersville University, Department of Earth Sciences.
Introduction
Methods
Acoustic Doppler Current Profiler (ADCP) measured current
velocities close to the coast are strongly influenced by the local tides and
there lacks a simplistic method of removing this influence from vessel
mounted observations. In this study, we demonstrate a new simple
technique that allows us to de-tide the observed ADCP velocities. The
technique is based on simple vector differencing method. The first step in
the process is to produce tidal velocities of the region using the ROMS
model. In the next step, we remove the tidal velocities from the ADCP
data using vector differencing at a number of points along the ships
track. An extensive error analysis will be carried out to assess the validity
of our de-tiding methods. Two sets of points will be used in this study.
The first set will contain points that lie inside the model domain and the
second set of points that lie on the model boundary. In this way, we can
determine the effect of the model boundaries on the above technique.
Results will determine the feasibility and accuracy of the technique.
Similar to various other studies in de-tiding ocean current data,
harmonic analysis was employed to both identify and remove the tidal
component from the ROMS model data. In the case of harmonic analysis
to identify tides, there were thirty-seven tidal harmonics that need to be
considered. All harmonics and observations beyond these known tidal
harmonics represent residual data or error. The thirty-seven tidal
harmonics are well known and studied. Each of the tidal harmonics has a
constant speed, or rates of change in the phase of the individual
constituent, which are readily available through NOAA via
tidesandcurrents.noaa.gov. Harmonic analysis begins by calculating the
thirty-seven sets of tidal coefficients, Ak and Bk, for each of the data points
for both the u and v components.
Objectives
•Provide a simple yet effective technique to remove tidal component from
vessel mounted ADCP data
•Test the validity of the de-tiding technique
•Apply method at varying depths as well as other BIOME cruises
Materials
The Regional Ocean Model System (ROMS) used in our study
has been configured for the Chesapeake Bay and adjacent shelf regions
with a terrain-following or orthogonal curvilinear coordinate system. This
study will utilize ROMS output data for July 4-5, 2006 with a temporal
resolution of 1 hour. Figure 1 visualizes this coordinate system in a
gridded model domain with land areas masked
BIOME (Bio-Physical Interactions in Coastal Margin Ecosystems)
IV is one of a collection of seasonal oceanographic cruises, which
monitor various meteorological, biological, and physical interactions in
the coastal region offshore of Delaware, Maryland, and Virginia. The
cruises observe and interpret the seasonal variability of these
interactions. Figure 2 displays the ship track of the BIOME lV cruise.
The ADCP data collected for this study was collected from BIOME
cruises using a RD (Now RDI) “Workhorse” 600 KHz with a nominal
range of 60 m. The 1st bin resided 2.11m beneath the surface. For all
intensive purposes this will be considered the surface. Each successive
been below the surface bin is 1 meter in width.
Initial Results Continued
Figure 4 and figure 5 compare the u and v components respectively. They
also include the resulting de-tided components. In figure 4, the ROMS
model seems to do a fair job representing the observed u-component of the
ADCP. The v-components, found in figure 5, display a larger dissimilarity
between the observed and modeled v-components. This discrepancy is a
topic of further research and needs to be considered when analyzing the
validity of the de-tided values. The de-tided components are, as one would
expect, significantly smaller then the observed and modeled values. This
provides some reassurance to that fact that the currents in the region are
heavily influenced by the local tides. A proper statistical analysis is needed
to properly evaluate the validity of this technique.
Ax  B

2kt 
2kt 

y t  y  Ak cos
 Bk sin 


 n 
 n 
k 1 
n /2
The raw data from the ADCP was processed using VMDAS and

converted
into short term and long term averages, which span 5 and 15
minutes respectively. WinADCP was then used to convert those short
term and long term averages into Matlab variables. Using the U and V
components with the vessels influence removed the locations are
matched with the model output for the same time period. A simple vector
differencing method is used to estimate the de-tided current velocities at
each location where ADCP data was available within the domain of the
model. For this study, eighteen U and V components from the ancillary
ADCP data were matched in both space and time with data points from
ROMS . The resultant current velocities represent shelf currents with the
tidal components removed.
Initial Results
To date we have applied these concepts to get our first glimpse that
the de-tided ADCP observations. We expected to see a significant shift in the
direction from the original observations to the de-tided observations due to
the strong tidal influence in and around the mouth of the Chesapeake bay. In
addition, we also expected to see a substantial reduction in the magnitude of
the ADCP observations after the tidal influence was removed. The results of
Figure 3 (below) support our original hypotheses that the de-tided vectors
(black) indeed show a directional shift as well as a reduction in magnitude.
Further analysis is required to properly identify and inspect the vectors that
did not support our hypothesis.
Figure 4: Plot of observed ADCP u component, ROMS u
component and the de-tided u component.
Figure 5: Plot of observed ADCP v component, ROMS
v component and the de-tided v component.
Summary
In summary, we found that the best approach to de-tiding the
ADCP data collected during the BIOME lV cruise would be with the use
of a regional ocean model. Given the cruise track, it was found that the
Chesapeake Bay Regional Ocean Model System would be the best fit
for this particular study. Using harmonic analysis to identify the tidal
currents we were able to use simple vector subtraction to remove the
tidal component from the velocities collected by the ADCP. We look to
continue to use this method to remove the tidal component from the
ADCP data at depth.
Future Research
An error analysis will be carried out to estimate the validity of our detiding method. In addition, further comparisons will be made between
The above steps will be again used at different depths to get a current
profile of the inner shelf region. With a gridded field of the inner and
outer shelf currents, ancillary BIOME cruise data can be used to identify
coastal processes such as upwelling and relate them to satellite and
biological data collected over the area of interest. The physical regime
can be characterized by the CTD data collected during BIOME IV. Wind
stress and upwelling indexes could also be estimated from data obtained
from the Chesapeake Bay Light Tower and NDBC buoy located nearest
to the cruise transect.
References
Wilks, D. S., Statistical Methods in the Atmospheric Sciences, 2nd Edition, International Geophysics Series,
Vol. 91, ElsevierInc., Boston, 2006, Chaps. 8, 9.
Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P., Numerical Recipes in C++, The Art of
Scientific Computing, 2nd Edition, Cambridge University Press, New York, 2002, Chap. 2.
Acknowledgements
Figure 1: Display of the domain of the Chesapeake
Bay ROMS used for this study.
Millersville University Earth Sciences Department
John and Tiffany Moisan - NASA Wallops Island Flight Facility
M_MAP Tool Box
Bruce Lipphardt, University of Delaware
Figure 2: BOIME IV ship
track
TEMPLATE DESIGN © 2008
www.PosterPresentations.com
Grants
Figure 3: Vector plot of original ADCP observations (Black) and the de-tided observations (Red).
Note that since this is a time series the data was collected over the course of two days.
AGU Student Travel Grant, Spring 2010
Millersville University Student Research Grant, Spring 2009
Neimeyer-Hodgson Student Research Grant, Fall 2008