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Autonomous Ocean Sampling Network (AOSN)

"The Autonomous Ocean Sampling Network (AOSN) project seeks to bring together sophisticated new robotic vehicles with advanced ocean models to improve our ability to observe and predict the ocean.  Key to our effort is the development of control strategies to command our mobile vehicles to places where their data will be most useful.  We call this 'adaptive sampling.'  The ability to predict physical properties of the ocean, such as temperature and current, and their biological (ecosystem productivity) and chemical (nutrient fertilization) are central to our program. The operational system includes data collection by smart and adaptive platforms and sensors that relay information to a shore in near real-time (hours) where it is assimilated into numerical models, that help visualize the four dimensional fields and predict future conditions. The success of the AOSN program depends on the collaboration of a network of institutions spread across the United States."
Excerpt from the AOSN Home page at the Monterey Bay Aquarium Research Institute (MBARI)




Introduction

 
During August 2003, the AOSN-II field trial was held in Monterey Bay. A staggering number of different observational platforms were deployed in and around the bay during that month. Two numerical models were used to predict the state of Monterey Bay, for the first time, assimilating much of the in-situ data that was collected. The main physical process of interest during AOSN-II was coastal upwelling, although other features such as eddy circulations were investigated.

My own research in the AOSN project has two goals:
(1) To understand how atmospheric processes affect the ocean behavior in and around Monterey Bay
(2) To develop a reliable ensemble-based adaptive sampling strategy to select the time and type of autonomous observations to be collected, to improve the analysis or forecast of a physical process of interest (e.g. upwelling, mesoscale eddies)


The AOSN-II field trial gave an opportunity to develop ensembles of Regional Ocean Modeling System (ROMS) forecasts, based on (a) Initial perturbations using the breeding technique, and (b) Phase-shifted perturbations of COAMPS model wind stress fields. Once the ensembles were stable and running, the Ensemble Transform Kalman Filter (ETKF) adaptive sampling strategy was developed.



Collaborators


Yi Chao, Zhijin Li, Jei-Kook Choi (NASA Jet Propulsion Laboratory). ROMS Ocean Model.
Craig Bishop (NRL Monterey). Atmospheric forcing, adaptive sampling, ensemble generation.
Pierre Lermusiaux (DEAS, Harvard University). Adaptive sampling, ensemble generation.



Preliminary Results
(date = ensemble initialization time)

ANIMATED GIF OF 3-KM COAMPS SURFACE WINDS DURING AOSN (WARNING: 25MB!!)

Aug 18
Aug 19
Aug 20
Aug 22
Aug 24
Aug 25
Aug 26
Aug 27
Aug 28
Aug 31

There are hundreds of plots for each day  (green=available, red=not available)


COAMPS Wind Stress at Ocean Surface ("CO" in tables)

The COAMPS control forecast, issued by the Naval Research Laboratory, Monterey is given for 0h, 24h, 48h and 72h.
A phase-shift method based on Autoregressive functions is used to perturb the COAMPS wind stress fields in space and time. The phase shift functions "lx" and "ly" are shown in the top panels for each time, for each of the 8 ensemble members (there is a similar phase shift "tau" in time, not shown)

The wind stress fields u_s and v_s are perturbed in the following manner:

u_s (x,y,t) --> u_s (x+lx,y+ly,t+tau)
v_s (x,y,t) --> v_s (x+lx,y+ly,t+tau)

Using this technique, it is expected that realistic atmospheric features are preserved, and that ensemble variance is largest in areas where the gradient is highest. The ensemble generation technique still need to be refined considerably, but the first attempt is encouraging.

ROMS Ocean Model

The ROMS model (labeled "ctrl") was run each day at JPL, with all available and quality-checked AOSN-II data being assimilated using a 3d-Var scheme. The model consists of 3 nested grids, with resolution 15, 5 and 1.67 km. The innermost domain covers Monterey Bay.

Each of the 8 ROMS ensemble members was run to 2-3 days on the JPL Supercomputer, with the corresponding perturbed COAMPS atmospheric forcing, and initial condition derived from breeding. The forecast ocean currents, temperature and salinity fields are shown for depths of 0m, 10m, 30m, 50m and 200m in the Monterey Bay region.

ETKF Summary Maps

Based on the ROMS ensembles, the ETKF adaptive sampling (or "targeting") strategy was tested near the end of AOSN-II. Some maps are linked from the lower frame. The "summary map" gives the predicted reduction in forecast error variance as a function of the location in which a targeted observation is taken. Hence, the best location to take an observation is where the value is highest (red shading). Unlike our atmospheric targeting to date, these summary maps are 3-dimensional fields. Only horizontal and vertical cross-sections are shown here. In principle, one can draw a hypothetical glider track through the maxima of the ETKF result. Alternatively, the ETKF calculations can be computed for any combination of prescribed observational paths, over a series of times. This is the direction in which we'd like to head, but first the ETKF needs to be evaluated before taking this ambitious step. Currently, I don't have tremendous faith in the ETKF results since the number of ensemble members (8 at most) is far too small, and the ensemble perturbation technique above needs more development. Improved computing power over the next few years will alleviate the small-ensemble problem considerably.

For more details on the adaptive sampling using the ETKF, click here.



The Near Future

1. The Error Subspace Statistical Estimation approach of Lermusiaux and Robinson (1999) has some theoretical similarities with the ETKF. A theoretical inter-comparison between the two strategies in data assimilation, ensemble generation, and adaptive sampling will be performed in the very near future. The relative strengths of the two techniques can be exploited to produce an improved ensemble-based strategy for adaptive sampling.

2. The breeding technique for ensemble initialization will be replaced by the ETKF technique of Wang and Bishop (2003), once the observation operators and error variance estimates for the myriad of observation types are available.

3. One of the primary mechanisms for producing an upwelling event is strong sustained winds, that last for several days. Typically, these winds prevail from the north-west during the California summer. The sensitivity of the ROMS ocean model to changes in atmospheric wind fields needs investigation. The development of a fully coupled atmosphere-ocean model requires considerable research and is probably some time away, but the simple sensitivity studies using prescribed forcings and fluxes may still shed valuable light on how the atmospheric fields are affecting the oceanic state.

4. The ETKF targeting strategy needs to be evaluated. First, the ability of the ETKF to predict the variance of "signals" will be assessed. A signal represents the difference in two ocean state estimates; one of which has assimilated the targeted observations, whereas the other has ignored the targeted observations. The techniques of Majumdar et al. (2001) and Bishop et al. (2003) will be used to test whether the ETKF is making reliable predictions. As mentioned above, there are several different applications of the ETKF that can be developed and evaluated. However, large, reliable ensembles are necessary (although by no means sufficient) for the ETKF to make trustworthy predictions of the effects of targeted observations.



Longer-Term Goal

A reliable adaptive sampling strategy can in principle be used to deploy autonomous vehicles along any path, at any time, to improve forecast skill. The development of such a strategy that is consistent with a state-of-the-art data assimilation scheme that assimilates observations nearly continuously in time is the ultimate goal. The intelligent deployment of autonomous vehicles in the ocean (and atmosphere) is likely to lead to improved forecasts and understanding of key physical processes of interest.



Links

MBARI AOSN Home Page
Monterey Bay '03
AOSN Image Page
JPL ROMS
Harvard Group
Princeton/Caltech



Photos (click to enlarge)

glider rescue

spudgun


Last Updated: May 2 2004