This article discusses the challenges of developing a regional ocean prediction model for the Philippine Archipelago, a complex area in terms of geometry, bathymetry-dominated dynamics and variability, and strong local and remote wind forcing, where there are limited temporal and spatial ocean measurements. We used the Regional Ocean Modeling System (ROMS) for real-time forecasting during the Philippine Straits Dynamics Experiment (2007–2009) observational program. The article focuses on the prediction experiments before and during the exploratory cruise period, June 6–July 3, 2007. The gathered observations were not available in real time, so the 4-Dimensional Variational (4D-Var) data assimilation experiments were carried out in hindcast mode. The best estimate of ocean state (nowcast) is determined by combining satellite-derived products for sea surface temperature and height, and subsurface temperature and salinity measurements from several hydrographic assets over a sequential five-day data assimilation window. The largest source of forecast uncertainty is from the prescribed lateral boundary conditions in the nearby Pacific Ocean, especially excessive salt flux. This result suggests that remote forcing and inflows from the Pacific are crucial for predicting ocean circulation in the Philippine Archipelago region. The lateral boundary conditions are derived from 1/12° global HYbrid Coordinate Ocean Model (HYCOM) daily snapshots. The incremental, strong-constraint 4D-Var data assimilation successfully decreased temperature and salinity errors of the real-time, nonassimilative control forecast by 38% and 49%, respectively.

Arango, H.G., J.C. Levin, E.N. Curchitser, B. Zhang, A.M. Moore, W. Han, A.L. Gordon, C.M. Lee, and J.B. Girton. 2011. Development of a hindcast/forecast model for the Philippine Archipelago. Oceanography 24(1):58–69, https://doi.org/10.5670/oceanog.2011.04.

Agarwal, A. 2009. Statistical Field Estimation and Scale Estimation for Complex Coastal Regions and Archipelagos. MS Thesis, Massachusetts Institute of Technology, Department of Mechanical Engineering, May 2009.
1. Bengtsson, L., M. Ghil, and E. Källen. 1981. Dynamic Meteorology: Data Assimilation Methods. Springer-Verlag, 330 pp.
2. Bennett, A.F. 1992. Inverse Methods in Physical Oceanography. Cambridge University Press, 347 pp. [CrossRef]
3. Bennett, A.F. 2002. Inverse Modeling of the Ocean and Atmosphere. Cambridge University Press, 234 pp. [CrossRef]
4. Bleck, R. 2002. An oceanic general circulation model framed in hybrid isopycnic-cartesian coordinates. Ocean Modeling 4:55–88. [CrossRef]
5. Boyer, T., S. Levitus, H. Garcia, R.A. Locarnini, C. Stephens, and J. Antonov. 2005. Objective analyses of annual, seasonal, and monthly temperature and salinity for the World Ocean on a 0.25° grid. International Journal of Climatology 25(7):931–945. [CrossRef]
6. Broquet, G., C.A. Edwards, A.M. Moore, B.S. Powell, M. Veneziani, and J.D. Doyle. 2009. Application of 4D-Variational data assimilation to the California Current System. Dynamics of Atmospheres and Oceans 48:69–92. [CrossRef]
7. Broquet, G., A.M. Moore, H.G. Arango, and C.A. Edwards. 2010. Corrections to ocean surface forcing in the California Current System using 4D-variational data assimilation. Ocean Modelling 36:116–132. [CrossRef]
8. Cummings, J.A. 2005. Operational multivariate ocean data assimilation. Quarterly Journal of the Royal Meteorological Society 131(613):3,583–3,604.
9. Daley, R. 1991. Atmospheric Data Analysis. Cambridge University Press, 457 pp.
10. Durski, S.M., S.M. Glenn, and D.B. Haidvogel. 2004. Vertical mixing schemes in the coastal ocean: Comparison of the level 2.5 Mellor-Yamada scheme with an enhanced version of the K profile parameterization. Journal of Geophysical Research 109, C01015. [CrossRef]
11. Egbert, G.D., A.F. Bennett, and M.G.G. Foreman. 1994. TOPEX/POSEIDON tides estimated using a global inverse model. Journal of Geophysical Research 99:24,821–24,852. [CrossRef]
12. Egbert, G.D., and S.Y. Erofeeva. 2002. Efficient inverse modeling of barotropic ocean tides. Journal of Atmospheric and Oceanic Technology 19:183–204. [CrossRef]
13. Fairall, C.W., E.F. Bradley, J.E. Hare, A.A. Grachev, and J.B. Edson. 2003. Bulk parameterization of air-sea fluxes: Updates and verification for the COARE algorithm. Journal of Climate 16:571–591. [CrossRef]
14. Flather, R.A. 1976. A tidal model of the northwest European continental shelf. Memoires de la Société Royale des Sciences de Liège 10:141–164.
15. Ghil, M., and P. Malanotte-Rizzoli. 1991. Data assimilation in meteorology and oceanography. Advances in Geophysics 33:141–266. [CrossRef]
16. Girton, J.B., B.S. Chinn, and M.H. Alford. 2011. Internal wave climates of the Philippine seas. Oceanography 24(1):100–111. [CrossRef]
17. Gordon, A.L., J. Sprintall, and A. Ffield. 2011. Regional oceanography of the Philippine Archipelago. Oceanography 24(1):14–27. [CrossRef]
18. Haidvogel, D.B., H. Arango, W.P. Budgell, B.D. Cornuelle, E. Curchitser, E. Di Lorenzo, K. Fennel, W.R. Geyer, A.J. Hermann, L. Lanerolle, and others. 2008. Ocean forecasting in terrain-following coordinates: Formulation and skill assessment of the Regional Ocean Modeling System. Journal of Computational Physics 227(7):3,595–3,624.
19. Haidvogel, D.B., H.G. Arango, K. Hedstrom, A. Beckmann, P. Malanotte-Rizzoli, and A.F. Shchepetkin. 2000. Model evaluation experiments in the North Atlantic Basin: Simulations in nonlinear terrain-following coordinates. Dynamics of Atmospheres and Oceans 32:239–281. [CrossRef]
20. Han, W., A.M. Moore, J. Levin, B. Zhang, H.G. Arango, E. Curchitser, E. Di Lorenzo, A.L. Gordon, and J. Lin. 2009. Seasonal surface ocean circulation and dynamics in the Philippine Archipelago region during 2004–2008. Dynamics of Atmospheres and Oceans 47:114–137. [CrossRef]
21. Hurlburt, H.E., E.J. Metzger, J. Sprintall, S.N. Riedlinger, R.A. Arnone, T. Shinoda, and X. Xu. 2011. Circulation in the Philippine Archipelago simulated by 1/12° and 1/25° global HYCOM and EAS NCOM. Oceanography 24(1):28–47. [CrossRef]
22. Ingleby, B., and M. Huddleston. 2007. Quality control of ocean temperature and salinity profiles: Historical and real-time data. Journal of Marine Systems 65:158–175. [CrossRef]
23. Kleist, D.T., D.F. Parrish, J.C. Derber, R. Treadon, W.-S. Wu, and S. Lord. 2009. Introduction of the GSI into the NCEP Global Data Assimilation System. Weather and Forecasting 24:1,691–1,705.
24. May, P.W., J.D. Doyle, J.D. Pullen, and L.T. David. 2011. Two-way coupled atmosphere-ocean modeling of the PhilEx Intensive Observational Periods. Oceanography 24(1):48–57. [CrossRef]
25. Moore, A.M., H.G. Arango, E. Di Lorenzo, B.D. Cornuelle, A.J. Miller, and D.J. Neilson. 2004. A comprehensive ocean prediction and analysis system based on the tangent linear and adjoint of a regional ocean model. Ocean Modelling 7:227–258. [CrossRef]
26. Moore, A.M., H.G. Arango, E. Di Lorenzo, A.J. Miller, and B.D. Cornuelle. 2009. An adjoint sensitivity analysis of the Southern California Current circulation and ecosystem. Journal of Physical Oceanography 39:702–720. [CrossRef]
27. Moore, A.M., H.G. Arango, G. Broquet, B.S. Powell, J. Zavala-Garay, and A.T. Weaver. 2011a. The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems, Part I: Formulation and overview. Progress in Oceanography, submitted.
28. Moore, A.M., H.G. Arango, G. Broquet, C. Edwards, M. Veneziani, B.S. Powell, D. Foley, J. Doyle, D. Costa, and P. Robinson. 2011b. The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems, Part II: Performance and applications to the California Current System. Progress in Oceanography, submitted.
29. Moore, A.M., H.G. Arango, G. Broquet, C. Edwards, M. Veneziani, B.S. Powell, D. Foley, J. Doyle, D. Costa, and P. Robinson. 2011c. The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems, Part III: Observation impact and observation sensitivity in the California Current System. Progress in Oceanography, submitted.
30. Powell, B.S., H.G. Arango, A.M. Moore, E. Di Lorenzo, R.F. Milliff, and D. Foley. 2008. 4DVAR data assimilation in the Intra-Americas Sea with the Regional Ocean Modeling System (ROMS). Ocean Modelling 25:173–188. [CrossRef]
31. Powell, B.S., A.M. Moore, H.G. Arango, E. Di Lorenzo, R.F. Milliff, and R.R. Leben. 2009. Near real-time ocean circulation assimilation and prediction in the Intra-Americas Sea with ROMS. Dynamics of Atmospheres and Oceans 48:46–68. [CrossRef]
32. Rio, M.-H., P. Schaeffer, J.-M. Lemoine, and F. Hernandez. 2005. Estimation of the ocean mean dynamic topography through the combination of altimetric data, in-situ measurements, and GRACE geoid: From global to regional studies. Proceedings of the GOCINA [Geoid and Ocean Circulation in the North Atlantic] International Workshop. April 13–15, 2005, Luxembourg.
33. Shchepetkin, A.F., and J.C. McWilliams. 2003. A method for computing horizontal pressure-gradient force in an oceanic model with a nonaligned vertical coordinate. Journal of Geophysical Research 108(C3), 3090. [CrossRef]
34. Shchepetkin, A.F., and J.C. McWilliams. 2005. The Regional Ocean Modeling System: A split-explicit, free-surface, topography following coordinates ocean model. Ocean Modelling 9:347–404. [CrossRef]
35. Shchepetkin, A.F., and J.C. McWilliams. 2009. Computational kernel algorithms for fine-scale, multiprocess, longtime oceanic simulations. Pp. 119–182 in Handbook of Numerical Analysis: Computational Methods for the Atmosphere and Oceans. R.M. Teman and J.J. Tribbia, eds, Elsevier Science. [CrossRef]
36. Smith, W.H.F., and D.T. Sandwell. 1997. Global sea floor topography from satellite altimetry and ship depth soundings. Science 277:1,956–1,962.
37. Tarantola, A. 1987. Inverse Problem Theory: Methods for Data Filtering and Model Parameter Estimation. Elsevier, 613 pp.
38. Wang, X., and Y. Chao. 2004. Simulated sea surface salinity variability in the tropical Pacific. Geophysical Research Letters 31, L02302. [CrossRef]
39. Warner, J.C., C.R. Sherwood, H.G. Arango, and R.P. Signell. 2005. Performance of four turbulence closure methods implemented using a generic length scale method. Ocean Modelling 8:81–113. [CrossRef]
40. Willis, J.K., D. Roemmich, and B.D. Cornuelle. 2004. Interannual variability in upper ocean heat content, temperature, and thermosteric expansion on global scales. Journal of Geophysical Research 109, C12036. [CrossRef]
41. Wunsch, C. 1996. The Ocean Circulation Inverse Problem. Cambridge University Press, 442 pp. [CrossRef]
42. Zhang, W.G., J.L. Wilkin, and H.G. Arango. 2010a. Towards an integrated observation and modeling system in the New York Bight using variational methods. Part I: 4DVAR data assimilation. Ocean Modelling 35:119–133. [CrossRef]
43. Zhang, W.G., J.L. Wilkin, and J.C. Levin. 2010b. Towards an integrated observation and modeling system in the New York Bight using variational methods. Part II: Repressenter-based observing strategy evaluation. Ocean Modelling 35:134–145.

This is an open access article made available under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format as long as users cite the materials appropriately, provide a link to the Creative Commons license, and indicate the changes that were made to the original content. Images, animations, videos, or other third-party material used in articles are included in the Creative Commons license unless indicated otherwise in a credit line to the material. If the material is not included in the article’s Creative Commons license, users will need to obtain permission directly from the license holder to reproduce the material.