Oceanic data assimilation study in northern Chile: use of a 3DVAR method

ISI Document Delivery No.: 189IY Times Cited: 0 Cited Reference Count: 13 Cited References: Capet X, 2008, J PHYS OCEANOGR, V38, P44, DOI 10.1175/2007JPO3672.1 Chao Y, 2009, DEEP-SEA RES PT II, V56, P100, DOI 10.1016/j.dsr2.2008.08.011 Debreu L, 2012, OCEAN MODEL, V49-50, P1, DOI 10.1016/j.ocemod.2012.03.003 Drevillon M, 2008, J OPER OCEANOGR, V1, P51 Li ZJ, 2008, J GEOPHYS RES-OCEANS, V113, DOI 10.1029/2008JC004928 Li ZJ, 2008, J ATMOS OCEAN TECH, V25, P2074, DOI 10.1175/2008JTECHO594.1 Marchesiello P., 2008, MERCATOR OCEAN Q NEW, V30, P38 National Center for Environmental Prediction (NCEP), 2003, 442 NCEP EMC PARRISH DF, 1992, MON WEATHER REV, V120, P1747, DOI 10.1175/1520-0493(1992)1202.0.CO;2 Penven P., 2007, ENVIRON MODELL SOFTW, V23, P2007 Penven P, 2006, OCEAN MODEL, V12, P157, DOI 10.1016/j.ocemod.2005.05.002 Shchepetkin AF, 2005, OCEAN MODEL, V9, P347, DOI 10.1016/j.ocemod.2004.08.002 Weaver AT, 2005, Q J ROY METEOR SOC, V131, P3605, DOI 10.1256/qj.05.119 Sepulveda, Hector H. Marchesiello, Patrick Li, Zhijin project "Implementation de ROMS-3DVAR pour affiner le downscaling des previsions MERCATOR (projet previ-ROMS)"; project INNOVA-CORFO [07CN13IXM-A 150] Funding provided by project "Implementation de ROMS-3DVAR pour affiner le downscaling des previsions MERCATOR (projet previ-ROMS)". MERCATOR, Fr. and project INNOVA-CORFO 07CN13IXM-A 150. 0 UNIV CATOLICA DE VALPARAISO VALPARAISO LAT AM J AQUAT RESWe report the use of a 3-dimensional variational (3DVAR) data assimilation method as part of a numerical model off northern Chile. The numerical model is part of an ocean forecasting project that aims to understand the impact of environmental variability on the distribution of biological species in the area. We assimilated data from a simulated ocean observing system to recover a known state, obtaining a significantly smaller error when compared to a numerical run with no assimilation. Our results validate the computational implementation of the code, and allow us to evaluate the impact of the choice of data in the assimilation process: the assimilation of sea surface height being particularly important. We note that the assimilation of surface data propagates properly to greater depths and reduces the error with reference to the known state. This was possible by using covariance error matrices calculated previously for the California coastal area. The implementation of the data assimilation module is relatively simple and permits its use in operational forecasting systems, and for the design and evaluation of future ocean observational systems