Evaluation of ocean models using observed and simulated drifter trajectories: Impact of sea surface height on synthetic profiles for data assimilation

Abstract

[1] The impact of global Navy Layered Ocean Model (NLOM) sea surface height (SSH) on global Navy Coastal Ocean Model (NCOM) nowcasts of ocean currents is investigated in a series of experiments. The studies focus on two primary aspects: the role of NLOM horizontal resolution and the role of differences between the SSH means in NLOM and the Modular Ocean Data Assimilation System (MODAS) climatology. To evaluate the impact of changes to the assimilation system, we compare observed drifter trajectories with trajectories simulated using global NCOM over 7-day timescales. The results indicate general improvement in NCOM currents as a result of increasing NLOM horizontal resolution. The effects of accounting for the differences between NLOM and MODAS mean that SSH is less clear, with some regions showing a decline in simulation skill while others show improvement or little impact. These outcomes supported recommendations for operational transition to 1/32°global NLOM with continued refinement to methods accounting for differences in mean SSH. (2007), Evaluation of ocean models using observed and simulated drifter trajectories: Impact of sea surface height on synthetic profiles for data assimilation,

MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting – Part 2: Numerical simulations and validations

“…The papers of Reed et al (1994) and Al-Rabeh et al (2000) showed a qualitative comparison between drifting buoy trajectories and modeled trajectories. In more recent papers, quantitative metrics, based on the separation distance between modeled and observed trajectories, are presented (Price et al, 2006;Barron et al, 2007;Caballero et al, 2008;Sotillo et al, 2008;Huntley et al, 2011;Cucco et al, 2012). A skill score, based on separation distance normalized by the trajectory length, has been recently proposed by Liu and Weisberg (2011).…”

“…Price et al (2006) found that separation distance between modeled and drifters trajectories is 78 km after 3 days, and 229 km after 20 days. RMSE over the integration time, estimated by Barron et al (2007), ranges from 10 to 25 km after 1 day and from 50 to 150 km after 7 days. Caballero et al (2008) showed that after 3 days observed and modeled trajectories separate by 23 km, while after 7 days the separation increases to 46 km.…”

The Crossover Time as an Evaluation of Ocean Models Against Persistence

“…The local circulation memory is likely maintained for longer in slowly evolving dynamics, improving the performance of persistence trajectories. Therefore, the ocean model has a tougher persistence benchmark where Lagrangian trajectories are inherently more predictable in the model, that is, in areas of lower square root of the velocity variance (Barron et al, 2007;Özgökmen et al, 2000).…”

“…An example of a reasonable choice of M is the commonly used Lagrangian separation distance (Barron et al, 2007;Berta et al, 2015;Castellari et al, 2001;Liu et al, 2014;Phillipson & Toumi, 2017;Rixen & Ferreira-Coelho, 2007;Muscarella et al, 2015), that is, the separation distance between simulated trajectories (computed via an advection scheme) and observed drifters (km):…”

“…For quantifying improvements in ocean current forecasts, particularly the Lagrangian predictability, persistence has been typically taken as either a constant last known position (Barron et al, 2007;Kuang et al, 2012;Schmidt & Gangopadhyay, 2013;Ullman et al, 2006) or constant last known velocity (Paldor et al, 2004;Rixen & Ferreira-Coelho, 2007) of a surface-drifting buoy held constant. Surface-drifting buoys provide unique observations of the ocean currents capturing various scales of motion (Lumpkin et al, 2017).…”