OSSE Assessment of Underwater Glider Arrays to Improve Ocean Model Initialization for Tropical Cyclone Prediction

• Published in: Journal of atmospheric and oceanic technology 2020-03, Vol.37 (3), p.467-487
• Main Authors: Halliwell, George R., Goni, Gustavo J., Mehari, Michael F., Kourafalou, Villy H., Baringer, Molly, Atlas, Robert
• Format: Article
• Language: English
• Subjects: Advection; Arrays; Atmospheric models; Bias; Computer simulation; Corrections; Covariance matrix; Cyclones; Data assimilation; Data collection; Design; Enthalpy; Error reduction; Experiments; General circulation models; Gliders; Heat content; Hurricanes; Localization; Mathematical models; Mesoscale features; Mesoscale phenomena; Ocean circulation; Ocean models; Oceans; Profilers; Profiles; Salinity; Sea surface; Sea surface temperature; Separation; Storms; Surface temperature; Tapering; Temperature requirements; Tropical climate; Tropical cyclone intensities; Tropical cyclones; Underwater; Underwater gliders; Underwater vehicles; Weather forecasting
• ISSN: 0739-0572; 1520-0426
• DOI: 10.1175/JTECH-D-18-0195.1

Credible tropical cyclone (TC) intensity prediction by coupled models requires accurate forecasts of enthalpy flux from ocean to atmosphere, which in turn requires accurate forecasts of sea surface temperature cooling beneath storms. Initial ocean fields must accurately represent ocean mesoscale features and the associated thermal and density structure. Observing system simulation experiments (OSSEs) are performed to quantitatively assess the impact of assimilating profiles collected from multiple underwater gliders deployed over the western North Atlantic Ocean TC region, emphasizing advantages gained by profiling from moving versus stationary platforms. Assimilating ocean profiles collected repeatedly at fixed locations produces large root-mean-square error reduction only within ~50 km of each profiler for two primary reasons. First, corrections performed during individual update cycles tend to introduce unphysical eddy structure resulting from smoothing properties of the background error covariance matrix and the tapering of innovations by a localization radius function. Second, advection produces rapid nonlinear error growth at larger distances from profiler locations. The ability of each individual moving glider to cross gradients and map mesoscale structure in its vicinity substantially reduces this nonlinear error growth. Glider arrays can be deployed with horizontal separation distances that are 50%–100% larger than those of fixed-location profilers to achieve similar mesoscale error reduction. By contrast, substantial larger-scale bias reduction in upper-ocean heat content can be achieved by deploying profiler arrays with separation distances up to several hundred kilometers, with moving gliders providing only modest additional improvement. Expected sensitivity of results to study region and data assimilation method is discussed.