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Gleb Panteleev
,
Max Yaremchuk
, and
Oceana Francis

Abstract

We analyzed the feasibility of the reconstruction of the spatially varying rheological parameters through the four-dimensional variational data assimilation of the sea ice velocity, thickness, and concentration into the viscoplastic (VP) sea ice model. The feasibility is assessed via idealized variational data assimilation experiments with synthetic observations configured for a 1-day data assimilation window in a 50 × 40 rectangular basin forced by the open boundaries, winds, and ocean currents and should be viewed as a first step in the developing the similar algorithms which can be applied for the more advanced sea ice models. It is found that “true” spatial variability (∼5.8 kN m−2) of the internal maximum ice strength parameter P * can be retrieved from observations with reasonable accuracy of 2.3–5.3 kN m−2, when an observation of the sea ice state is available daily in each grid point. Similar relative accuracy was achieved for the reconstruction of the compactness strength parameter α. The yield curve eccentricity e is found to be controlled by the data with less efficiency, but the spatial mean value of e could be still reconstructed with a similar degree of confidence. The accuracy of P * , α, and e retrievals is found to increase in regions of stronger ice velocity convergence, providing prospects for better processing of the observations collected during the recent MOSAiC experiment. The accuracy of the retrievals strongly depends on the number of the control variables characterizing the rheological parameter fields.

Restricted access
Yuchun Gao
,
Shengyi Jiao
,
Kai Fu
,
Xueying Zeng
, and
Xianqing Lv

Abstract

The adjoint assimilation method has been widely used in various ocean models, and a series of technical schemes have been developed at the same time. Open boundary conditions (OBCs) in the two-dimensional (2D) tidal model of the M2 tidal constituent in the Bohai and the Yellow Seas (BYS) were inverted successfully using the adjoint assimilation methods in previous studies. However, the cost function in the adjoint assimilation method usually used the L2 norm in the past, which is difficult to maintain the robustness of the method when there are outliers. Meanwhile, using the L1 norm with strong robustness will shield the outliers’ information fully. Therefore, we propose a new scheme that replaces the L2 norm with the Huber function to improve the robustness of the adjoint assimilation method and absorb the data’s useful information to some extent. This scheme was verified in the ideal experiments in which magnitudes of the misfit vector were significantly reduced and the quality control (QC) process was simplified consequently. In the practical experiments, the introduction of the Huber function improved the accuracy of inversion in the inshore area using mixed data containing tide gauges and satellite altimetry. With this scheme, the root-mean-square errors (RMSEs) between the estimation and the observed values at tide gauge stations were reduced from ∼8 cm with the original scheme to ∼6 cm. Testing the new scheme in more complex models and how it might be affected remains a topic for future study.

Significance Statement

The adjoint assimilation method has been effectively applied in various ocean models. The cost function in the adjoint assimilation is usually in the form of the L2 norm, which presents poor robustness. By using the Huber function instead of the L2 norm as the cost function, we proposed a new scheme that can perfectly handle the potential outliers in data and noticeably improve the robustness of the adjoint assimilation method. The new method was applied to the inversion of tidal open boundary conditions of the M2 constituent in the Bohai and the Yellow Seas. Both the ideal and practical experiments verified the effectiveness of the developed scheme.

Restricted access
Bruno Ferron
,
P. Bouruet Aubertot
,
Y. Cuypers
, and
C. Vic

Abstract

To calculate a turbulent kinetic energy dissipation rate from the microstructure vertical shear of the horizontal velocity via a spectral analysis, shear spectra need first to be cleaned from vibrations of the moving vehicle. Unambiguously, this study shows that the spectral cleaning must be applied all over the frequency range and not only at frequencies larger than 10 Hz, as a recent study suggested. For a Vertical Microstructure Profiler (VMP-6000), not correcting for vehicle vibrations below 10 Hz leads to overestimated dissipation rates from 50% to 700% for usual downcast velocities and for weak dissipation rates (ε < 1 × 10−9 W kg−1). Vibrations concern all vehicles, but the exact vibrational frequency signature depends on the vehicle shape and its downcast velocity. In any case, a spectral cleaning over the whole frequency range is strongly advised. This study also reports on a systematic low bias of inferred dissipation rates induced by the spectral cleaning when too few degrees of freedom are available for the cleaning, which is usually the default of the standard processing. Whatever the dissipation rate level, not correcting for the bias leads to underestimated dissipation rates by a factor 1.4–2.7 (with usual parameters), the exact amplitude of the bias depending on the number of degrees of freedom and on the number of independent accelerometer axes used for the cleaning. It is strongly advised that such a bias be taken into account to recompute dissipation rates of past datasets and for future observations.

Restricted access
Jhon A. Castro-Correa
,
Stephanie A. Arnett
,
Tracianne B. Neilsen
,
Lin Wan
, and
Mohsen Badiey

Abstract

The presence of internal waves (IWs) in the ocean alters the isotropic properties of sound speed profiles (SSPs) in the water column. Changes in the SSPs affect underwater acoustics since most of the energy is dissipated into the seabed due to the downward refraction of sound waves. In consequence, variations in the SSP must be considered when modeling acoustic propagation in the ocean. Empirical orthogonal functions (EOFs) are regularly employed to model and represent SSPs using a linear combination of basis functions that capture the sound speed variability. A different approach is to use dictionary learning to obtain a learned dictionary (LD) that generates a nonorthogonal set of basis functions (atoms) that generate a better sparse representation. In this paper, the performance of EOFs and LDs are evaluated for sparse representation of SSPs affected by the passing of IWs. In addition, an LD-based supervised framework is presented for SSP classification and is compared with classical learning models. The algorithms presented in this work are trained and tested on data collected from the Shallow Water Experiment 2006. Results show that LDs yield lower reconstruction error than EOFs when using the same number of bases. In addition, overcomplete LDs are demonstrated to be a robust method to classify SSPs during low, medium, and high IW activity, reporting accuracy that is comparable to and sometimes higher than that of standard supervised classification methods.

Restricted access
Samuel Brenner
,
Jim Thomson
,
Luc Rainville
,
Daniel Torres
,
Martin Doble
,
Jeremy Wilkinson
, and
Craig Lee

Abstract

Properties of the surface mixed layer (ML) are critical for understanding and predicting atmosphere–sea ice–ocean interactions in the changing Arctic Ocean. Mooring measurements are typically unable to resolve the ML in the Arctic due to the need for instruments to remain below the surface to avoid contact with sea ice and icebergs. Here, we use measurements from a series of three moorings installed for one year in the Beaufort Sea to demonstrate that upward-looking acoustic Doppler current profilers (ADCPs) installed on subsurface floats can be used to estimate ML properties. A method is developed for combining measured peaks in acoustic backscatter and inertial shear from the ADCPs to estimate the ML depth. Additionally, we use an inverse sound speed model to infer the summer ML temperature based on offsets in ADCP altimeter distance during open-water periods. The ADCP estimates of ML depth and ML temperature compare favorably with measurements made from mooring temperature sensors, satellite SST, and from an autonomous Seaglider. These methods could be applied to other extant mooring records to recover additional information about ML property changes and variability.

Open access
Jacob T. Carlin
,
Edwin L. Dunnavan
,
Alexander V. Ryzhkov
, and
Mariko Oue

Abstract

Quasi-vertical profiles (QVPs) of polarimetric radar data have emerged as a powerful tool for studying precipitation microphysics. Various studies have found enhancements in specific differential phase K dp in regions of suspected secondary ice production (SIP) due to rime splintering. Similar K dp enhancements have also been found in regions of sublimating snow, another proposed SIP process. This work explores these K dp signatures for two cases of sublimating snow using nearly collocated S- and Ka-band radars. The presence of the signature was inconsistent between the radars, prompting exploration of alternative causes. Idealized simulations are performed using a radar beam-broadening model to explore the impact of nonuniform beam filling (NBF) on the observed reflectivity Z and K dp within the sublimation layer. Rather than an intrinsic increase in ice concentration, the observed K dp enhancements can instead be explained by NBF in the presence of sharp vertical gradients of Z and K dp within the sublimation zone, which results in a K dp bias dipole. The severity of the bias is sensitive to the Z gradient and radar beamwidth and elevation angle, which explains its appearance at only one radar. In addition, differences in scanning strategies and range thresholds during QVP processing can constructively enhance these positive K dp biases by excluding the negative portion of the dipole. These results highlight the need to consider NBF effects in regions not traditionally considered (e.g., in pure snow) due to the increased K dp fidelity afforded by QVPs and the subsequent ramifications this has on the observability of sublimational SIP.

Significance Statement

Many different processes can cause snowflakes to break apart into numerous tiny pieces, including when they evaporate into dry air. Purported evidence of this phenomenon has been seen in data from some weather radars, but we noticed it was not seen in data from others. In this work we use case studies and models to show that this signature may actually be an artifact from the radar beam becoming too big and there being too much variability of the precipitation within it. While this breakup process may actually be occurring in reality, these results suggest we may have trouble observing it with typical weather radars.

Restricted access
Guangping Liu
,
Yongping Jin
,
Youduo Peng
,
Deshun Liu
, and
Liang Liu

Abstract

A new full-ocean-depth macroorganisms pressure-retaining sampler (FMPS) was designed to collect pressure-retaining macroorganisms samples from the abyssal seafloor. A mathematical model for pressure compensation in the FMPS recovery process was developed. The effects of FMPS structural parameters, pressure compensator structural parameters, and sampling environment on the pressure retention performance of FMPS were analyzed. Using the developed FMPS engineering prototype, FMPS internal pressure test, high-pressure chamber simulation sampling, and pressure-retaining test was carried out. The test results show that the key components of FMPS can carry 115 MPa pressure, FMPS can complete the sampling action in the high-pressure chamber of 115 MPa, the pressure is maintained at 105.5 MPa, and the pressure drop rate (ratio of pressure drop during FMPS recovery to sampling point pressure) is 9.13%; the experimental results are consistent with the theoretical calculation. The test verified the feasibility of FMPS design and the reliability of pressure retention, providing a theoretical basis and technical support for the design and manufacture of full-ocean-depth sampling devices.

Restricted access
Shakeel Asharaf
,
Derek J. Posselt
,
Faozi Said
, and
Christopher S. Ruf

Abstract

Global Navigation Satellite System Reflectometry (GNSS-R)-based wind retrieval techniques use the global positioning system (GPS) signals scattered from the ocean surface in the forward direction, and can potentially work in all weather conditions. An overview of recent progress made in the Cyclone Global Navigation Satellite System (CYGNSS) level-2 surface wind products is given. To this end, four publicly released CYGNSS surface wind products—Science Data Record (SDR) v2.1, SDR v3.0, Climate Data Record (CDR) v1.1, and science wind speed product NOAA v1.1—are validated quantitatively against high-quality data from tropical buoy arrays. The latest released CYGNSS wind products (e.g., CDR v1.1, SDR v3.0, NOAA v1.1), as compared with these tropical buoy data, significantly outperform the SDR v2.1. Moreover, the uncertainty among these products is found to be less than 2 m s−1 root-mean-squared difference, meeting the NASA science mission level-1 uncertainty requirement for wind speeds below 20 m s−1. The quality of the CYGNSS wind is further assessed under different precipitation conditions in low winds, and in large-scale convective regions. Results show that the presence of rain appears to cause a slightly positive wind speed bias in all CYGNSS data. Nonetheless, the outcomes are encouraging for the recently released CYGNSS wind products in general, and for CYGNSS data in regions with precipitating deep convection. The overall comparison indicates a significant improvement in wind speed quality and sample size when going from the older version to any of the newer datasets.

Restricted access
Bernadette M. Sloyan
,
Christopher C. Chapman
,
Rebecca Cowley
, and
Anastase A. Charantonis

Abstract

In situ observations are vital to improving our understanding of the variability and dynamics of the ocean. A critical component of the ocean circulation are the strong, narrow and highly variable western boundary currents. Ocean moorings that extend from the sea floor to the surface remain the most effective and efficient method to fully observe these currents. For various reasons mooring instruments may not provide continuous records. Here we assess the application of the Iterative Completion Self Organising Maps (ITCOMPSOM) machine learning technique to fill observational data gaps in a 7.5 year time-series of the East Australian Current. The method was validated by withholding parts of fully known profiles, and reconstructing them. For 20% random withholding of known velocity data validation statistics of the u- and v-velocity components are R 2 coefficients of 0.70 and 0.88 and, root mean square errors of 0.038 m s−1 and 0.064 m s−1, respectively. Withholding 100 days of known velocity profiles over a depth range between 60 m to 700 m has mean profile residual differences between true and predicted u- and v-velocity of 0.009 m s−1 and 0.02 m s−1, respectively. The ITCOMPSOM also reproduces the known velocity variability. For 20% withholding of temperature and salinity data root mean square error of 0.04 and 0.38°C, respectively, are obtained. The ITCOMPSOM validation statistics are significantly better than those obtained when standard data filling methods are used. We suggest that machine learning techniques can be an appropriate method to fill missing data and enable production of observational-derived data products.

Open access