Browse

You are looking at 21 - 30 of 5,062 items for :

  • Journal of Atmospheric and Oceanic Technology x
  • Refine by Access: All Content x
Clear All
Tara Howatt, Stephanie Waterman, and Tetjana Ross

Abstract

Turbulence plays a key role in many oceanic processes, but a shortage of turbulence observations impedes its exploration. Parameterizations of turbulence applied to readily-available CTD data can be useful in expanding our understanding of the space-time variability of turbulence. Typically tested and applied to shipboard data, these parameterizations have not been rigorously tested on data collected by underwater gliders, which show potential to observe turbulence in conditions that ships cannot. Using data from a 10-day glider survey in a coastal shelf environment, we compare estimates of turbulent dissipation from the finescale parameterization and Thorpe scale method to those estimated from microstructure observations collected on the same glider platform. We find that the finescale parameterization captures the magnitude and statistical distribution of dissipation, but cannot resolve spatiotemporal features in this relatively shallow water depth. In contrast, the Thorpe scale method more successfully characterizes the spatiotemporal distribution of turbulence; however, the magnitude of dissipation is overestimated, largely due to limitations on the detectable density overturn size imposed by the typical glider CTD sampling frequency of 0.5 Hz and CTD noise. Despite these limitations, turbulence parameterizations provide a viable opportunity to use CTD data collected by the multitude of gliders sampling the ocean to develop greater insight into the space-time variability of ocean turbulence and the role of turbulence in oceanic processes.

Restricted access
Ganesh Gopalakrishnan, Bruce D. Cornuelle, Matthew R. Mazloff, Peter F. Worcester, and Matthew A. Dzieciuch

Abstract

The 2010–2011 North Pacific Acoustic Laboratory (NPAL) Philippine Sea experiment measured travel times between six acoustic transceiver moorings in a 660–km diameter ocean acoustic tomography array in the Northern Philippine Sea (NPS). The travel-time series compare favorably with travel times computed for a yearlong series of state estimates produced for this region using the Massachusetts Institute of Technology general circulation model–Estimating the Circulation and Climate of the Ocean four-dimensional variational (MITgcm-ECCO 4DVAR) assimilation system constrained by satellite sea surface height and sea surface temperature observations and by Argo temperature and salinity profiles. Fluctuations in the computed travel times largely match the fluctuations in the measurements caused by the intense mesoscale eddy field in the NPS, providing a powerful test of the observations and state estimates. The computed travel times tend to be shorter than the measured travel times, however, reflecting a warm bias in the state estimates. After processing the travel times to remove tidal signals and extract the low-frequency variability, the differences between the measured and computed travel times were used in addition to SSH, SST, and Argo temperature and salinity observations to further constrain the model and generate improved state estimates. The assimilation of the travel times reduced the misfit between the measured and computed travel times, while not increasing the misfits with the other assimilated observations. The state estimates that used the travel times are more consistent with temperature measurements from an independent oceanographic mooring than the state estimates that did not incorporate the travel times.

Restricted access
Naoki HIROSE, Tianran LIU, Katsumi TAKAYAMA, Katsuto UEHARA, Takeshi TANEDA, and Young Ho KIM

Abstract

This study clarifies the necessity of an extraordinary large coefficient of vertical viscosity for dynamical ocean modeling in a shallow and narrow strait with complex bathymetry. Sensitivity experiments and objective analyses imply that background momentum viscosity is at the order of 100 cm2/s, while tracer diffusivity estimates are on the order of 0.1 cm2/s. The physical interpretation of these estimates is also discussed in the last part of this paper. To obtain reliable solutions, this study introduces cyclic application of the dynamical response to each parameter to minimize the number of long-term sensitivity experiments. The recycling Green’s function method yields weaker bottom friction and enhanced latent heat flux simultaneously with the increased viscosity in high-resolution modeling of the Tsushima/Korea Strait.

Restricted access
Stephen S. Leroy, Chi O. Ao, Olga P. Verkhoglyadova, and Mayra I. Oyola

Abstract

Bayesian interpolation has previously been proposed as a strategy to construct maps of radio occultation (RO) data, but that proposition did not consider the diurnal dimension of RO data. In this work, the basis functions of Bayesian interpolation are extended into the domain of the diurnal cycle, thus enabling monthly mapping of radio occultation data in synoptic time and analysis of the atmospheric tides. The basis functions are spherical harmonics multiplied by sinusoids in the diurnal cycle up to arbitrary spherical harmonic degree and diurnal cycle harmonic. Bayesian interpolation requires a regularizer to impose smoothness on the fits it produces, thereby preventing the overfitting of data. In this work, a formulation for the regularizer is proposed and the most probable values of the parameters of the regularizer determined. Special care is required when obvious gaps in the sampling of the diurnal cycle are known to occur in order to prevent the false detection of statistically significant high-degree harmonics of the diurnal cycle in the atmosphere. Finally, this work probes the ability of Bayesian interpolation to generate a valid uncertainty analysis of the fit. The postfit residuals of Bayesian interpolation are dominated not by measurement noise but by unresolved variability in the atmosphere, which is statistically nonuniform across the globe, thus violating the central assumption of Bayesian interpolation. The problem is ameliorated by constructing maps of RO data using Bayesian interpolation that partially resolve the temporal variability of the atmosphere, constructing maps for approximately every 3 days of RO data.

Restricted access
Guangyao Dai, Xiaoye Wang, Kangwen Sun, Songhua Wu, Xiaoquan Song, Rongzhong Li, Jiaping Yin, and Xitao Wang

Abstract

A practical method for instrumental calibration and aerosol optical properties retrieval based on coherent Doppler lidar (CDL) and sun photometer is presented in this paper. To verify its feasibility and accuracy, this method is applied into three field experiments in 2019 and 2020. In this method, multiwavelength (440, 670, 870, and 1020 nm) aerosol optical depth (AOD) from sun-photometer measurements are used to estimate AOD at 1550 nm and calibrate integrated CDL backscatter signal. Then it is validated by comparing the retrieved calibrated AOD at 1550 nm from CDL signal and that from sun-photometer measurements. Good agreement between them with the correlation of 0.96, the RMSE of 0.0085, and the mean relative error of 22% is found. From the comparison results of these three experiments, sun photometer is verified to be an effective reference instrument for the calibration of CDL return signal and the aerosol optical properties measurement with CDL is feasible. It is expected to promote the study on the aerosol flux and transport mechanism in the planetary boundary layer with the widely deployed CDLs.

Open access
Orrin Lancaster, Remo Cossu, Sebastien Boulay, Scott Hunter, and Tom E. Baldock

Abstract

Wave measurements from a new, low-cost, real-time wave buoy (Spotter) are investigated in a comparative study as part of a site characterization study at a wave energy candidate site at King Island, Tasmania, Australia. Measurements from the Sofar Ocean Spotter buoy are compared with concurrent measurements from a Teledyne RD Instrument (RDI) 1200 kHz Work Horse ADCP and two RBRsolo3 D wave16 pressure loggers. The comparison period between 8 August and 12 October 2019 provides both the shallowest and longest continuous published comparison undertaken with the Spotter buoy. Strong agreement was evident between the Spotter buoy and RDI ADCP of key wave parameters including the significant wave height, peak wave period, and mean wave direction, with the mean values of those parameters across the full deployment period agreeing within 3%. Surface wave spectra and directional spectra are also analyzed with good agreement observed over the majority of the frequency domain, although the Spotter buoy records approximately 17% less energy within a narrow frequency band near the peak frequency when compared to the RDI ADCP. Measurements derived from the pressure loggers routinely underestimated the significant wave height and overestimated the mean wave period over the deployment period. The comparison highlights the suitability of the Spotter buoy for low-cost wave resource studies, with accurate measurements of key parameters and spectra observed.

Restricted access
Yingkai Sha, David John Gagne II, Gregory West, and Roland Stull

Abstract

We present a novel approach for the automated quality control (QC) of precipitation for a sparse station observation network within the complex terrain of British Columbia, Canada. Our QC approach uses convolutional neural networks (CNNs) to classify bad observation values, incorporating a multiclassifier ensemble to achieve better QC performance. We train CNNs using human QC’d labels from 2016 to 2017 with gridded precipitation and elevation analyses as inputs. Based on the classification evaluation metrics, our QC approach shows reliable and robust performance across different geographical environments (e.g., coastal and inland mountains), with 0.927 area under curve (AUC) and type I/type II error lower than 15%. Based on the saliency-map-based interpretation studies, we explain the success of CNN-based QC by showing that it can capture the precipitation patterns around, and upstream of the station locations. This automated QC approach is an option for eliminating bad observations for various applications, including the preprocessing of training datasets for machine learning. It can be used in conjunction with human QC to improve upon what could be accomplished with either method alone.

Open access
Xingru Feng, Junchuan Sun, Dezhou Yang, Baoshu Yin, Guandong Gao, and Weiqi Wan

Abstract

Reasonable parameterization of air–sea momentum flux is important for the accuracy of ocean and atmosphere simulations, and in the numerical model, the parameterization of the air–sea momentum flux becomes a problem of parameterization of the sea surface wind stress drag coefficient (C d). In this study, five kinds of typical C d parameterization methods were assessed in the simulation of two typhoon cases, one of which was a supertyphoon and another was a common severe typhoon, based on an atmosphere–wave–ocean coupled model. Based on the two case studies, it was found that the typhoon path and minimum sea level pressure were not very sensitive to C d parameterizations, though the spatial distribution of C d and its variation with wind speed were all very different across the parameterization methods. However, C d has a significant effect on the wind speed, and at high wind speed, the simulated maximum wind speed compared better with the observation in the experiment that adopted the C d calculation method considering the effects of sea spray. Also, C d plays an important role in the feedback processes between atmosphere and ocean during the typhoon process, through its effect on the air–sea heat and momentum flux, SST, ocean mixed layer depth, ocean currents, etc. The results of this study answered the question of how the C d affects the atmosphere and ocean during the typhoon process, and to what extent they are affected, which can help to explain or even further improve the simulation results.

Open access
Michael O’Malley, Adam M. Sykulski, Romuald Laso-Jadart, and Mohammed-Amin Madoui

Abstract

We provide a novel method for computing the most likely path taken by drifters between arbitrary fixed locations in the ocean. We also provide an estimate of the travel time associated with this path. Lagrangian pathways and travel times are of practical value not just in understanding surface velocities, but also in modeling the transport of oceanborne species such as planktonic organisms and floating debris such as plastics. In particular, the estimated travel time can be used to compute an estimated Lagrangian distance, which is often more informative than Euclidean distance in understanding connectivity between locations. Our method is purely data driven and requires no simulations of drifter trajectories, in contrast to existing approaches. Our method scales globally and can simultaneously handle multiple locations in the ocean. Furthermore, we provide estimates of the error and uncertainty associated with both the most likely path and the associated travel time.

Open access
Simon P. de Szoeke

Abstract

A small integrated oceanographic thermometer with a nominal response time of 1 s was affixed to a floating hose “sea snake” towed near the bow of a research vessel. The sensor measured the near-surface ocean temperature accurately and in agreement with other platforms. The effect of conduction and evaporation is modeled for a sensor impulsively alternated between water and air. Large thermal mass makes most sea snake thermometers insensitive to temperature impulses. The smaller 1-s thermometer cooled by evaporation, but the sensor never reached the wet-bulb temperature. The cooling was less than 6% of the (~2.7°C) difference between the ocean temperature and the wet-bulb temperature in 99% of 2-s−1 samples. Filtering outliers, such as with a median, effectively removes the evaporative cooling effect from 1- or 10-min average temperatures.

Open access