Ocean Field Research Articles

Dynamic mode decomposition for underwater acoustics on autonomous platforms

A sufficiently accurate representation of the ocean physics is needed for reliable underwater acoustics forecasting. Ocean physics are often simulated by high-dimensional data-assimilative numerical ocean simulations. Due to operational constraints such as computing, memory, communication-bandwidth, etc., it is infeasible to run these numerical simulations onboard the platforms. In this work, we employ the onboard data-assimilative incremental, Low-Rank Dynamic Mode Decomposition (iLRDMD) for forecast transmission, onboard computation, and data assimilation. The iLRDMD is an adaptive reduced-order model (ROM) that uses proper orthogonal decomposition (POD) and DMD for compression and transmission of high-fidelity ocean forecast to communication-disadvantaged platforms. It provides reduced-order onboard forecasting and also enables efficient updates of the DMD model itself from inputs from remote centers. Finally, when sparse observations are made by the platform, the iLRDM uses Bayesian data assimilation to correct both the forecasts and the DMD model. We demonstrate the use of deterministic and data-assimilative iLRDMD forecasted ocean fields for underwater acoustics computations in real ocean examples. We further explore the use of our adaptive ROM for joint reduction of the ocean physics and acoustics fields.

A DBULSTM-Adaboost Model for Sea Surface Temperature Prediction.

Sea surface temperature (SST) is an important parameter to measure the energy and heat balance of sea surface. The change of sea surface temperature has an important impact on the marine ecosystem, marine climate and marine environment. Therefore, sea surface temperature prediction has become an significant research direction in the field of ocean. This article proposes a DBULSTM-Adaboost model based on ensemble learning. The model is composed of Deep Bidirectional and Unidirectional Long Short Term Memory (DBULSTM) and Adaboost strong learner. DBULSTM can capture the forward and backward dependence of time series, and the DBULSTM model is integrated with Adaboost strong learner to reduce the variance and bias of prediction and realize the short and medium term prediction of SST at a single point scale. Experimental results show that the model can improve the accuracy and stability of SST prediction. Experiments on the East China Sea and South China Sea with different prediction lengths show that the model is almost superior to other classical models in different sea areas and at different prediction levels. Compared with full-connected LSTM (FC-LSTM) model, the root-mean-square error is reduced by about 0.1.

Analysis of propagating wave structures of the cold bosonic atoms in a zig-zag optical lattice via comparison with two different analytical techniques.

The wave propagation has the significant role in the field of coastal engineering and ocean. In the geographical fields, waves are primary source of environmental process owed to energy conveyance on floating structure. This study aims to investigate the system of cold bosonic atoms in zig-zag optics lattices. The solitonic patterns of the considered model successfully surveyed by using two integrated analytical techniques new extended direct algebraic and frac{G'}{G^{2}} expansion method. The exact solutions are presented by rational, trigonometric, hyperbolic and exponential functions. On the basis of solitons, we need to show that which one is more integrated and robust scheme. These solutions will help to understood the dynamics of cold bosonic atoms in zig-zag optical lattices and the several other systems. Three dimensional as well as two dimensional comparison presented for a cold bosonic atoms model solutions which are revealed diagrammatically for appropriate parameters by using Mathematica. This study will help physicists to predict some new hypothesis and theories in the field of mathematical physics.

Biogeography and Population Divergence of Microeukaryotes Associated with Fluids and Chimneys in the Hydrothermal Vents of the Southwest Indian Ocean.

ABSTRACTDeep-sea hydrothermal vents have been proposed as oases for microbes, but microeukaryotes as key components of the microbial loop have not been well studied. Based on high-throughput sequencing and network analysis of the 18S rRNA gene, distinct biogeographical distribution patterns and impacting factors were revealed from samples in the three hydrothermal fields of the southwest Indian Ocean, where higher gene abundance of microeukaryotes appeared in chimneys. The microeukaryotes in the fluids might be explained by hydrogeochemical heterogeneity, especially that of the nitrate and silicate concentrations, while the microeukaryotes in the chimneys coated with either Fe oxides or Fe-Si oxyhydroxides might be explained by potentially different associated prokaryotic groups. Population divergence of microeukaryotes, especially clades of parasitic Syndiniales, was observed among different hydrothermal fluids and chimneys and deserves further exploration to gain a deeper understanding of the trophic relationships and potential ecological function of microeukaryotes in the deep-sea extreme ecosystems, especially in the complex deep-sea chemoautotrophic habitats.IMPORTANCE Deep-sea hydrothermal vents have been proposed as oases for microbes, but microeukaryotes as key components of the microbial loop have not been well studied. Based on high-throughput sequencing and network analysis of the 18S rRNA gene, population divergence of microeukaryotes, especially clades of parasitic Syndiniales, was observed among different hydrothermal fields. This might be attributed to the hydrogeochemical heterogeneity of fluids and to the potentially different associated prokaryotic groups in chimneys.

Bridging Gaps in the Climate Observation Network: A Physics‐Based Nonlinear Dynamical Interpolation of Lagrangian Ice Floe Measurements via Data‐Driven Stochastic Models

AbstractModeling and understanding sea ice dynamics in marginal ice zones rely on measurements of sea ice. Lagrangian observations of ice floes provide insight into the dynamics of sea ice, the ocean, and the atmosphere. However, optical satellite images are susceptible to atmospheric noise, leading to gaps in the retrieved time series of floe positions. This paper presents an efficient and statistically accurate nonlinear dynamical interpolation framework for recovering missing floe observations. It exploits a balanced physics‐based and data‐driven construction to address the challenges posed by the high‐dimensional and nonlinear nature of the coupled atmosphere‐ice‐ocean system, where effective reduced‐order stochastic models, nonlinear data assimilation, and simultaneous parameter estimation are systematically integrated. The new method succeeds in recovering the locations, curvatures, angular displacements, and the associated strong non‐Gaussian distributions of the missing floes in the Beaufort Sea. It also accurately estimates floe thickness and recovers the unobserved underlying ocean field with an appropriate uncertainty quantification, advancing our understanding of Arctic climate.

Regional earth system model for CORDEX‐South Asia: A comparative assessment of RESM and ESM over the tropical Indian Ocean

AbstractUnderstanding climate variability requires good quality high resolution spatially and temporally varying ocean fields due to its decisive role in regulating the region's climate, including the Indian summer monsoon. In this regard, we employed a new high‐resolution regional earth system model (RESM), namely ROM over CORDEX South Asia. We demonstrated the performance of the RESM and its added value over the global earth system model, namely MPI‐ESM, in simulating the hydrographic characteristics and associated mechanism over the tropical Indian Ocean (TIO). ROM shows better skill than MPI‐ESM in simulating the near‐surface and subsurface characteristics of the ocean. However, larger added values are noticed for subsurface (>350 m) thermal structure. MPI‐ESM's cold sea surface temperature (SST) bias is reduced in ROM which is associated with weaker winds, anomalous cyclonic wind stress curl, enhanced stratifications, and a shallower mixed layer, resulting in greater upper‐ocean heating. The reduced SST bias is also consistent with improved ocean meridional heat transport (OMHT) in ROM. For example, reduced southward export of OMHT over the Arabian Sea increases the surface warming by ~4%, reducing the RMSE by ~0.2–0.6°C and becoming closer to observation. The anomalous cyclonic wind stress curl, in turn, caused mixed layer stratifications in the western Indian Ocean. The advective heat transfer from the south‐eastern Indian Ocean to the western Indian Ocean reduced oceanic cooling by vertical processes, overcame the cooling by the net loss of surface heat fluxes, and favoured the TIO's surface warming.

Phytoplankton size-class contributions to new and regenerated production during the EXPORTS Northeast Pacific Ocean field deployment

The NASA EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) program was established to better quantify the pathways of the biological carbon pump in order to gain a more comprehensive understanding of global carbon export efficiency. The summer 2018 field campaign in the vicinity of Ocean Station Papa (Station P; 50°N, 145°W) in the Northeast Pacific Ocean yielded evidence of low phytoplankton biomass and primary productivity dominated by small cells (<5 µm) that are reliant on recycled nutrients. Using combined 13C/15N stable isotope incubations, we calculated an average depth-integrated dissolved inorganic carbon uptake (net primary production) rate of 23.1 mmol C m–2 d–1 throughout the euphotic zone with small cells contributing 88.9% of the total daily DIC uptake. Average depth-integrated NO3– uptake rates were 1.5 mmol N m–2 d–1 with small cells contributing 73.4% of the total daily NO3– uptake. Estimates of new and regenerated production fluctuated, with small cells continuing to dominate both forms of production. The daily mixed-layer f-ratio ranged from 0.17 to 0.38 for the whole community, consistent with previous studies, which indicates a predominance of regenerated production in this region, with small and large cells (≥5 μm) having average f-ratios of 0.28 and 0.82, respectively. Peak phytoplankton biomass, total primary productivity and new production occurred between Julian Days 238 and 242 of our observation period, driven primarily by an increase in carbon and nitrate assimilation rates without apparent substantial shifts in the phytoplankton size-class structure. Our findings demonstrate the importance of small cells in performing the majority of net primary production and new production and the modest productivity fluctuations that occur in this iron-limited region of the Northeast Pacific Ocean, driven by ephemeral increases in new production, which could have significant ramifications for carbon export over broad timescales.

JCOPE-FGO: an eddy-resolving quasi-global ocean reanalysis product

In the present research, we provide a brief description and assessment of the oceanic fields analyzed in the newly developed eddy-resolving quasi-global ocean reanalysis product, named the Japan Coastal Ocean Predictability Experiments-Forecasting Global Ocean (JCOPE-FGO). This product covers the quasi-global ocean with a horizontal resolution of 0.1° × 0.1°. Validations of analyzed temperature and salinity fields by JCOPE-FGO against in situ observations revealed that our product can capture various aspects of observed hydrographic structures in the world ocean, including frontal structures near the surface and thermohaline properties of water masses, as well as their spatiotemporal variability. Furthermore, we have assessed dynamical fields analyzed in JCOPE-FGO using satellite altimeters and surface drifters, and found that our product can represent the mean state and variability of the upper ocean circulation in many regions. A notable feature of JCOPE-FGO is the inclusion of an updated global river runoff, and impacts of river forcing have been assessed by an additional reanalysis experiment without river forcing. We found that the removal of continental river discharge leads to dramatic changes in the near-surface salinity and related fields around river mouths of large rivers, but large changes are mostly confined to narrow regions near the coast. As an example of the substantial impact of river runoff, we discuss the dispersion of low-salinity water from the Mississippi river to the Gulf of Mexico: a comparison between the analyzed salinity fields from both reanalysis products with those from satellite observations demonstrated that the inclusion of river runoff is essential for an accurate representation of its seasonal variability. Several key issues that warrant further improvements are discussed for future development.

Modeling, Control and Experiments of a Novel Underwater Vehicle with Dual Operating Modes for Oceanographic Observation

Multifarious and continuous oceanographic observation data is of considerable significance to oceanographic research and military application. In this paper, a novel oceanographic observation method with dual-modal underwater vehicle able to switch between the Argo and Glider modes is proposed and is studied experimentally in the South China Sea. Using the Newton–Euler formulation, a complete dynamic model considering the variation in gravity/buoyancy due to mode switching is thus established. Then, based on Lyapunov stability criterion, a nonlinear adaptive inverse controller with multiple inputs and multiple outputs is developed which enables the observation platform to freely switch between various modes. By analyzing the numerical and experimental data of a single mode, the results demonstrate that the proposed method can supplement the observation data and improve the accuracy of oceanographic data. Finally, based on experimental data acquired by the dual-modal underwater vehicle in the South China Sea, three-dimensional regional oceanic temperature and salinity fields are constructed with the spatio-temporal Kriging method, and feature surfaces are extracted from the oceanic fields. Furthermore, the proposed method can be applied in other stirring dynamic regions, such as ocean eddies, ocean fronts, filaments, etc.

Marine Drifting Trajectory Prediction Based on LSTM-DNN Algorithm

In this paper, the long short-term memory with dense neural network (LSTM-DNN) is first introduced to calculate marine drifting trajectory. Based on the Internet of Things technology and the LSTM-DNN algorithm, the marine drifting trajectory model is established. In this model, the information such as wind field, temperature field, ocean current motion field, and target attributes are included, and the influences of the above information on the trajectory model are studied in detail. In order to verify the proposed model, the marine experiments are carried out in the end. The results show that the predicted trajectory data matches well with the experimental trajectory data. By introducing DNN into the algorithm, computational accuracy of drifting trajectory can be significantly improved compared with the conventional LSTM-based prediction model. A detailed comparison of the two algorithms has also been given in the paper. The proposed remote sensing of marine drifting trajectory model can provide a high accurate trajectory prediction and will lead an important guidance in the marine search and rescue work.

Poetic Dwelling of the Ocean: Appreciating the Man Who Has Never Seen the Sea from Heidegger’s Poetic View

Heidegger believed that the origin of a nation’s historic existence is art. In the artistic world, we can expose the alienation of reality, uncover the original beauty, and return to poetic human habitation. In Le Clézio’s works The Man Who Has Never Seen the Sea, bystanders, as “intermediaries”, construct virtual dreams to escape from the city’s dreams. In the interaction with the natural ocean field, Daniel, a pure child messenger, is shaped, and his original experience is used to restore the original language, so as to realize the original awakening of human beings and create a poetic dwelling.

Solar and lunar daily geomagnetic variations and their equivalent current systems observed by Swarm

This paper describes solar and lunar daily variations of the geomagnetic field over low- and mid-latitude regions, using vector magnetometer data from Swarm satellites at altitudes of sim500 km during the solar minimum years of 2017–2020. The average solar variation of the geomagnetic field is within the range of ±14 nT, while the lunar variation is within ±2 nT. The latter is comparable to the ocean tidal field. A spherical harmonic analysis is performed on the solar and lunar variations to evaluate their internal and external equivalent current systems. The results show that both the solar and lunar variations are mainly of internal origin, which can be attributed to combined effects of ionospheric dynamo currents and induced underground currents. Global patterns of the internal solar and lunar current systems are consistent with the corresponding external current systems previously reported based on ground observations. The Swarm external currents are mainly in the meridional direction, and are likely associated with interhemispheric field-aligned currents. Both the internal and external current systems depend on the season and longitude.Graphical

Cloud phase and macrophysical properties over the Southern Ocean during the MARCUS field campaign

Abstract. To investigate the cloud phase and macrophysical properties over the Southern Ocean (SO), the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF2) was installed on the Australian icebreaker research vessel (R/V) Aurora Australis during the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign (41 to 69∘ S, 60 to 160∘ E) from October 2017 to March 2018. To examine cloud properties over the midlatitude and polar regions, the study domain is separated into the northern (NSO) and southern (SSO) parts of the SO, with a demarcation line of 60∘ S. The total cloud fractions (CFs) were 77.9 %, 67.6 %, and 90.3 % for the entire domain, NSO and SSO, respectively, indicating that higher CFs were observed in the polar region. Low-level clouds and deep convective clouds are the two most common cloud types over the SO. A new method was developed to classify liquid, mixed-phase, and ice clouds in single-layered, low-level clouds (LOW), where mixed-phase clouds dominate with an occurrence frequency (Freq) of 54.5 %, while the Freqs of the liquid and ice clouds were 10.1 % (most drizzling) and 17.4 % (least drizzling). The meridional distributions of low-level cloud boundaries are nearly independent of latitude, whereas the cloud temperatures increased by ∼8 K, and atmospheric precipitable water vapor increased from ∼5 mm at 69∘ S to ∼18 mm at 43∘ S. The mean cloud liquid water paths over NSO were much larger than those over SSO. Most liquid clouds occurred over NSO, with very few over SSO, whereas more mixed-phase clouds occurred over SSO than over NSO. There were no significant differences for the ice cloud Freq between NSO and SSO. The ice particle sizes are comparable to cloud droplets and drizzle drops and well mixed in the cloud layer. These results will be valuable for advancing our understanding of the meridional and vertical distributions of clouds and can be used to improve model simulations over the SO.

A Novel Machine Learning Method for Accelerated Modeling of the Downwelling Irradiance Field in the Upper Ocean

The downwelling irradiance is a crucial part of the air-sea heat flux and a major energy source for the photosynthesis processes in the upper ocean. The modeling of the irradiance field is often time-consuming in conventional Monte Carlo (MC) simulations. We propose an accelerated model based on machine learning to significantly reduce the computational cost. By introducing a generalized beam spreading function, we transform the raw data generated with the MC simulation into training data of reduced dimensions, which is then used to develop an artificial neural network. To validate the machine learning model, we use the MC model to simulate the irradiance field under a broadband wave field. For both clear and turbid seawater, the irradiance field predicted by the machine learning model agrees well with the MC simulation result. Compared with the MC simulation that is computationally expensive, our machine learning model is O(1,000) times faster.

On the hydrodynamic responses of a multi-column TLP floating offshore wind turbine model

The offshore floating wind turbine system is a key piece of equipment for harvesting wind energy from the ocean field. However, the structural safety analysis and design of such equipment largely require a suitable platform to accurately obtain the dynamic characteristics. In the present study, an attempt was made to investigate the dynamic responses of the proposed 5-MW WindStar multi-column tension-leg-platform (TLP) system under different ocean environment conditions. Typical motion responses of the WindStar system, including the surge and pitch, and a series of measurements obtained from wave basin tests, such as nacelle acceleration and tendon tension, were compared with those of simulated results from the FAST code under four hydrodynamic load conditions. Additionally, the second-order wave load was incorporated with the first-order wave load, so as to allow for accurate evaluation of the high-frequency response of the floating offshore wind turbine (FOWT). Further, the effect of high-order wave loads on the high-frequency responses of the TLP FOWT was elucidated. The second-order frequency wave load was found to be a significant factor in determining the motion responses of the WindStar TLP system, and the results from FAST were found to agree well with those of the experiment. The coupled method of experiments and numerical analysis can be effectively used to understand the high-frequency dynamic responses of TLP FOWT systems, such as the WindStar TLP.

Impact of ocean-atmosphere coupling on present and future Köppen-Geiger climate classification in Europe

The effect of air-sea coupling in the simulation of the European climate is assessed through a climate type classification that uses surface temperature and precipitation from a regional atmosphere-ocean coupled model and from its atmospheric component. The atmospheric setup in both models is the same, differing only in the representation of the oceanic fields. The simulations cover the present and future-time climate under the RCP8.5 CMIP5 scenario. Climate type distributions obtained from both coupled and uncoupled simulations are similar to those obtained from ERA5 for the 1976–2005 period. Both models simulate colder climate types for present-time in southern and northeastern regions compared to ERA5, possibly due to a weaker influence of the Atlantic circulation, and drier climate types in some western Mediterranean areas. Also, for present-time coupling leads to more humid winters (relatively drier summers) in some zones of north Spain and south France, and drier climates in some western Mediterranean spots. Based on simulations with these models under the RCP8.5 scenario, we find that by the end of the 21st century (2070–2099) the climate type distribution changes in more than 50% of the domain. While both models project the reduction of regions with cold climate types and the expansion of those with hot summers and hot arid climate types, these changes affect a larger area in the coupled simulation. These differences may be related to a drier signal in the coupled simulation, especially during summer, due to the influence of colder surface water in the North Atlantic Ocean and the Mediterranean Sea. Using a climate classification to evaluate the annual cycles of the simulated temperature and precipitation data provides a novel insight into the impact of air-ocean coupling on the representation of the climate, and consequently into the simulated impact on ecosystems and human activities in Europe.

Development of a forecast-oriented kilometre-resolution ocean–atmosphere coupled system for western Europe and sensitivity study for a severe weather situation

Abstract. To improve high-resolution numerical environmental prediction, it is essential to represent ocean–atmosphere interactions properly, which is not the case in current operational regional forecasting systems used in western Europe. The objective of this paper is to present a new forecast-oriented coupled ocean–atmosphere system. This system uses the state-of-the-art numerical models AROME (cy43t2) and NEMO (v3.6) with a horizontal resolution of 2.5 km. The OASIS coupler (OASIS3MCT-4.0), implemented in the SurfEX surface scheme and in NEMO, is used to perform the communications between models. A sensitivity study of this system is carried out using 7 d simulations from 12 to 19 October 2018, characterized by extreme weather events (storms and heavy precipitation) in the area of interest. Comparisons with in situ and L3 satellite observations show that the fully coupled simulation reproduces the spatial and temporal evolution of the sea surface temperature and 10 m wind speed quantitatively well. Sensitivity analysis of ocean–atmosphere coupling shows that the use of an interactive and high-resolution sea surface temperature (SST), in contrast to actual numerical weather prediction (NWP) where SST is constant, modifies the atmospheric circulation and the location of heavy precipitation. Simulated oceanic fields show a large sensitivity to coupling when compared to the operational ocean forecast. The comparison to two distinct forced ocean simulations highlights that this sensitivity is mainly controlled by the change in the atmospheric model used to drive NEMO (AROME vs. IFS operational forecast), and less by the interactive air–sea exchanges. In particular, the oceanic boundary layer depths can vary by more than 40 % locally, between the two ocean-only experiments. This impact is amplified by the interactive coupling and is attributed to positive feedback between sea surface cooling and evaporation.

GeoCLR: Georeference Contrastive Learning for Efficient Seafloor Image Interpretation

This paper describes georeference contrastive learning of visual representation (GeoCLR) for efficient training of deep-learning convolutional neural networks (CNNs). The method leverages georeference information by generating a similar image pair using images taken of nearby locations, and contrasting these with an image pair that is far apart. The underlying assumption is that images gathered within a close distance are more likely to have similar visual appearance, where this can be reasonably satisfied in seafloor robotic imaging applications where image footprints are limited to edge lengths of a few meters and are taken so that they overlap along a vehicle’s trajectory, whereas seafloor substrates and habitats have patch sizes that are far larger. A key advantage of this method is that it is self-supervised and does not require any human input for CNN training. The method is computationally efficient, where results can be generated between dives during multi-day autonomous underwater vehicle (AUV) missions using computational resources that would be accessible during most oceanic field trials. We apply GeoCLR to habitat classification on a dataset that consists of ~86,000 images gathered using an AUV. We demonstrate how the latent representations generated by GeoCLR can be used to efficiently guide human annotation efforts, where the semi-supervised framework improves classification accuracy by an average of 10.2% compared to the state-of-the-art SimCLR using the same CNN and equivalent number of human annotations for training.

Adaptive Network Design for Multiple Gliders Observation of Mesoscale Eddy

With its excellent endurance, good maneuverability and track controllability, glider has become one of the important equipment to obtain ocean field data. The network observation of multiple gliders will be the main approach of ocean observation in the future. However, how to plan the glider path in a reasonable way and how to design the optimal observation network consisting of multiple gliders in an eddy have not been well solved. As an effort to tackle this problem, an adaptive network design algorithm for multiple gliders in mesoscale eddies observation which referred as “Optimal Sampling” is proposed in this paper. Simulation experiments of tracking a target eddy in the South China Sea (SCS) show that the proposed algorithm cannot only realize the adaptive network design for multiple gliders, but also ensure the uniform sampling inside the eddy. Compared with the traditional method which samples eddy along a fixed path, the observation data obtained by the solution proposed in this paper are more valuable to describe the essential temperature characteristics of eddies. The residual errors computed from the interpolation of the sampled field is also smaller.

Validation of the CFOSAT Scatterometer Data With Buoy Observations and Tests of Operational Application to Extreme Weather Forecasts in Taiwan Strait

AbstractThe China‐France Oceanography Satellite (CFOSAT) launched in 2018 is equipped with the Chinese Scatterometer (CSCAT), which is designed to measure high‐precision sea surface wind fields in the global ocean. This study aims to validate the CFOSAT wind fields with moored buoy‐measured ground truth data from August 2019 to July 2021. The test area was chosen as the Taiwan Strait, which is an important navigation channel and rich fishery grounds in the East Asia. It is also a high wind speed area particularly during typhoons and cold air passages. Thus, the accurate wind forecasts are crucially needed to avoid capsizing and casualties. The validation results give the correlation coefficient (R2) of 0.92, the root mean square error (RMSE) of 1.75 m s−1, and the absolute deviation (AD) of 1.10 m s−1 for the wind speeds, and R2 of 0.96, the RMSE of 21.87°, and the AD of 1.17° for the wind directions. During cold air passages, the AD of wind speeds decreases to 1.03 m s−1, which is smaller than 1.17 m s−1 during typhoons. Using the CFOSAT wind as historical observation data for refining of forecast work was tested by model output statistics revised method. The test results show that the refined forecast results of wind fields are improved up to 4%–17%. Thus, the validation and test results obtained in this study indicate that updating satellite winds would have great contributions to refined forecasting of local sea surface winds.