Browsing by Author "Yan, Chi"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Reconstructing High-Resolution Ocean Subsurface and Interior Temperature and Salinity Anomalies From Satellite Observations(IEEE Transactions on Geoscience and Remote Sensing, 2021-09-21) Meng, Lingsheng; Yan, Chi; Zhuang, Wei; Geng, Xupu; Yan, Xiao-HaiAccurately retrieving ocean interior parameters from remote sensing observations is essential for ocean and climate studies because direct observations are sparse and costly. Furthermore, high-resolution structure of seawater properties is critical for understanding the oceanic processes and changes on multiple scales. Here, we designed a new method based on a deep neural network to retrieve subsurface temperature anomaly (STA) and subsurface salinity anomaly (SSA) in the Pacific Ocean at high (1/4°) and super (1/12°) horizontal resolution. We utilized multisource satellite-observed sea surface data (e.g., sea level, temperature, salinity, and wind vector) as inputs. The results revealed that our model retrieved the high- and super-resolution STA/SSA with high accuracy, and the model was reliable in a wide range of depths (near surface to 4000 m) and times (all months in 2014). Regarding the high-resolution STA (SSA) estimation, the average coefficient of determination ( R2 ) was 0.984 (0.966), and the average root-mean-squared error (RMSE) was 0.068 °C (0.016 psu). For the super-resolution STA, the average R2 was 0.988 and RMSE was 0.093 °C. Here, we established an effective technique that improved the resolution and accuracy of estimating the ocean interior parameters from satellite observation. The new technique provides some new insights into oceanic observation and dynamics.Item Reconstruction of Three-Dimensional Temperature and Salinity Fields From Satellite Observations(Journal of Geophysical Research: Oceans, 2021-11-07) Meng, Lingsheng; Yan, Chi; Zhuang, Wei; Zhang, Weiwei; Yan, Xiao-HaiObservation of the ocean is crucial to the studies of ocean dynamics, climate change, and biogeochemical cycle. However, current oceanic observations are patently insufficient, because the in situ observations are of difficulty and high cost while the satellite remote-sensed measurements are mainly the sea surface data. To make up for the shortage of ocean interior data and make full use of the abundant satellite data, here we develop a data-driven deep learning model to estimate ocean subsurface and interior variables from satellite-observed sea surface data. Exclusively and simply using satellite data, three-dimensional ocean temperature and salinity fields are successfully reconstructed, which are at 26 level depths from 0 to 2,000 m. We further design a scheme to increase the horizontal resolution from 1° to 1/4°, which is higher than the Argo gridded data. Estimations from our model are accurate, reliable, and stable for a wide range of research areas and periods. Dynamic height fields that are derived from the estimated temperature and salinity, as well as the associated ocean geostrophic flows, are also calculated and analyzed, which indicates the potentials of our model for reconstructing the ocean circulation fields as well. This study enriches oceanic observations with respect to vertical dimension and horizontal resolution, which can largely make up for the paucity of the subsurface and deep ocean observation, both before and during Argo era. This work also provides some new foundations for and insights into geoscience and climate change fields. Plain Language Summary: Ocean data are important for ocean science and climate change studies; however, oceanic observations are difficult and costly and thus are still very sparse in space and time. Satellites have been providing plentiful oceanic observations, but these data are only at the sea surface. To fully utilize the copious satellite data and to make up for the shortage of ocean interior data. Here, we establish a deep learning model to connect the surface ocean with the subsurface and deep oceans, through which, subsurface and deep ocean temperature and salinity data are estimated from the surface data observed by satellites. We also design a new scheme to improve the horizontal resolution of the obtained data. The results show that our model successfully reconstructed the three-dimensional field data of temperature and salinity. Our model could facilitate ocean science studies by largely enriching the ocean data availability.Item Wind turbine wakes: from numerical modeling to machine learning(University of Delaware, 2018) Yan, ChiTwo main topics are studied in this research. First, the importance of compressibility effects of large horizontal-axis wind turbines are systematically assessed using the Blade Element Moment (BEM) method and unsteady Reynolds-Averaged Navier-Stokes (RANS) simulations. Second, a deep neural network (NN) with transfer learning ability are proposed for efficient wind farm power estimation. ☐ The tips of large horizontal-axis wind turbines can easily reach high speeds, thus raising the concern that compressibility effects may influence turbine wakes and ultimately power production. All past studies have assumed that these effects are negligible. In Chapter \ref{chap2}, compressibility effects are assessed in terms of blade aerodynamic properties and variable density separately. Using the BEM method, we find that under normal operating conditions (i.e., wind speed $<\sim$15 m s$^{-1}$ and tip speed ratio TSR $<\sim12$) aerodynamic corrections to the lift and drag coefficients of the blades have a minimal impact, thus the incompressible coefficients are adequate. In Chapter \ref{chap3}, compressibility effects are assessed in terms of variable-density, numerical simulations of a single turbine and two aligned turbines, modeled via the actuator line model with the default aerodynamic coefficients, are conducted using both the traditional incompressible and a compressible framework. The flow field around the single turbine and its power performance are affected by compressibility and both show a strong dependency on TSR. Wind speed and turbulent kinetic energy (TKE) differences between compressible and incompressible results origin from the rotor tip region but then impact the entire wind turbine wake. Power production is lower by 8\% under normal operating conditions (TSR$\sim$8) and 20\% lower for TSR$\sim$12 due to compressibility effects. When a second turbine is added, the front turbine experiences similar effects as the single-turbine case, but TKE differences are enhanced while wind speed differences are reduced after the second turbine in the overlapping wakes. These findings suggest that compressibility effects play a more important role than previously thought on power production and, due to the acceptable additional computational cost of the compressible simulations, should be taken into account in future wind farm studies. ☐ In Chapter \ref{chap4}, a deep neural network is trained and validated using three years of one-minute observations of wind speed, direction, and power generated at the offshore Lillgrund wind farm (Sweden). In its traditional form, the NN is used to generate a new two-dimensional power curve, which predicts with high accuracy (error $\sim2\%$) the power of the entire Lillgrund wind farm based on wind speed and direction. By contrast, manufacturers only provide one-dimensional power curves (i.e., power as a function of wind speed) for a single turbine. The second innovative application is the use of a geometric model (GM) to calculate two simple geometric properties to replace wind direction in the NN. The resulting GM-trained NN has the powerful feature of being applicable to any wind farm, not just Lillgrund. A validation at the onshore N{\o}rrek{\ae}r wind farm in Denmark demonstrates the high accuracy (error $\sim6\%$) and transfer-learning ability of the GM-trained NN.