In-situ ocean wave observations are critical to improve model skill and validate remote sensing wave measurements. Historically, such observations are extremely sparse due to the large costs and complexity of traditional wave buoys and sensors. In this work, we present a recently deployed network of free-drifting satellite-connected surface weather buoys that provide long-dwell coverage of surface weather in the northern Pacific Ocean basin. To evaluate the leading-order improvements to model forecast skill using this distributed sensor network, we implement a widely-used data assimilation technique and compare forecast skill to the same model without data assimilation. Even with a basic assimilation strategy as used here, we find remarkable improvements to forecast accuracy from the incorporation of wave buoy observations, with a 27% reduction in root-mean-square error in significant waveheights overall. For an extreme event, where forecast accuracy is particularly relevant, we observe considerable improvements in both arrival time and magnitude of the swell on the order of 6 hours and 1 m, respectively. Our results show that distributed ocean networks can meaningfully improve model skill, at extremely low cost. Refinements to the assimilation strategy are straightforward to achieve and will result in immediate further modelling gains.
The review describes the development of ideas Gury Ivanovich Marchuk in the field of variational data assimilation for ocean models applied in particular in coupled models for long-range weather forecasts. Particular attention is paid to the optimization of boundary conditions on rigid boundaries. As idealized and realistic model configurations are considered. It is shown that the optimization allows us to determine the most sensitive model operators and bring the model solution closer to the assimilated data.
A real time assimilation and forecasting system for coastal currents is presented. The purpose of the system is to deliver current analyses and forecasts based on assimilation of high frequency radar surface current measurements. The local Vessel Traffic Service monitoring the ship traffic to two oil terminals on the coast of Norway received the analyses and forecasts in real time. A new assimilation method based on optimal interpolation is presented where spatial covariances derived from an ocean model are used instead of simplified mathematical formulations. An array of high frequency radar …
The feasibility of global ocean state estimation by sequential data assimilation is demonstrated. The model componenet of the assimilator is the GROB version of the MPIMET ocean circulation model HOPE. Assimilation uses the Fokker-Planck representation of the Kalman Filter. This approach determines the temporal evolution of error statistics by integration of the Fokker-Planck Equation. Phase space advection and diffusion are obtained from histogram techniques considering the model as a black box. For efficiency, the estimate combines nudging and Kalman Filtering. The ocean state is estimated f…
Through ensemble-based data assimilation (DA), we address one of the most notorious difficulties in phase-resolved ocean wave forecast, regarding the deviation of numerical solution from the true surface elevation due to the chaotic nature of and underrepresented physics in the nonlinear wave models. In particular, we develop a coupled approach of the high-order spectral (HOS) method with the ensemble Kalman filter (EnKF), through which the measurement data can be incorporated into the simulation to improve the forecast performance. A unique feature in this coupling is the mismatch between the…
A formulation is developed to assimilate ocean-wave data into the Numerical Flow Analysis (NFA) code. NFA is a Cartesian-based implicit Large-Eddy Simulation (LES) code with Volume of Fluid (VOF) interface capturing. The sequential assimilation of data into NFA permits detailed analysis of ocean-wave physics with higher bandwidths than is possible using either other formulations, such as High-Order Spectral (HOS) methods, or field measurements. A framework is provided for assimilating the wavy and vortical portions of the flow. Nudging is used to assimilate wave data at low wavenumbers, and th…
Coupled data assimilation (CDA) distinctively appears as a main concern in numerical weather and climate prediction with major efforts put forward worldwide. The core issue is the scale separation acting as a barrier that hampers the propagation of the information across model components. We provide a brief survey of CDA, and then focus on CDA using the ensemble Kalman filter (EnKF). We consider first coupled equations with temporal scale difference and deduce that: (i) cross components effects are strong from the slow to the fast scale, but, (ii) intra-component effects are much stronger in t…
Lagrangian data assimilation of complex nonlinear turbulent flows is an important but computationally challenging topic. In this article, an efficient data-driven statistically accurate reduced-order modeling algorithm is developed that significantly accelerates the computational efficiency of Lagrangian data assimilation. The algorithm starts with a Fourier transform of the high-dimensional flow field, which is followed by an effective model reduction that retains only a small subset of the Fourier coefficients corresponding to the energetic modes. Then a linear stochastic model is developed …
Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity
Department of Applied Mathematics, University of California, Santa Cruz, CA 95064, USA（应用数学系，加州大学圣克鲁兹分校）；Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA 95064, USA（电气与计算机工程系，加州大学圣克鲁兹分校）；Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA（海洋、地球与大气科学系，北卡罗来纳州立大学）
Forecasting ocean drift trajectories are important for many applications, including search and rescue operations, oil spill cleanup and iceberg risk mitigation. In an operational setting, forecasts of drift trajectories are produced based on computationally demanding forecasts of three-dimensional ocean currents. Herein, we investigate a complementary approach for shorter time scales by using a recent state-of-the-art implicit equal-weights particle filter applied to a simplified ocean model. To achieve this, we present a new algorithmic design for a data-assimilation system in which all compo…
Physics-informed neural networks for phase-resolved data assimilation and prediction of nonlinear ocean waves
Hamburg University of Technology（汉堡工业大学）；Imperial College London（帝国理工学院）；University of Oxford（牛津大学）；Universitat Politécnica de Catalunya（加泰罗尼亚理工大学）；China University of Petroleum（中国石油大学）；College of Oceanography（海洋学院）；Space Informatics（空间信息学院）
Generative data assimilation highlights fronts as key regulators of ocean energy cascade
University of Washington（华盛顿大学）；California Institute of Technology（加州理工学院）；ENS, PSL Université, Ecole Polytechnique, Sorbonne Université, CNRS（法国高等师范学院、巴黎文理研究大学、巴黎综合理工学院、索邦大学、法国国家科学研究中心）
The understanding of nonlinear, high dimensional flows, e.g, atmospheric and ocean flows, is critical to address the impacts of global climate change. Data Assimilation techniques combine physical models and observational data, often in a Bayesian framework, to predict the future state of the model and the uncertainty in this prediction. Inherent in these systems are noise (Gaussian and non-Gaussian), nonlinearity, and high dimensionality that pose challenges to making accurate predictions. To address these issues we investigate the use of both model and data dimension reduction based on techn…
Microwave remote sensors mounted on center pivot irrigation systems provide a feasible approach to obtain soil moisture information, in the form of water content maps, for the implementation of closed-loop irrigation. Major challenges such as significant time delays in the soil moisture measurements, the inability of the sensors to provide soil moisture information in instances where the center pivot is stationary, and the inability of the sensors to provide soil moisture information in the root zone reduce the usability of the water content maps in the effective implementation of closed-loop …
Simulation-informed deep learning for enhanced SWOT observations of fine-scale ocean dynamics
IMT Atlantique（大西洋高等电信学院）；CNRS（法国国家科学研究中心）；INRIA（法国国家信息与自动化研究所）；Ifremer（法国海洋开发研究院）；Jet Propulsion Laboratory（喷气推进实验室）；California Institute of Technology（加州理工学院）
The accumulated remote sensing data of altimeters and scatterometers have provided a new opportunity to forecast the ocean states and improve the knowledge in ocean/atmosphere exchanges. Few previous studies have focused on sea level anomaly (SLA) multi-step forecasting by multivariate deep learning for different modalities. For this paper, a novel multimodal fusion approach named MMFnet is used for SLA multi-step forecasting in South China Sea (SCS). First, a grid forecasting network is trained by an improved Convolutional Long Short-Term Memory (ConvLSTM) network on daily multiple remote sen…
A one-year time series of moored high-resolution temperature T-sensor data from 1455 m depth on a 3900 m long line in about 5300 m of water in the NE-Atlantic Canary Basin are dominated by salinity (over-)compensated intrusions arising from the effects of Mediterranean outflow waters, which are commonly organized as Meddies. During the passage of a Meddy-core above the T-sensors, no intrusions were observed, thereby making it possible to use the temperature records to quantify turbulence parameters. The present data show that these ocean-interior turbulence estimates are from short-lived (less…
This paper presents a combined ocean and oil model for adaptive placement of sensors in the immediate aftermath of oilspills. A key feature of this model is the ability to correct its predictions of spill location using continual measurement feedback from a low number of deployed sensors. This allows for a model of relatively low complexity compared to existing models, which in turn enables fast predictions. The focus of this paper is upon the modelling aspects and in-particular the trade-off between complexity and numerical efficiency. The presented model contains relevant ocean, wind and wav…
Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study. The incorporation of machine learning (ML) techniques offers exciting possibilities for advancing the capacity and speed of established methods and also for making substantial and serendipitous discoveries. Beyond vast amounts of complex data ubiquitous in many modern scientific fields, the study of the ocean poses a combination of unique challenges that ML can help address. The observational data available is largely spatially sparse, limited to the surface, and with …
Improved estimation of hydrometeorological states from down-sampled observations and background model forecasts in a noisy environment, has been a subject of growing research in the past decades. Here, we introduce a unified framework that ties together the problems of downscaling, data fusion and data assimilation as ill-posed inverse problems. This framework seeks solutions beyond the classic least squares estimation paradigms by imposing proper regularization, which are constraints consistent with the degree of smoothness and probabilistic structure of the underlying state. We review releva…
The daily cycle of heating and cooling of the near-surface ocean may be quite different in a shallow lagoon with a few meters deep seafloor that can be heated directly by the sun. If important, the solar radiation will affect the local benthic communities. To study the physical processes associated with the daily cycle of south-Pacific lagoon Bora Bora, a vertical string of five high-resolution temperature sensors was moored at a 2-m deep site for 3 weeks. Besides the standard ocean warming (approximately during daytime) and cooling (approximately nighttime), the sensors show relatively highes…
This paper presents the first estimate of the seasonal cycle of ocean and sea ice net heat and freshwater (FW) fluxes around the boundary of the Arctic Ocean. The ocean transports are estimated primarily using 138 moored instruments deployed in September 2005 to August 2006 across the four main Arctic gateways: Davis, Fram and Bering Straits, and the Barents Sea Opening (BSO). Sea ice transports are estimated from a sea ice assimilation product. Monthly velocity fields are calculated with a box inverse model that enforces volume and salinity conservation. The resulting net ocean and sea ice he…
University of Maryland, College Park（马里兰大学帕克分校）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is in the form of noisy partial measurements of the past and present state of the dynamical system. Recently there have been several promising data-driven approaches to forecasting of chaotic dynamical systems using machine learning. Particularly promising among these are hybrid approaches that combine machine learning with a knowledge-based model, where a machine-learning technique is used to correct the imperfections in the knowledge-based model. Such imperfections may be due to incomple…
The inertial subrange of turbulence in a density stratified environment is the transition from internal waves to isotropic turbulence, but it is unclear how to interpret its extension to anisotropic stratified turbulence. Knowledge about stratified turbulence is relevant for the dispersal of suspended matter in geophysical flows, such as in most of the ocean. For studying internal-wave-induced ocean-turbulence moored high-resolution temperature (T-)sensors are used. Spectra from observations on episodic quasi-convective internal wave breaking above a steep slope of large seamount Josephine in …
Modeling and understanding sea ice dynamics in marginal ice zones relies on acquiring Lagrangian ice floe measurements. 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 effec…
In the stably stratified ocean, small-scale turbulence is important for vertical exchange and hence for the mixing of water masses and suspended matter. To observationally study turbulent motions and the buoyancy- and shear-generators behind them, a 100-m tall array of high-resolution temperature (T) sensors was moored at a 2480-m deep seafloor near the steep continental slope of the Western Mediterranean Sea. The area is dominated by boundary flow, (sub-)mesoscale eddies, internal waves, wintertime dense water formation, and very weak vertical density stratification. Various physical oceanogr…
Borey: A High-Resolution Regional Atmosphere-Ocean-Sea Ice-Wave Forecasting System and Hindcast Dataset for the Barents and Kara Seas
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）；Shirshov Institute of Oceanology, Russian Academy of Sciences（俄罗斯科学院谢尔肖夫海洋研究所）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Autonomous Non-Profit Organization Artificial Intelligence Research Institute (AIRI)（人工智能研究院（AIRI））
AEROS aims to develop a nanosatellite as a precursor of a future system of systems, which will include assets and capabilities of both new and existing platforms operating in the Ocean and Space, equipped with state-of-the-art sensors and technologies, all connected through a communication network linked to a data gathering, processing and dissemination system. This constellation leverages scientific and economic synergies emerging from New Space and the opportunities in prospecting, monitoring, and valuing the Ocean in a sustainable manner, addressing the demand for improved spatial, temporal…
The Biogeochemical-Argo (BGC-Argo) program is building a network of globally distributed, sensor-equipped robotic profiling floats, improving our understanding of the climate system and how it is changing. These floats, however, are limited in the number of variables measured. In this study, we train neural networks to predict silicate and phosphate values in the Southern Ocean from temperature, pressure, salinity, oxygen, nitrate, and location and apply these models to earth system model (ESM) and BGC-Argo data to expand the utility of this ocean observation network. We trained our neural net…
This article explores the concepts of ocean wave multivariate multistep forecasting, reconstruction and feature selection. We introduce recurrent neural network frameworks, integrated with Bayesian hyperparameter optimization and Elastic Net methods. We consider both short- and long-term forecasts and reconstruction, for significant wave height and output power of the ocean waves. Sequence-to-sequence neural networks are being developed for the first time to reconstruct the missing characteristics of ocean waves based on information from nearby wave sensors. Our results indicate that the Adam …
