A comparison is carried out between two operational wave forecasting/assimilation models for the North Sea, with the emphasis on the assimilation schemes. One model is the WAM model, in combination with an optimal interpolation method (OIP). The other model, DASWAM, consists of the third generation wave model PHIDIAS in combination with an approximate implementation of the adjoint method.
In an experiment over the period February 19 - March 30, 1993, the models are driven by the same wind field (HIRLAM analysis winds), and the same observation data set is assimilated. This set consists of a) spectra from three pitch-and-roll buoys and b) Synthetic Aperture Radar (SAR) spectra from the ERS-1 satellite. Three analysis/forecast runs are performed: one without assimilation, one with assimilation of buoy measurements only, and one with all data assimilated. For validation, observations from four buoys, altimeter data from ERS-1 and Topex-Poseidon, and scatterometer data from ERS-1 are used.
A detailed analysis of the "Wadden Storm" (February 20-22) shows the very different nature of the two assimilation schemes: the wave and wind field corrections of the WAM/OIP scheme are all in the vicinity of the observations, whereas the DASWAM adjustments are more of a global nature. The impact of some individual buoy and SAR observations is visualized. A comparison of the performance of the two schemes is somewhat obscured by the very different behaviour of the two first-guess runs.
A statistical analysis over the whole 39-day period gives the following results. In a comparison with buoy observations it is shown that a positive impact of wave data assimilation remains until about 12 hours in forecast in
Integration of renewable power sources into grids remains an active research and development area, particularly for less developed renewable energy technologies such as wave energy converters (WECs). WECs are projected to have strong early market penetration for remote communities, which serve as natural microgrids. Hence, accurate wave predictions to manage the interactions of a WEC array with microgrids is especially important. Recently developed, low-cost wave measurement buoys allow for operational assimilation of wave data at remote, site specific locations where real-time data have previ…
Ensemble data assimilation methods such as the Ensemble Kalman Filter (EnKF) are a key component of probabilistic weather forecasting. They represent the uncertainty in the initial conditions by an ensemble which incorporates information coming from the physical model with the latest observations. High-resolution numerical weather prediction models ran at operational centers are able to resolve non-linear and non-Gaussian physical phenomena such as convection. There is therefore a growing need to develop ensemble assimilation algorithms able to deal with non-Gaussianity while staying computati…
The first aim of this work is to construct a Finite Elements model for quasi-geostrophic equation. This model is analyzed through FEniCS interface. As a second purpose, it shows the potential of the control theory to treat Data Assimilation large scale problems. In section one the finite element approximation of the quasi-geostrophic model is described. In section two some numerical results on different domains are shown. In section three a comparison between Navier Stokes model and quasi-geostrophic one is discussed. Last section is dedicated to a more complex data assimilation/control proble…
Data assimilation is uniquely challenging in weather forecasting due to the high dimensionality of the employed models and the nonlinearity of the governing equations. Although current operational schemes are used successfully, our understanding of their long-term error behaviour is still incomplete. In this work, we study the error of some simple data assimilation schemes in the presence of unbounded (e.g. Gaussian) noise on a wide class of dissipative dynamical systems with certain properties, including the Lorenz models and the 2D incompressible Navier-Stokes equations. We exploit the prope…
This chapter describes a novel approach for the treatment of model error in geophysical data assimilation. In this method, model error is treated as a deterministic process fully correlated in time. This allows for the derivation of the evolution equations for the relevant moments of the model error statistics required in data assimilation procedures, along with an approximation suitable for application to large numerical models typical of environmental science. In this contribution we first derive the equations for the model error dynamics in the general case, and then for the particular situ…
Data assimilation is used in numerical simulations of laboratory experiments in a stratified, rotating fluid. The experiments are performed on the large Coriolis turntable (Grenoble, France), which achieves a high degree of similarity with the ocean, and the simulations are performed with a two-layer shallow water model. Since the flow is measured with a high level of precision and resolution, a detailed analysis of a forecasting system is feasible. Such a task is much more difficult to undertake at the oceanic scale because of the paucity of observations and problems of accuracy and data samp…
The space weather community has begun to use frontier methods such as data assimilation, machine learning, and ensemble modeling to advance current operational forecasting efforts. This was highlighted by a multi-disciplinary session at the 2017 American Geophysical Union Meeting, 'Frontier Solar-Terrestrial Science Enabled by the Combination of Data-Driven Techniques and Physics-Based Understanding', with considerable discussion surrounding ensemble techniques. Here ensemble methods are described in detail; using a set of predictions to improve on a single-model output, for example taking a s…
Data assimilation procedures have been developed for thermospheric models using satellite density measurements as part of the EU Framework Package 7 ATMOP Project. Two models were studied; one a general circulation model, TIEGCM, and the other a semi-empirical drag temperature model, DTM. Results of runs using data assimilation with these models were compared with independent density observations from CHAMP and GRACE satellites throughout solar cycles 23 and 24. Time periods of 60 days were examined at solar minimum and maximum, including the 2003 Hallowe'en storms. The differences between the…
This paper addresses the impact of assimilating data from the Earth Networks Total Lightning Network (ENTLN) during two cases of severe weather. Data from the ENTLN serve as a substitute for those from the upcoming launch of the GOES Lightning Mapper (GLM). We use the Weather Research and Forecast (WRF) model and variational data assimilation techniques at 9 km spatial resolution. The main goal is to examine the potential impact of lightning observations from the future GLM. Previous efforts to assimilate lightning observations mainly utilized nudging approaches. We develop three more sophisti…
Google Research（谷歌研究院）；Technical University of Munich（慕尼黑工业大学）；Harvard University（哈佛大学）
Variational data assimilation optimizes for an initial state of a dynamical system such that its evolution fits observational data. The physical model can subsequently be evolved into the future to make predictions. This principle is a cornerstone of large scale forecasting applications such as numerical weather prediction. As such, it is implemented in current operational systems of weather forecasting agencies across the globe. However, finding a good initial state poses a difficult optimization problem in part due to the non-invertible relationship between physical states and their correspo…
Hybrid machine learning data assimilation for marine biogeochemistry
University of Reading（雷丁大学）；National Centre for Earth Observation（国家地球观测中心）；Plymouth Marine Laboratory（普利茅斯海洋实验室）；University of Bologna（博洛尼亚大学）；Mercator Ocean International（国际海洋气象中心）
Huracan: A skillful end-to-end data-driven system for ensemble data assimilation and weather prediction
Microsoft Corporation（微软公司）；Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies（教育部地球系统科学部门地球系统建模重点实验室，全球变化研究机构）；Tsinghua University（清华大学）
The paper presents experiments of driving a physics-based thermosphere model by assimilating electron density (Ne) and temperature (Tn) data using the ensemble adjustment Kalman filter (EAKF) technique. This study not only helps to gauge the accuracy of the assimilation, to explain the inherent model bias, and to understand the limitations of the framework, but it also establishes EAKF as a viable technique in the presence of realistic data assimilation scenarios to forecast the highly dynamical thermosphere. The results from perfect model scenarios show that data assimilation changes and, mor…
This paper provides a detailed theoretical analysis of methods to approximate the solutions of high-dimensional (>10^6) linear Bayesian problems. An optimal low-rank projection that maximizes the information content of the Bayesian inversion is proposed and efficiently constructed using a scalable randomized SVD algorithm. Useful optimality results are established for the associated posterior error covariance matrix and posterior mean approximations, which are further investigated in a numerical experiment consisting of a large-scale atmospheric tracer transport source-inversion problem. This …
Accurate specification and prediction of the ionosphere-thermosphere (IT) environment, driven by external forcing, is crucial to the space community. In this work, we present a new transformative framework for data assimilation and calibration of the physical IT models. The framework has two main components: (i) the development of a quasi-physical dynamic reduced order model (ROM) that uses a linear approximation of the underlying dynamics and effect of the drivers, and (ii) data assimilation and calibration of the ROM through estimation of the ROM coefficients that represent the model paramet…
Observations from the Odin/Sub-Millimetre Radiometer (SMR) instrument have been assimilated into the DIAMOND model (Dynamic Isentropic Assimilation Model for OdiN Data), in order to estimate the chemical ozone (O$_{3}$) loss in the stratosphere. This data assimilation technique is described in Sagi and Murtagh (2016), in which it was used to study the inter-annual variability in ozone depletion during the entire Odin operational time and in both hemispheres. Our study focuses on the Arctic region, where two O$_{3}$ destruction mechanisms play an important role, involving halogen and nitrogen c…
Continuous data assimilation (CDA) is successfully implemented for the first time for efficient dynamical downscaling of a global atmospheric reanalysis. A comparison of the performance of CDA with the standard grid and spectral nudging techniques for representing long- and short-scale features in the downscaled fields using the Weather Research and Forecast (WRF) model is further presented and analyzed. The WRF model is configured at 25km horizontal resolution and is driven by 250km initial and boundary conditions from NCEP/NCAR reanalysis fields. Downscaling experiments are performed over a …
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…
This study aims at introducing two conservative thermodynamic variables (moist-air entropy potential temperature and total water content) into a one-dimensional variational data assimilation system (1D-Var) to demonstrate the benefit for future operational assimilation schemes. This system is assessed using microwave brightness temperatures from a ground-based radiometer installed during the field campaign SOFOG3D dedicated to fog forecast improvement. An underlying objective is to ease the specification of background error covariance matrices that are highly dependent on weather conditions wh…
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）
RealBench: Benchmarking Data-Driven Numerical Weather Forecasting Under Operational Conditions and Extreme Event Challenges
The Hong Kong University of Science and Technology（香港科学与技术大学）；Nanjing University（南京大学）；Southern University of Science and Technology（南方科技大学）；Shanghai AI Laboratory（上海人工智能实验室）；Shanghai TechWind Technology Co., Ltd.（上海技风科技有限公司）
Probabilistic Emulation of the Community Radiative Transfer Model Using Machine Learning
University of Colorado, Boulder（科罗拉多大学博尔德分校）；University Center for Atmospheric Research（大学大气研究中心）；Joint Center for Satellite Data Assimilation（卫星数据同化联合中心）
The 4D-Var method for filtering partially observed nonlinear chaotic dynamical systems consists of finding the maximum a-posteriori (MAP) estimator of the initial condition of the system given observations over a time window, and propagating it forward to the current time via the model dynamics. This method forms the basis of most currently operational weather forecasting systems. In practice the optimization becomes infeasible if the time window is too long due to the non-convexity of the cost function, the effect of model errors, and the limited precision of the ODE solvers. Hence the window…
Two drifting wave buoys were deployed in the central Arctic Ocean, north of the Laptev Sea, where there are historically no wave observations available. An experimental wave buoy was deployed alongside a commercial buoy. The inter-buoy comparison showed that the experimental buoy measured wave heights and periods accurately; so the buoy data were used to study the predictability of a wave-ice model in this region. The first event we focused was when both buoys observed a sudden decrease in significant wave heights $H_{m0}$. The sudden decrease was caused by the change of wind directions from a…
