Coastal observing and forecasting system for the German Bight – estimates of hydrophysical states

Abstract. A coastal observing system for Northern and Arctic Seas (COSYNA) aims at construction of a long-term observatory for the German part of the North Sea, elements of which will be deployed as prototype modules in Arctic coastal waters. At present a coastal prediction system deployed in the area of the German Bight integrates near real-time measurements with numerical models in a pre-operational way and provides continuously state estimates and forecasts of coastal ocean state. The measurement suite contributing to the pre-operational set up includes in situ time series from stationary stations, a High-Frequency (HF) radar system measuring surface currents, a FerryBox system and remote sensing data from satellites. The forecasting suite includes nested 3-D hydrodynamic models running in a data-assimilation mode, which are forced with up-to-date meteorological forecast data. This paper reviews the present status of the system and its recent upgrades focusing on developments in the field of coastal data assimilation. Model supported data analysis and state estimates are illustrated using HF radar and FerryBox observations as examples. A new method combining radial surface current measurements from a single HF radar with a priori information from a hydrodynamic model is presented, which optimally relates tidal ellipses parameters of the 2-D current field and the M2 phase and magnitude of the radials. The method presents a robust and helpful first step towards the implementation of a more sophisticated assimilation system and demonstrates that even using only radials from one station can substantially benefit state estimates for surface currents. Assimilation of FerryBox data based on an optimal interpolation approach using a Kalman filter with a stationary background covariance matrix derived from a preliminary model run which was validated against remote sensing and in situ data demonstrated the capabilities of the pre-operational system. Data assimilation significantly improved the performance of the model with respect to both SST and SSS and demonstrated a good skill not only in the vicinity of the Ferry track, but also over larger model areas. The examples provided in this study are considered as initial steps in establishing new coastal ocean products enhanced by the integrated COSYNA-observations and numerical modelling.

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Blending Surface Currents from HF Radar Observations and Numerical Modeling: Tidal Hindcasts and Forecasts

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-13-00164.1 ◽

2015 ◽

Vol 32 (2) ◽

pp. 256-281 ◽

Cited By ~ 32

Author(s):

E. V. Stanev ◽

F. Ziemer ◽

J. Schulz-Stellenfleth ◽

J. Seemann ◽

J. Staneva ◽

...

Keyword(s):

Numerical Modeling ◽

German Bight ◽

Spatial Scales ◽

Surface Current ◽

Hf Radar ◽

Optimal Interpolation ◽

Kelvin Wave ◽

Arctic Seas ◽

Current Estimate ◽

Radar Observations

AbstractAn observation network operating three Wellen Radars (WERAs) in the German Bight, which are part of the Coastal Observing System for Northern and Arctic Seas (COSYNA), is presented in detail. Major consideration is given to expanding the patchy observations over the entire German Bight on a 1-km grid and producing state estimates at intratidal scales, and 6- and 12-h forecasts. This was achieved with the help of the proposed spatiotemporal optimal interpolation (STOI) method, which efficiently uses observations and simulations from a free model run within an analysis window of one or two tidal cycles. In this way the method maximizes the use of available observations and can be considered as a step toward the “best surface current estimate.” The performance of the analysis was investigated based on the achieved reduction of the misfit between model and observations. The complex dynamics of the study domain was illustrated based on the spatial and temporal changes of tidal ellipses for the M2 and M4 constituents from HF radar observations. It was demonstrated that blending observations and numerical modeling facilitates physical interpretation of processes such as the nonlinear distortion of the Kelvin wave in the coastal zone and in particular in front of the Elbe and Weser estuaries. Comparisons with in situ data acquired outside the area covered by the HF radar demonstrated that the analysis method is able to propagate the HF radar information to larger spatial scales.

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Ocean forecasting for the German Bight: from regional to coastal scales

Ocean Science ◽

10.5194/os-12-1105-2016 ◽

2016 ◽

Vol 12 (5) ◽

pp. 1105-1136 ◽

Cited By ~ 19

Author(s):

Emil V. Stanev ◽

Johannes Schulz-Stellenfleth ◽

Joanna Staneva ◽

Sebastian Grayek ◽

Sebastian Grashorn ◽

...

Keyword(s):

German Bight ◽

Unstructured Grid ◽

Wind Waves ◽

Radar Data ◽

Coastal Ocean ◽

Hf Radar ◽

Regional Models ◽

Arctic Seas ◽

The North ◽

Ocean Forecasting

Abstract. This paper describes recent developments based on advances in coastal ocean forecasting in the fields of numerical modeling, data assimilation, and observational array design, exemplified by the Coastal Observing System for the North and Arctic Seas (COSYNA). The region of interest is the North and Baltic seas, and most of the coastal examples are for the German Bight. Several pre-operational applications are presented to demonstrate the outcome of using the best available science in coastal ocean predictions. The applications address the nonlinear behavior of the coastal ocean, which for the studied region is manifested by the tidal distortion and generation of shallow-water tides. Led by the motivation to maximize the benefits of the observations, this study focuses on the integration of observations and modeling using advanced statistical methods. Coastal and regional ocean forecasting systems do not operate in isolation but are linked, either weakly by using forcing data or interactively using two-way nesting or unstructured-grid models. Therefore, the problems of downscaling and upscaling are addressed, along with a discussion of the potential influence of the information from coastal observatories or coastal forecasting systems on the regional models. One example of coupling coarse-resolution regional models with a fine-resolution model interface in the area of straits connecting the North and Baltic seas using a two-way nesting method is presented. Illustrations from the assimilation of remote sensing, in situ and high-frequency (HF) radar data, the prediction of wind waves and storm surges, and possible applications to search and rescue operations are also presented. Concepts for seamless approaches to link coastal and regional forecasting systems are exemplified by the application of an unstructured-grid model for the Ems Estuary.

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Hyperspectral Remote Sensing of the Coastal Ocean: Adaptive Sampling and Forecasting of In situ Optical Properties

10.21236/ada624963 ◽

2001 ◽

Author(s):

Oscar Schofield ◽

Scott Glenn ◽

Dale Haidvogel ◽

Frederick Grassle

Keyword(s):

Remote Sensing ◽

Optical Properties ◽

Adaptive Sampling ◽

Coastal Ocean ◽

Hyperspectral Remote Sensing

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Remote sensing of surface currents and waves by the HF radar WERA

Seventh International Conference on Electronic Engineering in Oceanography - Technology Transfer from Research to Industry ◽

10.1049/cp:19970686 ◽

1997 ◽

Cited By ~ 11

Author(s):

K.-W. Gurgel

Keyword(s):

Remote Sensing ◽

Hf Radar ◽

Surface Currents

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Data assimilation with High-Frequency (HF) radar surface currents at a marine renewable energy test site

Renewable Energies Offshore ◽

10.1201/b18973-28 ◽

2015 ◽

pp. 189-193 ◽

Cited By ~ 2

Author(s):

L Ren ◽

S Nash ◽

M Hartnett

Keyword(s):

Renewable Energy ◽

Data Assimilation ◽

High Frequency ◽

Test Site ◽

Hf Radar ◽

Surface Currents ◽

Marine Renewable Energy

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Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4

Geoscientific Model Development ◽

10.5194/gmd-13-1267-2020 ◽

2020 ◽

Vol 13 (3) ◽

pp. 1267-1284 ◽

Cited By ~ 2

Author(s):

Theo Baracchini ◽

Philip Y. Chu ◽

Jonas Šukys ◽

Gian Lieberherr ◽

Stefan Wunderle ◽

...

Keyword(s):

Remote Sensing ◽

Data Assimilation ◽

Satellite Remote Sensing ◽

Model Performance ◽

Remote Sensing Data ◽

Lake Ecosystem ◽

Sensing Data ◽

Localization Scheme ◽

Spatiotemporal Scales

Abstract. The understanding of physical dynamics is crucial to provide scientifically credible information on lake ecosystem management. We show how the combination of in situ observations, remote sensing data, and three-dimensional hydrodynamic (3D) numerical simulations is capable of resolving various spatiotemporal scales involved in lake dynamics. This combination is achieved through data assimilation (DA) and uncertainty quantification. In this study, we develop a flexible framework by incorporating DA into 3D hydrodynamic lake models. Using an ensemble Kalman filter, our approach accounts for model and observational uncertainties. We demonstrate the framework by assimilating in situ and satellite remote sensing temperature data into a 3D hydrodynamic model of Lake Geneva. Results show that DA effectively improves model performance over a broad range of spatiotemporal scales and physical processes. Overall, temperature errors have been reduced by 54 %. With a localization scheme, an ensemble size of 20 members is found to be sufficient to derive covariance matrices leading to satisfactory results. The entire framework has been developed with the goal of near-real-time operational systems (e.g., integration into meteolakes.ch).

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Forecasting of Surface Currents via Correcting Wind Stress with Assimilation of High-Frequency Radar Data in a Three-Dimensional Model

Advances in Meteorology ◽

10.1155/2016/8950378 ◽

2016 ◽

Vol 2016 ◽

pp. 1-12 ◽

Cited By ~ 7

Author(s):

Lei Ren ◽

Stephen Nash ◽

Michael Hartnett

Keyword(s):

Data Assimilation ◽

High Frequency ◽

Three Dimensional ◽

Hf Radar ◽

Surface Velocity ◽

Shear Stresses ◽

Surface Currents ◽

Dimensional Model ◽

Three Dimensional Model ◽

The Impact

This paper details work in assessing the capability of a hydrodynamic model to forecast surface currents and in applying data assimilation techniques to improve model forecasts. A three-dimensional model Environment Fluid Dynamics Code (EFDC) was forced with tidal boundary data and onshore wind data, and so forth. Surface current data from a high-frequency (HF) radar system in Galway Bay were used for model intercomparisons and as a source for data assimilation. The impact of bottom roughness was also investigated. Having developed a “good” water circulation model the authors sought to improve its forecasting ability through correcting wind shear stress boundary conditions. The differences in surface velocity components between HF radar measurements and model output were calculated and used to correct surface shear stresses. Moreover, data assimilation cycle lengths were examined to extend the improvements of surface current’s patterns during forecasting period, especially for north-south velocity component. The influence of data assimilation in model forecasting was assessed using a Data Assimilation Skill Score (DASS). Positive magnitude of DASS indicated that both velocity components were considerably improved during forecasting period. Additionally, the improvements of RMSE for vector direction over domain were significant compared with the “free run.”

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Numerical modeling of mesospheric mountain wave propagation observed in Southern Argentina during the SOUTHTRAC campaign

10.5194/egusphere-egu2020-19489 ◽

2020 ◽

Author(s):

Tyler Mixa ◽

Andreas Dörnbrack ◽

Bernd Kaifler ◽

Markus Rapp

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Numerical Models ◽

Airborne Lidar ◽

Tierra Del Fuego ◽

Polar Night ◽

Research Aircraft ◽

Complex Propagation ◽

The Antarctic

<p>We present numerical simulations of a deep orographic gravity wave (GW) event observed by the ALIMA airborne lidar on 11-12 September 2019 over Southern Argentina. The measurements are taken from the 2019 SOUTHTRAC Campaign, employing a comprehensive suite of remote sensing and in-situ instruments onboard the HALO research aircraft to study the stratospheric GW hotspot over Tierra del Fuego and the Antarctic Peninsula. Wind conditions on 11-12 September exhibit local and large-scale directional shear from the ground to the polar night jet, creating a complex propagation environment supporting multiple orientations of GW propagation and strong potential for local GW breaking and secondary GW generation. Using high resolution numerical models, we simulate the 3D evolution of the orographic GW field to analyze<span> the remote sensing and in-situ measurements from the event.</span></p>

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