scholarly journals Model-to-model data assimilation method for fine resolution ocean modelling

2021 ◽  
Author(s):  
Georgy I. Shapiro ◽  
Jose M. Gonzalez-Ondina
Keyword(s):  
Data Assimilation ◽  
Ocean Model ◽  
Quality Data ◽  
Computationally Efficient ◽  
Fine Grid ◽  
True Solution ◽  
Grid Nodes ◽  
Two Stages ◽  
Fine Resolution ◽  
Mean Square Errors

Abstract. An effective and computationally efficient method is presented for data assimilation in a high-resolution (child) ocean model, which is nested into a coarse-resolution good quality data assimilating (parent) model. The method named Data Assimilation with Stochastic-Deterministic Downscaling (SDDA) reduces bias and root mean square errors (RMSE) of the child model and does not allow the child model to drift away from reality. The basic idea is to assimilate data from the parent model instead of actual observations. In this way, the child model is physically aware of observations via the parent model. The method allows to avoid a complex process of assimilating the same observations which were already assimilated into the parent model. The method consists of two stages: (1) downscaling the parent model output onto the child model grid using Stochastic-Deterministic Downscaling, and (2) applying a simplified Kalman gain formula to each of the fine grid nodes. The method is illustrated in a synthetic case where the true solution is known, and the child model forecast (before data assimilation) is simulated by adding various types of errors. The SDDA method reduces the child model bias to the same level as in the parent model and reduces the RMSE typically by a factor of 2 to 5.

An application of a data assimilation method based on the diffusion stochastic process theory using altimetry data in Atlantic

2016 ◽  
Vol 31 (3) ◽  
Author(s):  
Konstantin P. Belyaev ◽  
Andrey A. Kuleshov ◽  
Clemente A. S. Tanajura
Keyword(s):  
Stochastic Process ◽  
Data Assimilation ◽  
Interpolation Method ◽  
Planck Equation ◽  
Ocean Model ◽  
Sea Surface ◽  
Process Theory ◽  
Optimal Interpolation ◽  
Computationally Efficient ◽  
The Impact

AbstractA data assimilation (DA) method based on the application of the diffusion stochastic process theory, particularly, of the Fokker-Planck equation, is considered. The method was introduced in the previous works; however, it is substantially modified and extended to the multivariate case in the current study. For the first time, the method is here applied to the assimilation of sea surface height anomalies (SSHA) into the Hybrid Coordinate Ocean Model (HYCOM) over the Atlantic Ocean. The impact of assimilation of SSHA is investigated and compared with the assimilation by an Ensemble Optimal Interpolation method (EnOI). The time series of the analyses produced by both assimilation methods are evaluated against the results from a free model run without assimilation. This study shows that the proposed assimilation technique has some advantages in comparison with EnOI analysis. Particularly, it is shown that it provides slightly smaller error and is computationally efficient. The method may be applied to assimilate other data such as observed sea surface temperature and vertical profiles of temperature and salinity.

scholarly journals On the Impact of Unmanned Aerial System Observations on Numerical Weather Prediction in the Coastal Zone

2018 ◽  
Vol 146 (2) ◽  
pp. 599-622 ◽  
Author(s):  
David D. Flagg ◽  
James D. Doyle ◽  
Teddy R. Holt ◽  
Daniel P. Tyndall ◽  
Clark M. Amerault ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Research Laboratory ◽  
Weather Prediction ◽  
Ocean Model ◽  
Naval Research ◽  
Unmanned Aerial System ◽  
Numerical Weather ◽  
The Impact

Abstract The Trident Warrior observational field campaign conducted off the U.S. mid-Atlantic coast in July 2013 included the deployment of an unmanned aerial system (UAS) with several payloads on board for atmospheric and oceanic observation. These UAS observations, spanning seven flights over 5 days in the lowest 1550 m above mean sea level, were assimilated into a three-dimensional variational data assimilation (DA) system [the Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] used to generate analyses for a numerical weather prediction model [the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] with a coupled ocean model [the Naval Research Laboratory Navy Coastal Ocean Model (NCOM)]. The impact of the assimilated UAS observations on short-term atmospheric prediction performance is evaluated and quantified. Observations collected from 50 radiosonde launches during the campaign adjacent to the UAS flight paths serve as model forecast verification. Experiments reveal a substantial reduction of model bias in forecast temperature and moisture profiles consistently throughout the campaign period due to the assimilation of UAS observations. The model error reduction is most substantial in the vicinity of the inversion at the top of the model-estimated boundary layer. Investigations reveal a consistent improvement to prediction of the vertical position, strength, and depth of the boundary layer inversion. The relative impact of UAS observations is explored further with experiments of systematic denial of data streams from the NAVDAS DA system and removal of individual measurement sources on the UAS platform.

scholarly journals Tasman leakage in a fine-resolution ocean model

2012 ◽  
Vol 39 (6) ◽  
pp. n/a-n/a ◽  
Author(s):  
Erik van Sebille ◽  
Matthew H. England ◽  
Jan D. Zika ◽  
Bernadette M. Sloyan
Keyword(s):  
Ocean Model ◽  
Fine Resolution

scholarly journals Adjusting the Wind Stress Drag Coefficient in Storm Surge Forecasting Using an Adjoint Technique

2013 ◽  
Vol 30 (3) ◽  
pp. 590-608 ◽  
Author(s):  
Shiqiu Peng ◽  
Yineng Li ◽  
Lian Xie
Keyword(s):  
Data Assimilation ◽  
Drag Coefficient ◽  
Storm Surge ◽  
Wind Stress ◽  
Weather Prediction ◽  
Ocean Model ◽  
Error Sources ◽  
Empirical Parameter ◽  
Forecasting Errors ◽  
Storm Surge Forecasting

Abstract A three-dimensional ocean model and its adjoint model are used to adjust the drag coefficient in the calculation of wind stress for storm surge forecasting. A number of identical twin experiments (ITEs) with different error sources imposed are designed and performed. The results indicate that when the errors come from the wind speed, the drag coefficient is adjusted to an “optimal value” to compensate for the wind errors, resulting in significant improvements of the specific storm surge forecasting. In practice, the “true” drag coefficient is unknown and the wind field, which is usually calculated by an empirical parameter model or a numerical weather prediction model, may contain large errors. In addition, forecasting errors may also come from imperfect model physics and numerics, such as insufficient resolution and inaccurate physical parameterizations. The results demonstrate that storm surge forecasting errors can be reduced through data assimilation by adjusting the drag coefficient regardless of the error sources. Therefore, although data assimilation may not fix model imperfection, it is effective in improving storm surge forecasting by adjusting the wind stress drag coefficient using the adjoint technique.

Entrainment and Transport in Idealized Three-Dimensional Gravity Current Simulation

2006 ◽  
Vol 23 (9) ◽  
pp. 1249-1269 ◽  
Author(s):  
Yu-Heng Tseng ◽  
David E. Dietrich
Keyword(s):  
Gravity Current ◽  
Vertical Mixing ◽  
Three Dimensional ◽  
Ocean Model ◽  
Numerical Dissipation ◽  
Good Convergence ◽  
Low Viscosity ◽  
Denmark Strait ◽  
Dimensional Gravity ◽  
Fine Resolution

Abstract A purely z-coordinate Dietrich/Center for Air Sea Technology (DieCAST) ocean model is applied to the Dynamics of Overflow Mixing and Entrainment (DOME) idealized bottom density current problem that is patterned after the Denmark Strait. The numerical results show that the background viscosity plays a more important role than the chosen coordinate system in the entrainment and mixing if the background viscosity is not small enough. Both higher horizontal viscosity and coarser resolution leads to slower along-slope propagation. Reducing vertical mixing parameterization also leads to slower along-slope propagation with thicker plume size vertically. The simulation gives consistent results for the moderate- and fine-resolution runs. At a very coarse grid the dense water descends more slowly and is mainly dominated by diffusion. Time-averaged downstream transport and entrainment are not very sensitive to viscosity after the flow reaches its quasi-steady status. However, more realistic eddies and flow structures are found in low-viscosity runs. The results show good convergence of the resolved flow as expected and clarify the effects of numerical dissipation/mixing on overflow modeling. Larger numerical dissipation is not required nor recommended in z-coordinate models.

scholarly journals Variational data assimilation with superparameterization

2015 ◽  
Vol 2 (2) ◽  
pp. 513-536 ◽  
Author(s):  
I. Grooms ◽  
Y. Lee
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Climate Models ◽  
Low Cost ◽  
Ocean Model ◽  
Variational Data Assimilation ◽  
Scale Model ◽  
Small Scale ◽  
New Applications ◽  
Lorenz 96

Abstract. Superparameterization (SP) is a multiscale computational approach wherein a large scale atmosphere or ocean model is coupled to an array of simulations of small scale dynamics on periodic domains embedded into the computational grid of the large scale model. SP has been successfully developed in global atmosphere and climate models, and is a promising approach for new applications. The authors develop a 3D-Var variational data assimilation framework for use with SP; the relatively low cost and simplicity of 3D-Var in comparison with ensemble approaches makes it a natural fit for relatively expensive multiscale SP models. To demonstrate the assimilation framework in a simple model, the authors develop a new system of ordinary differential equations similar to the two-scale Lorenz-'96 model. The system has one set of variables denoted {Yi}, with large and small scale parts, and the SP approximation to the system is straightforward. With the new assimilation framework the SP model approximates the large scale dynamics of the true system accurately.

scholarly journals Data Assimilation for Ionospheric Space-Weather Forecasting in the Presence of Model Bias

2021 ◽  
Vol 7 ◽  
Author(s):  
Juan Durazo ◽  
Eric J. Kostelich ◽  
Alex Mahalov
Keyword(s):  
Data Assimilation ◽  
Space Weather ◽  
Bias Correction ◽  
Geomagnetic Storms ◽  
Circulation Model ◽  
Storm Event ◽  
Electron Content ◽  
Systematic Bias ◽  
Total Electron ◽  
Mean Square Errors

The dynamics of many models of physical systems depend on the choices of key parameters. This paper describes the results of some observing system simulation experiments using a first-principles model of the Earth’s ionosphere, the Thermosphere Ionosphere Electrodynamics Global Circulation Model (TIEGCM), which is driven by parameters that describe solar activity, geomagnetic conditions, and the state of the thermosphere. Of particular interest is the response of the ionosphere (and predictions of space weather generally) during geomagnetic storms. Errors in the overall specification of driving parameters for the TIEGCM (and similar dynamical models) may be especially large during geomagnetic storms, because they represent significant perturbations away from more typical interactions of the earth-sun system. Such errors can induce systematic biases in model predictions of the ionospheric state and pose difficulties for data assimilation methods, which attempt to infer the model state vector from a collection of sparse and/or noisy measurements. Typical data assimilation schemes assume that the model produces an unbiased estimate of the truth. This paper tests one potential approach to handle the case where there is some systematic bias in the model outputs. Our focus is on the TIEGCM when it is driven with solar and magnetospheric inputs that are systematically misspecified. We report results from observing system experiments in which synthetic electron density vertical profiles are generated at locations representative of the operational FormoSat-3/COSMIC satellite observing platforms during a moderate (G2, Kp = 6) geomagnetic storm event on September 26–27, 2011. The synthetic data are assimilated into the TIEGCM using the Local Ensemble Transform Kalman Filter with a state-augmentation approach to estimate a small set of bias-correction factors. Two representative processes for the time evolution of the bias in the TIEGCM are tested: one in which the bias is constant and another in which the bias has an exponential growth and decay phase in response to strong geomagnetic forcing. We show that even simple approximations of the TIEGCM bias can reduce root-mean-square errors in 1-h forecasts of total electron content (a key ionospheric variable) by 20–45%, compared to no bias correction. These results suggest that our approach is computationally efficient and can be further refined to improve short-term predictions (∼1-h) of ionospheric dynamics during geomagnetic storms.

Sign in / Sign up

Export Citation Format

Share Document