scholarly journals On determining initial conditions and parameters in a simple coupled atmosphere-ocean model by adjoint data assimilation

1998 ◽  
Vol 50 (4) ◽  
pp. 534-544 ◽  
Author(s):  
Jingxi Lu ◽  
William W. Hsieh
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Ocean Model

scholarly journals A Potential Density Gradient Dependent Analysis Scheme for Ocean Multiscale Data Assimilation

2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
Hongli Fu ◽  
Jinkun Yang ◽  
Wei Li ◽  
Xinrong Wu ◽  
Guijun Han ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Density Gradient ◽  
Initial Conditions ◽  
Ocean Model ◽  
Potential Density ◽  
3 Dimensional ◽  
Ocean Data Assimilation ◽  
Variational Framework ◽  
Analysis Scheme ◽  
Density Surface

This study addresses how to maintain oceanic mixing along potential density surface in ocean data assimilation (ODA). It is well known that the oceanic mixing across the potential density surface is much weaker than that along the potential density surface. However, traditional ODA schemes allow the mixing across the potential density surface and thus may result in extra assimilation errors. Here, a new ODA scheme that uses potential density gradient information of the model background to rescale observational adjustment is designed to improve the quality of assimilation. The new scheme has been tested using a regional ocean model within a multiscale 3-dimensional variational framework. Results show that the new scheme effectively prevents the excessive unphysical projection of observational information in the direction across potential density surface and thus improves assimilation quality greatly. Forecast experiments also show that the new scheme significantly improves the model forecast skills through providing more dynamically consistent initial conditions

scholarly journals Ensemble forecasting greatly expands the prediction horizon for ocean mesoscale variability

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Prasad G. Thoppil ◽  
Sergey Frolov ◽  
Clark D. Rowley ◽  
Carolyn A. Reynolds ◽  
Gregg A. Jacobs ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Horizontal Resolution ◽  
Ocean Model ◽  
Mesoscale Dynamics ◽  
Predictive Skill ◽  
Freshwater Transport ◽  
Prediction Horizon ◽  
Initial Location ◽  
Ocean Models ◽  
High Uncertainty

AbstractMesoscale eddies dominate energetics of the ocean, modify mass, heat and freshwater transport and primary production in the upper ocean. However, the forecast skill horizon for ocean mesoscales in current operational models is shorter than 10 days: eddy-resolving ocean models, with horizontal resolution finer than 10 km in mid-latitudes, represent mesoscale dynamics, but mesoscale initial conditions are hard to constrain with available observations. Here we analyze a suite of ocean model simulations at high (1/25°) and lower (1/12.5°) resolution and compare with an ensemble of lower-resolution simulations. We show that the ensemble forecast significantly extends the predictability of the ocean mesoscales to between 20 and 40 days. We find that the lack of predictive skill in data assimilative deterministic ocean models is due to high uncertainty in the initial location and forecast of mesoscale features. Ensemble simulations account for this uncertainty and filter-out unconstrained scales. We suggest that advancements in ensemble analysis and forecasting should complement the current focus on high-resolution modeling of the ocean.

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 Implementation of a 3-D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

2013 ◽  
Vol 6 (2) ◽  
pp. 3581-3610
Author(s):  
S. Federico
Keyword(s):  
Relative Humidity ◽  
Data Assimilation ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Wind Profiler ◽  
Short Term ◽  
Recursive Filters ◽  
Data Assimilation System ◽  
Rams Model ◽  
Assimilation System

Abstract. This paper presents the current status of development of a three-dimensional variational data assimilation system. The system can be used with different numerical weather prediction models, but it is mainly designed to be coupled with the Regional Atmospheric Modelling System (RAMS). Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height. Important features of the data assimilation system are the use of incremental formulation of the cost-function, and the use of an analysis space represented by recursive filters and eigenmodes of the vertical background error matrix. This matrix and the length-scale of the recursive filters are estimated by the National Meteorological Center (NMC) method. The data assimilation and forecasting system is applied to the real context of atmospheric profiling data assimilation, and in particular to the short-term wind prediction. The analyses are produced at 20 km horizontal resolution over central Europe and extend over the whole troposphere. Assimilated data are vertical soundings of wind, temperature, and relative humidity from radiosondes, and wind measurements of the European wind profiler network. Results show the validity of the analysis solutions because they are closer to the observations (lower RMSE) compared to the background (higher RMSE), and the differences of the RMSEs are consistent with the data assimilation settings. To quantify the impact of improved initial conditions on the short-term forecast, the analyses are used as initial conditions of a three-hours forecast of the RAMS model. In particular two sets of forecasts are produced: (a) the first uses the ECMWF analysis/forecast cycle as initial and boundary conditions; (b) the second uses the analyses produced by the 3-D-Var scheme as initial conditions, then is driven by the ECMWF forecast. The improvement is quantified by considering the horizontal components of the wind, which are measured at a-synoptic times by the European wind profiler network. The results show that the RMSE is effectively reduced at the short range (1–2 h). The results are in agreement with the set-up of the numerical experiment.

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.

scholarly journals Using adjoint sensitivity as a local structure function in variational data assimilation

2001 ◽  
Vol 8 (6) ◽  
pp. 347-355 ◽  
Author(s):  
G. Hello ◽  
F. Bouttier
Keyword(s):  
Data Assimilation ◽  
Structure Function ◽  
Local Structure ◽  
Initial Conditions ◽  
Storm Track ◽  
Variational Data Assimilation ◽  
Sensitive Area ◽  
Adjoint Sensitivity ◽  
Weather Events ◽  
Gradient Fields

Abstract. One approach recently proposed in order to improve the forecast of weather events, such as cyclogenesis, is to increase the number of observations in areas depending on the flow configuration. These areas are obtained using, for example, the sensitivity to initial conditions of a selected predicted cyclone. An alternative or complementary way is proposed here. The idea is to employ such an adjoint sensitivity field as a local structure function within variational data assimilation, 3D-Var in this instance. Away from the sensitive area, observation increments project on the initial fields with the usual climatological (or weakly flow-dependent, in the case of 4D-Var) structure functions. Within the sensitive area, the gradient fields are projected using all the available data in the zone, conventional or extra, if any. The formulation of the technique is given and the approach is further explained by using a simple 1D scheme. The technique is implemented in the ARPEGE/IFS code and applied to 11 FASTEX (Fronts and Atlantic Storm-Track Experiment) cyclone cases, together with the targeted observations performed at the time of the campaign. The new approach is shown to allow for the desired stronger impact of the available observations and to systematically improve the forecasts of the FASTEX cyclones, unlike the standard 3D-Var.

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.

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