scholarly journals Data assimilation of sea surface temperature and salinity using basin-scale EOF reconstruction: a feasibility study in the NE Baltic Sea

2020 ◽  
Author(s):  
Mihhail Zujev ◽  
Jüri Elken ◽  
Priidik Lagemaa
Keyword(s):  
Data Assimilation ◽  
Surface Temperature ◽  
Baltic Sea ◽  
Large Scale ◽  
Model Performance ◽  
Empirical Orthogonal Functions ◽  
Reconstruction Technique ◽  
Orthogonal Functions ◽  
Fine Grid ◽  
Basin Scale

Abstract. The tested data assimilation (DA) method based on EOF (Empirical Orthogonal Functions) reconstruction of observations decreased RMSD of surface temperature (SST) and salinity (SSS) in reference to observations in the NE Baltic Sea by 22 % and 34 %, respectively, compared to the control run without DA. The method is based on the covariance estimates from the long period model data. The amplitudes of the pre-calculated gravest EOF modes are estimated from point observations using least-squares optimization; the method builds the variables on the regular grid. The study used FerryBox observations along four ship tracks from 1 May to 31 December 2015, and observations from research vessels. In the reconstruction, this data amount was compressed into daily averages over 5’ N X 10’ E coarse grid. Skill was tested based on daily averages on the 0.5’ N X 1’ E original fine grid of the model. DA with EOF reconstruction technique was found feasible for further implementation studies, since: 1) the method that works on the large-scale patterns (mesoscale features are neglected by taking only the gravest EOF modes) improves the high-resolution model performance by comparable or even better degree than in the other published studies, 2) the method is computationally effective.

scholarly journals Data assimilation of sea surface temperature and salinity using basin-scale reconstruction from empirical orthogonal functions: a feasibility study in the northeastern Baltic Sea

Ocean Science ◽  
2021 ◽  
Vol 17 (1) ◽  
pp. 91-109
Author(s):  
Mihhail Zujev ◽  
Jüri Elken ◽  
Priidik Lagemaa
Keyword(s):  
Data Assimilation ◽  
Surface Temperature ◽  
Baltic Sea ◽  
Large Scale ◽  
Model Performance ◽  
Empirical Orthogonal Functions ◽  
Reconstruction Technique ◽  
Orthogonal Functions ◽  
Fine Grid ◽  
Basin Scale

Abstract. The tested data assimilation (DA) method based on EOF (Empirical Orthogonal Functions) reconstruction of observations decreased centred root-mean-square difference (RMSD) of surface temperature (SST) and salinity (SSS) in reference to observations in the NE Baltic Sea by 22 % and 34 %, respectively, compared to the control run without DA. The method is based on the covariance estimates from long-term model data. The amplitudes of the pre-calculated dominating EOF modes are estimated from point observations using least-squares optimization; the method builds the variables on a regular grid. The study used a large number of in situ FerryBox observations along four ship tracks from 1 May to 31 December 2015, and observations from research vessels. Within DA, observations were reconstructed as daily SST and SSS maps on the coarse grid with a resolution of 5 × 10 arcmin by N and E (ca. 5 nautical miles) and subsequently were interpolated to the fine grid of the prognostic model with a resolution of 0.5 × 1 arcmin by N and E (ca. 0.5 nautical miles). The fine-grid observational fields were used in the DA relaxation scheme with daily interval. DA with EOF reconstruction technique was found to be feasible for further implementation studies, since (1) the method that works on the large-scale patterns (mesoscale features are neglected by taking only the leading EOF modes) improves the high-resolution model performance by a comparable or even better degree than in the other published studies, and (2) the method is computationally effective.

scholarly journals Data-Driven Interpolation of Sea Level Anomalies Using Analog Data Assimilation

2019 ◽  
Vol 11 (7) ◽  
pp. 858 ◽  
Author(s):  
Redouane Lguensat ◽  
Phi Huynh Viet ◽  
Miao Sun ◽  
Ge Chen ◽  
Tian Fenglin ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Level ◽  
Large Scale ◽  
Empirical Orthogonal Functions ◽  
Data Driven ◽  
Optimal Interpolation ◽  
Observation System ◽  
Orthogonal Functions ◽  
Sea Level Anomalies ◽  
Recent Developments

From the recent developments of data-driven methods as a means to better exploit large-scale observation, simulation and reanalysis datasets for solving inverse problems, this study addresses the improvement of the reconstruction of higher-resolution Sea Level Anomaly (SLA) fields using analog strategies. This reconstruction is stated as an analog data assimilation issue, where the analog models rely on patch-based and Empirical Orthogonal Functions (EOF)-based representations to circumvent the curse of dimensionality. We implement an Observation System Simulation Experiment (OSSE) in the South China Sea. The reported results show the relevance of the proposed framework with a significant gain in terms of Root Mean Square Error (RMSE) for scales below 100 km. We further discuss the usefulness of the proposed analog model as a means to exploit high-resolution model simulations for the processing and analysis of current and future satellite-derived altimetric data with regard to conventional interpolation schemes, especially optimal interpolation.

scholarly journals A Hierarchy of Data-Based ENSO Models

2005 ◽  
Vol 18 (21) ◽  
pp. 4425-4444 ◽  
Author(s):  
D. Kondrashov ◽  
S. Kravtsov ◽  
A. W. Robertson ◽  
M. Ghil
Keyword(s):  
El Niño ◽  
Additive Noise ◽  
Polynomial Regression ◽  
El Nino ◽  
Southern Oscillation ◽  
Model Performance ◽  
Empirical Orthogonal Functions ◽  
Quadratic Model ◽  
Orthogonal Functions ◽  
Optimizing Model

Abstract Global sea surface temperature (SST) evolution is analyzed by constructing predictive models that best describe the dataset’s statistics. These inverse models assume that the system’s variability is driven by spatially coherent, additive noise that is white in time and are constructed in the phase space of the dataset’s leading empirical orthogonal functions. Multiple linear regression has been widely used to obtain inverse stochastic models; it is generalized here in two ways. First, the dynamics is allowed to be nonlinear by using polynomial regression. Second, a multilevel extension of classic regression allows the additive noise to be correlated in time; to do so, the residual stochastic forcing at a given level is modeled as a function of variables at this level and the preceding ones. The number of variables, as well as the order of nonlinearity, is determined by optimizing model performance. The two-level linear and quadratic models have a better El Niño–Southern Oscillation (ENSO) hindcast skill than their one-level counterparts. Estimates of skewness and kurtosis of the models’ simulated Niño-3 index reveal that the quadratic model reproduces better the observed asymmetry between the positive El Niño and negative La Niña events. The benefits of the quadratic model are less clear in terms of its overall, cross-validated hindcast skill; this model outperforms, however, the linear one in predicting the magnitude of extreme SST anomalies. Seasonal ENSO dependence is captured by incorporating additive, as well as multiplicative forcing with a 12-month period into the first level of each model. The quasi-quadrennial ENSO oscillatory mode is robustly simulated by all models. The “spring barrier” of ENSO forecast skill is explained by Floquet and singular vector analysis, which show that the leading ENSO mode becomes strongly damped in summer, while nonnormal optimum growth has a strong peak in December.

scholarly journals The Influence of European Climate Variability Mechanism on Air Temperatures in Romania

2014 ◽  
Vol 8 (1) ◽  
pp. 5-16 ◽  
Author(s):  
Nicoleta Ionac ◽  
Monica Matei
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Large Scale ◽  
Empirical Orthogonal Functions ◽  
Spatial And Temporal Variability ◽  
Orthogonal Functions ◽  
Mean Values ◽  
European Climate ◽  
Air Temperatures ◽  
And Shifts

Abstract The present paper investigates on the spatial and temporal variability of maximum and minimum air-temperatures in Romania and their connection to the European climate variability. The European climate variability is expressed by large scale parameters, which are roughly represented by the geopotential height at 500 hPa (H500) and air temperature at 850 hPa (T850). The Romanian data are represented by the time series at 22 weather stations, evenly distributed over the entire country’s territory. The period that was taken into account was 1961-2010, for the summer and winter seasons. The method of empirical orthogonal functions (EOF) has been used, in order to analyze the connection between the temperature variability in Romania and the same variability at a larger scale, by taking into consideration the atmosphere circulation. The time series associated to the first two EOF patterns of local temperatures and large-scale anomalies were considered with regard to trends and shifts in their mean values. The non- Mann-Kendall and Pettitt parametric tests were used in this respect. The results showed a strong correlation between T850 parameter and minimum and maximum air temperatures in Romania. Also, the ample variance expressed by the first EOF configurations suggests a connection between local and large scale climate variability.

scholarly journals Understanding past and future sea surface temperature trends in the Baltic Sea

2021 ◽  
Author(s):  
C. Dutheil ◽  
H. E. M. Meier ◽  
M. Gröger ◽  
F. Börgel
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Baltic Sea ◽  
Heat Fluxes ◽  
Sea Surface ◽  
Atmospheric Forcing ◽  
The Baltic Sea ◽  
Climate Simulations ◽  
Basin Scale ◽  
The Baltic

AbstractThe Baltic Sea is one of the fastest-warming semi-enclosed seas in the world over the last decades, yielding critical consequences on physical and biogeochemical conditions and on marine ecosystems. Although long-term trends in sea surface temperature (SST) have long been attributed to trends in air temperature, there are however, strong seasonal and sub-basin scale heterogeneities of similar magnitude than the average trend which are not fully explained. Here, using reconstructed atmospheric forcing fields for the period 1850–2008, oceanic climate simulations were performed and analyzed to identify areas of homogenous SST trends using spatial clustering. Our results show that the Baltic Sea can be divided into five different areas of homogeneous SST trends: the Bothnian Bay, the Bothnian Sea, the eastern and western Baltic proper, and the southwestern Baltic Sea. A classification tree and sensitivity experiments were carried out to analyze the main drivers behind the trends. While ice cover explains the seasonal north/south warming contrast, the changes in surface winds and air-sea temperature anomalies (along with changes in upwelling frequencies and heat fluxes) explain the SST trends differences between the sub-basins of the southern part of the Baltic Sea. To investigate future warming trends climate simulations were performed for the period 1976–2099 using two RCP scenarios. It was found that the seasonal north/south gradient of SST trends should be reduced in the future due to the vanishing of sea ice, while changes in the frequency of upwelling and heat fluxes explained the lower future east/west gradient of SST trend in fall. Finally, an ensemble of 48 climate change simulations has revealed that for a given RCP scenario the atmospheric forcing is the main source of uncertainty. Our results are useful to better understand the historical and future changes of SST in the Baltic Sea, but also in terms of marine ecosystem and public management, and could thus be used for planning sustainable coastal development.

scholarly journals A METHODOLOGY TO SIMULATE MEDIUM TERM MORPHOLOGICAL CHANGES IN A PRACTICAL COMPUTING TIME

2011 ◽  
Vol 1 (32) ◽  
pp. 58
Author(s):  
Nestor Jimenez ◽  
Roberto Mayerle
Keyword(s):  
Baltic Sea ◽  
Morphological Changes ◽  
Morphological Evolution ◽  
Computing Time ◽  
Empirical Orthogonal Functions ◽  
Medium Term ◽  
Orthogonal Functions ◽  
Preliminary Results ◽  
Realistic Representation ◽  
Set Up

In this paper the assessment of the preliminary results of a methodology to enable predictions of medium-term morphodynamics accounting for the effects of storms is carried out. The methodology integrates the approaches based on a morphological acceleration factor and on the empirical orthogonal functions to account respectively for the morphological changes on the medium and short term. In a very simplified fashion, the effects of the storms are represented by a superposition of most relevant bathymetrical changes. The effectiveness of the methodology was assessed for a coastal stretch along the German Baltic Sea. The analysis of the simulations of morphodynamics for a period of 10 years showed that the method is able to predict volumetric changes along the coastal stretches reasonably well. However it fails to describe the spatial variation of the morphological changes near the coast. Sensitivity studies show also that the results are significantly affected by the set-up scheme of the methodology. Preliminary results during the assessment of the methodology gave clues about the evolution of the morphology of the German Baltic Sea coast. The methodology can be used as a practical tool for initial assessments of tendencies of morphological evolution. Obviously, in this investigation, the method proposed to account for to the storms is a simplified representation of the reality. In this regard, further research is needed to include a more realistic representation of the chronology taking into account their intensity.

scholarly journals DINEOF reconstruction of clouded images including error maps – application to the Sea-Surface Temperature around Corsican Island

Ocean Science ◽  
2006 ◽  
Vol 2 (2) ◽  
pp. 183-199 ◽  
Author(s):  
J.-M. Beckers ◽  
A. Barth ◽  
A. Alvera-Azcárate
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Empirical Orthogonal Functions ◽  
Sea Surface ◽  
Optimal Interpolation ◽  
Orthogonal Functions ◽  
Ligurian Sea ◽  
Additional Information ◽  
Error Covariance ◽  
Data Coverage

Abstract. We present an extension to the Data INterpolating Empirical Orthogonal Functions (DINEOF) technique which allows not only to fill in clouded images but also to provide an estimation of the error covariance of the reconstruction. This additional information is obtained by an analogy with optimal interpolation. It is shown that the error fields can be obtained with a clever rearrangement of calculations at a cost comparable to that of the interpolation itself. The method is presented on the reconstruction of sea-surface temperature in the Ligurian Sea and around the Corsican Island (Mediterranean Sea), including the calculation of inter-annual variability of average surface values and their expected errors. The application shows that the error fields are not only able to reflect the data-coverage structure but also the covariances of the physical fields.

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