scholarly journals Forecast Divergences of a Global Wave Model

2005 ◽  
Vol 133 (8) ◽  
pp. 2148-2162 ◽  
Author(s):  
Diana J. M. Greenslade ◽  
Ian R. Young
Keyword(s):  
Correlation Length ◽  
Surface Wind ◽  
Wave Model ◽  
Surface Wind Speed ◽  
Length Scale ◽  
Similar Distribution ◽  
Background Error ◽  
Latitudinal Dependence ◽  
Wave Models ◽  
Seasonal Signal

Abstract One of the main limitations to current wave data assimilation systems is the lack of an accurate representation of the structure of the background errors. One method that may be used to determine background errors is the “NMC method.” This method examines the forecast divergence component of the background error growth by considering differences between forecasts of different ranges valid at the same time. In this paper, the NMC method is applied to global forecasts of significant wave height (SWH) and surface wind speed (U10). It is found that the isotropic correlation length scale of the SWH forecast divergence (LSWH) has considerable geographical variability, with the longest scales just to the south of the equator in the eastern Pacific Ocean, and the shortest scales at high latitudes. The isotropic correlation length scale of the U10 forecast divergence (LU10) has a similar distribution with a stronger latitudinal dependence. It is found that both LSWH and LU10 increase as the forecast period increases. The increase in LSWH is partly due to LU10 also increasing. Another explanation is that errors in the analysis or the short-range SWH forecast propagate forward in time and disperse and their scale becomes larger. It is shown that the forecast divergence component of the background error is strongly anisotropic with the longest scales perpendicular to the likely direction of propagation of swell. In addition, in regions where the swell propagation is seasonal, the forecast divergence component of the background error shows a similar strong seasonal signal. It is suggested that the results of this study provide a lower bound to the description of the total background error in global wave models.

scholarly journals Emulation of an ensemble Kalman filter algorithm on a flood wave propagation model

2013 ◽  
Vol 10 (6) ◽  
pp. 6963-7001
Author(s):  
S. Barthélémy ◽  
S. Ricci ◽  
O. Pannekoucke ◽  
O. Thual ◽  
P. O. Malaterre
Keyword(s):  
Correlation Length ◽  
Computational Cost ◽  
Error Variance ◽  
Observation Point ◽  
Propagation Model ◽  
Length Scale ◽  
Background Error ◽  
Background Error Covariance ◽  
Covariance Functions ◽  
Error Covariance

Abstract. This study describes the emulation of an Ensemble Kalman Filter (EnKF) algorithm on a 1-D flood wave propagation model. This model is forced at the upstream boundary with a random variable with gaussian statistics and a correlation function in time with gaussian shape. This allows for, in the case without assimilation, the analytical study of the covariance functions of the propagated signal anomaly. This study is validated numerically with an ensemble method. In the case with assimilation with one observation point, where synthetical observations are generated by adding an error to a true state, the dynamic of the background error covariance functions is not straightforward and a numerical approach using an EnKF algorithm is prefered. First, those numerical experiments show that both background error variance and correlation length scale are reduced at the observation point. This reduction of variance and correlation length scale is propagated downstream by the dynamics of the model. Then, it is shown that the application of a Best Linear Unbiased Estimator (BLUE) algorithm using the background error covariance matrix converged from the EnKF algorithm, provides the same results as the EnKF but with a cheaper computational cost, thus allowing for the use of data assimilation in the context of real time flood forecasting. Moreover it was demonstrated that the reduction of background error correlation length scale and variance at the observation point depends on the error observation statistics. This feature is quantified by abacus built from linear regressions over a limited set of EnKF experiments. These abacus that describe the background error variance and the correlation length scale in the neighboring of the observation point combined with analytical expressions that describe the background error variance and the correlation length scale away from the observation point provide parametrized models for the variance and the correlation length scale. Using this parametrized variance and correlation length scale with a diffusion operator makes it possible to model the converged background error covariance matrix from the EnKF without actually integrating the EnKF algorithm. This method was finally applied to a case with two different observation point with different error statistics. It was shown that the results of this emulated EnKF (EEnKF) in terms of background error variance, correlation length scale and analyzed water level is close to those of the EnKF but with a significantly reduced computational cost.

Estimation and Impact of Nonuniform Horizontal Correlation Length Scales for Global Ocean Physical Analyses

2014 ◽  
Vol 31 (10) ◽  
pp. 2330-2349 ◽  
Author(s):  
Andrea Storto ◽  
Simona Masina ◽  
Srdjan Dobricic
Keyword(s):  
Data Assimilation ◽  
Correlation Length ◽  
Global Ocean ◽  
Length Scale ◽  
Background Error ◽  
Length Scales ◽  
Skill Scores ◽  
Uniform Background ◽  
Analysis System ◽  
The Impact

Abstract Optimally modeling background-error horizontal correlations is crucial in ocean data assimilation. This paper investigates the impact of releasing the assumption of uniform background-error correlations in a global ocean variational analysis system. Spatially varying horizontal correlations are introduced in the recursive filter operator, which is used for modeling horizontal covariances in the Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) analysis system. The horizontal correlation length scales (HCLSs) were defined on the full three-dimensional model space and computed from both a dataset of monthly anomalies with respect to the monthly climatology and through the so-called National Meteorological Center (NMC) method. Different formulas for estimating the correlation length scale are also discussed and applied to the two forecast error datasets. The new formulation is tested within a 12-yr period (2000–11) in the ½° resolution system. The comparison with the data assimilation system using uniform background-error horizontal correlations indicates the superiority of the former, especially in eddy-dominated areas. Verification skill scores report a significant reduction of RMSE, and the use of nonuniform length scales improves the representation of the eddy kinetic energy at midlatitudes, suggesting that uniform, latitude, or Rossby radius-dependent formulations are insufficient to represent the geographical variations of the background-error correlations. Furthermore, a small tuning of the globally uniform value of the length scale was found to have a small impact on the analysis system. The use of either anomalies or NMC-derived correlation length scales also has a marginal effect with respect to the use of nonuniform HCLSs. On the other hand, the application of overestimated length scales has proved to be detrimental to the analysis system in all areas and for all parameters.

scholarly journals The Effect of Thinning and Superobservations in a Simple One-Dimensional Data Analysis with Mischaracterized Error

2018 ◽  
Vol 146 (4) ◽  
pp. 1181-1195 ◽  
Author(s):  
Ross N. Hoffman
Keyword(s):  
Correlation Length ◽  
Weighted Least Squares ◽  
Simple Problem ◽  
Length Scale ◽  
Observation Error ◽  
Analysis Method ◽  
Analysis Problem ◽  
Background Error ◽  
One Dimensional ◽  
Error Correlation

A one-dimensional (1D) analysis problem is defined and analyzed to explore the interaction of observation thinning or superobservation with observation errors that are correlated or systematic. The general formulation might be applied to a 1D analysis of radiance or radio occultation observations in order to develop a strategy for the use of such data in a full data assimilation system, but is applied here to a simple analysis problem with parameterized error covariances. Findings for the simple problem include the following. For a variational analysis method that includes an estimate of the full observation error covariances, the analysis is more sensitive to variations in the estimated background and observation error standard deviations than to variations in the corresponding correlation length scales. Furthermore, if everything else is fixed, the analysis error increases with decreasing true background error correlation length scale and with increasing true observation error correlation length scale. For a weighted least squares analysis method that assumes the observation errors are uncorrelated, best results are obtained for some degree of thinning and/or tuning of the weights. Without tuning, the best strategy is superobservation with a spacing approximately equal to the observation error correlation length scale.

Testing the Limits and Breakdown of the Nonacceleration Theorem for Orographic Stationary Waves

2020 ◽  
Vol 77 (5) ◽  
pp. 1513-1529
Author(s):  
Nicholas J. Lutsko
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Surface Wind ◽  
Circulation Model ◽  
Wave Model ◽  
Stationary Wave ◽  
Surface Wind Speed ◽  
Maximum Height ◽  
Stationary Waves ◽  
Mean Flow

Abstract The nonacceleration theorem states that the torque exerted on the atmosphere by orography is exactly balanced by the convergence of momentum by the stationary waves that the orography excites. This balance is tested in simulations with a stationary wave model and with a dry, idealized general circulation model (GCM), in which large-scale orography is placed at the latitude of maximum surface wind speed. For the smallest mountain considered (maximum height H = 0.5 m), the nonacceleration balance is nearly met, but the damping in the stationary wave model induces an offset between the stationary eddy momentum flux (EMF) convergence and the mountain torque, leading to residual mean flow changes. A stationary nonlinearity appears for larger mountains (H ≥ 10 m), driven by preferential deflection of the flow around the poleward flank of the orography, and causes further breakdown of the nonacceleration balance. The nonlinearity grows as H is increased, and is stronger in the GCM than in the stationary wave model, likely due to interactions with transient eddies. The midlatitude jet shifts poleward for H ≤ 2 km and equatorward for larger mountains, reflecting changes in the transient EMFs, which push the jet poleward for smaller mountains and equatorward for larger mountains. The stationary EMFs consistently force the jet poleward. These results add to our understanding of how orography affects the atmosphere’s momentum budget, providing insight into how the nonacceleration theorem breaks down; the roles of stationary nonlinearities and transients; and how orography affects the strength and latitude of eddy-driven jets.

Recurrence Characteristics Analysis of Near-Surface Wind Speed Signal with Different Sampling Frequencies

2014 ◽  
Vol 599-601 ◽  
pp. 1605-1609 ◽  
Author(s):  
Ming Zeng ◽  
Zhan Xie Wu ◽  
Qing Hao Meng ◽  
Jing Hai Li ◽  
Shu Gen Ma
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Recurrence Plot ◽  
Sampling Frequency ◽  
Near Surface ◽  
Gas Leakage ◽  
Time Period ◽  
Characteristics Analysis ◽  
Quantification Analysis

The wind is the main factor to influence the propagation of gas in the atmosphere. Therefore, the wind signal obtained by anemometer will provide us valuable clues for searching gas leakage sources. In this paper, the Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA) are applied to analyze the influence of recurrence characteristics of the wind speed time series under the condition of the same place, the same time period and with the sampling frequency of 1hz, 2hz, 4.2hz, 5hz, 8.3hz, 12.5hz and 16.7hz respectively. Research results show that when the sampling frequency is higher than 5hz, the trends of recurrence nature of different groups are basically unchanged. However, when the sampling frequency is set below 5hz, the original trend of recurrence nature is destroyed, because the recurrence characteristic curves obtained using different sampling frequencies appear cross or overlapping phenomena. The above results indicate that the anemometer will not be able to fully capture the detailed information in wind field when its sampling frequency is lower than 5hz. The recurrence characteristics analysis of the wind speed signals provides an important basis for the optimal selection of anemometer.

Ocean surface wind speed retrieval from C-band quad-polarized SAR measurements at optimal spatial resolution

2020 ◽  
Vol 12 (2) ◽  
pp. 155-164
Author(s):  
He Fang ◽  
William Perrie ◽  
Gaofeng Fan ◽  
Tao Xie ◽  
Jingsong Yang
Keyword(s):  
Wind Speed ◽  
Spatial Resolution ◽  
Surface Wind ◽  
Ocean Surface ◽  
Surface Wind Speed ◽  
Ocean Surface Wind
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