Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part II: CGCM Experiments*

2015 ◽  
Vol 143 (11) ◽  
pp. 4645-4659 ◽  
Author(s):  
Feiyu Lu ◽  
Zhengyu Liu ◽  
Shaoqing Zhang ◽  
Yun Liu ◽  
Robert Jacob
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Model Framework ◽  
Strongly Coupled ◽  
Low Level ◽  
Atmosphere Temperature ◽  
Noise Ratio ◽  
Coupled Data Assimilation

Abstract This paper uses a fully coupled general circulation model (CGCM) to study the leading averaged coupled covariance (LACC) method in a strongly coupled data assimilation (SCDA) system. The previous study in a simple coupled climate model has shown that, by calculating the coupled covariance using the leading averaged atmospheric states, the LACC method enhances the signal-to-noise ratio and improves the analysis quality of the slow model component compared to both the traditional weakly coupled data assimilation without cross-component adjustments (WCDA) and the regular SCDA using the simultaneous coupled covariance (SimCC). Here in Part II, the LACC method is tested with a CGCM in a perfect-model framework. By adding the observational adjustments from the low-level atmosphere temperature to the sea surface temperature (SST), the SCDA using LACC significantly reduces the SST error compared to WCDA over the globe; it also improves from the SCDA using SimCC, which performs better than the WCDA only in the deep tropics. The improvement in SST analysis is a result of the enhanced signal-to-noise ratio in the LACC method, especially in the extratropical regions. The improved SST analysis also benefits the subsurface ocean temperature and low-level atmosphere temperature analyses through dynamic and statistical processes.

Does the Signal-to-Noise Paradox Exist in Subseasonal Predictions?

2021 ◽  
Author(s):  
Wei Zhang ◽  
Baoqiang Xiang ◽  
Ben Kirtman ◽  
Emily Becker
Keyword(s):  
Signal To Noise Ratio ◽  
Current Model ◽  
Model Development ◽  
Coupled Model ◽  
Seasonal Prediction ◽  
Signal To Noise ◽  
Model Framework ◽  
Noise Ratio ◽  
Decadal Climate ◽  
New Generation

<p>One of the emerging topics in climate prediction is the issue of the so-called “signal-to-noise paradox”, characterized by too small signal-to-noise ratio in current model predictions that cannot reproduce the realistic signal. Recent studies have suggested that seasonal-to-decadal climate can be more predictable than ever expected due to the paradox. But no studies, to the best of our knowledge, have been focused on whether the signal-to-noise paradox exists in subseasonal predictions. The present study seeks to address the existence of the paradox in subseasonal predictions based on (i) coupled model simulations participating in phase 5 and phase 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively), and (ii) subseasonal hindcast outputs from the Subseasonal Experiment (SubX) and the Subseasonal-to-Seasonal Prediction (S2S) projects. Of particular interest is the possible existence of the paradox in the new generation of GFDL SPEAR model, through the diagnosis of which may help identify potential issues in the new forecast system to guide future model development and initialization. Here we investigate the paradox issue using two methods: the ratio of predictable component defined as the ratio of predictable component in the real world to the signal-to-noise ratio in models and the persistence/dispersion characteristics estimated from a Markov model framework. The preliminary results suggest a potentially widespread occurrence of the signal-to-noise paradox in subseasonal predictions, further implying some room for improvement in future ensemble-based subseasonal predictions.</p>

scholarly journals Strongly Coupled Data Assimilation Experiments with Linearized Ocean–Atmosphere Balance Relationships

2018 ◽  
Vol 146 (4) ◽  
pp. 1233-1257 ◽  
Author(s):  
Andrea Storto ◽  
Matthew J. Martin ◽  
Bruno Deremble ◽  
Simona Masina
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Circulation Model ◽  
Variational Data Assimilation ◽  
Global Ocean ◽  
Background Error ◽  
Near Surface ◽  
Strongly Coupled ◽  
Coupled Data Assimilation ◽  
The Impact

Coupled data assimilation is emerging as a target approach for Earth system prediction and reanalysis systems. Coupled data assimilation may be indeed able to minimize unbalanced air–sea initialization and maximize the intermedium propagation of observations. Here, we use a simplified framework where a global ocean general circulation model (NEMO) is coupled to an atmospheric boundary layer model [Cheap Atmospheric Mixed Layer (CheapAML)], which includes prognostic prediction of near-surface air temperature and moisture and allows for thermodynamic but not dynamic air–sea coupling. The control vector of an ocean variational data assimilation system is augmented to include 2-m atmospheric parameters. Cross-medium balances are formulated either through statistical cross covariances from monthly anomalies or through the application of linearized air–sea flux relationships derived from the tangent linear approximation of bulk formulas, which represents a novel solution to the coupled assimilation problem. As a proof of concept, the methodology is first applied to study the impact of in situ ocean observing networks on the near-surface atmospheric analyses and later to the complementary study of the impact of 2-m air observations on sea surface parameters, to assess benefits of strongly versus weakly coupled data assimilation. Several forecast experiments have been conducted for the period from June to December 2011. We find that especially after day 2 of the forecasts, strongly coupled data assimilation provides a beneficial impact, particularly in the tropical oceans. In most areas, the use of linearized air–sea balances outperforms the statistical relationships used, providing a motivation for implementing coupled tangent linear trajectories in four-dimensional variational data assimilation systems. Further impacts of strongly coupled data assimilation might be found by retuning the background error covariances.

scholarly journals Fast Periodic Stimulation (FPS): A highly effective approach in fMRI brain mapping

2017 ◽  
Author(s):  
Xiaoqing Gao ◽  
Francesco Gentile ◽  
Bruno Rossion
Keyword(s):  
Brain Mapping ◽  
Signal To Noise Ratio ◽  
Fold Increase ◽  
Signal To Noise ◽  
Retest Reliability ◽  
Low Level ◽  
Periodic Stimulation ◽  
Noise Ratio ◽  
Test Retest Reliability ◽  
Very High

SummaryFunctional magnetic resonance imaging (fMRI) is a major technique for human brain mapping. We present a Fast Periodic Stimulation (FPS) fMRI approach, demonstrating its high effectiveness in defining category-selective brain regions. Observers see a dynamic stream of widely variable natural object images alternating at a fast rate (6 images/sec). Every 9 seconds, a short burst of variable face images contrasting with objects in pairs induces an objective 0.111 Hz face-selective neural response in the ventral occipito-temporal cortex and beyond. A model-free Fourier analysis achieves a two-fold increase in signal-to-noise ratio compared to a conventional block-design approach with identical stimuli. Periodicity of category contrast and random variability among images minimize low-level visual confounds while preserving naturalness of the stimuli, leading to the highest values (80-90%) of test-retest reliability yet reported in this area of research. FPS-fMRI opens a new avenue for understanding brain function with low temporal resolution methods.HighlightsFPS-fMRI achieves a two-fold increase in peak SNR over conventional approachFPS-fMRI reveals comprehensive extended face-selective areas including ATLFPS-fMRI achieves high specificity by minimizing influence of low-level visual cuesFPS-fMRI achieves very high test-retest reliability (80%-90%) in spatial activation mapeTOC BlurbIn BriefGao et al. present a novel FPS-fMRI approach, which achieves a two-fold increase in peak signal-to-noise ratio in defining the neural basis of visual categorization while preserving ecological validity, minimizing low-level visual confounds and reaching very high (80%-90%) test-retest reliability.

scholarly journals Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part III: Assimilation of Real World Reanalysis

2020 ◽  
Vol 148 (6) ◽  
pp. 2351-2364
Author(s):  
Jingzhe Sun ◽  
Zhengyu Liu ◽  
Feiyu Lu ◽  
Weimin Zhang ◽  
Shaoqing Zhang
Keyword(s):  
Data Assimilation ◽  
State Estimation ◽  
Real World ◽  
General Circulation ◽  
Correlation Coefficients ◽  
Circulation Model ◽  
Reanalysis Data ◽  
Real World Data ◽  
Strongly Coupled ◽  
Coupled Data Assimilation

Abstract Recent studies proposed leading averaged coupled covariance (LACC) as an effective strongly coupled data assimilation (SCDA) method to improve the coupled state estimation over weakly coupled data assimilation (WCDA) in a coupled general circulation model (CGCM). This SCDA method, however, has been previously evaluated only in the perfect model scenario. Here, as a further step toward evaluating LACC for real world data assimilation, LACC is evaluated for the assimilation of reanalysis data in a CGCM. Several criteria are used to evaluate LACC against the benchmark WCDA. It is shown that despite significant model bias, LACC can improve the coupled state estimation over WCDA. Compared to WCDA, LACC increases the globally averaged anomaly correlation coefficients (ACCs) of sea surface temperature (SST) by 0.036 and atmosphere temperature at the bottom level (Ts) by 0.058. However, there also exist regions where WCDA outperforms LACC. Although the reduction in the anomaly root-mean-square error (RMSE) is not as consistently clear as the increase in ACC, LACC can largely correct the biased model climatology.

Low-level signals radiomonitoring using periodic spectrum samples analysis

2021 ◽  
Author(s):  
A.A. Potapov
Keyword(s):  
Radio Frequency ◽  
Detection Method ◽  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Spectrum Analyzer ◽  
Signal To Noise ◽  
Recording Time ◽  
Low Level ◽  
Periodic Spectrum ◽  
Noise Ratio

The article presents practical implementation of energy detection method based upon non-parametric statistics computed using periodic spectrum samples provided by measuring equipment. The method enables efficient detection and monitoring for signals with low or negative signal-to-noise ratio. The method's sensitivity is limited by measuring equipment inherent noise fluctuations and can be a priory experimentally established for certain experimental hardware settings and desirable spectrum samples lengths. Sensitivity thresholds (in terms of signal-to-noise ratio) for reliable (with probability > 0,98) signal detection for a typical spectrum analyzer used in experiment varied from –11 dB to + 0,6 dB for spectrum samples lengths ranged between 30 000 and 470 spectrums respectively. The suggested energy detection method can be used for unstable and intermittent signals detection, which are active (or above sensitivity threshold) only for a fraction of spectrum sample recording time. The method is independent of signal's modulation (if any is used), amplitude variability profile and signal's probability distribution features. Experimentally determined sensitivity threshold levels for real radio frequency signals coincided within 1,9 dB tolerances with corresponding levels estimated from spectrum analyzer inherent noise fluctuations for all implemented spectrum samples lengths. The data recording time for abovementioned spectrum samples lengths ranged between 207 and 3,2 seconds respectively and was entirely hardware-dependent parameter. Experiment proved equal efficiency and reliability of the suggested method for reliable detection for both white noise signal (generated by analog generator) and broadcasted LTE signal (generated by cellular base stations), which were affected by multi-path propagation effects and average signal level instability due to subscribers time-varying activity. The experiment showed the proposed energy detection method besides detection of low-level radio frequency signals (down to –11 dB SNR) provides highly reliable assessment of the detected signal's signal-to-noise ratio with 0,6 dB tolerance and 0,95 probability. The energy detection method demonstrated zero level of false detections when there was no signal at the spectrum analyzer input (the input port of the instrument was terminated by a matched load), which is essential for method applicability in tasks of highly reliable detection of low-level signals from various types of sources. Taking into account specifications of available hardware, required sensitivity level and limits for data recording time it is possible to choose optimal length of spectrum sample for the energy detection method, which would be the most reasonable for any task in question. The energy detection method based upon non-parametric statistics computed using periodic spectrum samples can be effectively used in detection and radiomonitoring of low-level signals, in radio frequency electromagnetic compatibility research tasks and radio propagation path properties analysis in high loss environment.

Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part I: Simple Model Study*

2015 ◽  
Vol 143 (9) ◽  
pp. 3823-3837 ◽  
Author(s):  
Feiyu Lu ◽  
Zhengyu Liu ◽  
Shaoqing Zhang ◽  
Yun Liu
Keyword(s):  
Data Assimilation ◽  
Signal To Noise Ratio ◽  
Coupled Model ◽  
Coupled System ◽  
Asymmetric Structure ◽  
Multiple Model ◽  
Strongly Coupled ◽  
Model Study ◽  
Coupled Data Assimilation ◽  
Model Components

Abstract This paper studies a new leading averaged coupled covariance (LACC) method for the strongly coupled data assimilation (SCDA). The SCDA not only uses the coupled model to generate the forecast and assimilate observations into multiple model components like the weakly coupled version (WCDA), but also applies a cross update using the coupled covariance between variables from different model components. The cross update could potentially improve the balance and quality of the analysis, but its implementation has remained a great challenge in practice because of different time scales between model components. In a typical extratropical coupled system, the ocean–atmosphere correlation shows a strong asymmetry with the maximum correlation occurring when the atmosphere leads the ocean by about the decorrelation time of the atmosphere. The LACC method utilizes such asymmetric structure by using the leading forecasts and observations of the fast atmospheric variable for cross update, therefore, increasing the coupled correlation and enhancing the signal-to-noise ratio in calculating the coupled covariance. Here it is applied to a simple coupled model with the ensemble Kalman filter (EnKF). With the LACC method, the SCDA reduces the analysis error of the oceanic variable by over 20% compared to the WCDA and 10% compared to the SCDA using simultaneous coupled covariance. The advantage of the LACC method is more notable when the system contains larger errors, such as in the cases with smaller ensemble size, bigger time-scale difference, or model biases.

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

Experiments in Bright-Field Imaging with Hollow-Cone Illumination

1981 ◽  
Vol 39 ◽  
pp. 226-227
Author(s):  
W. Kunath ◽  
K. Weiss ◽  
E. Zeitler
Keyword(s):  
Signal To Noise Ratio ◽  
Carbon Support ◽  
Electronic System ◽  
Optic Axis ◽  
Hollow Cone ◽  
Signal To Noise ◽  
Bright Field ◽  
Noise Ratio ◽  
Single Field ◽  
Field Imaging

Bright-field images taken with axial illumination show spurious high contrast patterns which obscure details smaller than 15 ° Hollow-cone illumination (HCI), however, reduces this disturbing granulation by statistical superposition and thus improves the signal-to-noise ratio. In this presentation we report on experiments aimed at selecting the proper amount of tilt and defocus for improvement of the signal-to-noise ratio by means of direct observation of the electron images on a TV monitor.Hollow-cone illumination is implemented in our microscope (single field condenser objective, Cs = .5 mm) by an electronic system which rotates the tilted beam about the optic axis. At low rates of revolution (one turn per second or so) a circular motion of the usual granulation in the image of a carbon support film can be observed on the TV monitor. The size of the granular structures and the radius of their orbits depend on both the conical tilt and defocus.

Information capacity and bandwidth limitations in the SEM

1989 ◽  
Vol 47 ◽  
pp. 84-85
Author(s):  
D. C. Joy ◽  
R. D. Bunn
Keyword(s):  
Signal To Noise Ratio ◽  
Critical Dimension ◽  
Information Capacity ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Sem Image ◽  
Probe Size ◽  
Minimum Number ◽  
Noise Ratio ◽  
Signal Channel

The information available from an SEM image is limited both by the inherent signal to noise ratio that characterizes the image and as a result of the transformations that it may undergo as it is passed through the amplifying circuits of the instrument. In applications such as Critical Dimension Metrology it is necessary to be able to quantify these limitations in order to be able to assess the likely precision of any measurement made with the microscope.The information capacity of an SEM signal, defined as the minimum number of bits needed to encode the output signal, depends on the signal to noise ratio of the image - which in turn depends on the probe size and source brightness and acquisition time per pixel - and on the efficiency of the specimen in producing the signal that is being observed. A detailed analysis of the secondary electron case shows that the information capacity C (bits/pixel) of the SEM signal channel could be written as :

Speech Reception in the Presence of Classroom Noise

1979 ◽  
Vol 10 (4) ◽  
pp. 221-230 ◽  
Author(s):  
Veronica Smyth
Keyword(s):  
Signal To Noise Ratio ◽  
Learning Processes ◽  
Speech Discrimination ◽  
Signal To Noise ◽  
Speech Reception ◽  
Monosyllabic Words ◽  
Noise Ratio

Three hundred children from five to 12 years of age were required to discriminate simple, familiar, monosyllabic words under two conditions: 1) quiet, and 2) in the presence of background classroom noise. Of the sample, 45.3% made errors in speech discrimination in the presence of background classroom noise. The effect was most marked in children younger than seven years six months. The results are discussed considering the signal-to-noise ratio and the possible effects of unwanted classroom noise on learning processes.

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