A Study of Subseasonal Predictability

Abstract The predictability of weekly averaged circulation anomalies in the Northern Hemisphere, and diabatic heating anomalies in the Tropics, is investigated in a linear inverse model (LIM) derived from their observed simultaneous and time-lag correlation statistics. In both winter and summer, the model's forecast skill at week 2 (days 8–14) and week 3 (days 15–21) is comparable to that of a comprehensive global medium-range forecast (MRF) model developed at the National Centers for Environmental Prediction (NCEP). Its skill at week 3 is actually higher on average, partly due to its better ability to forecast tropical heating variations and their influence on the extratropical circulation. The geographical and temporal variations of forecast skill are also similar in the two models. This makes the much simpler LIM an attractive tool for assessing and diagnosing atmospheric predictability at these forecast ranges. The LIM assumes that the dynamics of weekly averages are linear, asymptotically stable, and stochastically forced. In a forecasting context, the predictable signal is associated with the deterministic linear dynamics, and the forecast error with the unpredictable stochastic noise. In a low-order linear model of a high-order chaotic system, this stochastic noise represents the effects of both chaotic nonlinear interactions and unresolved initial components on the evolution of the resolved components. Its statistics are assumed here to be state independent. An average signal-to-noise ratio is estimated at each grid point on the hemisphere and is then used to estimate the potential predictability of weekly variations at the point. In general, this predictability is about 50% higher in winter than summer over the Pacific and North America sectors; the situation is reversed over Eurasia and North Africa. Skill in predicting tropical heating variations is important for realizing this potential skill. The actual LIM forecast skill has a similar geographical structure but weaker magnitude than the potential skill. In this framework, the predictable variations of forecast skill from case to case are associated with predictable variations of signal rather than of noise. This contrasts with the traditional emphasis in studies of shorter-term predictability on flow-dependent instabilities, that is, on the predictable variations of noise. In the LIM, the predictable variations of signal are associated with variations of the initial state projection on the growing singular vectors of the LIM's propagator, which have relatively large amplitude in the Tropics. At times of strong projection on such structures, the signal-to-noise ratio is relatively high, and the Northern Hemispheric circulation is not only potentially but also actually more predictable than at other times.

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The first data release of LAMOST low-resolution single-epoch spectra

Research in Astronomy and Astrophysics ◽

10.1088/1674-4527/21/10/249 ◽

2021 ◽

Vol 21 (10) ◽

pp. 249

Author(s):

Zhong-Rui Bai ◽

Hao-Tong Zhang ◽

Hai-Long Yuan ◽

Dong-Wei Fan ◽

Bo-Liang He ◽

...

Keyword(s):

Radial Velocity ◽

Signal To Noise Ratio ◽

Time Lag ◽

Spectral Lines ◽

Low Resolution ◽

Signal To Noise ◽

Data Release ◽

Repeat Exposure ◽

Noise Ratio ◽

Single Epoch

Abstract LAMOST Data Release 5, covering ∼17 000 deg2 from –10° to 80° in declination, contains 9 million co-added low-resolution spectra of celestial objects, each spectrum combined from repeat exposure of two to tens of times during Oct 2011 to Jun 2017. In this paper, we present the spectra of individual exposures for all the objects in LAMOST Data Release 5. For each spectrum, the equivalent width of 60 lines from 11 different elements are calculated with a new method combining the actual line core and fitted line wings. For stars earlier than F type, the Balmer lines are fitted with both emission and absorption profiles once two components are detected. Radial velocity of each individual exposure is measured by minimizing χ 2 between the spectrum and its best template. The database for equivalent widths of spectral lines and radial velocities of individual spectra are available online. Radial velocity uncertainties with different stellar type and signal-to-noise ratio are quantified by comparing different exposure of the same objects. We notice that the radial velocity uncertainty depends on the time lag between observations. For stars observed in the same day and with signal-to-noise ratio higher than 20, the radial velocity uncertainty is below 5km s−1, and increases to 10 km s−1 for stars observed in different nights.

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Eddy-covariance data with low signal-to-noise ratio: time-lag determination, uncertainties and limit of detection

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-8-2913-2015 ◽

2015 ◽

Vol 8 (3) ◽

pp. 2913-2955 ◽

Cited By ~ 5

Author(s):

B. Langford ◽

W. Acton ◽

C. Ammann ◽

A. Valach ◽

E. Nemitz

Keyword(s):

Eddy Covariance ◽

Random Error ◽

Limit Of Detection ◽

Sampling Error ◽

Signal To Noise Ratio ◽

Time Lag ◽

Systematic Bias ◽

Signal To Noise ◽

Cross Covariance ◽

Noise Ratio

Abstract. All eddy-covariance flux measurements are associated with random uncertainties which are a combination of sampling error due to natural variability in turbulence and sensor noise. The former is the principal error for systems where the signal-to-noise ratio of the analyser is high, as is usually the case when measuring fluxes of heat, CO2 or H2O. Where signal is limited, which is often the case for measurements of other trace gases and aerosols, instrument uncertainties dominate. We are here applying a consistent approach based on auto- and cross-covariance functions to quantifying the total random flux error and the random error due to instrument noise separately. As with previous approaches, the random error quantification assumes that the time-lag between wind and concentration measurement is known. However, if combined with commonly used automated methods that identify the individual time-lag by looking for the maximum in the cross-covariance function of the two entities, analyser noise additionally leads to a systematic bias in the fluxes. Combining datasets from several analysers and using simulations we show that the method of time-lag determination becomes increasingly important as the magnitude of the instrument error approaches that of the sampling error. The flux bias can be particularly significant for disjunct data, whereas using a prescribed time-lag eliminates these effects (provided the time-lag does not fluctuate unduly over time). We also demonstrate that when sampling at higher elevations, where low frequency turbulence dominates and covariance peaks are broader, both the probability and magnitude of bias are magnified. We show that the statistical significance of noisy flux data can be increased (limit of detection can be decreased) by appropriate averaging of individual fluxes, but only if systematic biases are avoided by using a prescribed time-lag. Finally, we make recommendations for the analysis and reporting of data with low signal-to-noise and their associated errors.

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Towards standardized processing of eddy covariance flux measurements of carbonyl sulfide

Atmospheric Measurement Techniques ◽

10.5194/amt-13-3957-2020 ◽

2020 ◽

Vol 13 (7) ◽

pp. 3957-3975

Author(s):

Kukka-Maaria Kohonen ◽

Pasi Kolari ◽

Linda M. J. Kooijmans ◽

Huilin Chen ◽

Ulli Seibt ◽

...

Keyword(s):

Data Processing ◽

Eddy Covariance ◽

High Frequency ◽

Signal To Noise Ratio ◽

Time Lag ◽

Carbonyl Sulfide ◽

Signal To Noise ◽

Flux Measurements ◽

Noise Ratio ◽

Processing Steps

Abstract. Carbonyl sulfide (COS) flux measurements with the eddy covariance (EC) technique are becoming popular for estimating gross primary productivity. To compare COS flux measurements across sites, we need standardized protocols for data processing. In this study, we analyze how various data processing steps affect the calculated COS flux and how they differ from carbon dioxide (CO2) flux processing steps, and we provide a method for gap-filling COS fluxes. Different methods for determining the time lag between COS mixing ratio and the vertical wind velocity (w) resulted in a maximum of 15.9 % difference in the median COS flux over the whole measurement period. Due to limited COS measurement precision, small COS fluxes (below approximately 3 pmol m−2 s−1) could not be detected when the time lag was determined from maximizing the covariance between COS and w. The difference between two high-frequency spectral corrections was 2.7 % in COS flux calculations, whereas omitting the high-frequency spectral correction resulted in a 14.2 % lower median flux, and different detrending methods caused a spread of 6.2 %. Relative total uncertainty was more than 5 times higher for low COS fluxes (lower than ±3 pmol m−2 s−1) than for low CO2 fluxes (lower than ±1.5 µmol m−2 s−1), indicating a low signal-to-noise ratio of COS fluxes. Due to similarities in ecosystem COS and CO2 exchange, we recommend applying storage change flux correction and friction velocity filtering as usual in EC flux processing, but due to the low signal-to-noise ratio of COS fluxes, we recommend using CO2 data for time lag and high-frequency corrections of COS fluxes due to the higher signal-to-noise ratio of CO2 measurements.

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Eddy-covariance data with low signal-to-noise ratio: time-lag determination, uncertainties and limit of detection

Atmospheric Measurement Techniques ◽

10.5194/amt-8-4197-2015 ◽

2015 ◽

Vol 8 (10) ◽

pp. 4197-4213 ◽

Cited By ~ 45

Author(s):

B. Langford ◽

W. Acton ◽

C. Ammann ◽

A. Valach ◽

E. Nemitz

Keyword(s):

Eddy Covariance ◽

Random Error ◽

Limit Of Detection ◽

Sampling Error ◽

Signal To Noise Ratio ◽

Time Lag ◽

Systematic Bias ◽

Signal To Noise ◽

Cross Covariance ◽

Noise Ratio

Abstract. All eddy-covariance flux measurements are associated with random uncertainties which are a combination of sampling error due to natural variability in turbulence and sensor noise. The former is the principal error for systems where the signal-to-noise ratio of the analyser is high, as is usually the case when measuring fluxes of heat, CO2 or H2O. Where signal is limited, which is often the case for measurements of other trace gases and aerosols, instrument uncertainties dominate. Here, we are applying a consistent approach based on auto- and cross-covariance functions to quantify the total random flux error and the random error due to instrument noise separately. As with previous approaches, the random error quantification assumes that the time lag between wind and concentration measurement is known. However, if combined with commonly used automated methods that identify the individual time lag by looking for the maximum in the cross-covariance function of the two entities, analyser noise additionally leads to a systematic bias in the fluxes. Combining data sets from several analysers and using simulations, we show that the method of time-lag determination becomes increasingly important as the magnitude of the instrument error approaches that of the sampling error. The flux bias can be particularly significant for disjunct data, whereas using a prescribed time lag eliminates these effects (provided the time lag does not fluctuate unduly over time). We also demonstrate that when sampling at higher elevations, where low frequency turbulence dominates and covariance peaks are broader, both the probability and magnitude of bias are magnified. We show that the statistical significance of noisy flux data can be increased (limit of detection can be decreased) by appropriate averaging of individual fluxes, but only if systematic biases are avoided by using a prescribed time lag. Finally, we make recommendations for the analysis and reporting of data with low signal-to-noise and their associated errors.

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Determination of the Best Emulsion for the Microscopy of Crystals

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100096886 ◽

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.

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Experiments in Bright-Field Imaging with Hollow-Cone Illumination

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100097703 ◽

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.

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Information capacity and bandwidth limitations in the SEM

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100152392 ◽

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 :

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Speech Reception in the Presence of Classroom Noise

Language Speech and Hearing Services in Schools ◽

10.1044/0161-1461.1004.221 ◽

1979 ◽

Vol 10 (4) ◽

pp. 221-230 ◽

Cited By ~ 2

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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Quantifying the Effects of Background Speech Babble on Preschool Children's Novel Word Learning in a Multisession Paradigm: A Preliminary Study

Journal of Speech Language and Hearing Research ◽

10.1044/2019_jslhr-h-19-0083 ◽

2020 ◽

Vol 63 (1) ◽

pp. 345-356

Author(s):

Meital Avivi-Reich ◽

Megan Y. Roberts ◽

Tina M. Grieco-Calub

Keyword(s):

Word Learning ◽

Signal To Noise Ratio ◽

Preschool Age ◽

Exposure Condition ◽

Signal To Noise ◽

Verbal Recall ◽

Referent Selection ◽

Main Effect ◽

Noise Ratio ◽

Novel Word Learning

Purpose This study tested the effects of background speech babble on novel word learning in preschool children with a multisession paradigm. Method Eight 3-year-old children were exposed to a total of 8 novel word–object pairs across 2 story books presented digitally. Each story contained 4 novel consonant–vowel–consonant nonwords. Children were exposed to both stories, one in quiet and one in the presence of 4-talker babble presented at 0-dB signal-to-noise ratio. After each story, children's learning was tested with a referent selection task and a verbal recall (naming) task. Children were exposed to and tested on the novel word–object pairs on 5 separate days within a 2-week span. Results A significant main effect of session was found for both referent selection and verbal recall. There was also a significant main effect of exposure condition on referent selection performance, with more referents correctly selected for word–object pairs that were presented in quiet compared to pairs presented in speech babble. Finally, children's verbal recall of novel words was statistically better than baseline performance (i.e., 0%) on Sessions 3–5 for words exposed in quiet, but only on Session 5 for words exposed in speech babble. Conclusions These findings suggest that background speech babble at 0-dB signal-to-noise ratio disrupts novel word learning in preschool-age children. As a result, children may need more time and more exposures of a novel word before they can recognize or verbally recall it.

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