Spectral Noise Level Research Articles

Analyzing Stellar Spectra for PRV by Accurate Modeling and Retrieval of Telluric Absorption Features

Ground-based Precision Radial Velocity (PRV) measurements are inevitably impeded by contamination from telluric absorption features, particularly in the infrared region. Thus, it is crucial to improve modeling of the telluric absorption features down to the spectral noise level. As part of the efforts towards improved PRV measurements, we have taken an existing atmospheric trace gas retrieval algorithm (GFIT) and have successfully adapted it to fit the telluric absorption features in stellar spectra down to the spectral noise level (typically ∼1%). We have established a stellar spectral fitting processing pipeline, Stellar-GFIT, to analyze a series of stellar spectra observed by two spectrographs, PARVI (1.1–1.76 μm) commissioned at the Palomar Observatory (Palomar Mountain, CA) and iSHELL (1–5 μm) deployed on the IRTF (Mauna Kea, HI). For this, we have (1) implemented a Gaussian instrumental line shape function, (2) generated atmospheric models (consisting of temperature, pressure, and volume mixing ratios of all the known trace gases) for the particular observation sites and times, (3) employed the most up-to-date spectroscopic parameters in the target spectral regions, and finally (4) developed a series of spectral fitting intervals of ∼60 cm−1 width, i.e., micro-windows, customized to the individual orders of each spectrograph. Stellar-GFIT is also capable of handling non-telluric features, such as transitions from a gas cell placed in the starlight beam and stellar features if a model spectrum template is available for the target star. We present spectrum fits from the observations of various target stars and discuss the performance and advantages of our novel approach. One of the major strengths of Stellar-GFIT is an ability to adjust the abundance of atmospheric trace gases simultaneously with determining the stellar doppler shift, mitigating any adverse impacts of short-timescale variations of water vapor.

Parameter estimation from the Lyα forest in the Fourier space using an information-maximizing neural network

Our aim is to present a robust parameter estimation with simulated forest spectra from Sherwood-Relics simulations suite by using an information-maximizing neural network (IMNN) to extract maximal information from 1D-transmitted flux in the Fourier space. We performed 1D estimations using IMNN for intergalactic medium (IGM) thermal parameters $T_0$ and gamma at $z=2-4$, and cosmological parameters $ and s $ at $z=3-4$. We compared our results with estimates from the power spectrum using the posterior distribution from a Markov Chain Monte Carlo (MCMC). We then checked the robustness of IMNN estimates against deviation in spectral noise levels, continuum uncertainties, and instrumental smoothing effects. Using mock forest sightlines from the publicly available CAMELS project, we also checked the robustness of the trained IMNN on a different simulation. As a proof of concept, we demonstrated a 2D-parameter estimation for $T_0$ and photoionization rates, $ HI We obtain improved estimates of $T_0$ and gamma using IMNN over the standard MCMC approach. These estimates are also more robust against signal-to-noise deviations at $z=2$ and 3. At $z=4$, the sensitivity to noise deviations is on par with MCMC estimates. The IMNN also provides $T_0$ and gamma estimates that are robust against continuum uncertainties by extracting small-scale continuum-independent information from the Fourier domain. In the cases of $ and s $, the IMNN performs on par with MCMC but still offers a significant speed boost in estimating parameters from a new dataset. The improved estimates with IMNN are seen for high instrumental resolution (FWHM=6 At medium or low resolutions, the IMNN performs similarly to MCMC, suggesting an improved extraction of small-scale information with IMNN. We also find that IMNN estimates are robust against the choice of simulation. By performing a 2D-parameter estimation for $T_0$ and $ HI $, we also demonstrate how to take forward this approach observationally in the future.

Application of the non-uniform Fourier transform to non-uniformly sampled Fourier transform spectrometers

Resampling by interpolation is the traditional method to process interferograms from non-uniformly sampled Fourier transform spectrometers. The non-uniform fast Fourier transform (NUFFT) is an alternative approach that has been mostly overlooked. With the aid of experiments on a high-resolution interferometer with a variety of optical sources, these two methods are compared. It is found that the NUFFT is comparable to interpolation in spectral profile shape and spectral noise levels and is better in spectral amplitude and computer performance. A significant advantage is also found in the case of under-sampling and noise performance by NUFFT due to the unique non-periodic nature of non-uniform sampling. In addition, a novel implementation of NUFFT is presented and analysed.

Inter-Subject Variability in the Dependence of Medial-Olivocochlear Reflex Strength on Noise Bandwidth.

The objective of the study was to quantify inter-subject variability in the dependence of the medial-olivocochlear reflex (MOCR) on noise bandwidth. Of specific interest was whether inter-subject variability in MOCR dependence on bandwidth explained variability in the MOCR response elicited by wideband noise. Thirty-two young adults with normal hearing participated in the study. Click-evoked otoacoustic emissions were measured in the ipsilateral ear with and without noise presented in the contralateral ear. Presentation of contralateral noise served to activate the MOCR. The MOCR was activated using five different noise stimuli with bandwidths ranging from 1- to 5-octaves wide (center frequency of 2 kHz; bandwidth incremented in 1-octave steps). Noise spectral levels (19.6 dB SPL/Hz) were held constant across all bandwidths. MOCR metrics included the normalized-percent change in the otoacoustic emission (OAE), the MOCR-induced OAE magnitude shift, and the MOCR-induced OAE phase shift. Linear mixed-effect models were fit to model the dependence of MOCR-induced OAE magnitude and phase changes on noise bandwidth. The use of a mixed-effect modeling approach allowed for the estimation of subject-specific model parameters that capture on- and off-frequency contributions to the MOCR effects. Regression analysis was performed to evaluate the predictive capacity of subject-specific model parameters on the MOCR response elicited by wideband noise. All OAE-based MOCR metrics increased as the noise bandwidth increased from 1- to 5-octaves wide. The dependence of MOCR-induced OAE magnitude and phase shifts on activator bandwidth was well approximated using a linear model with intercept and slope terms. On average, MOCR-induced magnitude and phase shifts increased at a rate of 0.3 dB/octave and 0.01 cycles/octave, respectively, as bandwidth extended beyond the predicted region of OAE generation. A statistically significant random effect of subject was found for both the intercept and slope parameter of each model. Subject-specific slope estimates were statistically significant predictors of a repeated measure of the wideband MOCR response. A higher slope was predictive of larger wideband MOCR effects. MOCR-induced changes to the OAE are greatest when the MOCR is elicited using wideband noise. Variability in the process of spectral integration within the MOCR pathway appears to explain, in part, inter-subject variability in OAE-based estimates of the MOCR response elicited by wideband noise.

Quantitative analysis of Cu XANES spectra using linear combination fitting of binary mixtures simulated by FEFF9

X-ray absorption near edge structure (XANES) is a powerful tool to probe the fingerprint of local structures, and when coupled with X-ray microscopy, the small spot size enables one to probe very specific regions of interest in a device or material, e.g. interfaces/bulk, different grains, good/bad electrical areas. In this work, we investigate the use of linear combination fitting (LCF) of XANES spectra for the particular case of Cu doping in CdTe. We show that the experimental data seem to be accurately represented by standards of Cu2Te and its substoichiometric counterpart, Cu1.43Te. We use Cu in CdTe as a case study to evaluate the accuracy of linear combination fitting using simulated standards, given that experimental standards for certain phases (e.g. Cu1.43Te) or defect structures (e.g. vacancies) cannot be readily obtained. We discuss how spectral features of the FEFF9-simulated standards, fitting ranges, and noise levels all dictate the accuracy of this type of analysis. We show that the greater the spectral difference between the two standards, the better the LCF is able to differentiate between the two structures and to tolerate experimental noise. Finally, we estimate the error of the fitted weights for different spectral features and noise levels and propose a framework to study local structures semi-quantitatively by using binary mixtures of FEFF9-simulated standards.

Evaluation of the Event Detection Level of the Cuban Seismic Network

Abstract The detection level of a seismic network is a measure of its effective ability to record small earthquakes in a given area. It can vary in both space and time and depends on several factors such as meteorological conditions, anthropic noise, local soil conditions—all factors that affect the seismic noise level—as well as the quality and operating condition of the instruments. The ability to estimate the level of detection is of tremendous importance both in the design of a new network and in determining whether a given network can recognize seismicity consistently or needs to be improved in some of its parts. In this article, we determine the detection level of the Cuban seismic network using the empirically estimated seismic noise spectral level at each station site and some theoretical relationships to predict the signal amplitude of a seismic event at individual stations. The minimum local detectable magnitude thus depends on some network parameters such as the signal-to-noise ratio and the number of stations used in the calculation. We also demonstrate the effectiveness of our predictions by comparing the estimated detection level with those empirically determined from one year of data (i.e., the year 2020) of the Cuban seismic catalog. Our analysis shows, on the one hand, in which areas the current Cuban network should be improved, also depending on the regional pattern of faults, and, on the other hand, indicates the magnitude threshold that can be assumed homogeneously for the catalog of Cuban earthquakes in 2020. Because the adopted method can use current measurements of the seismic noise level (e.g., daily), the proposed analysis can also be configured for continuous monitoring of network state quality.

Studies on Cross-modal Feature-based Mapping from Voice-source to Texture through Image Association by Listening Speech

The direct correlations between modality driven parameters of voice-source and texture were investigated. A perceptual experiment was conducted using vowel sounds with three representative phonation differences (modal, creaky and breathy) and texture images annotated with semantic terms. For quantitative analyses, acoustic features measuring vocal fold vibration, periodicity, spectral noise level, fundamental frequency and energy were calculated. Computational textural features containing coarseness, contrast, directionality, busyness, complexity, strength and brightness were extracted. The results showed that the most important feature is the amplitude difference between the first two harmonics (H1-H2). H1-H2 significantly correlates to coarseness, contrast, busyness, complexity, strength and brightness. Harmonic-to-Noise Ratios (HNRs) highly correlate to coarseness, busyness, complexity and strength. Significant correlations were also observed between Cepstral Peak Prominence (CPP) & coarseness, fundamental frequency (F0) & complexity, brightness and energy & strength. These parametric correlations can serve as basic scientific knowledge for cross-modal mapping.

Stability evaluation of the PROSPECT model for leaf chlorophyll content retrieval

The radiative transfer model, PROSPECT, has been widely applied for retrieving leaf biochemical traits. However, little work has been conducted to evaluate the stability of the PROSPECT model with consideration of multiple factors (i.e., spectral resolution, signal-to-noise ratio, plant growth stages, and treatments). This study aims to investigate the stability of the PROSPECT model for retrieving leaf chlorophyll (Chl) content (Cab). Leaf hemispherical reflectance and transmittance of oilseed rape were acquired at different spectral resolutions, noise levels, growth stages, and nitrogen treatments. The Chl content was also measured destructively by using a microplate spectrophotometer. The performance of the PROSPECT model was compared with a commonly used random forest (RF) model. The results showed that the prediction accuracy of PROSPECT and RF models for Cab did not produce significant differences under varied spectral resolutions ranging from 1 to 20 nm. The ranges of the relative root mean square errors (rRMSE) of the PROSPECT and RF models were 12%-13% and 11.70%-12.86%, respectively. However, the performance of both models for leaf Chl retrieval was strongly influenced by the noise level with the rRMSE of 13-15.37% and 12.04%-15.80% for PROSPECT and RF, respectively. For different growth stages, the PROSPECT model had similar prediction accuracies (rRMSE = 9.26%-12.41%) to the RF model (rRMSE = 9.17%-12.70%). Furthermore, the superiority of the PROSPECT model (rRMSE = 10.10%-12.82%) over the RF model (rRMSE = 11.81%-15.47%) was prominently observed when tested with plants growth at different nitrogen treatment levels. The results demonstrated that the PROSPECT model has a more stable performance than the RF model for all datasets in this study. Keywords: leaf chlorophyll content, oilseed rape, PROSPECT, spectral resolution, spectral noise, nitrogen treatment DOI: 10.25165/j.ijabe.20211405.6340 Citation: Zhai L, Wan L, Sun D W, Abdalla A, Zhu Y M, Li X R, et al. Stability evaluation of the PROSPECT model for leaf chlorophyll content retrieval. Int J Agric & Biol Eng, 2021; 14(5): 189–198.

Prediction uncertainty of wind-generated noise spectra from wind speed.

The prediction of wind-generated noise spectral levels at one frequency is typically based on a linear regression function, which is defined over the logarithm of the 10-m wind speed. However, despite its widespread success, the linear regression model does not pay attention to its prediction uncertainty because it makes point predictions. The main reasons for the uncertainty in the predicted value of the wind-generated noise level are that it cannot be uniquely determined by 10-m wind speed and its measurements may be corrupted by other sources of ambient noise. To quantify the uncertainty in predictions in this scenario, a Bayesian treatment of linear regression models and its associated predictive distribution are applied, making distribution predictions instead of point predictions. Once the predictive distribution for one frequency has been fixed, its linear variants are used to obtain predictive distributions for other frequencies. The data for the ocean ambient noise and 10-m wind speed are collected from two deep-water experiments, conducted in the South China Sea, and reanalysis data sets of the European Centre For Medium-Range Weather Forecasts, respectively. Empirical expressions for the predictive distribution of noise spectra (0.5-10 kHz) at wind speeds from 3.3 to 14 m/s have been developed. The results indicate decreasing uncertainties with an increasing wind speed.

Earth’s Time-Variable Gravity from GRACE Follow-On K-Band Range-Rates and Pseudo-Observed Orbits

During its science phase from 2002–2017, the low-low satellite-to-satellite tracking mission Gravity Field Recovery And Climate Experiment (GRACE) provided an insight into Earth’s time-variable gravity (TVG). The unprecedented quality of gravity field solutions from GRACE sensor data improved the understanding of mass changes in Earth’s system considerably. Monthly gravity field solutions as the main products of the GRACE mission, published by several analysis centers (ACs) from Europe, USA and China, became indispensable products for quantifying terrestrial water storage, ice sheet mass balance and sea level change. The successor mission GRACE Follow-On (GRACE-FO) was launched in May 2018 and proceeds observing Earth’s TVG. The Institute of Geodesy (IfE) at Leibniz University Hannover (LUH) is one of the most recent ACs. The purpose of this article is to give a detailed insight into the gravity field recovery processing strategy applied at LUH; to compare the obtained gravity field results to the gravity field solutions of other established ACs; and to compare the GRACE-FO performance to that of the preceding GRACE mission in terms of post-fit residuals. We use the in-house-developed MATLAB-based GRACE-SIGMA software to compute unconstrained solutions based on the generalized orbit determination of 3 h arcs. K-band range-rates (KBRR) and kinematic orbits are used as (pseudo)-observations. A comparison of the obtained solutions to the results of the GRACE-FO Science Data System (SDS) and Combination Service for Time-variable Gravity Fields (COST-G) ACs, reveals a competitive quality of our solutions. While the spectral and spatial noise levels slightly differ, the signal content of the solutions is similar among all ACs. The carried out comparison of GRACE and GRACE-FO KBRR post-fit residuals highlights an improvement of the GRACE-FO K-band ranging system performance. The overall amplitude of GRACE-FO post-fit residuals is about three times smaller, compared to GRACE. GRACE-FO post-fit residuals show less systematics, compared to GRACE. Nevertheless, the power spectral density of GRACE-FO and GRACE post-fit residuals is dominated by similar spikes located at multiples of the orbital and daily frequencies. To our knowledge, the detailed origin of these spikes and their influence on the gravity field recovery quality were not addressed in any study so far and therefore deserve further attention in the future. Presented results are based on 29 monthly gravity field solutions from June 2018 until December 2020. The regularly updated LUH-GRACE-FO-2020 time series of monthly gravity field solutions can be found on the website of the International Centre for Global Earth Models (ICGEM) and in LUH’s research data repository. These operationally published products complement the time series of the already established ACs and allow for a continuous and independent assessment of mass changes in Earth’s system.

The spectrogram, method of reassignment, and frequency-domain beamforming.

A smeared spectrogram is a result of the smoothing kernel in the short-time Fourier-transform (STFT). Besides the smeared energy, time and frequency phase information is also smeared, i.e., spectral components may contain imprecise phase information. The STFT is also used as the basis for more advanced signal processing techniques such as frequency-domain beamforming and cross correlation (CC). Both methods seek the delay time between signals by exploring phase-shifts in the frequency domain. Due to the inexact phase information in some of the time-frequency elements, their phase shifts are incorrect. This study re-introduces the reassigned spectrogram (RS) as a measure to fix the STFT artifacts. Moreover, it is shown that by using the RS, phase shifts can be optimized and improve beamforming and CC results. Synthetic and recorded data are used to show the advantage of using the RS in time-frequency analysis, CC, and beamforming. Results show that, subject to certain constraints, the RS provides exact time-frequency representation of deterministic signals and significantly improve CC and beamforming results. Array analysis of infrasonic signals shows that better results are obtained by either the RS- or STFT-based analysis depending on the signals' spectral components and noise levels.

Road surface influence on electric vehicle noise emission at urban speed

Abstract Considering the relative quietness of electric motors, tyre/road interaction has become the prominent source of noise emission from Electric Vehicles (EVs). This study deals with the potential influence of the road surface on EV noise emission, especially in urban area. A pass-by noise measurement campaign has been carried out on a reference test track, involving six different road surfaces and five electric passenger car models in different vehicle segments. The immunity of sound recordings to background noise was considered with care. The overall and spectral pass-by noise levels have been analysed as a function of the vehicle speed for each couple of road surface and EV model. It was found that the type of EV has few influence on the noise classification of the road surfaces at 50 km/h. However, the noise level difference between the quietest and the loudest road surface depends on the EV model, with an average close to 6 dBA, showing the potential effect of the road surface on noise reduction in the context of growing EV fleet in urban area. The perspective based on an average passenger EV in a future French or European electric fleet is addressed.

Temperature and Emissivity Inversion Accuracy of Spectral Parameter Changes and Noise of Hyperspectral Thermal Infrared Imaging Spectrometers

The emergence of hyperspectral thermal infrared imaging spectrometers makes it possible to retrieve both the land surface temperature (LST) and the land surface emissivity (LSE) simultaneously. However, few articles focus on the problem of how the instrument’s spectral parameters and instrument noise level affect the LST and LSE inversion errors. In terms of instrument development, this article simulated three groups of hyperspectral thermal infrared data with three common spectral parameters and each group of data includes tens of millions of simulated radiances of 1525 emissivity curves with 17 center wavelength shift ratios, 6 full width at half maximum (FWHM) change ratios and 6 noise equivalent differential temperatures (NEDTs) under 15 atmospheric conditions with 6 object temperatures, inverted them by two temperature and emissivity separation methods (ISSTES and ARTEMISS), and analyzed quantitatively the effects of the spectral parameters change and noise of an instrument on the LST and LSE inversion errors. The results show that: (1) center wavelength shifts and noise affect the inversion errors strongly, while FWHM changes affect them weakly; (2) the LST and LSE inversion errors increase with the center wavelength shift ratio in a quadratic function and increase with FWHM change ratio slowly and linearly for both the inversion methods, however they increase with NEDT in an S-curve for ISSTES while they increase with NEDT slightly and linearly for ARTEMISS. During the design and development of a hyperspectral thermal infrared instrument, it is highly recommended to keep the potential center wavelength shift within 1 band and keep NEDT within 0.1K (corresponding LST error < 1K and LSE error < 0.015) for normal applications and within 0.03K (corresponding LST error < 0.5K and LSE error < 0.01) for better application effect and level.

Noise and spectral stability of deep-UV gas-filled fiber-based supercontinuum sources driven by ultrafast mid-IR pulses

Deep-UV (DUV) supercontinuum (SC) sources based on gas-filled hollow-core fibers constitute perhaps the most viable solution towards ultrafast, compact, and tunable lasers in the UV spectral region, which can even also extend into the mid-infrared (IR). Noise and spectral stability of such broadband sources are key parameters that define their true potential and suitability towards real-world applications. In order to investigate the spectral stability and noise levels in these fiber-based DUV sources, we generate an SC spectrum that extends from 180 nm (through phase-matched dispersive waves - DWs) to 4 μm by pumping an argon-filled hollow-core anti-resonant fiber at a mid-IR wavelength of 2.45 μm. We characterize the long-term stability of the source over several days and the pulse-to-pulse relative intensity noise (RIN) of the DW at 275 nm. The results indicate no sign of spectral degradation over 110 hours, but the RIN of the DW pulses at 275 nm is found to be as high as 33.3%. Numerical simulations were carried out to investigate the spectral distribution of the RIN and the results confirm the experimental measurements and that the poor noise performance is due to the high RIN of the mid-IR pump laser, which was hitherto not considered in numerical modelling of these sources. The results presented herein provide an important step towards an understanding of the noise mechanism underlying such complex light-gas nonlinear interactions and demonstrate the need for pump laser stabilization.

Characterizing the Performance of New Seismic Stations in Southeastern Region, Korea Using Seismic Noise Levels

We performed seismic noise level analysis to access the proper functioning of 11 newly established seismic stations in the southeastern region of Korea. One-hour long segments of seismograms were selected from the continuous data of the 3 elements for 61 days from March 1, 2019. For each segment of data, the power spectral density (PSD) was estimated from the continuous back ground noise data of the 3 elements for periods ranging from 0.02~100 s. The median noise levels (NLs) of the stations were compared with the new noise model (NNM) of USGS and NLs of station TJN installed in a tunnel on a granite basement. We observed that the NLs of the newly installed seismometers were between the upper and lower limit of the NNM. In a comparison with the noise level of station TJN, the new seismometers had their own noteworthy features. The NLs from accelerometers (Epi-sensors) were ~ 40 dB higher than the NLs from velocimeters (STS-sensors) for periods > 10 s, which is because the small and light Epi-sensors are sensitive to environmental changes. Daily and weekly variations in spectral noise level were observed clearly in short periods 10 sec. Overall the functioning of the new velocimeter and accelerometer stations was reliable for periods ranging from 0.02~100 s and 0.02~10 s, respectively.

Windowing Design and Performance Assessment for Mitigation of Spectrum Leakage

This paper investigates the windowing design and performance assessment for mitigation of spectral leakage. A pretreatment method to reduce the spectral leakage is developed. In addition to selecting appropriate window functions, the Welch method is introduced. Windowing is implemented by multiplying the input signal with a windowing function. The periodogram technique based on Welch method is capable of providing good resolution if data length samples are selected optimally. Windowing amplitude modulates the input signal so that the spectral leakage is evened out. Thus, windowing reduces the amplitude of the samples at the beginning and end of the window, altering leakage. The influence of various window functions on the Fourier transform spectrum of the signals was discussed, and the characteristics and functions of various window functions were explained. In addition, we compared the differences in the influence of different data lengths on spectral resolution and noise levels caused by the traditional power spectrum estimation and various window-function-based Welch power spectrum estimations.

Comparison of spectral and spatial denoising techniques in the context of High Definition FT-IR imaging hyperspectral data

The recent emergence of High Definition (HD) FT-IR and Quantum Cascade Laser (QCL) Microscopes elevated the IR imaging field very close to clinical timescales. However, the speed of acquisition and data quality are still the critical factors in reaching the clinic. Denoising offers aide in both aspects if performed properly. However, there is a lack of a direct comparison of the efficiency of denoising techniques in IR imaging in general. To achieve such comparison within a rigorous framework and obtaining the critical information about signal loss, a simulated dataset strongly bound by experimental parameters was created. Using experimental structural and spectral information and experimental noise levels data as an input for the simulation, a direct comparison of spatial (Fourier transform, Mean Filter, Weighted Mean Filter, Gauss Filter, Median Filter, spatial Wavelets and Deep Neural Networks) and spectral (Savitzky-Golay, Fourier transform, Principal Component Analysis, Minimum Noise Fraction and spectral Wavelets) denoising schemes was enabled. All of these techniques were compared on the simulated dataset, taking into account SNR gain, signal distortion and sensitivity to tuning parameters as comparison metrics. Later, the best techniques were applied to experimental data for validation. The results presented here clearly show the benefit of using hyperspectral denoising schemes such as PCA and MNF which outperform other methods.

Investigation of the relationship between rock strain and radon concentration in the tidal frequency-range

Changes in the radon gas concentration can precede geodynamic processes associated with tectonic, volcanic activities and earthquakes. For this reason the relationship between rock strain and radon concentration is an important scientific issue to be answered. According to the complexity of the radon emanation process influenced by environmental effects, the interpretation of radon concentration variation as a possible precursor of geodynamic processes is not yet resolved unambiguously. The Sopronbánfalva Geodynamic Observatory in Hungary is one of the few places where radon concentration and rock strain variations are simultaneously monitored. The object of this study is to investigate the connection between indoor radon concentration and rock strain in the tidal frequency-range on the basis of seven-year long data series measured in years from 2009 till 2015. The relationship between rock deformation and radon concentration was investigated together with the temperature and barometric pressure effects. It was found that the strain induced radon concentration variations are in the order of 10–100 Bq nstr−1, while the concentration variations bear more considerable similarity and relation to the temperature and barometric variations. The theoretical tide at the location of the measurement site and tidal components computed from strain, radon concentration, barometric pressure and temperature data were compared with each other. Spectral and tidal analysis of data demonstrated that only the thermally induced solar components S1 and S2 are present in the radon concentration but their amplitudes hardly exceed the spectral noise level. The principal lunar semidiurnal M2 and diurnal O1 tidal waves cause the largest rock strain variations. The lack of the O1 and M2 constituents in the radon concentration confirms the fact that the detected S1 and S2 tidal components appear due to the barometric tide and the daily variations of the temperature and barometric pressure.

Electron density inversed by plasma lines induced by suprathermal electron in the ionospheric modification experiment

Incoherent scatter radar (ISR) is the most powerful ground-based measurement facility to study the ionosphere. The plasma lines are not routinely detected by the incoherent scatter radar due to the low intensity, which falls below the measured spectral noise level of the incoherent scatter radar. The plasma lines are occasionally enhanced by suprathermal electrons through the Landau damping process and detectable to the incoherent scatter radar. In this study, by using the European Incoherent Scatter Association (EISCAT) UHF incoherent scatter radar, the experiment observation presents that the enhanced plasma lines were observed. These plasma lines were considered as manifest of the suprathermal electrons generated by the high-frequency heating wave during the ionospheric modification. The electron density profile is also obtained from the enhanced plasma lines. This study can be a promising technique for obtaining the accurate electron density during ionospheric modification experiment.

Cloud-Assisted Retrieval of Lower-Stratospheric Water Vapor from Nadir-View Satellite Measurements

AbstractThis study examines the feasibility of retrieving lower-stratospheric water vapor using a nadir infrared hyperspectrometer, with the focus on the detectability of small-scale water vapor variability. The feasibility of the retrieval is examined using simulation experiments that model different instrument settings. These experiments show that the infrared spectra, measured with sufficient spectral coverage, resolution, and noise level, contain considerable information content that can be used to retrieve lower-stratospheric water vapor. Interestingly, it is found that the presence of an opaque cloud layer at the tropopause level can substantially improve the retrieval performance, as it helps remove the degeneracy in the retrieval problem. Under this condition, elevated lower-stratospheric water vapor concentration—for instance, caused by convective moistening—can be detected with an accuracy of 0.09 g m−2 using improved spaceborne hyperspectrometers. The cloud-assisted retrieval is tested using the measurements of the Atmospheric Infrared Sounder (AIRS). Validation against collocated aircraft data shows that the retrieval can detect the elevated water vapor concentration caused by convective moistening.