On the infrared spectrum of solid hydrogen deuteride

1990 ◽  
Vol 68 (4-5) ◽  
pp. 422-427 ◽  
Author(s):  
A. R. W. McKellar ◽  
M. J. Clouter
Keyword(s):  
Nearest Neighbor ◽  
Far Infrared ◽  
Solid Hydrogen ◽  
High Spectral Resolution ◽  
Fourier Transform Spectrometer ◽  
Hydrogen Deuteride ◽  
Mid Infrared ◽  
Integrated Intensity ◽  
Recent Theoretical Calculation ◽  
Good Agreement

The spectrum of solid hydrogen deuteride has been studied in the far-infrared (80–200 cm−1) and mid-infrared (3600–4500 cm−1) regions using a Fourier transform spectrometer and a moderately high spectral resolution of 0.09 cm−1. Spectra of the liquid were also recorded for comparison purposes. The pure rotational R0(0) transition at 88 cm−1 was found to have an integrated intensity of 1.78 ± 0.10 cm−2, in good agreement with a recent theoretical prediction. The shape of R0(0) appears to be best represented as the sum of two near-Lorentzian components. These may be related to the presence or absence of nearest neighbor H2 or D2 impurities in the crystal. The fundamental band in the mid-infrared is more complicated, and its detailed shape is not completely understood at this time. The profile of the band in the 3710 cm−1 region of the R1(0) transition is compared with a recent theoretical calculation. The presence of numerous sharp lines due to Q1(1) transitions in impurity H2 molecules around 4150 cm−1 is also noted.

Two-phonon absorption in InSb, InAs, and GaAs

1976 ◽  
Vol 54 (16) ◽  
pp. 1676-1682 ◽  
Author(s):  
E. S. Koteles ◽  
W. R. Datars
Keyword(s):  
Phonon Dispersion ◽  
Inelastic Neutron Scattering ◽  
Far Infrared ◽  
Inelastic Neutron ◽  
Model Fit ◽  
Fourier Transform Spectrometer ◽  
Phonon Density ◽  
Phonon Dispersion Curves ◽  
Phonon Absorption ◽  
Good Agreement

Far-infrared absorption in the III–V compound semiconductors InSb, InAs, and GaAs has been measured using a Fourier transform spectrometer. The high-resolution spectra of the three materials were found to be very similar. Features on the spectra were assigned to two-phonon sum and difference processes with the aid of two-phonon density-of-states curves for InSb and GaAs calculated from a shell model fit to phonon dispersion curves. Interpretation of the spectrum of InAs was possible because of its similarity to that of InSb and GaAs. The frequencies of phonons at certain points in the Brillouin zone of InSb and GaAs determined from the mode assignments to the infrared spectra were in good agreement with previous measurements by inelastic neutron scattering and Raman scattering.

Antarctic cloud detection and classification from far and mid infrared downwelling radiance spectra: performances optimization and results

2020 ◽  
Author(s):  
Davide Magurno ◽  
Tiziano Maestri ◽  
William Cossich ◽  
Gianluca Di Natale ◽  
Luca Palchetti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Far Infrared ◽  
Mixed Phase ◽  
Radiative Properties ◽  
High Spectral Resolution ◽  
Ice Clouds ◽  
Mid Infrared ◽  
Clear Sky ◽  
Threat Score ◽  
Mixed Phase Clouds

<p>This work aims at determining the best performing mid and far-infrared (MIR and FIR) joint spectral interval to identify and classify clouds in the Antarctic region by mean of a machine learning algorithm.</p><p>About 1700 spectral-resolved radiances, collected during 2013 by the ground based Radiation Explorer in the Far InfraRed-Prototype for Applications and Development, REFIR-PAD (Palchetti et al., 2015) at Dome C, Antarctic Plateau, are selected in coincidence with the co-located with backscatter and depolarization profiles derived from a tropospheric lidar system (Ricaud et al., 2017) to pre-classify clear sky, ice clouds, or mixed phase clouds.</p><p>A machine learning cloud identification and classification algorithm named CIC (Maestri et al., 2019), trained with a pre-selected set of REFIR-PAD spectra, is applied to this dataset by assuming that no other information than the spectrum itself is known.</p><p>The CIC algorithm is applied by considering different spectral intervals, in order to maximize the classification results for each class (clear sky, ice clouds, mixed phase clouds). A CIC "threat score" is defined as the classification true positives divided by the sum of true positives, false positives, and false negatives. The maximization of the threat score is used to assess the algorithm performances that span from 58% to 96% in accordance with the selected interval. The best performing spectral range is the 380-1000 cm<sup>-1</sup>. The result, besides suggesting the importance of a proper algorithm calibration in accordance with the used sensor, highlights the fundamental role of the FIR part of the spectrum.</p><p>The calibrated CIC algorithm is then applied to a larger REFIR-PAD dataset of about 90000 spectra collected from 2012 to 2015. Some results of the full dataset cloud classification are also presented.</p><p>The present work contributes to the preparatory studies for the Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission that has recently been selected as ESA’s 9<sup>th</sup> Earth Explorer mission, scheduled for launch in 2026. </p><p> </p><p>References:</p><p><span>Maestri, T., Cossich, W., and Sbrolli, I., 2019: Cloud identification and classification from high spectral resolution data in the far infrared and mid-infrared, Atmos. Meas. Tech., 12, pp. 3521 - 3540</span></p><p><span>Palchetti, L., Bianchini, G., Di Natale, G., and Del Guasta, M., 2015: Far infrared radiative properties of water vapor and clouds in Antarctica. Bull. Amer. Meteor. Soc., 96, 1505–1518, doi: http://dx.doi.org/10.1175/BAMS-D-13-00286.1.</span></p><p><span>Ricaud, P., Bazile, E., del Guasta, M., Lanconelli, C., Grigioni, P., and Mahjoub, A., 2017: Genesis of diamond dust, ice fog and thick cloud episodes observed and modelled above Dome C, Antarctica, Atmos. Chem. Phys., 17, 5221–5237, https://doi.org/10.5194/acp-17-5221-2017.</span></p>

scholarly journals Cloud identification and classification from high spectral resolution data in the far and mid infrared

2019 ◽  
Author(s):  
Tiziano Maestri ◽  
William Cossich ◽  
Iacopo Sbrolli
Keyword(s):  
Spectral Resolution ◽  
Learning Algorithm ◽  
Far Infrared ◽  
Synthetic Dataset ◽  
High Spectral Resolution ◽  
Spectral Radiance ◽  
Electromagnetic Spectrum ◽  
Cloud Detection ◽  
Mid Infrared ◽  
Univariate Distribution

Abstract. A new Cloud Identification and Classification algorithm, named CIC, is presented. CIC is a machine-learning algorithm, based on Principal Component Analysis, able to perform a cloud detection and scene classification using a univariate distribution and a threshold, which serves as a binary classifier. CIC is tested on a widespread synthetic dataset of high spectral resolution radiances in the far and mid infrared part of the spectrum simulating measures from the ESA Earth Explorer Fast Track 9 competing mission FORUM (Far Infrared Outgoing Radiation Understanding and Monitoring) that is currently (2018/19) undergoing the industrial and scientific Phase-A studies. Simulated spectra are representatives of many diverse climatic areas, ranging from the tropical to polar regions. Application of the algorithm to the synthetic dataset provides high scores for clear/cloud identification, especially when optimisation processes are performed. One of the main results consists in pointing out the high information content of spectral radiance in the far-infrared region of the electromagnetic spectrum to identify cloudy scenes specifically thin cirrus clouds.

scholarly journals Cloud identification and classification from high spectral resolution data in the far infrared and mid-infrared

2019 ◽  
Vol 12 (7) ◽  
pp. 3521-3540 ◽  
Author(s):  
Tiziano Maestri ◽  
William Cossich ◽  
Iacopo Sbrolli
Keyword(s):  
Spectral Resolution ◽  
Similarity Index ◽  
Far Infrared ◽  
Synthetic Dataset ◽  
High Spectral Resolution ◽  
Training Dataset ◽  
Spectral Radiance ◽  
Electromagnetic Spectrum ◽  
Mid Infrared ◽  
Univariate Distribution

Abstract. A new cloud identification and classification algorithm named CIC is presented. CIC is a machine learning algorithm, based on principal component analysis, able to perform a cloud detection and scene classification using a univariate distribution of a similarity index that defines the level of closeness between the analysed spectra and the elements of each training dataset. CIC is tested on a widespread synthetic dataset of high spectral resolution radiances in the far- and mid-infrared part of the spectrum, simulating measurements from the Fast Track 9 mission FORUM (Far-Infrared Outgoing Radiation Understanding and Monitoring), competing for the ESA Earth Explorer programme, which is currently (2018 and 2019) undergoing industrial and scientific Phase A studies. Simulated spectra are representatives of many diverse climatic areas, ranging from the tropical to polar regions. Application of the algorithm to the synthetic dataset provides high scores for clear or cloud identification, especially when optimisation processes are performed. One of the main results consists of pointing out the high information content of spectral radiance in the far-infrared region of the electromagnetic spectrum to identify cloudy scenes, specifically thin cirrus clouds. In particular, it is shown that hit scores for clear and cloudy spectra increase from about 70 % to 90 % when far-infrared channels are accounted for in the classification of the synthetic dataset for tropical regions.

VIBRATIONAL FREQUENCY PERTURBATIONS IN THE RAMAN SPECTRUM OF COMPRESSED GASEOUS HYDROGEN

1964 ◽  
Vol 42 (6) ◽  
pp. 1058-1069 ◽  
Author(s):  
A. D. May ◽  
G. Varghese ◽  
J. C. Stryland ◽  
H. L. Welsh
Keyword(s):  
Raman Band ◽  
Intermolecular Forces ◽  
Hydrogen Gas ◽  
High Spectral Resolution ◽  
Intermolecular Potential ◽  
Dispersion Forces ◽  
Frequency Shifts ◽  
Linear Coefficient ◽  
Relative Population ◽  
Good Agreement

The frequencies of the Q(J) lines of the fundamental Raman band of compressed hydrogen gas were measured with high spectral resolution for a series of densities from 25 to 400 Amagat units at 300 °K and 85 °K. The frequency shifts are expressed as a power series in the gas density. The linear coefficient at a given temperature has the form aJ = ai + ae(nJ/n), where ai, constant for all the Q lines, can be interpreted in terms of isotropic intermolecular forces, and ae(nJ/n), proportional to the relative population of the initial J level, arises from the inphase coupled oscillation of pairs of molecules. The temperature variation of ai is analyzed on the basis of the Lennard-Jones intermolecular potential and the molecular pair distribution function. The repulsive overlap forces and the attractive dispersion forces give, respectively, positive and negative contributions to ai, which can be characterized by the empirical parameters Krep and Katt. The values of Katt and ae are in good agreement with calculations based on the polarizability model of the dispersion forces. The relation of the results to the Raman frequency shifts in solid hydrogen is discussed.

Comparing the use of mid-infrared versus far-infrared lasers for mitigating damage growth on fused silica

2010 ◽  
Vol 49 (14) ◽  
pp. 2606 ◽  
Author(s):  
Steven T. Yang ◽  
Manyalibo J. Matthews ◽  
Selim Elhadj ◽  
Diane Cooke ◽  
Gabriel M. Guss ◽  
...  
Keyword(s):  
Far Infrared ◽  
Fused Silica ◽  
Infrared Lasers ◽  
Damage Growth ◽  
Mid Infrared ◽  
Far Infrared Lasers

Imaging Fourier transform spectrometer with photoconductive detector arrays: an application to the AKARI far-infrared instrument

2008 ◽  
Author(s):  
Mitsunobu Kawada ◽  
Hidenori Takahashi ◽  
Noriko Murakami ◽  
Yoko Okada ◽  
Akiko Yasuda ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Far Infrared ◽  
Fourier Transform Spectrometer ◽  
Detector Arrays ◽  
Photoconductive Detector

Mid-infrared Fourier-transform spectrometer based on metamaterial materal cladding suspended silicon waveguides

2022 ◽  
Author(s):  
Thi Thuy Duong Dinh ◽  
xavier leroux ◽  
Natnicha Koompai ◽  
Daniele Melati ◽  
Miguel Montesinos Ballester ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Fourier Transform Spectrometer ◽  
Mid Infrared ◽  
Silicon Waveguides

Design of an efficient broadband far-infrared fourier-transform spectrometer

1999 ◽  
Vol 38 (18) ◽  
pp. 3945 ◽  
Author(s):  
Bruno Carli ◽  
Alessandra Barbis ◽  
John E. Harries ◽  
Luca Palchetti
Keyword(s):  
Fourier Transform ◽  
Far Infrared ◽  
Fourier Transform Spectrometer

scholarly journals Analysis of new species retrieved from MIPAS

2014 ◽  
Vol 56 ◽  
Author(s):  
Shaomin Cai ◽  
Anu Dudhia
Keyword(s):  
Fourier Transform ◽  
New Species ◽  
High Resolution ◽  
Trace Gases ◽  
Michelson Interferometer ◽  
Emission Spectra ◽  
Fourier Transform Spectrometer ◽  
Mid Infrared ◽  
The Third ◽  
Atmospheric Sounding

The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument which operated on the Envisat satellite from 2002-2012 is a Fourier transform spectrometer for the measurement of high-resolution gaseous emission spectra at the Earth's limb. It operates in the near- to mid-infrared, where many of the main atmospheric trace gases have important emission features. The initial operational products were profiles of Temperature, H2O, O3, CH4, N2O, HNO3, and NO2, and this list was recently extended to include N2O5, ClONO2, CFC-11 and CFC-12. Here we present preliminary results of retrievals of the third set of species under consideration for inclusion in the operational processor: HCN, CF4, HCFC-22, COF2 and CCl4.

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