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

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.

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

Atmospheric Measurement Techniques ◽

10.5194/amt-12-3521-2019 ◽

2019 ◽

Vol 12 (7) ◽

pp. 3521-3540 ◽

Cited By ~ 2

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.

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Time-resolved mid-infrared dual-comb spectroscopy

Scientific Reports ◽

10.1038/s41598-019-53825-8 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 10

Author(s):

Muhammad A. Abbas ◽

Qing Pan ◽

Julien Mandon ◽

Simona M. Cristescu ◽

Frans J. M. Harren ◽

...

Keyword(s):

Spectral Resolution ◽

Vibrational Excitation ◽

High Spectral Resolution ◽

Acquisition Time ◽

Time Resolved ◽

Short Acquisition Time ◽

Mid Infrared ◽

Infrared Wavelength ◽

Potential Applications ◽

Kinetics Of

AbstractDual-comb spectroscopy can provide broad spectral bandwidth and high spectral resolution in a short acquisition time, enabling time-resolved measurements. Specifically, spectroscopy in the mid-infrared wavelength range is of particular interest, since most of the molecules have their strongest rotational-vibrational transitions in this “fingerprint” region. Here we report time-resolved mid-infrared dual-comb spectroscopy, covering ~300 nm bandwidth around 3.3 μm with 6 GHz spectral resolution and 20 μs temporal resolution. As a demonstration, we study a CH4/He gas mixture in an electric discharge, while the discharge is modulated between dark and glow regimes. We simultaneously monitor the production of C2H6 and the vibrational excitation of CH4 molecules, observing the dynamics of both processes. This approach to broadband, high-resolution, and time-resolved mid-infrared spectroscopy provides a new tool for monitoring the kinetics of fast chemical reactions, with potential applications in various fields such as physical chemistry and plasma/combustion analysis.

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Hybrid high spectral resolution broadband mid-infrared SFG spectrometer development and demonstration

Optics Express ◽

10.1364/oe.433617 ◽

2021 ◽

Author(s):

Karolis Madeikis ◽

Robertas Kananavicius ◽

Rokas Danilevicius ◽

Audrius Zaukevicius ◽

Januskevicius Regimantas ◽

...

Keyword(s):

Spectral Resolution ◽

High Spectral Resolution ◽

Mid Infrared

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

10.5194/egusphere-egu2020-9036 ◽

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

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.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.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.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-1. 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.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.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&#8217;s 9th Earth Explorer mission, scheduled for launch in 2026.&#160;&#160;References: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 - 3540Palchetti, 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&#8211;1518, doi: http://dx.doi.org/10.1175/BAMS-D-13-00286.1.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&#8211;5237, https://doi.org/10.5194/acp-17-5221-2017.

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EXES: The Echelon-cross-echelle Spectrograph for SOFIA

Journal of Astronomical Instrumentation ◽

10.1142/s2251171718400135 ◽

2018 ◽

Vol 07 (04) ◽

pp. 1840013 ◽

Cited By ~ 11

Author(s):

M. J. Richter ◽

C. N. DeWitt ◽

M. McKelvey ◽

E. Montiel ◽

R. McMurray ◽

...

Keyword(s):

Spectral Resolution ◽

Infrared Astronomy ◽

High Spectral Resolution ◽

Echelle Spectrograph ◽

Mid Infrared ◽

Low Background ◽

And Performance ◽

Operational Modes ◽

Echelon Grating

The Echelon-cross-echelle spectrograph (EXES) is a high spectral resolution, mid-infrared spectrograph designed for and operated on the Stratospheric Observatory for Infrared Astronomy (SOFIA). EXES has multiple operational modes, but is optimized for high spectral resolution. The heart of the instrument is a one meter long, diamond-machined echelon grating. EXES also uses a 10242 Si:As detector optimized for low-background flux. We will discuss the design, operation and performance of EXES.

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Comparing Information Content of Upwelling Far-Infrared and Midinfrared Radiance Spectra for Clear Atmosphere Profiling

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-11-00113.1 ◽

2012 ◽

Vol 29 (4) ◽

pp. 510-526 ◽

Cited By ~ 9

Author(s):

Aronne Merrelli ◽

David D. Turner

Keyword(s):

Water Vapor ◽

Absorption Band ◽

Information Content ◽

Spectral Resolution ◽

Far Infrared ◽

Forward Model ◽

High Spectral Resolution ◽

Model Errors ◽

Noise Levels ◽

Sensor Noise

Abstract The information content of high-spectral-resolution midinfrared (MIR; 650–2300 cm−1) and far-infrared (FIR; 200–685 cm−1) upwelling radiance spectra is calculated for clear-sky temperature and water vapor profiles. The wavenumber ranges of the two spectral bands overlap at the central absorption line in the CO2 ν2 absorption band, and each contains one side of the full absorption band. Each spectral band also includes a water vapor absorption band; the MIR contains the first vibrational–rotational absorption band, while the FIR contains the rotational absorption band. The upwelling spectral radiances are simulated with the line-by-line radiative transfer model (LBLRTM), and the retrievals and information content analysis are computed using standard optimal estimation techniques. Perturbations in the surface temperature and in the trace gases methane, ozone, and nitrous oxide (CH4, O3, and N2O) are introduced to represent forward-model errors. Each spectrum is observed by a simulated infrared spectrometer, with a spectral resolution of 0.5 cm−1, with realistic spectrally varying sensor noise levels. The modeling and analysis framework is applied identically to each spectral range, allowing a quantitative comparison. The results show that for similar sensor noise levels, the FIR shows an advantage in water vapor profile information content and less sensitivity to forward-model errors. With a higher noise level in the FIR, which is a closer match to current FIR detector technology, the FIR information content drops and shows a disadvantage relative to the MIR.

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