A temporal stability study of calibration functions coefficients in the pure rotational Raman lidar technique during tropospheric temperature measurements

2016 ◽  
Author(s):  
V. V. Gerasimov ◽  
V. V. Zuev ◽  
V. L. Pravdin ◽  
A. V. Pavlinskiy ◽  
D. P. Nakhtigalova
1997 ◽  
Vol 36 (12) ◽  
pp. 2594 ◽  
Author(s):  
Keith D. Evans ◽  
S. Harvey Melfi ◽  
Richard A. Ferrare ◽  
David N. Whiteman

1983 ◽  
Vol 22 (19) ◽  
pp. 2984 ◽  
Author(s):  
Yu. F. Arshinov ◽  
S. M. Bobrovnikov ◽  
V. E. Zuev ◽  
V. M. Mitev

Author(s):  
Lawrence D. Shriberg ◽  
Edythe A. Strand ◽  
Marios Fourakis ◽  
Kathy J. Jakielski ◽  
Sheryl D. Hall ◽  
...  

Purpose Three previous articles provided rationale, methods, and several forms of validity support for a diagnostic marker of childhood apraxia of speech (CAS), termed the pause marker (PM). Goals of the present article were to assess the validity and stability of the PM Index (PMI) to scale CAS severity. Method PM scores and speech, prosody, and voice precision-stability data were obtained for participants with CAS in idiopathic, neurogenetic, and complex neurodevelopmental disorders; adult-onset apraxia of speech consequent to stroke and primary progressive apraxia; and idiopathic speech delay. Three studies were completed including criterion and concurrent validity studies of the PMI and a temporal stability study of the PMI using retrospective case studies. Results PM scores were significantly correlated with other signs of CAS precision and stability. The best fit of the distribution of PM scores to index CAS severity was obtained by dividing scores into 4 ordinal severity classifications: mild, mild-moderate, moderate-severe, and severe. Severity findings for the 4 classifications and retrospective longitudinal findings from 8 participants with CAS supported the validity and stability of the PMI. Conclusion Findings support research and clinical use of the PMI to scale the severity of CAS.


2018 ◽  
Vol 176 ◽  
pp. 01017 ◽  
Author(s):  
Giovanni Martucci ◽  
Valentin Simeonov ◽  
Ludovic Renaud ◽  
Alexander Haefele

RAman Lidar for Meteorological Observations (RALMO) is operated at MeteoSwiss and provides continuous measurements of water vapor and temperature since 2010. While the water vapor has been acquired by a Licel acquisition system since 2008, the temperature channels have been migrated to a Fastcom P7888 acquisition system, since August 2015. We present a characterization of this new acquisition system, namely its dead-time, desaturation, temporal stability of the Pure Rotational Raman signals and the retrieval of the PRR-temperature.


2019 ◽  
Vol 12 (11) ◽  
pp. 5801-5816 ◽  
Author(s):  
Shayamila Mahagammulla Gamage ◽  
Robert J. Sica ◽  
Giovanni Martucci ◽  
Alexander Haefele

Abstract. We present a new method for retrieving temperature from pure rotational Raman (PRR) lidar measurements. Our optimal estimation method (OEM) used in this study uses the full physics of PRR scattering and does not require any assumption of the form for a calibration function nor does it require fitting of calibration factors over a large range of temperatures. The only calibration required is the estimation of the ratio of the lidar constants of the two PRR channels (coupling constant) that can be evaluated at a single or multiple height bins using a simple analytic expression. The uncertainty budget of our OEM retrieval includes both statistical and systematic uncertainties, including the uncertainty in the determination of the coupling constant on the temperature. We show that the error due to calibration can be reduced significantly using our method, in particular in the upper troposphere when calibration is only possible over a limited temperature range. Some other advantages of our OEM over the traditional Raman lidar temperature retrieval algorithm include not requiring correction or gluing to the raw lidar measurements, providing a cutoff height for the temperature retrievals that specifies the height to which the retrieved profile is independent of the a priori temperature profile, and the retrieval's vertical resolution as a function of height. The new method is tested on PRR temperature measurements from the MeteoSwiss RAman Lidar for Meteorological Observations system in clear and cloudy sky conditions, compared to temperature calculated using the traditional PRR calibration formulas, and validated with coincident radiosonde temperature measurements in clear and cloudy conditions during both daytime and nighttime.


2021 ◽  
Author(s):  
Noemi Franco ◽  
Paolo Di Girolamo ◽  
Donato Summa ◽  
Benedetto De Rosa ◽  
Andreas Behrendt ◽  
...  

<p>An end-to-end model has been developed in order to simulate the expected performance of a space-borne Raman Lidar, with a specific focus on the Atmospheric Thermodynamics LidAr in Space – ATLAS proposed as a “mission concept” to the ESA in the frame of the “Earth Explorer-11 Mission Ideas” Call. The numerical model includes a forward module, which simulates the lidar signals with their statistical uncertainty, and a retrieval module able to provide vertical profiles of atmospheric water vapour mixing ratio and temperature based on the analyses of the simulated signals. Specifically, the forward module simulates the interaction mechanisms of laser radiation with the atmospheric constituents and the behavior of all the devices present in the experimental system(telescope, optical reflecting and transmitting components, avalanche photodiodes, ACCDs). An analytical expression of the lidar equation for the water vapour and molecular nitrogen roto-vibrational Raman signals and the pure rotational Raman signals from molecular oxygen and nitrogen is used. The analytically computed signals are perturbed by simulating their shot-noise through Poisson statistics. Perturbed signals thus take into account the fluctuations in the number of photons reaching the detector over a certain time interval. The simulator also provides an estimation of the background due to the solar contribution. Daylight background includes three distinct terms: a cloud-free atmospheric contribution, a surface contribution and a cloud contribution[1]. Background is calculated as a function of the solar zenith angle. In order to better estimatethe background contribution, an integration on slant path is performed instead of a classical parallel-planes approximation. The proposed numerical model allows to better simulate solar background for high solar zenith angles, even higher than 90 degrees. Signals simulated through the forward model are then fed into the retrieval module. A background subtraction scheme is used to remove the solar contribution and a vertical averaging is performed to smooth the signals. Based on the application of the roto-vibrational Raman lidar technique, the vertical profile of atmospheric water vapour mixing ratio is obtained from the power ratio of the water vapour to a reference signal, such as molecular nitrogen roto-vibrational Raman signal or an alternative temperature-independent reference signal. A vertical profile of temperature is then obtained through the ratio of high-to-low quantum number rotational Raman signals by the application of the pure rotational Raman lidar technique. Both atmospheric water vapour mixing ratio and temperature measurements require the determination of calibration constants, which can be obtained from the comparison with simultaneous and co-located measurements from a different sensor [2]. The simulator finally provides statistical (RMS) and systematic (bias) uncertainties. Estimates are provided in terms of percentage and absolute (g/kg) uncertainty for water vapour mixing ratio measurements and in terms of absolute uncertainty (K) for temperature measurements.</p><p><strong> </strong><strong>References</strong></p><p>1 - P.Di Girolamo et al., "Spaceborne profiling of atmospheric temperature and particle extinction with pure rotational Raman lidar and of relative humidity in combination with differential absorption lidar: performance simulations"Appl.Opt. 45, 2474-2494(2006)</p><p>2 - P.Di Girolamo et al., "Space-borne profiling of atmospheric thermodynamic variables with Raman lidar: performance simulations,"Opt.Express 26, 8125-8161(2018)</p>


1997 ◽  
Vol 36 (24) ◽  
pp. 5987 ◽  
Author(s):  
Michael R. Gross ◽  
Thomas J. McGee ◽  
Richard A. Ferrare ◽  
Upendra N. Singh ◽  
Patrick Kimvilakani

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