scholarly journals Comparing scattering ratio products retrieved from ALADIN/Aeolus and CALIOP/CALIPSO observations: sensitivity, comparability, and temporal evolution

2021 ◽  
Author(s):  
Artem Feofilov ◽  
Hélène Chepfer ◽  
Vincent Noel ◽  
Rodrigo Guzman ◽  
Cyprien Gindre ◽  
...  
Keyword(s):  
Temporal Evolution ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Satellite Observation ◽  
Middle Level ◽  
Orthogonal Polarization ◽  
Lower Sensitivity ◽  
Global Changes ◽  
Cloud Detection ◽  
Scattering Ratio

Abstract. The spaceborne active sounders have been contributing invaluable vertically resolved information of atmospheric optical properties since the launch of CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) in 2006. To ensure the continuity of climate studies and monitoring the global changes, one has to understand the differences between lidars operating at different wavelengths, flying at different orbits, and utilizing different observation geometries, receiving paths, and detectors. In this article, we show the results of an intercomparison study of ALADIN (Atmospheric Laser Doppler INstrument) and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidars using their scattering ratio (SR) products for the period of 28/06/2019−31/12/2019. We suggest an optimal set of collocation criteria (Δdist < 1º; Δtime < 6 h), which would give a representative set of collocated profiles and we show that for such a pair of instruments the theoretically achievable cloud detection agreement for the data collocated with aforementioned criteria is 0.77 ± 0.17. The analysis of a collocated database consisting of ~78000 pairs of collocated nighttime SR profiles revealed the following: (a) in the cloud-free area, the agreement is good indicating low frequency of false positive cloud detections by both instruments; (b) the cloud detection agreement is better for the lower layers. Above ~7 km, the ALADIN product demonstrates lower sensitivity because of lower backscatter at 355 nm and because of lower signal-to-noise ratio; (c) in 50 % of the analyzed cases when ALADIN reported a low cloud not detected by CALIOP, the middle level cloud hindered the observations and perturbed the ALADIN’s retrieval indicating the need for quality flag refining for such scenarios; (d) large sensitivity to lower clouds leads to skewing the ALADIN’s cloud peaks down by ~0.5 ± 0.4 km, but this effect does not alter the polar stratospheric cloud peak heights; (e) temporal evolution of cloud agreement quality does not reveal any anomaly for the considered period, indicating that hot pixels and laser degradation effects in ALADIN have been mitigated at least down to the uncertainties in the following cloud detection agreement values: 61 ± 16 %, 34 ± 18 % 24 ± 10 %, 26 ± 10 %, and 22 ± 12 % at 0.75 km, 2.25 km, 6.75 km, 8.75 km, and 10.25 km, respectively.

scholarly journals CALIPSO Lidar Description and Performance Assessment

2009 ◽  
Vol 26 (7) ◽  
pp. 1214-1228 ◽  
Author(s):  
William H. Hunt ◽  
David M. Winker ◽  
Mark A. Vaughan ◽  
Kathleen A. Powell ◽  
Patricia L. Lucker ◽  
...  
Keyword(s):  
Laser Energy ◽  
Signal To Noise Ratio ◽  
Satellite Observation ◽  
Quality Data ◽  
Special Treatment ◽  
Orthogonal Polarization ◽  
Polarization Sensitivity ◽  
Data Products ◽  
And Performance ◽  
Energy Signal

Abstract This paper provides background material for a collection of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) algorithm papers that are to be published in the Journal of Atmospheric and Oceanic Technology. It provides a brief description of the design and performance of CALIOP, a three-channel elastic backscatter lidar on the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. After more than 2 yr of on-orbit operation, CALIOP performance continues to be excellent in the key areas of laser energy, signal-to-noise ratio, polarization sensitivity, and overall long-term stability, and the instrument continues to produce high-quality data products. There are, however, some areas where performance has been less than ideal. These include short-term changes in the calibration coefficients at both wavelengths as the satellite passes between dark and sunlight, some radiation-induced effects on both the detectors and the laser when passing through the South Atlantic Anomaly, and slow transient recovery on the 532-nm channels. Although these issues require some special treatment in data analysis, they do not seriously detract from the overall quality of the level 2 data products.

scholarly journals Calibration of global MODIS cloud amount using CALIOP cloud profiles

2020 ◽  
Author(s):  
Andrzej Z. Kotarba
Keyword(s):  
Cloud Amount ◽  
Satellite Observation ◽  
Cloud Fraction ◽  
Orthogonal Polarization ◽  
Polar Regions ◽  
Cloud Detection ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Mask

Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection procedure classifies instantaneous fields of view (IFOV) as either confident cloudy, probably cloudy, probably clear, or confident clear. The cloud amount calculation requires quantitative cloud fractions to be assigned to these classes. The operational procedure used by NASA assumes that confident clear and probably clear IFOV are cloud-free (cloud fraction 0 %), while the remaining categories are completely filled with clouds (cloud fraction 100 %). This study demonstrates that this best guess approach is unreliable, especially on a regional/ local scale. We use data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument flown on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission, collocated with MODIS/ Aqua IFOV. Based on 33,793,648 paired observations acquired in January and July 2015, we conclude that actual cloud fractions to be associated with MODIS cloud mask categories are 21.5 %, 27.7 %, 66.6 %, and 94.7 %. Spatial variability is significant, even within a single MODIS algorithm path, and the operational approach introduces uncertainties of up to 30 % of cloud amount, notably in the polar regions at night, and in selected locations over the northern hemisphere. Applications of MODIS data at ~10 degrees resolution (or finer) should first assess the extent of the error. Uncertainties were related to the efficiency of the cloud masking algorithm. Until the algorithm can be significantly modified, our method is a robust way to calibrate (correct) MODIS estimates. It can be also used for MODIS/ Terra data, and other missions where the footprint is collocated with CALIPSO.

scholarly journals On the optimal method for evaluating cloud products from passive satellite imagery using CALIPSO-CALIOP data: example investigating the CM SAF CLARA-A1 dataset

2013 ◽  
Vol 6 (5) ◽  
pp. 1271-1286 ◽  
Author(s):  
K.-G. Karlsson ◽  
E. Johansson
Keyword(s):  
Satellite Imagery ◽  
Winter Season ◽  
Satellite Observation ◽  
Orthogonal Polarization ◽  
Polar Regions ◽  
Cloud Detection ◽  
Optimal Method ◽  
Tropical Regions ◽  
Boundary Layer Clouds ◽  
High Level

Abstract. A method for detailed evaluation of a new satellite-derived global 28 yr cloud and radiation climatology (Climate Monitoring SAF Clouds, Albedo and Radiation from AVHRR data, named CLARA-A1) from polar-orbiting NOAA and Metop satellites is presented. The method combines 1 km and 5 km resolution cloud datasets from the CALIPSO-CALIOP (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation – Cloud-Aerosol Lidar with Orthogonal Polarization) cloud lidar for estimating cloud detection limitations and the accuracy of cloud top height estimations. Cloud detection is shown to work efficiently for clouds with optical thicknesses above 0.30 except for at twilight conditions when this value increases to 0.45. Some misclassifications of cloud-free surfaces during daytime were revealed for semi-arid land areas in the sub-tropical and tropical regions leading to up to 20% overestimated cloud amounts. In addition, a substantial fraction (at least 20–30%) of all clouds remains undetected in the polar regions during the polar winter season due to the lack of or an inverted temperature contrast between Earth surfaces and clouds. Subsequent cloud top height evaluation took into account the derived information about the cloud detection limits. It was shown that this has fundamental importance for the achieved results. An overall bias of −274 m was achieved compared to a bias of −2762 m when no measures were taken to compensate for cloud detection limitations. Despite this improvement it was concluded that high-level clouds still suffer from substantial height underestimations, while the opposite is true for low-level (boundary layer) clouds. The validation method and the specifically collected satellite dataset with optimal matching in time and space are suggested for a wider use in the future for evaluation of other cloud retrieval methods based on passive satellite imagery.

scholarly journals Comparison of Two Different Cloud Climatologies Derived from CALIOP-Attenuated Backscattered Measurements (Level 1): The CALIPSO-ST and the CALIPSO-GOCCP

2013 ◽  
Vol 30 (4) ◽  
pp. 725-744 ◽  
Author(s):  
H. Chepfer ◽  
G. Cesana ◽  
D. Winker ◽  
B. Getzewich ◽  
M. Vaughan ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Coupled Model ◽  
Orthogonal Polarization ◽  
Cloud Detection ◽  
Model Intercomparison ◽  
Spatiotemporal Resolution ◽  
Wide Range ◽  
Intercomparison Project ◽  
Level 1 ◽  
High Level

Abstract Two different cloud climatologies have been derived from the same NASA–Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)-measured attenuated backscattered profile (level 1, version 3 dataset). The first climatology, named Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations–Science Team (CALIPSO-ST), is based on the standard CALIOP cloud mask (level 2 product, version 3), with the aim to document clouds with the highest possible spatiotemporal resolution, taking full advantage of the CALIOP capabilities and sensitivity for a wide range of cloud scientific studies. The second climatology, named GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP), is aimed at a single goal: evaluating GCM prediction of cloudiness. For this specific purpose, it has been designed to be fully consistent with the CALIPSO simulator included in the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) used within version 2 of the CFMIP (CFMIP-2) experiment and phase 5 of the Coupled Model Intercomparison Project (CMIP5). The differences between the two datasets in the global cloud cover maps—total, low level (P &gt; 680 hPa), midlevel (680 &lt; P &lt; 440 hPa), and high level (P &lt; 440 hPa)—are frequently larger than 10% and vary with region. The two climatologies show significant differences in the zonal cloud fraction profile (which differ by a factor of almost 2 in some regions), which are due to the differences in the horizontal and vertical averaging of the measured attenuated backscattered profile CALIOP profile before the cloud detection and to the threshold used to detect clouds (this threshold depends on the resolution and the signal-to-noise ratio).

scholarly journals Calibration of global MODIS cloud amount using CALIOP cloud profiles

2020 ◽  
Vol 13 (9) ◽  
pp. 4995-5012
Author(s):  
Andrzej Z. Kotarba
Keyword(s):  
Cloud Amount ◽  
Satellite Observation ◽  
The United States ◽  
Local Scale ◽  
Cloud Fraction ◽  
Orthogonal Polarization ◽  
Polar Regions ◽  
Cloud Detection ◽  
North West ◽  
Modis Data

Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection procedure classifies instantaneous fields of view (IFOVs) as either “confident clear”, “probably clear”, “probably cloudy”, or “confident cloudy”. The cloud amount calculation requires quantitative cloud fractions to be assigned to these classes. The operational procedure used by the MODIS Science Team assumes that confident clear and probably clear IFOVs are cloud-free (cloud fraction 0 %), while the remaining categories are completely filled with clouds (cloud fraction 100 %). This study demonstrates that this “best-guess” approach is unreliable, especially on a regional/local scale. We use data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument flown on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission, collocated with Aqua MODIS IFOV. Based on 33 793 648 paired observations acquired in January and July 2015, we conclude that actual cloud fractions to be associated with MODIS cloud mask categories are 21.5 %, 27.7 %, 66.6 %, and 94.7 %. Spatial variability is significant, even within a single MODIS algorithm path, and the operational approach introduces uncertainties of up to 30 % of cloud amount, notably in polar regions at night, and in selected locations over the Northern Hemisphere (e.g. China, the north-west coast of Africa, and eastern parts of the United States). Consequently, applications of MODIS data on a regional/local scale should first assess the extent of the uncertainty. We suggest using CALIPSO-based cloud fractions to improve MODIS cloud amount estimates. This approach can also be used for Terra MODIS data, and other passive cloud imagers, where the footprint is collocated with CALIPSO.

Structured methods for improving flow measurement accuracy in pipelines

2020 ◽  
pp. 30-35
Author(s):  
Gurami N. Akhobadze
Keyword(s):  
Flow Measurement ◽  
Measurement Accuracy ◽  
Directional Coupler ◽  
Signal To Noise Ratio ◽  
Scattered Wave ◽  
Orthogonal Polarization ◽  
Flow Sensors ◽  
Information Signal ◽  
Mass Flows ◽  
Processing Device

In the age of digital transformation of production processes in industry and science the development and design of intelligent flow sensors for granular and liquid substances transferring through pipelines becomes more important. With this in view new approaches for improving the accuracy of microwave flowmeters are proposed. Taking into account the characteristics ofelectromagnetic waves propagating through a pipeline, a wave scattered by inhomogeneities of the controlled medium is analyzed. Features of the transformation of the polarized scattered wave limiting the geometric dimensions of the pipeline and optimizing the values of the useful scattered signal are revealed. Expediency of collection of the information signal with orthogonal polarization of the scattered wave and through a directional coupler is substantiated. The method of estimating the measurement accuracy with reference to the signal-to-noise ratio at the input of the processing device is given. The research results can be used in cryogenic machine engineering to measure volume and mass flows of liquid cryogenic products.

Temporal evolution of reactive and resistive nonlinear instabilities

1987 ◽  
Vol 38 (3) ◽  
pp. 473-481 ◽  
Author(s):  
D. B. Melrose
Keyword(s):  
Electron Number Density ◽  
Temporal Evolution ◽  
Random Phase ◽  
Low Frequency ◽  
Rate Of Change ◽  
Number Density ◽  
Wave Instability ◽  
Langmuir Waves ◽  
Secular Evolution ◽  
Coherent Wave

A kinetic theory for nonlinear processes involving Langmuir waves, developed in an earlier paper, is extended through consideration of three aspects of the temporal evolution, (i) Following Falk & Tsytovich (1975). the dynamic equation for the rate of change of one amplitude at t is expressed as an integral over T of the product of two amplitudes at t – T and a kernel functionf(T); two generalizations of Falk & Tsytovich's form (f(T) ∝ T) that satisfy the requirement f(∞) = 0 are identified, (ii) It is shown that the low-frequency or beat disturbance may be described in terms of fluctuations in the electron number density, and that its time evolution involves an operator that is essentially the inverse of f(t). (iii) The transition from oscillatory evolution in the reactive or ‘coherent-wave’ version of the three-wave instability to the secular evolution of the resistive or ‘random-phase’ version is discussed qualitatively.

Denoising for full-waveform inversion with expanded prediction-error filters

Geophysics ◽  
2021 ◽  
pp. 1-54
Author(s):  
Milad Bader ◽  
Robert G. Clapp ◽  
Biondo Biondi
Keyword(s):  
High Frequency ◽  
Prediction Error ◽  
Signal To Noise Ratio ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Frequency Data ◽  
Signal To Noise ◽  
Frequency Signal ◽  
Full Waveform

Low-frequency data below 5 Hz are essential to the convergence of full-waveform inversion towards a useful solution. They help build the velocity model low wavenumbers and reduce the risk of cycle-skipping. In marine environments, low-frequency data are characterized by a low signal-to-noise ratio and can lead to erroneous models when inverted, especially if the noise contains coherent components. Often field data are high-pass filtered before any processing step, sacrificing weak but essential signal for full-waveform inversion. We propose to denoise the low-frequency data using prediction-error filters that we estimate from a high-frequency component with a high signal-to-noise ratio. The constructed filter captures the multi-dimensional spectrum of the high-frequency signal. We expand the filter's axes in the time-space domain to compress its spectrum towards the low frequencies and wavenumbers. The expanded filter becomes a predictor of the target low-frequency signal, and we incorporate it in a minimization scheme to attenuate noise. To account for data non-stationarity while retaining the simplicity of stationary filters, we divide the data into non-overlapping patches and linearly interpolate stationary filters at each data sample. We apply our method to synthetic stationary and non-stationary data, and we show it improves the full-waveform inversion results initialized at 2.5 Hz using the Marmousi model. We also demonstrate that the denoising attenuates non-stationary shear energy recorded by the vertical component of ocean-bottom nodes.

The enhanced deep Plug-and-play super-resolution algorithm with residual channel attention networks

2021 ◽  
pp. 1-10
Author(s):  
Hongguang Pan ◽  
Fan Wen ◽  
Xiangdong Huang ◽  
Xinyu Lei ◽  
Xiaoling Yang
Keyword(s):  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Super Resolution ◽  
Structural Similarity ◽  
Plug And Play ◽  
Attention Networks ◽  
Frequency Information ◽  
Blind Deblurring ◽  
Adaptive Adjustment ◽  
Deep Feature Extraction

In the field of super-resolution image reconstruction, as a learning-based method, deep plug-and-play super-resolution (DPSR) algorithm can be used to find the blur kernel by using the existing blind deblurring methods. However, DPSR is not flexible enough in processing images with high- and low-frequency information. Considering a channel attention mechanism can distinguish low-frequency information and features in low-resolution images, in this paper, we firstly introduce this mechanism and design a new residual channel attention networks (RCAN); then the RCAN is adopted to replace deep feature extraction part in DPSR to achieve the adaptive adjustment of channel characteristics. Through four test experiments based on Set5, Set14, Urban100 and BSD100 datasets, we find that, under different blur kernels and different scale factors, the average peak signal to noise ratio (PSNR) and structural similarity (SSIM) values of our proposed method increase by 0.31dB and 0.55%, respectively; under different noise levels, the average PSNR and SSIM values increase by 0.26dB and 0.51%, respectively.

Low-frequency multiplex CARS microscopy with a high-repetition near-infrared supercontinuum laser

2021 ◽  
Author(s):  
Yusuke Arashida ◽  
Atsushi Taninaka ◽  
Takayuki Ochiai ◽  
Hiroyuki Mogi ◽  
Shoji YOSHIDA ◽  
...  
Keyword(s):  
Near Infrared ◽  
High Efficiency ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
High Sensitivity ◽  
High Repetition ◽  
Spectrum Shaping ◽  
Crystal Phase Transition ◽  
Wavenumber Region ◽  
Anti Stokes

Abstract We have developed a multiplex Coherent anti-Stokes Raman scattering (CARS) microscope effective for low-wavenumber measurement by combining a high-repetition supercontinuum light source of 1064 nm and an infrared high-sensitivity InGaAs diode array. This system could observe the low-wavenumber region down to 55 cm-1 with high sensitivity. In addition, using spectrum shaping and spectrum modulation techniques, we simultaneously realized a wide bandwidth (<1800 cm-1), high wavenumber resolution (9 cm-1), high efficiency, and increasing signal to noise ratio by reducing the effect of the background shape in low-wavenumber region. Spatial variation of a sulfur crystal phase transition with metastable states was visualized.

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