scholarly journals A novel approach for the extraction of cloud motion vectors using airglow imager measurements

2015 ◽  
Vol 8 (9) ◽  
pp. 3893-3901 ◽  
Author(s):  
S. Satheesh Kumar ◽  
T. Narayana Rao ◽  
A. Taori
Keyword(s):  
Meridional Wind ◽  
Noise Removal ◽  
Search Window ◽  
Motion Vectors ◽  
Good Correspondence ◽  
Cloud Motion ◽  
Cloud Height ◽  
Novel Approach ◽  
Wind Velocities ◽  
Cloud Tracking

Abstract. The paper explores the possibility of implementing an advanced photogrammetric technique, generally employed for satellite measurements, on airglow imager, a ground-based remote sensing instrument primarily used for upper atmospheric studies, measurements of clouds for the extraction of cloud motion vectors (CMVs). The major steps involved in the algorithm remain the same, including image processing for better visualization of target elements and noise removal, identification of target cloud, setting a proper search window for target cloud tracking, estimation of cloud height, and employing 2-D cross-correlation to estimate the CMVs. Nevertheless, the implementation strategy at each step differs from that of satellite, mainly to suit airglow imager measurements. For instance, climatology of horizontal winds at the measured site has been used to fix the search window for target cloud tracking. The cloud height is estimated very accurately, as required by the algorithm, using simultaneous collocated lidar measurements. High-resolution, both in space and time (4 min), cloud imageries are employed to minimize the errors in retrieved CMVs. The derived winds are evaluated against MST radar-derived winds by considering it as a reference. A very good correspondence is seen between these two wind measurements, both showing similar wind variation. The agreement is also found to be good in both the zonal and meridional wind velocities with RMSEs < 2.4 m s−1. Finally, the strengths and limitations of the algorithm are discussed, with possible solutions, wherever required.

scholarly journals A novel approach for the extraction of cloud motion vectors using airglow imager measurements

2015 ◽  
Vol 8 (3) ◽  
pp. 2657-2682
Author(s):  
S. Satheesh Kumar ◽  
T. Narayana Rao ◽  
A. Taori
Keyword(s):  
Meridional Wind ◽  
Noise Removal ◽  
Search Window ◽  
Motion Vectors ◽  
Good Correspondence ◽  
Cloud Motion ◽  
Cloud Height ◽  
Novel Approach ◽  
Wind Velocities ◽  
Cloud Tracking

Abstract. The paper explores the possibility of implementing an advanced photogrammetric technique, generally employed for satellite measurements, on airglow imager, a ground-based remote sensing instrument primarily used for upper atmospheric studies, measurements of clouds for the extraction of cloud motion vectors (CMVs). The major steps involved in the algorithm remain the same, including image processing for better visualization of target elements and noise removal, identification of target cloud, setting a proper search window for target cloud tracking, estimation of cloud height, and employing 2-D cross-correlation to estimate the CMVs. Nevertheless, the implementation strategy at each step differs from that of satellite, mainly to suit airglow imager measurements. For instance, climatology of horizontal winds at the measured site has been used to fix the search window for target cloud tracking. The cloud height is estimated very accurately, as required by the algorithm, using simultaneous collocated Lidar measurements. High-resolution, both in space and time (4 min), cloud imageries are employed to minimize the errors in retrieved CMVs. The derived winds are evaluated against MST radar-derived winds by considering it as a reference. A very good correspondence is seen between these two wind measurements, both showing similar wind variation. The agreement is also found to be good in the both zonal and meridional wind velocities with RMSEs < 2.4 m s−1. At the end, the strengths and limitations of the algorithm are discussed, with possible solutions, wherever required.

scholarly journals A Wind Study of Venus’s Cloud Top: New Doppler Velocimetry Observations

Atmosphere ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 2
Author(s):  
Ruben Gonçalves ◽  
Pedro Machado ◽  
Thomas Widemann ◽  
Francisco Brasil ◽  
José Ribeiro
Keyword(s):  
Spatial Variability ◽  
Local Time ◽  
Zonal Wind ◽  
Meridional Wind ◽  
Space Mission ◽  
Horizontal Wind ◽  
Doppler Velocimetry ◽  
Middle Latitudes ◽  
Wind Velocities ◽  
Wind Stability

At Venus’s cloud top, the circulation is dominated by the superroration, where zonal wind speed peaks at ∼100 ms−1, in the low-to-middle latitudes. The constraining of zonal and meridional circulations is essential to understanding the mechanisms driving the superrotation of Venus’s atmosphere, which are still poorly understood. We present new Doppler velocimetry measurements of horizontal wind velocities at Venus’s cloud top, around 70 km altitude. These results were based on March 2015 observations at the Canada–France–Hawaii Telescope (CFHT, Mauna Kea, Hawaii), using ESPaDOnS. The Doppler velocimetry method used has already successfully provided zonal and meridional results in previous works led by P. Machado and R. Gonçalves, proving to be a good reference ground-based technique in the study of the dynamics of Venus’s atmosphere. These observations were carried out between 27 and 29 March 2015, using the Echelle SpectroPolarimetric Device for the Observation of Stars (ESPaDOnS) which provides simultaneous visible-near IR spectra from 370 to 1050 nm, with a spectral resolution of 81000 allowing wind field characterization in the scattered Franuhofer solar lines by Venus’s cloud top on the dayside. The zonal velocities are consistent with previous results while also showing evidence of spatial variability, along planetocentric latitude and longitude (local-time). The meridional wind circulation presents a notably constant latitudinal structure with null velocities at lower latitudes, below 10∘ N–S, and peak velocities of ∼30 ms−1, centered around 35∘ N–S. The uncertainty of the meridional wind results from ground observations is of the same order as the uncertainty of meridional wind retrieved by space-based observations using cloud-tracking, as also shown by previous work led by R. Gonçalves and published in 2020. These March 2015 measurements present a unique and valuable contribution to the study of horizontal wind at the cloud top, from a period when Doppler velocimetry was the only available method to do so, since no space mission was orbiting Venus between Venus Express ending in January 2015 and Akatsuki’s orbit insertion in December 2015. These results from new observations provide (1) constraints on zonal wind temporal and spatial variability (latitude and local time), (2) constraints on the meridional wind latitudinal profile, (3) additional evidence of zonal and meridional wind stability for the period between 2011 and 2015 (along previous Doppler results) (4) further evidence of the consistency and robustness of our Doppler velocimetry method.

scholarly journals Prediction of Solar Power Using Near-Real Time Satellite Data

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5865
Author(s):  
Abhnil Amtesh Prasad ◽  
Merlinde Kay
Keyword(s):  
Real Time ◽  
Satellite Data ◽  
Solar Power ◽  
Short Term ◽  
Motion Vectors ◽  
Clear Sky ◽  
Cloud Motion ◽  
Visible Images ◽  
Grid Operators ◽  
Short Term Prediction

Solar energy production is affected by the attenuation of incoming irradiance from underlying clouds. Often, improvements in the short-term predictability of irradiance using satellite irradiance models can assist grid operators in managing intermittent solar-generated electricity. In this paper, we develop and test a satellite irradiance model with short-term prediction capabilities using cloud motion vectors. Near-real time visible images from Himawari-8 satellite are used to derive cloud motion vectors using optical flow estimation techniques. The cloud motion vectors are used for the advection of pixels at future time horizons for predictions of irradiance at the surface. Firstly, the pixels are converted to cloud index using the historical satellite data accounting for clear, cloudy and cloud shadow pixels. Secondly, the cloud index is mapped to the clear sky index using a historical fitting function from the respective sites. Thirdly, the predicated all-sky irradiance is derived by scaling the clear sky irradiance with a clear sky index. Finally, a power conversion model trained at each site converts irradiance to power. The prediction of solar power tested at four sites in Australia using a one-month benchmark period with 5 min ahead prediction showed that errors were less than 10% at almost 34–60% of predicted times, decreasing to 18–26% of times under live predictions, but it outperformed persistence by >50% of the days with errors <10% for all sites. Results show that increased latency in satellite images and errors resulting from the conversion of cloud index to irradiance and power can significantly affect the forecasts.

scholarly journals Two Stepped Majority Voting for Efficient EEG based Emotion Classification

2020 ◽  
Author(s):  
aras Masood Ismael ◽  
Ömer F Alçin ◽  
Karmand H Abdalla ◽  
Abdulkadir k sengur
Keyword(s):  
Emotion Recognition ◽  
Nearest Neighbor ◽  
Noise Removal ◽  
Majority Voting ◽  
Emotion Classification ◽  
K Nearest Neighbor ◽  
Discrimination Accuracy ◽  
Novel Approach ◽  
Knn Classifier ◽  
Low Pass

Abstract In this paper, a novel approach that is based on two-stepped majority voting is proposed for efficient EEG based emotion classification. Emotion recognition is important for human-machine interactions. Facial-features and body-gestures based approaches have been generally proposed for emotion recognition. Recently, EEG based approaches become more popular in emotion recognition. In the proposed approach, the raw EEG signals are initially low-pass filtered for noise removal and band-pass filters are used for rhythms extraction. For each rhythm, the best performed EEG channels are determined based on wavelet-based entropy features and fractal dimension based features. The k-nearest neighbor (KNN) classifier is used in classification. The best five EEG channels are used in majority voting for getting the final predictions for each EEG rhythm. In the second majority voting step, the predictions from all rhythms are used to get a final prediction. The DEAP dataset is used in experiments and classification accuracy, sensitivity and specificity are used for performance evaluation metrics. The experiments are carried out to classify the emotions into two binary classes such as high valence (HV) vs low valence (LV) and high arousal (HA) vs low arousal (LA). The experiments show that 86.3% HV vs LV discrimination accuracy and 85.0% HA vs LA discrimination accuracy is obtained. The obtained results are also compared with some of the existing methods. The comparisons show that the proposed method has potential in the use of EEG based emotion classification.

DE-Based RBFNs for Classification With Special Attention to Noise Removal and Irrelevant Features

2018 ◽  
pp. 218-243
Author(s):  
Ch. Sanjeev Kumar Dash ◽  
Ajit Kumar Behera ◽  
Sarat Chandra Nayak
Keyword(s):  
Feature Selection ◽  
Radial Basis Function ◽  
Basis Function ◽  
Missing Values ◽  
Information Gain ◽  
Noise Removal ◽  
Network Efficiency ◽  
Novel Approach ◽  
Radial Basis ◽  
Filter Approach

This chapter presents a novel approach for classification of dataset by suitably tuning the parameters of radial basis function networks with an additional cost of feature selection. Inputting optimal and relevant set of features to a radial basis function may greatly enhance the network efficiency (in terms of accuracy) at the same time compact its size. In this chapter, the authors use information gain theory (a kind of filter approach) for reducing the features and differential evolution for tuning center and spread of radial basis functions. Different feature selection methods, handling missing values and removal of inconsistency to improve the classification accuracy of the proposed model are emphasized. The proposed approach is validated with a few benchmarking highly skewed and balanced dataset retrieved from University of California, Irvine (UCI) repository. The experimental study is encouraging to pursue further extensive research in highly skewed data.

scholarly journals Comparison of MISR and Meteosat-9 cloud-motion vectors

2011 ◽  
Vol 116 (D24) ◽  
pp. n/a-n/a ◽  
Author(s):  
Katrin Lonitz ◽  
Ákos Horváth
Keyword(s):  
Motion Vectors ◽  
Cloud Motion

scholarly journals A Multiplicative Noise Removal Approach Based on Partial Differential Equation Model

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Bo Chen ◽  
Jin-Lin Cai ◽  
Wen-Sheng Chen ◽  
Yan Li
Keyword(s):  
Fourth Order ◽  
Multiplicative Noise ◽  
Additive Noise ◽  
Speckle Noise ◽  
Noise Removal ◽  
Equation Model ◽  
Superior Performance ◽  
Pde Model ◽  
Novel Approach ◽  
Fourth Order Pde

Multiplicative noise, also known as speckle noise, is signal dependent and difficult to remove. Based on a fourth-order PDE model, this paper proposes a novel approach to remove the multiplicative noise on images. In practice, Fourier transform and logarithm strategy are utilized on the noisy image to convert the convolutional noise into additive noise, so that the noise can be removed by using the traditional additive noise removal algorithm in frequency domain. For noise removal, a new fourth-order PDE model is developed, which avoids the blocky effects produced by second-order PDE model and attains better edge-preserve ability. The performance of the proposed method has been evaluated on the images with both additive and multiplicative noise. Compared with some traditional methods, experimental results show that the proposed method obtains superior performance on different PSNR values and visual quality.

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