scholarly journals Shallow cumuli cover and its uncertainties from ground-based lidar–radar data and sky images

2020 ◽  
Vol 13 (4) ◽  
pp. 2099-2117
Author(s):  
Erin A. Riley ◽  
Jessica M. Kleiss ◽  
Laura D. Riihimaki ◽  
Charles N. Long ◽  
Larry K. Berg ◽  
...  
Keyword(s):  
Cloud Cover ◽  
Single Layer ◽  
Radar Data ◽  
Cloud Detection ◽  
Data Merging ◽  
Data Products ◽  
Two Factors ◽  
Ground Based Lidar ◽  
The Impact ◽  
Wide Fov

Abstract. Cloud cover estimates of single-layer shallow cumuli obtained from narrow field-of-view (FOV) lidar–radar and wide-FOV total sky imager (TSI) data are compared over an extended period (2000–2017 summers) at the established United States Atmospheric Radiation Measurement mid-continental Southern Great Plains site. We quantify the impacts of two factors on hourly and sub-hourly cloud cover estimates: (1) instrument-dependent cloud detection and data merging criteria and (2) FOV configuration. Enhanced observations at this site combine the advantages of the ceilometer, micropulse lidar (MPL) and cloud radar in merged data products. Data collected by these three instruments are used to calculate narrow-FOV cloud fraction (CF) as a temporal fraction of cloudy returns within a given period. Sky images provided by TSI are used to calculate the wide-FOV fractional sky cover (FSC) as a fraction of cloudy pixels within a given image. To assess the impact of the first factor on CF obtained from the merged data products, we consider two additional subperiods (2000–2010 and 2011–2017 summers) that mark significant instrumentation and algorithmic advances in the cloud detection and data merging. We demonstrate that CF obtained from ceilometer data alone and FSC obtained from sky images provide the most similar and consistent cloud cover estimates; hourly bias and root-mean-square difference (RMSD) are within 0.04 and 0.12, respectively. However, CF from merged MPL–ceilometer data provides the largest estimates of the multiyear mean cloud cover, about 0.12 (35 %) and 0.08 (24 %) greater than FSC for the first and second subperiods, respectively. CF from merged ceilometer–MPL–radar data has the strongest subperiod dependence with a bias of 0.08 (24 %) compared to FSC for the first subperiod and shows no bias for the second subperiod. The strong period dependence of CF obtained from the combined ceilometer–MPL–radar data is likely results from a change in what sensors are relied on to detect clouds below 3 km. After 2011, the MPL stopped being used for cloud top height detection below 3 km, leaving the radar as the only sensor used in cloud top height retrievals. To quantify the FOV impact, a narrow-FOV FSC is derived from the TSI images. We demonstrate that FOV configuration does not modify the bias but impacts the RMSD (0.1 hourly, 0.15 sub-hourly). In particular, the FOV impact is significant for sub-hourly observations, where 41 % of narrow- and wide-FOV FSC differ by more than 0.1. A new “quick-look” tool is introduced to visualize impacts of these two factors through integration of CF and FSC data with novel TSI-based images of the spatial variability in cloud cover. The influence of cloud field organization, such cloud streets parallel to the wind direction, on narrow- and wide-FOV cloud cover estimates can be visually assessed.

scholarly journals Shallow Cumuli Cover and Its Uncertainties from Ground-based Lidar-Radar Data and Sky Images

2019 ◽  
Author(s):  
Erin A. Riley ◽  
Jessica M. Kleiss ◽  
Laura D. Riihimaki ◽  
Charles N. Long ◽  
Larry K. Berg ◽  
...  
Keyword(s):  
Cloud Cover ◽  
Single Layer ◽  
Radar Data ◽  
Cloud Detection ◽  
Data Merging ◽  
Data Products ◽  
Two Factors ◽  
Ground Based Lidar ◽  
The Impact ◽  
Wide Fov

Abstract. Cloud cover estimates of single-layer shallow cumuli obtained from narrow field-of-view (FOV) lidar-radar and wide-FOV Total Sky Imager (TSI) data are compared over an extended period (2000–2017 summers) at the established United States Atmospheric Radiation Measurement mid-continental Southern Great Plains site. We quantify the impacts of two factors on hourly and sub-hourly cloud cover estimates: 1) instrument-dependent cloud detection and data merging criteria, and 2) FOV configuration. Popular enhanced observations at this site combine the advantages of the ceilometer, micropulse lidar (MPL) and cloud radar in merged data products, and are used to calculate temporal cloud fractions (CF). Sky images provide the spatial fractional sky cover (FSC) within the visible sky dome. To assess the impact of the first factor on CF obtained from the merged data products, we consider two additional sub-periods (2000–2010 and 2011–2017 summers) that mark significant instrumentation and algorithmic advances in the cloud detection and data merging. We demonstrate that CF obtained from ceilometer data alone and FSC obtained from sky images provide the most similar and consistent cloud cover estimates: bias and root-mean-square difference (RMSD) are within 0.04 and 0.12, respectively. Whereas CF from merged MPL-ceilometer data provides the largest estimates of the mean cloud cover: about 0.12 (35 %) and 0.08 (24 %) greater than FSC for the first and second sub-periods, respectively. CF from merged ceilometer-MPL-radar data has the strongest sub-period dependence with a bias of 0.08 (24 %) compared to FSC for the first sub-period and shows no bias for the second sub-period. To quantify the FOV impact, a narrow-FOV FSC is derived from the TSI images. We demonstrate that FOV configuration does not modify the bias, but impacts the RMSD (0.1 hourly, 0.15 sub-hourly). In particular, the FOV impact is significant for sub-hourly observations, where 41 % of narrow- and wide-FOV FSC differ by more than 0.1. A new "quick-look" tool is introduced to visualize impacts of these two factors through integration of CF and FSC data with novel TSI-based images of the spatial variability in cloud cover.

Comparison of polar stratospheric cloud detection and composition as observed by ground-based lidar and CALIOP at Dome C.

2021 ◽  
Author(s):  
Marcel Snels ◽  
Francesco Colao ◽  
Francesco Cairo ◽  
Ilir Shuli ◽  
Andrea Scoccione ◽  
...  
Keyword(s):  
Data Analysis ◽  
Detection Efficiency ◽  
Cloud Detection ◽  
Polar Stratospheric Cloud ◽  
Ground Based Lidar ◽  
Stratospheric Clouds ◽  
Observation Geometry ◽  
The Impact ◽  
Lidar Observations

<p>Polar stratospheric clouds have been observed at Dome C by a ground-based lidar from 2014 up to the present, possibly in coincidence with nearby overpasses of the CALIPSO satellite, with the CALIOP lidar on board.</p><p>A thorough study has been made in terms of detection efficiency and composition classification of near coincident lidar observations, with the goal to identify the main biases between the two lidars.</p><p>When comparing ground-based lidar observations with nearby CALIOP overpasses, several biases might occur, due to the distance between ground-based lidar and nearest overpass, observation geometry and integration times and different algorithms used for data analysis.</p><p>The bias resulting from different data analysis has been reduced by applying an algorithm for PSC detection and composition classification to the ground-based data which is very similar to the V2 algorithm used for CALIOP.</p><p>By comparing 5 years of PSC observations at Dome C, considering both detection efficiency and composition of the observed PSCs, the impact of all biases will be discussed and possibly quantified. </p>

Errors in Cloud Detection over the Arctic Using a Satellite Imager and Implications for Observing Feedback Mechanisms

2010 ◽  
Vol 23 (7) ◽  
pp. 1894-1907 ◽  
Author(s):  
Yinghui Liu ◽  
Steven A. Ackerman ◽  
Brent C. Maddux ◽  
Jeffrey R. Key ◽  
Richard A. Frey
Keyword(s):  
Sea Ice ◽  
Energy Budget ◽  
Cloud Cover ◽  
Cloud Amount ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Cloud Detection ◽  
Sea Ice Extent ◽  
Feedback Mechanisms ◽  
The Impact

Abstract Arctic sea ice extent has decreased dramatically over the last 30 years, and this trend is expected to continue through the twenty-first century. Changes in sea ice extent impact cloud cover, which in turn influences the surface energy budget. Understanding cloud feedback mechanisms requires an accurate determination of cloud cover over the polar regions, which must be obtained from satellite-based measurements. The accuracy of cloud detection using observations from space varies with surface type, complicating any assessment of climate trends as well as the understanding of ice–albedo and cloud–radiative feedback mechanisms. To explore the implications of this dependence on measurement capability, cloud amounts from the Moderate Resolution Imaging Spectroradiometer (MODIS) are compared with those from the CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder (CALIPSO) satellites in both daytime and nighttime during the time period from July 2006 to December 2008. MODIS is an imager that makes observations in the solar and infrared spectrum. The active sensors of CloudSat and CALIPSO, a radar and lidar, respectively, provide vertical cloud structures along a narrow curtain. Results clearly indicate that MODIS cloud mask products perform better over open water than over ice. Regional changes in cloud amount from CloudSat/CALIPSO and MODIS are categorized as a function of independent measurements of sea ice concentration (SIC) from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). As SIC increases from 10% to 90%, the mean cloud amounts from MODIS and CloudSat–CALIPSO both decrease; water that is more open is associated with increased cloud amount. However, this dependency on SIC is much stronger for MODIS than for CloudSat–CALIPSO, and is likely due to a low bias in MODIS cloud amount. The implications of this on the surface radiative energy budget using historical satellite measurements are discussed. The quantified ice–water difference in MODIS cloud detection can be used to adjust estimated trends in cloud amount in the presence of changing sea ice cover from an independent dataset. It was found that cloud amount trends in the Arctic might be in error by up to 2.7% per decade. The impact of these errors on the surface net cloud radiative effect (“forcing”) of the Arctic can be significant, as high as 8.5%.

Establishing the timings of rainfall-triggered landslides using Sentinel-1 satellite radar data

2021 ◽  
Author(s):  
Katy Burrows ◽  
Odin Marc ◽  
Dominique Remy
Keyword(s):  
Time Series ◽  
Satellite Imagery ◽  
Cloud Cover ◽  
Heavy Rainfall ◽  
Hazard Mitigation ◽  
Radar Data ◽  
Mitigation Strategies ◽  
Radar Images ◽  
Satellite Radar ◽  
The Impact

<p>Heavy rainfall can trigger thousands of landslides, which have a significant effect on the landscape and can pose a hazard to people and infrastructure. Inventories of rainfall triggered landslides are used to improve our understanding of the physical mechanisms that cause the event, in assessing the impact of the event and in the development of hazard mitigation strategies. Inventories of rainfall-triggered landslides are most commonly generated using optical or multispectral satellite imagery, but such imagery is often obscured by cloud-cover associated with the rainfall event. Cloud-free optical satellite images may not be available until several weeks following an event. In the case where rain falls over a long period of time, for example during the monsoon season or successive typhoon events, the timing of the triggered landslides is usually poorly constrained. This lack of information on landslide timing limits both hazard mitigation strategies and our ability to model the physical processes behind the triggered landsliding.</p><p>Satellite radar has emerged recently as an alternative source of information on landslides. The removal of vegetation and movement of material due to a landslide alters the scattering properties of the Earth’s surface, thus giving landslides a signal in satellite radar imagery. Satellite radar data can be acquired in all weather conditions, and the regular and frequent acquisitions of the Sentinel-1 constellation, could allow landslide timing to be constrained to within a few days.  Satellite radar data has been successfully used in detecting the spatial distribution of landslides whose timing is known a-priori (for example those triggered by earthquakes). Here we demonstrate that time series of Sentinel-1 satellite radar images can also be used to achieve the opposite: the identification of landslide timing for an event whose spatial extent is known.</p><p>We analyse radar coherence and amplitude times series to identify changes in the time series associated with landslide occurrence. We compare pixels within each landslide with nearby pixels outside each landslide that have been identified to be similar in pre-rainfall Sentinel-1 and Sentinel-2 imagery. We test our methods on rainfall-triggered landslides in Nepal and Japan, both of which are mountainous countries that experience regular heavy rainfall events that are often obscured by cloud cover in optical satellite imagery.</p>

Self-Esteem and Loneliness as Predicting Factors of Arab Children Achievement İn Kuala Lumpur

2015 ◽  
Vol 5 (1) ◽  
pp. 199 ◽  
Author(s):  
Asma'a Abdel Fattah Alhoot ◽  
Ssekamanya Sıraje Abdallah
Keyword(s):  
Academic Achievement ◽  
Relevant Literature ◽  
Self Esteem ◽  
Kuala Lumpur ◽  
Arab Children ◽  
Predicting Factors ◽  
Two Factors ◽  
Arab Schools ◽  
And Gender ◽  
The Impact

Taking into consideration the fact that self-esteem and loneliness have an even more important role to play in students' learning, this study seeks to examine the correlation of these two factors with children academic performance. The study involved 499 (grade 4 to grade 9) Arab children studying at Arab schools in Kuala Lumpur-Malaysia. Data were collected via two questionnaires (one for loneliness and the other for self-esteem). The correlational data analysis yielded a negative correlation between loneliness and academic achievement while there is a positive correlation between self-esteem and achievement. Results also suggested that there is no correlation between students' gender, age, and academic achievement. Furthermore, the results revealed that self-esteem is a good predictor of achievement while loneliness and gender are not good predictors. The findings of the present study are discussed in relation to the relevant literature, taking into consideration the impact of children mental health on their academic achievement. Finally, recommendations for further research are presented.

Design of a Robust Memristive Spiking Neuromorphic System with Unsupervised Learning in Hardware

2021 ◽  
Vol 17 (4) ◽  
pp. 1-26
Author(s):  
Md Musabbir Adnan ◽  
Sagarvarma Sayyaparaju ◽  
Samuel D. Brown ◽  
Mst Shamim Ara Shawkat ◽  
Catherine D. Schuman ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Recognition Task ◽  
Single Layer ◽  
Process Variations ◽  
Spike Timing ◽  
System Level ◽  
Design Parameters ◽  
Digital Pulse ◽  
Neuromorphic System ◽  
The Impact

Spiking neural networks (SNN) offer a power efficient, biologically plausible learning paradigm by encoding information into spikes. The discovery of the memristor has accelerated the progress of spiking neuromorphic systems, as the intrinsic plasticity of the device makes it an ideal candidate to mimic a biological synapse. Despite providing a nanoscale form factor, non-volatility, and low-power operation, memristors suffer from device-level non-idealities, which impact system-level performance. To address these issues, this article presents a memristive crossbar-based neuromorphic system using unsupervised learning with twin-memristor synapses, fully digital pulse width modulated spike-timing-dependent plasticity, and homeostasis neurons. The implemented single-layer SNN was applied to a pattern-recognition task of classifying handwritten-digits. The performance of the system was analyzed by varying design parameters such as number of training epochs, neurons, and capacitors. Furthermore, the impact of memristor device non-idealities, such as device-switching mismatch, aging, failure, and process variations, were investigated and the resilience of the proposed system was demonstrated.

scholarly journals Measuring the contribution of specialist vocabulary knowledge to academic achievement: disentangling effects of multiple types of word knowledge

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Ahmed Masrai ◽  
James Milton ◽  
Dina Abdel Salam El-Dakhs ◽  
Heba Elmenshawy
Keyword(s):  
Academic Achievement ◽  
Academic Success ◽  
Success Factors ◽  
Vocabulary Knowledge ◽  
The Other ◽  
Word Knowledge ◽  
Academic Vocabulary ◽  
Vocabulary Size ◽  
Two Factors ◽  
The Impact

AbstractThis study investigates the idea that knowledge of specialist subject vocabulary can make a significant and measurable impact on academic performance, separate from and additional to the impact of general and academic vocabulary knowledge. It tests the suggestion of Hyland and Tse (TESOL Quarterly, 41:235–253, 2007) that specialist vocabulary should be given more attention in teaching. Three types of vocabulary knowledge, general, academic and a specialist business vocabulary factors, are tested against GPA and a business module scores among students of business at a college in Egypt. The results show that while general vocabulary size has the greatest explanation of variance in the academic success factors, the other two factors - academic and a specialist business vocabulary - make separate and additional further contributions. The contribution to the explanation of variance made by specialist vocabulary knowledge is double that of academic vocabulary knowledge.

scholarly journals Predictability of Deep Convection in Idealized and Operational Forecasts: Effects of Radar Data Assimilation, Orography, and Synoptic Weather Regime

2019 ◽  
Vol 148 (1) ◽  
pp. 63-81 ◽  
Author(s):  
Kevin Bachmann ◽  
Christian Keil ◽  
George C. Craig ◽  
Martin Weissmann ◽  
Christian A. Welzbacher
Keyword(s):  
Data Assimilation ◽  
Deep Convection ◽  
Radar Data ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Model Errors ◽  
Ensemble Prediction System ◽  
Beneficial Impact ◽  
The Impact ◽  
Radar Data Assimilation

Abstract We investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDA simulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale.

scholarly journals The impact of atmospheric stability and wind shear on vertical cloud overlap over the Tibetan Plateau

2018 ◽  
Vol 18 (10) ◽  
pp. 7329-7343 ◽  
Author(s):  
Jiming Li ◽  
Qiaoyi Lv ◽  
Bida Jian ◽  
Min Zhang ◽  
Chuanfeng Zhao ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Wind Shear ◽  
Cloud Cover ◽  
Atmospheric Stability ◽  
Length Scale ◽  
The Tibetan Plateau ◽  
Total Cloud Cover ◽  
Atmospheric Models ◽  
The Impact ◽  
Overlap Parameter

Abstract. Studies have shown that changes in cloud cover are responsible for the rapid climate warming over the Tibetan Plateau (TP) in the past 3 decades. To simulate the total cloud cover, atmospheric models have to reasonably represent the characteristics of vertical overlap between cloud layers. Until now, however, this subject has received little attention due to the limited availability of observations, especially over the TP. Based on the above information, the main aim of this study is to examine the properties of cloud overlaps over the TP region and to build an empirical relationship between cloud overlap properties and large-scale atmospheric dynamics using 4 years (2007–2010) of data from the CloudSat cloud product and collocated ERA-Interim reanalysis data. To do this, the cloud overlap parameter α, which is an inverse exponential function of the cloud layer separation D and decorrelation length scale L, is calculated using CloudSat and is discussed. The parameters α and L are both widely used to characterize the transition from the maximum to random overlap assumption with increasing layer separations. For those non-adjacent layers without clear sky between them (that is, contiguous cloud layers), it is found that the overlap parameter α is sensitive to the unique thermodynamic and dynamic environment over the TP, i.e., the unstable atmospheric stratification and corresponding weak wind shear, which leads to maximum overlap (that is, greater α values). This finding agrees well with the previous studies. Finally, we parameterize the decorrelation length scale L as a function of the wind shear and atmospheric stability based on a multiple linear regression. Compared with previous parameterizations, this new scheme can improve the simulation of total cloud cover over the TP when the separations between cloud layers are greater than 1 km. This study thus suggests that the effects of both wind shear and atmospheric stability on cloud overlap should be taken into account in the parameterization of decorrelation length scale L in order to further improve the calculation of the radiative budget and the prediction of climate change over the TP in the atmospheric models.

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