scholarly journals Relationships between lightning and properties of convective cloud clusters

2007 ◽  
Vol 34 (15) ◽  
Author(s):  
Joanna M. Futyan ◽  
Anthony D. Del Genio
Keyword(s):  
Convective Cloud ◽  
Cloud Clusters

A Wave-Relative Framework Analysis of AEW–MCS Interactions Leading to Tropical Cyclogenesis

2020 ◽  
Vol 148 (11) ◽  
pp. 4657-4671
Author(s):  
Kelly M. Núñez Ocasio ◽  
Jenni L. Evans ◽  
George S. Young
Keyword(s):  
West Africa ◽  
Thermodynamic Characteristics ◽  
Convective Cloud ◽  
Type Systems ◽  
Squall Line ◽  
Tropical Cyclogenesis ◽  
Framework Analysis ◽  
Convective Systems ◽  
Cloud Clusters ◽  
Mesoscale Convective

AbstractAn African easterly wave (AEW) and associated mesoscale convective systems (MCSs) dataset has been created and used to evaluate the propagation of MCSs, AEWs, and, especially, the propagation of MCSs relative to the AEW with which they are associated (i.e., wave-relative framework). The thermodynamic characteristics of AEW–MCS systems are also analyzed. The analysis is done for both AEW–MCS systems that develop into tropical cyclones and those that do not to quantify significant differences. It is shown that developing AEWs over West Africa are associated with a larger number of convective cloud clusters (CCCs; squall-line-type systems) than nondeveloping AEWs. The MCSs of developing AEWs propagate at the same speed of the AEW trough in addition to being in phase with the trough, whereas convection associated with nondeveloping AEWs over West Africa moves faster than the trough and is positioned south of it. These differences become important for the intensification of the AEW vortex as this slower-moving convection (i.e., moving at the same speed of the AEW trough) spends more time supplying moisture and latent heat to the AEW vortex, supporting its further intensification. An analysis of the rainfall rate (MCS intensity), MCS area, and latent heating rate contribution reveals that there are statistically significant differences between developing AEWs and nondeveloping AEWs, especially over West Africa where the fraction of extremely large MCS areas associated with developing AEWs is larger than for nondeveloping AEWs.

Convective Cloud Clusters and Squall Lines along coastal Amazon

2021 ◽  
Author(s):  
A. C. Sousa ◽  
L. A. Candido ◽  
P. Satyamurty
Keyword(s):  
Mixed Layer ◽  
Convective Cloud ◽  
La Niña ◽  
Squall Line ◽  
Mixing Ratio ◽  
Squall Lines ◽  
La Nina ◽  
Coastal Belt ◽  
Cloud Clusters ◽  
The Mean

AbstractMesoscale convective cloud clusters develop and organize in the form of squall lines along the coastal Amazon in the afternoon hours and propagate inland during the evening hours. The frequency, location, organization into lines and movement of the convective systems are determined by analyzing the “precipitation features” obtained from the TRMM satellite for the period 1998-2014. The convective clusters and their alignments into Amazon coastal squall lines are more frequent from December through July and they mostly stay within 170 km from the coast line. Their development and movement in the afternoon and evening hours of about 14 m s-1 are helped by the sea breeze. Negative phase of Atlantic Dipole and La Niña combined increase the frequency of convective clusters over coastal Amazon. Composite environmental conditions of 13 large Amazon coastal squall line cases in April show that conditional instability increases from 09 LT to 12 LT and the wind profiles show a jet like structure in low levels. The differences in the vertical profiles of temperature and humidity between the large squall line composites and no-squall line composites are weak. However, appreciable increase in the mean value of CAPE from 09 LT to 15 LT is found in large squall line composite. The mean mixing ratio of mixed layer at 09 LT in La Niña situations is significantly larger in the large squall line composite. Thus, CAPE and mixed layer mixing ratio are considered promising indicators of the convective activity over the coastal belt of the Amazon Basin.

The Amazonian Low-Level Jet and Its Connection to Convective Cloud Propagation and Evolution

2020 ◽  
Vol 148 (10) ◽  
pp. 4083-4099 ◽  
Author(s):  
Evandro M. Anselmo ◽  
Courtney Schumacher ◽  
Luiz A. T. Machado
Keyword(s):  
Convective Cloud ◽  
Moisture Flux ◽  
Time Of Day ◽  
Convective System ◽  
South American ◽  
Low Level ◽  
Convective Systems ◽  
Wind Speeds ◽  
Cloud Clusters ◽  
Low Level Jet

AbstractWe describe the existence of an Amazonian low-level jet (ALLJ) that can affect the propagation and life cycle of convective systems from the northeast coast of South America into central Amazonia. Horizontal winds from reanalysis were analyzed during March–April–May (MAM) of the two years (2014–15) of the GoAmazon2014/5 field campaign. Convective system tracking was performed using GOES-13 infrared imagery and classified into days with high and weak convective activity. The MAM average winds show a nocturnal enhancement of low-level winds starting near the coast in the early evening and reaching 1600 km inland by late morning. Mean 3-hourly wind speeds maximize at 9–10 m s−1 near 900 hPa, but individual days can have nighttime low-level winds exceeding 12 m s−1. Based on objective low-level wind criteria, the ALLJ is present 10%–40% of the time over the Amazon during MAM depending on the location and time of day. The evolution of the ALLJ across the Amazon impacts the frequency of occurrence of cloud clusters and the intensity of the moisture flux. In addition, the ALLJ is associated with the enhancement of northeasterly flow in the midtroposphere during active convective days, when vertical momentum transport may be occurring in the organized cloud clusters. During the weakly active convective period, the ALLJ is weaker near the coast but stronger across the central Amazon and appears to be linked more directly with the South American low-level jet.

scholarly journals Analysis on the Evolution and Microphysical Characteristics of Two Consecutive Hailstorms in Spring in Yunnan, China

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 63
Author(s):  
Sidou Zhang ◽  
Shiyin Liu ◽  
Tengfei Zhang
Keyword(s):  
Wind Speed ◽  
Cloud Model ◽  
Global Analysis ◽  
Maximum Velocity ◽  
Western Pacific Subtropical High ◽  
Convective Cloud ◽  
Radar Data ◽  
Reanalysis Data ◽  
Cloud Water ◽  
High Level

By using products of the cloud model, National Centers for Environmental Prediction (NCEP) Final Operational Global Analysis (FNL) reanalysis data, and Doppler weather radar data, the mesoscale characteristics, microphysical structure, and mechanism of two hail cloud systems which occurred successively within 24 h in southeastern Yunnan have been analyzed. The results show that under the influence of two southwest jets in front of the south branch trough (SBT) and the periphery of the western Pacific subtropical high (WPSH), the northeast-southwest banded echoes affect the southeastern Yunnan of China twice. Meanwhile, the local mesoscale radial wind convergence and uneven wind speed lead to the intense development of convective echoes and the occurrence of hail. The simulated convective cloud bands are similar to the observation. The high-level mesoscale convergence line leads to the development of convective cloud bands. The low-level wind direction or wind speed convergence and the high-level wind speed divergence form a deep tilted updraft, with the maximum velocity of 15 m·s−1 at the −40~−10 °C layer, resulting in the intense development of local convective clouds. The hail embryos form through the conversion or collision growth of cloud water and snowflakes and have little to do with rain and ice crystals. Abundant cloud water, especially the accumulation region of high supercooled water (cloud water) near the 0 °C layer, is the key to the formation of hail embryos, in which qc is up to 1.92 g·kg−1 at the −4~−2 °C layer. The hail embryos mainly grow by collision-coalescence (collision-freezing) with cloud water (supercooled cloud drops) and snow crystal riming.

scholarly journals Emergency Landing Spot Detection Algorithm for Unmanned Aerial Vehicles

2021 ◽  
Vol 13 (10) ◽  
pp. 1930
Author(s):  
Gabriel Loureiro ◽  
André Dias ◽  
Alfredo Martins ◽  
José Almeida
Keyword(s):  
Point Cloud ◽  
Principal Component ◽  
Detection Algorithm ◽  
Cloud Data ◽  
Safety Issues ◽  
Spot Detection ◽  
Cloud Clusters ◽  
Aerial Vehicle ◽  
Incoming Light ◽  
Landing Spot

The use and research of Unmanned Aerial Vehicle (UAV) have been increasing over the years due to the applicability in several operations such as search and rescue, delivery, surveillance, and others. Considering the increased presence of these vehicles in the airspace, it becomes necessary to reflect on the safety issues or failures that the UAVs may have and the appropriate action. Moreover, in many missions, the vehicle will not return to its original location. If it fails to arrive at the landing spot, it needs to have the onboard capability to estimate the best area to safely land. This paper addresses the scenario of detecting a safe landing spot during operation. The algorithm classifies the incoming Light Detection and Ranging (LiDAR) data and store the location of suitable areas. The developed method analyses geometric features on point cloud data and detects potential right spots. The algorithm uses the Principal Component Analysis (PCA) to find planes in point cloud clusters. The areas that have a slope less than a threshold are considered potential landing spots. These spots are evaluated regarding ground and vehicle conditions such as the distance to the UAV, the presence of obstacles, the area’s roughness, and the spot’s slope. Finally, the output of the algorithm is the optimum spot to land and can vary during operation. The proposed approach evaluates the algorithm in simulated scenarios and an experimental dataset presenting suitability to be applied in real-time operations.

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