The optical and microwave characteristics of dust storms over the Indo-Gangetic Plains

2022 ◽  
pp. 505-520
Author(s):  
Feng Jing ◽  
Ramesh P. Singh
2007 ◽  
Vol 41 (29) ◽  
pp. 6289-6301 ◽  
Author(s):  
A PRASAD ◽  
S SINGH ◽  
S CHAUHAN ◽  
M SRIVASTAVA ◽  
R SINGH ◽  
...  

2018 ◽  
Vol 24 (1) ◽  
Author(s):  
JASWINDER KAUR ◽  
SATYA NARAIN

The floristic exploration and critical examination of specimens collected of family Convolvulaceae from Upper Gangetic Plains of India, resulted in addition of 2 new records for the flora viz. Ipomoea littoralis and Ipomoea capitellata var. multilobata. Detailed description, phenology, ecology, distribution, locality, field number, type specimens examined, illustrations and other relevant notes are provided.


2017 ◽  
Vol 13 (4) ◽  
pp. 182-195
Author(s):  
Tahseen H Mubarak ◽  
◽  
Bruska A. Azhdar ◽  
Saib Thiab Alwan ◽  
Hussein S Mahmood

2020 ◽  
Vol 16 (1) ◽  
pp. 1-14
Author(s):  
Monim Jiboori ◽  
Nadia Abed ◽  
Mohamed Abdel Wahab

2020 ◽  
Vol 4 ◽  
pp. 78-95
Author(s):  
A.R. Ivanova ◽  
◽  
E.N. Skriptunova ◽  
N.I. Komasko ◽  
A.A. Zavialova ◽  
...  

A review of literature on the impact of dust and sand storms on the air transport operation is presented. Observational data on dust storms at the aerodromes of European Russia for the period of 2001-2019 are analyzed. The seasonal variations in dust transport episodes at aerodromes and its relationship with visibility changes are discussed. The characteristics of dusty air masses and advection are given. It is concluded that the frequency of dust transfer episodes for the aerodromes under study has decreased over the past five years, except for Gumrak aerodrome (Volgograd). Keywords: dust storm, sand storm, aviation, visibility, seasonal variations, aerodrome оf European Russia


2020 ◽  
Vol 67 (1) ◽  
pp. 16-21
Author(s):  
Sergey M. Bakirov ◽  
Sergey S. Eliseev

The modern level of agriculture is described by the introduction of renewable energy sources. New generation sprinkler machines are being put into production, in the power system of which solar panels are used. One of the factors that negatively affect the performance of solar cells in an open field is their dusting, which is formed as a result of dust storms and wind. Cleaning of the battery panels is carried out in various ways: manual, semi-automatic and automatic. Dust cleaning is included in maintenance. (Research purpose) The research purpose is to determine the conditions for performing the maintenance, which consists in cleaning solar panels in the field. (Materials and methods) Theoretical (analysis, hypothesis design), empirical (observation, testing), experimental (ascertaining experiment) methods has been used during research. (Results and discussion) The article describes an introduced parameter for estimating the level of dusting. The power loss indicator shows the ratio of the power of the dusted module to the power of the clean module. Unscheduled maintenance is affected by the distance of the solar module from the repair point, the power of the solar module, the loss from dusting, the frequency of maintenance and cost indicators. (Conclusions) It has been found the dependence of maintenance period of the solar module of the sprinkler machine on the distance to the sprinkler machine, to the point of maintenance and repair, the power loss coefficient in case of dusting of the solar module, the cost of performing maintenance, as well as the frequency of maintenance. Article describes the boundaries of the choice of operating mode of the sprinkler between unscheduled maintenance for cleaning the solar module and the acceptance of additional power of the sprinkler power supply system according to the criterion of minimum operating costs.


2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.


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