Some Dust Storm Conditions of the Southern High Plains

1952 ◽  
Vol 33 (6) ◽  
pp. 240-243 ◽  
Author(s):  
G. Frederick Warn

This paper summarizes some of the weather conditions which cause blowing dust in the Southern High Plains of Texas. These conditions include diurnal winds, whirlwinds and thunderstorms. A tentative classification of dust storms in this area is given in table form. The terms dust lift, dust swath, and dust ring are introduced.

Author(s):  
Sabur F. Abdullaev ◽  
Irina. N. Sokolik

Dust storms are commonly occurring phenomena in Tajikistan. The known aridity of the region is a major factor in promoting numerous dust storms. They have many diverse impacts on the environment and the climate of the region. The classification of dust storms and synoptic conditions related to their formation in Central Asia are discussed in the content of their diverse impact. We address dust optical properties that are representative of the region. Dust storms significantly reduce visibly and pose a human health threads. They also cause a significant impact on the radiative regime. As a result, dust storms may cause a decrease in temperature during daytime of up to 16 о С and an increase in temperature during night time from up to 7 о С compared to a clear day. 


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.


2021 ◽  
Vol 212 ◽  
pp. 105040
Author(s):  
Steven A. Mauget ◽  
Sushil K. Himanshu ◽  
Tim S. Goebel ◽  
Srinivasalu Ale ◽  
Robert J. Lascano ◽  
...  

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 141
Author(s):  
Emilie Aragnou ◽  
Sean Watt ◽  
Hiep Nguyen Duc ◽  
Cassandra Cheeseman ◽  
Matthew Riley ◽  
...  

Dust storms originating from Central Australia and western New South Wales frequently cause high particle concentrations at many sites across New South Wales, both inland and along the coast. This study focussed on a dust storm event in February 2019 which affected air quality across the state as detected at many ambient monitoring stations in the Department of Planning, Industry and Environment (DPIE) air quality monitoring network. The WRF-Chem (Weather Research and Forecast Model—Chemistry) model is used to study the formation, dispersion and transport of dust across the state of New South Wales (NSW, Australia). Wildfires also happened in northern NSW at the same time of the dust storm in February 2019, and their emissions are taken into account in the WRF-Chem model by using Fire Inventory from NCAR (FINN) as emission input. The model performance is evaluated and is shown to predict fairly accurate the PM2.5 and PM10 concentration as compared to observation. The predicted PM2.5 concentration over New South Wales during 5 days from 11 to 15 February 2019 is then used to estimate the impact of the February 2019 dust storm event on three health endpoints, namely mortality, respiratory and cardiac disease hospitalisation rates. The results show that even though as the daily average of PM2.5 over some parts of the state, especially in western and north western NSW near the centre of the dust storm and wild fires, are very high (over 900 µg/m3), the population exposure is low due to the sparse population. Generally, the health impact is similar in order of magnitude to that caused by biomass burning events from wildfires or from hazardous reduction burnings (HRBs) near populous centres such as in Sydney in May 2016. One notable difference is the higher respiratory disease hospitalisation for this dust event (161) compared to the fire event (24).


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