Surface weather observations of atmospheric dust over the southwest summer monsoon region

Ackerman, S. A.; Cox, Stephen K.

doi:10.1007/bf01032587

Cited by 82 publications

(58 citation statements)

References 19 publications

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“…This was done based on the fact that, when a dust layer is misclassified as cloud, the layer will likely be classified as an ice cloud in the downstream data processing by the ice-water discrimination algorithm (HuSI) using the depolarization ratio measurement. Dust storms occur normally in dry and hot air conditions over the source regions during daytime due to the enhanced convection in a deepened boundary layer (e.g., Ackerman and Cox 1987;N'Tchayi Mbourou et al 1997). By checking the midlayer temperature, the misclassified dense dust layers can be identified in most cases.…”

Section: B Misclassification Typesmentioning

confidence: 99%

The CALIPSO Lidar Cloud and Aerosol Discrimination: Version 2 Algorithm and Initial Assessment of Performance

Liu

Vaughan

Winker

et al. 2009

Journal of Atmospheric and Oceanic Technology

484

454

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The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite was launched in April 2006 to provide global vertically resolved measurements of clouds and aerosols. Correct discrimination between clouds and aerosols observed by the lidar aboard the CALIPSO satellite is critical for accurate retrievals of cloud and aerosol optical properties and the correct interpretation of measurements. This paper reviews the theoretical basis of the CALIPSO lidar cloud and aerosol discrimination (CAD) algorithm, and describes the enhancements made to the version 2 algorithm that is used in the current data release (release 2). The paper also presents a preliminary assessment of the CAD performance based on one full day (12 August 2006) of expert manual classification and on one full month (July 2006) of the CALIOP 5-km cloud and aerosol layer products. Overall, the CAD algorithm works well in most cases. The 1-day manual verification suggests that the success rate is in the neighborhood of 90% or better. Nevertheless, several specific layer types are still misclassified with some frequency. Among these, the most prevalent are dense dust and smoke close to the source regions. The analysis of the July 2006 data showed that the misclassification of dust as cloud occurs for ,1% of the total tropospheric cloud and aerosol features found. Smoke layers are misclassified less frequently than are dust layers. Optically thin clouds in the polar regions can be misclassified as aerosols. While the fraction of such misclassifications is small compared with the number of aerosol features found globally, caution should be taken when studies are performed on the aerosol in the polar regions. Modifications will be made to the CAD algorithm in future data releases, and the misclassifications encountered in the current data release are expected to be reduced greatly.

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Section: B Misclassification Typesmentioning

confidence: 99%

The CALIPSO Lidar Cloud and Aerosol Discrimination: Version 2 Algorithm and Initial Assessment of Performance

Liu

Vaughan

Winker

et al. 2009

Journal of Atmospheric and Oceanic Technology

484

454

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“…Surface winds raise a tremendous amount of dust from the loose sandy soil of the region and the dust is transported eastward over the neighboring states of India. Thus, the Thar desert and parts of western India become the center of intense dust activity before the onset of monsoon (Ackerman and Cox, 1989;Leon and Legrand, 2003). The average soil loss in the Thar desert is estimated to vary between 30 and 60 kg/m 2 /d during April to June (Sikka, 1997).…”

mentioning

confidence: 97%

Geochemistry of aerosols of northwestern part of India adjoining the Thar desert

Yadav

Rajamani

2004

Geochimica et Cosmochimica Acta

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“…The frequency of DS occurrence peaks during the pre-monsoon season (MarchMay), when dust aerosols are transported by south westerly winds from the arid and semi-arid regions around the Arabian Sea (Middleton, 1986;Ackerman and Cox, 1989). Some of these regions include the alluvial plains of Iraq, the plateau of Eastern Jordan, the Jazirah of Eastern Syria, the plains of Dhofar, the adjacent interior eastern Yemen, and the Empty-Quarter desert centred in Saudi Arabia (Maghrabi et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Characterization and Elemental Composition of Atmospheric Aerosol Loads during Springtime Dust Storm in Western Saudi Arabia

Alghamdi

Almazroui

Shamy

et al. 2015

Aerosol Air Qual. Res.

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Dust storm is a common phenomenonand, a severe environmental hazard in western Saudi Arabia. In this study, simultaneous measurement of PM 10 , PM 2.5 and PM 1.0 and elemental compositions analysis of PM 2.5 in Jeddah city during springtime (March 2012) dust storm (DS) and non-dust storm (non-DS) periods were carried out to investigate the impact of DS on the levels, characterization and elemental compositions of atmospheric particles. Results indicate that PM fractions concentrations were higher in DS environment compared to non-DS. The diurnal variation of PM fractions concentrations was uni-modal in non-DS environment and bimodal in DS. PM 1.0 /PM 10 , PM 2.5 /PM 10 and PM 2.5 /PM 2.5-10 ratios were relatively lower in DS, indicating that sand-dust events in spring carry much more coarse than fine particles to Jeddah. PM 10 , PM 2.5 and PM 1.0 in DS and PM 10 and PM 2.5 in non-DS might originate from similar sources. PM 10 , PM 2.5 , PM 1.0 in DS and PM 10 in non-DS were correlated negatively with relative humidity and positively with wind speed. PM 2.5 and PM 1.0 in non-DS were correlated positively with relative humidity and negatively with wind speed. The crustal elements accounted for 44.62 and 67.53% of the total concentrations of elements in non-DS and DS, respectively. The elements concentrations increased in DS, with highest DS/non-DS ratios for Ca, Si, Al and Fe. This indicates that the soil originating species contributed mainly in DS particles.The enrichment factors values and non-crustal fractions in both non-DS and DS indicate that the main sources of Na, Mg, Si, K, Ca, Ti, Cr, Mn, Fe, Rb and Sr are of a crustal type, whereas S, Cl, Co, Cu, Zn, Ga, As, Pb and Cd are anthropogenic. V and Ni in DS only are emitted from anthropogenic sources. The enrichment factors of these anthropogenic elements were lower in DS. They might originate mainly from local sources in Jeddah.

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Surface weather observations of atmospheric dust over the southwest summer monsoon region

Cited by 82 publications

References 19 publications

The CALIPSO Lidar Cloud and Aerosol Discrimination: Version 2 Algorithm and Initial Assessment of Performance

The CALIPSO Lidar Cloud and Aerosol Discrimination: Version 2 Algorithm and Initial Assessment of Performance

Geochemistry of aerosols of northwestern part of India adjoining the Thar desert

Characterization and Elemental Composition of Atmospheric Aerosol Loads during Springtime Dust Storm in Western Saudi Arabia

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