CloudSat's A-Train Exit and the Formation of the C-Train: An Orbital Dynamics Perspective

Braun, Barbara; Sweetser, Theodore H.; Clifford, Graham; Bartsch, Joseph

doi:10.1109/aero.2019.8741958

Cited by 17 publications

(12 citation statements)

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“…It orbited as a part of the A-constellation satellites at an altitude of about 705 km until early 2018, which cross the equator at 1:30 and 13:30 local time, and have a 16-day orbit cycle with cross track errors of ±10 km (Winker et al, 2003). In February 2018, CALIPSO transitioned to the C-constellation satellites, reaching its final altitude of roughly 688 km in October 2018 (Braun et al, 2019). Such a change in…”

Section: Satellite Observationsmentioning

confidence: 99%

“…It orbited as a part of the A‐constellation satellites at an altitude of about 705 km until early 2018, which cross the equator at 1:30 and 13:30 local time, and have a 16‐days orbit cycle with cross track errors of ±10 km (Winker et al., 2003). In February 2018, CALIPSO transitioned to the C‐constellation satellites, reaching its final altitude of roughly 688 km in October 2018 (Braun et al., 2019). Such a change in altitude is unlikely to have an impact on the results presented in this work as (i) less 13% of the CALIPSO measurements considered here were collected from February 2018 to December 2019 and (ii) nearly all aerosols are in the bottom 8 km of the atmosphere, and therefore, the AOD estimates will be largely the same.…”

Section: Datasetsmentioning

confidence: 99%

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Characteristics of Atmospheric Aerosols Over the UAE Inferred From CALIPSO and Sun Photometer Aerosol Optical Depth

Fissehaye

Francis

Fonseca

et al. 2021

Earth and Space Science

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This study provides insights on the composition and variability of atmospheric aerosols over the United Arab Emirates (UAE) by analyzing the atmospheric conditions together with 14-years (2006-2019) of aerosol optical depth (AOD) retrieved from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite, and 7 years of AOD measured from the ground-based Aerosol Robotic Network.We found that mineral dust is the most prevailing aerosol subtype. In addition, polluted dust and polluted continental aerosols are observed mostly in the cold season. The AOD is higher in spring and summer, when the atmospheric conditions are more favourable to the occurrence of dust events. Moreover, there is another peak in winter associated with dust storms triggered by mid-latitude baroclinic systems. In summer's daytime, extinction coefficients in excess of 0.2 km -1 are observed up to 3-4 km above the surface, as a result of the warmer and windier conditions. In the cold season and at night, the dust layers are confined to the lower atmosphere below 2 km. On a climatological time scale, we found that the AOD over the UAE has been decreasing since 2009, possibly due to the increasing trend in precipitation and changes in land use.This study highlights the large contribution of dust aerosols to the total aerosol load over the UAE and stresses on the need to account for mineral dust aerosols in climate-air pollution related studies as well as weather and air quality forecasts.

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Section: Satellite Observationsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Characteristics of Atmospheric Aerosols Over the UAE Inferred From CALIPSO and Sun Photometer Aerosol Optical Depth

Fissehaye

Francis

Fonseca

et al. 2021

Earth and Space Science

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“…There are slight differences in spatial sampling between the testing dataset and the validation and training datasets. We expect that this is due to a combination of the strict 2 min time difference we require of the collocations and the exit of CALIPSO from the A-train in late 2018 (Braun et al, 2019). We select 2019 for our testing dataset since it provides the most spatially and temporally complete dataset.…”

Section: Dataset Splittingmentioning

confidence: 99%

Evaluation of Visible Infrared Imaging Radiometer Suite (VIIRS) neural network cloud detection against current operational cloud masks

2021

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Abstract. Cloud properties are critical to our understanding of weather and climate variability, but their estimation from satellite imagers is a nontrivial task. In this work, we aim to improve cloud detection, which is the most fundamental cloud property. We use a neural network applied to Visible Infrared Imaging Radiometer Suite (VIIRS) measurements to determine whether an imager pixel is cloudy or cloud-free. The neural network is trained and evaluated using 4 years (2016–2019) of coincident measurements between VIIRS and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). We successfully address the lack of sun glint in the collocation dataset with a simple semi-supervised learning approach. The results of the neural network are then compared with two operational cloud masks: the Continuity MODIS-VIIRS Cloud Mask (MVCM) and the NOAA Enterprise Cloud Mask (ECM). We find that the neural network outperforms both operational cloud masks in most conditions examined with a few exceptions. The largest improvements we observe occur during the night over snow- or ice-covered surfaces in the high latitudes. In our analysis, we show that this improvement is not solely due to differences in optical-depth-based definitions of a cloud between each mask. We also analyze the differences in true-positive rate between day–night and land–water scenes as a function of optical depth. Such differences are a contributor to spatial artifacts in cloud masking, and we find that the neural network is the most consistent in cloud detection with respect to optical depth across these conditions. A regional analysis over Greenland illustrates the impact of such differences and shows that they can result in mean cloud fractions with very different spatial and temporal characteristics.

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“…It orbited as a part of the A-constellation satellites at an altitude of about 705 km until early 2018, which cross the equator at 1:30 and 13:30 local time, and have a 16-days orbit cycle with cross track errors of ±10 km (Winker et al, 2003). In February 2018, CALIPSO transitioned to the C-constellation satellites, reaching its final altitude of roughly 688 km in October 2018 (Braun et al, 2019). Such a change in altitude is unlikely to have an impact on the results presented in this work as (i) less 13% of the CALIPSO measurements considered here were collected from February 2018 to December 2019 and (ii) nearly all aerosols are in the bottom 8 km of the atmosphere, and therefore, the AOD estimates will be largely the same.…”

Section: Satellite Observationsmentioning

confidence: 99%

Characteristics of atmospheric aerosols over the UAE inferred from CALIPSO and Sun Photometer Aerosol Optical Depth

Nelli

Fissehaye

Francis

et al. 2020

Preprint

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Atmospheric aerosols have a wide range of impacts on the climate system. This includes interactions with the incoming solar radiation and clouds (Forkel et al., 2012), serving as a medium for transporting various organisms through vast distances (Azua-Bustos et al., 2019), and causing or enhancing respiratory, infectious, cardiovascular, and allergic diseases (Boersma et al., 2008). However, quantifying the effects of aerosols on the climate on both global and regional scales has never been a trivial problem, primarily due to their sporadic nature and large spatiotemporal variability (B. Liu et al., 2018). Hence, a considerable uncertainty of their net effect on the climate remains (Boucher et al., 2013).Atmospheric aerosols originate from different sources, namely biogenic (nature) and anthropogenic (human-related activities). Mineral dust aerosols are the dominant species in the atmosphere by virtue of their large contribution to the global aerosol loading (Tegen et al., 2002). These natural aerosols originate mostly from the Arabian, the Asian, and the Sahara deserts (Prospero, 2002). Dust mixed with biomass burning and urban pollution, frequently labeled as "polluted dust," is also an important contributor to atmospheric aerosols and is regularly observed in the Middle East, West Africa, and Central Asia (Kim et al., 2018).The cycle of atmospheric aerosols, from emission to transport and deposition, is strongly controlled by the atmospheric dynamics and thermodynamics (Kok et al., 2012). In the Arabian Gulf and surrounding region, the prevailing near-surface wind is from the northwest, locally known as Shamal winds (Bou Karam Francis et al., 2017;Yu et al., 2016). This large-scale wind interacts with local land-sea breeze circulations (Eager et al., 2008), which are stronger in the summer season when the land-sea temperature gradient is larger. As a result of the strong heating of the surface by the Sun, a thermal low develops in the region,

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CloudSat's A-Train Exit and the Formation of the C-Train: An Orbital Dynamics Perspective

Cited by 17 publications

References 1 publication

Characteristics of Atmospheric Aerosols Over the UAE Inferred From CALIPSO and Sun Photometer Aerosol Optical Depth

Characteristics of Atmospheric Aerosols Over the UAE Inferred From CALIPSO and Sun Photometer Aerosol Optical Depth

Evaluation of Visible Infrared Imaging Radiometer Suite (VIIRS) neural network cloud detection against current operational cloud masks

Characteristics of atmospheric aerosols over the UAE inferred from CALIPSO and Sun Photometer Aerosol Optical Depth

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