Lidar Observations of Volcanic Particles

Volcanic Ash ◽  
2016 ◽  
pp. 161-173 ◽  
Author(s):  
L. Mona ◽  
F. Marenco
Keyword(s):  
2016 ◽  
Vol 119 ◽  
pp. 23017 ◽  
Author(s):  
Carmen Córdoba-Jabonero ◽  
José Antonio Adame ◽  
James R. Campbell ◽  
Emilio Cuevas ◽  
Juan Pedro Díaz ◽  
...  

2015 ◽  
Vol 120 (7) ◽  
pp. 2881-2898 ◽  
Author(s):  
Giovanni Pitari ◽  
Glauco Di Genova ◽  
Eleonora Coppari ◽  
Natalia De Luca ◽  
Piero Di Carlo ◽  
...  

2016 ◽  
Vol 16 (4) ◽  
pp. 2155-2174 ◽  
Author(s):  
Franco Marenco ◽  
Ben Johnson ◽  
Justin M. Langridge ◽  
Jane Mulcahy ◽  
Angela Benedetti ◽  
...  

Abstract. Lidar observations of smoke aerosols have been analysed from six flights of the Facility for Airborne Atmospheric Measurements BAe-146 research aircraft over Brazil during the biomass burning season (September 2012). A large aerosol optical depth (AOD) was observed, typically ranging 0.4–0.9, along with a typical aerosol extinction coefficient of 100–400 Mm−1. The data highlight the persistent and widespread nature of the Amazonian haze, which had a consistent vertical structure, observed over a large distance ( ∼ 2200 km) during a period of 14 days. Aerosols were found near the surface; but the larger aerosol load was typically found in elevated layers that extended from 1–1.5 to 4–6 km. The measurements have been compared to model predictions with the Met Office Unified Model (MetUM) and the ECMWF-MACC model. The MetUM generally reproduced the vertical structure of the Amazonian haze observed with the lidar. The ECMWF-MACC model was also able to reproduce the general features of smoke plumes albeit with a small overestimation of the AOD. The models did not always capture localised features such as (i) smoke plumes originating from individual fires, and (ii) aerosols in the vicinity of clouds. In both these circumstances, peak extinction coefficients of the order of 1000–1500 Mm−1 and AODs as large as 1–1.8 were encountered, but these features were either underestimated or not captured in the model predictions. Smoke injection heights derived from the Global Fire Assimilation System (GFAS) for the region are compatible with the general height of the aerosol layers.


2014 ◽  
Vol 119 (6) ◽  
pp. 3295-3308 ◽  
Author(s):  
Tetsu Sakai ◽  
Narihiro Orikasa ◽  
Tomohiro Nagai ◽  
Masataka Murakami ◽  
Takuya Tajiri ◽  
...  

2016 ◽  
Vol 121 (2) ◽  
pp. 1483-1502 ◽  
Author(s):  
Cao Chen ◽  
Xinzhao Chu ◽  
Jian Zhao ◽  
Brendan R. Roberts ◽  
Zhibin Yu ◽  
...  

2011 ◽  
Vol 32 (5) ◽  
pp. 1269-1288 ◽  
Author(s):  
Dong Wu ◽  
Yongxiang Hu ◽  
M. Patrick McCormick ◽  
Fengqi Yan

2011 ◽  
Vol 4 (9) ◽  
pp. 1705-1712 ◽  
Author(s):  
S. A. Carn ◽  
T. M. Lopez

Abstract. We report attempted validation of Ozone Monitoring Instrument (OMI) sulfur dioxide (SO2) retrievals in the stratospheric volcanic cloud from Sarychev Peak (Kurile Islands) in June 2009, through opportunistic deployment of a ground-based ultraviolet (UV) spectrometer (FLYSPEC) as the volcanic cloud drifted over central Alaska. The volcanic cloud altitude (~12–14 km) was constrained using coincident CALIPSO lidar observations. By invoking some assumptions about the spatial distribution of SO2, we derive averages of FLYSPEC vertical SO2 columns for comparison with OMI SO2 measurements. Despite limited data, we find minimum OMI-FLYSPEC differences within measurement uncertainties, which support the validity of the operational OMI SO2 algorithm. However, our analysis also highlights the challenges involved in comparing datasets representing markedly different spatial and temporal scales. This effort represents the first attempt to validate SO2 in a stratospheric volcanic cloud using a mobile ground-based instrument, and demonstrates the need for a network of rapidly deployable instruments for validation of space-based volcanic SO2 measurements.


2020 ◽  
Vol 13 (1) ◽  
pp. 100
Author(s):  
Kazuho Araki ◽  
Yoshio Awaya

Gaps are important for growth of vegetation on the forest floor. However, monitoring of gaps in large areas is difficult. Airborne light detection and ranging (LiDAR) data make precise gap mapping possible. We formulated a method to describe changes in gaps by time-series tracking of gap area changes using three digital canopy height models (DCHMs) based on LiDAR data collected in 2005, 2011, and 2016 over secondary deciduous broadleaf forest. We generated a mask that covered merging or splitting of gaps in the three DCHMs and allowed us to identify their spatiotemporal relationships. One-fifth of gaps merged with adjacent gaps or split into several gaps between 2005 and 2016. Gap shrinkage showed a strong linear correlation with gap area in 2005, via lateral growth of gap-edge trees between 2005 and 2016, as modeled by a linear regression analysis. New gaps that emerged between 2005 and 2011 shrank faster than gaps present in 2005. A statistical model to predict gap lifespan was developed and gap lifespan was mapped using data from 2005 and 2016. Predicted gap lifespan decreased greatly due to shrinkage and splitting of gaps between 2005 and 2016.


Sign in / Sign up

Export Citation Format

Share Document