scholarly journals Modelling the volcanic ash plume from Eyjafjallajökull eruption (May 2010) over Europe: evaluation of the benefit of source term improvements and of the assimilation of aerosol measurements

2021 ◽  
Vol 21 (12) ◽  
pp. 3731-3747
Author(s):  
Matthieu Plu ◽  
Guillaume Bigeard ◽  
Bojan Sič ◽  
Emanuele Emili ◽  
Luca Bugliaro ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Source Term ◽  
Sensitivity Study ◽  
Numerical Dispersion ◽  
Horizontal Dispersion ◽  
Realistic Representation ◽  
Coordination System ◽  
Ash Plume ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Ground Based Lidar

Abstract. Numerical dispersion models are used operationally worldwide to mitigate the effect of volcanic ash on aviation. In order to improve the representation of the horizontal dispersion of ash plumes and of the 3D concentration of ash, a study was conducted using the MOCAGE model during the European Natural Airborne Disaster Information and Coordination System for Aviation (EUNADICS-AV) project. Source term modelling and assimilation of different data were investigated. A sensitivity study of source term formulation showed that a resolved source term, using the FPLUME plume rise model in MOCAGE, instead of a parameterised source term, induces a more realistic representation of the horizontal dispersion of the ash plume. The FPLUME simulation provides more concentrated and focused ash concentrations in the horizontal and the vertical dimensions than the other source term. The assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth has an impact on the horizontal dispersion of the plume, but this effect is rather low and local compared to source term improvement. More promising results are obtained with the continuous assimilation of ground-based lidar profiles, which improves the vertical distribution of ash and helps in reaching realistic values of ash concentrations. Using this configuration, the effect of assimilation may last for several hours and it may propagate several hundred kilometres downstream of the lidar profiles.

scholarly journals Modelling the volcanic ash plume from Eyjafjallajökull eruption (May 2010) over Europe: evaluation of the benefit of source term improvements and of the assimilation of aerosol measurements

2021 ◽  
Author(s):  
Matthieu Plu ◽  
Guillaume Bigeard ◽  
Bojan Sič ◽  
Emanuele Emili ◽  
Luca Bugliaro ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Source Term ◽  
Sensitivity Study ◽  
Numerical Dispersion ◽  
Dispersion Models ◽  
Horizontal Dispersion ◽  
Realistic Representation ◽  
Ash Plume ◽  
Ground Based Lidar ◽  
The Vertical Distribution

Abstract. Numerical dispersion models are used operationally worldwide to mitigate the effect of volcanic ash on aviation. In order to improve the representation of the horizontal dispersion of ash plumes and of the 3D concentration of ash, a study was conducted using the MOCAGE model during the EUNADICS-AV project. Source term modelling and assimilation of different data were investigated. A sensitivity study to source term formulation showed that a resolved source term, using the FPLUME plume-rise model in MOCAGE, instead of a parameterised source term, induces a more realistic representation of the horizontal dispersion of the ash plume. The FPLUME simulation provides more concentrated and focused ash concentrations in the horizontal and the vertical dimensions than the other source term. The assimilation of MODIS Aerosol Optical Depth has an impact on the horizontal dispersion the plume, but this effect is rather low and local, compared to source term improvement. More promising results are obtained with the continuous assimilation of ground-based lidar profiles, which improves the vertical distribution of ash and helps to reach realistic values of ash concentrations. The improvement can remain several hours after and several hundred kilometers away downstream to the assimilated profiles.

scholarly journals Airborne observations of the Eyjafjalla volcano ash cloud over Europe during air space closure in April and May 2010

2011 ◽  
Vol 11 (5) ◽  
pp. 2245-2279 ◽  
Author(s):  
U. Schumann ◽  
B. Weinzierl ◽  
O. Reitebuch ◽  
H. Schlager ◽  
A. Minikin ◽  
...  
Keyword(s):  
Mass Concentration ◽  
Volcanic Ash ◽  
Particle Density ◽  
Airborne Lidar ◽  
Particle Sedimentation ◽  
Space Closure ◽  
Ash Plume ◽  
Ground Based Lidar ◽  
Ash Cloud ◽  
Mass Concentrations

Abstract. Airborne lidar and in-situ measurements of aerosols and trace gases were performed in volcanic ash plumes over Europe between Southern Germany and Iceland with the Falcon aircraft during the eruption period of the Eyjafjalla volcano between 19 April and 18 May 2010. Flight planning and measurement analyses were supported by a refined Meteosat ash product and trajectory model analysis. The volcanic ash plume was observed with lidar directly over the volcano and up to a distance of 2700 km downwind, and up to 120 h plume ages. Aged ash layers were between a few 100 m to 3 km deep, occurred between 1 and 7 km altitude, and were typically 100 to 300 km wide. Particles collected by impactors had diameters up to 20 μm diameter, with size and age dependent composition. Ash mass concentrations were derived from optical particle spectrometers for a particle density of 2.6 g cm−3 and various values of the refractive index (RI, real part: 1.59; 3 values for the imaginary part: 0, 0.004 and 0.008). The mass concentrations, effective diameters and related optical properties were compared with ground-based lidar observations. Theoretical considerations of particle sedimentation constrain the particle diameters to those obtained for the lower RI values. The ash mass concentration results have an uncertainty of a factor of two. The maximum ash mass concentration encountered during the 17 flights with 34 ash plume penetrations was below 1 mg m−3. The Falcon flew in ash clouds up to about 0.8 mg m−3 for a few minutes and in an ash cloud with approximately 0.2 mg m−3 mean-concentration for about one hour without engine damage. The ash plumes were rather dry and correlated with considerable CO and SO2 increases and O3 decreases. To first order, ash concentration and SO2 mixing ratio in the plumes decreased by a factor of two within less than a day. In fresh plumes, the SO2 and CO concentration increases were correlated with the ash mass concentration. The ash plumes were often visible slantwise as faint dark layers, even for concentrations below 0.1 mg m−3. The large abundance of volatile Aitken mode particles suggests previous nucleation of sulfuric acid droplets. The effective diameters range between 0.2 and 3 μm with considerable surface and volume contributions from the Aitken and coarse mode aerosol, respectively. The distal ash mass flux on 2 May was of the order of 500 (240–1600) kg s−1. The volcano induced about 10 (2.5–50) Tg of distal ash mass and about 3 (0.6–23) Tg of SO2 during the whole eruption period. The results of the Falcon flights were used to support the responsible agencies in their decisions concerning air traffic in the presence of volcanic ash.

scholarly journals A model sensitivity study of the impact of clouds on satellite detection and retrieval of volcanic ash

2015 ◽  
Vol 8 (5) ◽  
pp. 1935-1949 ◽  
Author(s):  
A. Kylling ◽  
N. Kristiansen ◽  
A. Stohl ◽  
R. Buras-Schnell ◽  
C. Emde ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Liquid Water ◽  
Volcanic Ash ◽  
Spectral Band ◽  
Sensitivity Study ◽  
Transfer Model ◽  
Mass Loading ◽  
Ice Clouds ◽  
Medium Range Weather Forecast ◽  
The Impact

Abstract. Volcanic ash is commonly observed by infrared detectors on board Earth-orbiting satellites. In the presence of ice and/or liquid-water clouds, the detected volcanic ash signature may be altered. In this paper the sensitivity of detection and retrieval of volcanic ash to the presence of ice and liquid-water clouds was quantified by simulating synthetic equivalents to satellite infrared images with a 3-D radiative transfer model. The sensitivity study was made for the two recent eruptions of Eyjafjallajökull (2010) and Grímsvötn (2011) using realistic water and ice clouds and volcanic ash clouds. The water and ice clouds were taken from European Centre for Medium-Range Weather Forecast (ECMWF) analysis data and the volcanic ash cloud fields from simulations by the Lagrangian particle dispersion model FLEXPART. The radiative transfer simulations were made both with and without ice and liquid-water clouds for the geometry and channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The synthetic SEVIRI images were used as input to standard reverse absorption ash detection and retrieval methods. Ice and liquid-water clouds were on average found to reduce the number of detected ash-affected pixels by 6–12%. However, the effect was highly variable and for individual scenes up to 40% of pixels with mass loading >0.2 g m−2 could not be detected due to the presence of water and ice clouds. For coincident pixels, i.e. pixels where ash was both present in the FLEXPART (hereafter referred to as "Flexpart") simulation and detected by the algorithm, the presence of clouds overall increased the retrieved mean mass loading for the Eyjafjallajökull (2010) eruption by about 13%, while for the Grímsvötn (2011) eruption ash-mass loadings the effect was a 4% decrease of the retrieved ash-mass loading. However, larger differences were seen between scenes (standard deviations of ±30 and ±20% for Eyjafjallajökull and Grímsvötn, respectively) and even larger ones within scenes. The impact of ice and liquid-water clouds on the detection and retrieval of volcanic ash, implies that to fully appreciate the location and amount of ash, hyperspectral and spectral band measurements by satellite instruments should be combined with ash dispersion modelling.

scholarly journals Assimilation of ground versus lidar observations for PM<sub>10</sub> forecasting

2013 ◽  
Vol 13 (1) ◽  
pp. 269-283 ◽  
Author(s):  
Y. Wang ◽  
K. N. Sartelet ◽  
M. Bocquet ◽  
P. Chazette
Keyword(s):  
Data Assimilation ◽  
Western Europe ◽  
Sensitivity Study ◽  
Optimal Interpolation ◽  
Temporal Influence ◽  
Potential Impact ◽  
Ground Based Lidar ◽  
Observing System Simulation Experiment ◽  
Lidar Observations ◽  
Do So

Abstract. This article investigates the potential impact of future ground-based lidar networks on analysis and short-term forecasts of particulate matter with a diameter smaller than 10 μm (PM10). To do so, an Observing System Simulation Experiment (OSSE) is built for PM10 data assimilation (DA) using optimal interpolation (OI) over Europe for one month from 15 July to 15 August 2001. First, using a lidar network with 12 stations and representing the "true" atmosphere by a simulation called "nature run", we estimate the efficiency of assimilating the lidar network measurements in improving PM10 concentration for analysis and forecast. It is compared to the efficiency of assimilating concentration measurements from the AirBase ground network, which includes about 500 stations in western Europe. It is found that assimilating the lidar observations decreases by about 54% the root mean square error (RMSE) of PM10 concentrations after 12 h of assimilation and during the first forecast day, against 59% for the assimilation of AirBase measurements. However, the assimilation of lidar observations leads to similar scores as AirBase's during the second forecast day. The RMSE of the second forecast day is improved on average over the summer month by 57% by the lidar DA, against 56% by the AirBase DA. Moreover, the spatial and temporal influence of the assimilation of lidar observations is larger and longer. The results show a potentially powerful impact of the future lidar networks. Secondly, since a lidar is a costly instrument, a sensitivity study on the number and location of required lidars is performed to help define an optimal lidar network for PM10 forecasts. With 12 lidar stations, an efficient network in improving PM10 forecast over Europe is obtained by regularly spacing the lidars. Data assimilation with a lidar network of 26 or 76 stations is compared to DA with the previously-used lidar network. During the first forecast day, the assimilation of 76 lidar stations' measurements leads to a better score (the RMSE decreased by about 65%) than AirBase's (the RMSE decreased by about 59%).

Using a model-of-models approach and remote sensing technologies to improve flood disaster alerting

2020 ◽  
Author(s):  
Guy J.-P. Schumann ◽  
Margaret Glasscoe ◽  
Douglas Bausch ◽  
Marlon Pierce ◽  
Jun Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Open Access ◽  
Structural Damage ◽  
Flood Hazard ◽  
Remote Sensing Data ◽  
Observation Data ◽  
Flood Impacts ◽  
Coordination System ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Almost All

&lt;p&gt;Floods are happening regularly in almost all places of the world and impact people, societies and economies, causing widespread devastation that can be hard to recover from. Yet, accurately predicting and alerting for floods is challenging, primarily since flood events are very local in nature and processes causing a flood can be very complex. In an era of open-access geospatial data proliferation as well as data and model interoperability, it makes sense to leverage on existing data and models, many of which are underutilized by decision-making applications. Thus, the objective of the project is to develop an open-access rapid alerting and severity assessment component for global flooding based on existing models and observation data sources. We do this within the DisasterAWARE platform of the Pacific Disaster Center (PDC).&lt;/p&gt;&lt;p&gt;This paper will outline the proposed concept of model-of-models that will leverage existing flood-hazard modeling capabilities, illustrating products that we will leverage, such as: GLOFAS (Global Flood Forecasting Feeds) probabilistic hydrologic data, IMERG (The Integrated Multi-satellitE Retrievals for GPM) observed precipitation grids, GDACS (Global Disaster Alerting Coordination System) anomaly points, GFMS (Global Flood Monitoring System) depth above baseline grids, the NASA MODIS (Moderate Resolution Imaging Spectroradiometer) and Dartmouth Observatory flood maps, as well as new models as they are developed. We will further combine the flood hazard data with existing exposure data to estimate property loss using a probabilistic fragility approach. With the use of an end-to-end deep learning framework, structural damage will be detected using different remote sensing data. The approach will further incorporate other, non-routinely-generated remotely-sensed products for ground-truthing for areas and events where and when such products are available.&lt;/p&gt;&lt;p&gt;The existing resilience and capacity of communities to rapidly respond to and recover from flood impacts will be incorporated into the severity determination on an administrative area and watershed risk basis. This model-of-models approach will leverage major efforts, improve reliability and reduce false triggers by ensuring two or more models agree.&lt;/p&gt;

scholarly journals Volcanic ash detection and retrievals from MODIS data by means of Neural Networks

2011 ◽  
Vol 4 (3) ◽  
pp. 2567-2598 ◽  
Author(s):  
M. Picchiani ◽  
M. Chini ◽  
S. Corradini ◽  
L. Merucci ◽  
P. Sellitto ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Confusion Matrix ◽  
Classification Problem ◽  
Cloud Detection ◽  
Cloud Properties ◽  
Mandatory Requirement ◽  
Mt Etna ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Time Basis ◽  
Ash Cloud

Abstract. Volcanic ash clouds detection and retrieval represent a key issue for the aviation safety due to the harming effects they can provoke on aircrafts. A lesson learned from the recent Icelandic Eyjafjalla volcano eruption is the need to obtain accurate and reliable retrievals on a real time basis. The current most widely adopted procedures for ash detection and retrieval are based on the Brightness Temperature Difference (BTD) inversion observed at 11 and 12 μm that allows volcanic and meteo clouds discrimination. While ash cloud detection can be readily obtained, a reliable quantitative ash cloud retrieval can be so time consuming to prevent its utilization during the crisis phase. In this work a fast and accurate Neural Network (NN) approach to detect and retrieve volcanic ash cloud properties has been developed using multispectral IR measurements collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) over Mt. Etna volcano during 2001, 2002 and 2006 eruptive events. The procedure consists in two separate steps: the ash detection and ash mass retrieval. The detection is reduced to a classification problem by identifying two classes of "ashy" and "non-ashy" pixels in the MODIS images. Then the ash mass is estimated by means of the NN, replicating the BTD-based model performances. The results obtained from the entire procedure are very encouraging; indeed the confusion matrix for the test set has an accuracy greater than 90 %. Both ash detection and retrieval show a good agreement when compared to the results achieved by the BTD-based procedure. Moreover, the NN procedure is so fast to be extremely attractive in all the cases when the quick response time of the system is a mandatory requirement.

scholarly journals A novel retrieval of daytime atmospheric dust and volcanic ash heights through a synergy of AIRS infrared radiances and MODIS L2 optical depths

2015 ◽  
Vol 8 (1) ◽  
pp. 443-485 ◽  
Author(s):  
S. DeSouza-Machado ◽  
L. Strow ◽  
E. Maddy ◽  
O. Torres ◽  
G. Thomas ◽  
...  
Keyword(s):  
Atmospheric Dust ◽  
Atmospheric Infrared Sounder ◽  
Hyper Spectral ◽  
Mediterranean Regions ◽  
Moderate Resolution ◽  
Ash Plume ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Using Data ◽  
Infrared Radiances ◽  
Along Track

Abstract. We present a novel method to retrieve daytime atmospheric dust and ash plume heights using a synergy of infrared hyper-spectral radiances and retrieved visible optical depths. The method is developed using data from the Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS), both of which are on NASA's Aqua platform, and lends itself to also a χ2 height derivation based on the smallest bias between observations and calculations in the thermal infrared window. The retrieval methodology is validated against almost 30 months of dust centroid heights obtained from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIOP) data, and against ash plume heights obtained from the Advanced Along-Track Scanning Radiometer (AATSR) after the Puyehue Cordon Caulle volcanic eruption of June 2011. Comparisons are also made against Goddard Chemistry Aerosol Radiation and Transport (GOCART) climatological aerosol heights. In general there is good agreement between the heights from the CALIPSO data and the AIRS/MODIS retrieval, especially over the Atlantic and Mediterranean regions; over land one there are more noticeable differences. The AIRS/MODIS derived heights are within typically 25% of the CALIOP centroid heights.

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