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 Volcanic ash detection and retrievals using MODIS data by means of neural networks

2011 ◽  
Vol 4 (12) ◽  
pp. 2619-2631 ◽  
Author(s):  
M. Picchiani ◽  
M. Chini ◽  
S. Corradini ◽  
L. Merucci ◽  
P. Sellitto ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Classification Problem ◽  
Detection Accuracy ◽  
Modis Data ◽  
Cloud Properties ◽  
Mandatory Requirement ◽  
Mt Etna ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Time Basis

Abstract. Volcanic ash clouds detection and retrieval represent a key issue for aviation safety due to the harming effects on aircraft. A lesson learned from the recent Eyjafjallajokull eruption is the need to obtain accurate and reliable retrievals on a real time basis. In this work we have developed a fast and accurate Neural Network (NN) approach to detect and retrieve volcanic ash cloud properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) data in the Thermal InfraRed (TIR) spectral range. Some measurements collected during the 2001, 2002 and 2006 Mt. Etna volcano eruptions have been considered as test cases. The ash detection and retrievals obtained from the Brightness Temperature Difference (BTD) algorithm are used as training for the NN procedure that consists in two separate steps: ash detection and ash mass retrieval. The ash detection is reduced to a classification problem by identifying two classes: "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. A segmentation procedure has also been tested to remove the false ash pixels detection induced by the presence of high meteorological clouds. The segmentation procedure shows a clear advantage in terms of classification accuracy: the main drawback is the loss of information on ash clouds distal part. The results obtained are very encouraging; indeed the ash detection accuracy is greater than 90%, while a mean RMSE equal to 0.365 t km−2 has been obtained for the ash mass retrieval. Moreover, the NN quickness in results delivering makes the procedure extremely attractive in all the cases when the rapid response time of the system is a mandatory requirement.

scholarly journals Volcanic ash cloud detection from space: a comparison between the RSTASHtechnique and the water vapour corrected BTD procedure

2011 ◽  
Vol 2 (3) ◽  
pp. 263-277 ◽  
Author(s):  
Alessandro Piscini ◽  
Stefano Corradini ◽  
Francesco Marchese ◽  
Luca Merucci ◽  
Nicola Pergola ◽  
...  
Keyword(s):  
Water Vapour ◽  
Volcanic Ash ◽  
Cloud Detection ◽  
Volcanic Ash Cloud ◽  
Ash Cloud

scholarly journals Volcanic ash modeling with the NMMB-MONARCH-ASH model: quantification of offline modeling errors

2018 ◽  
Vol 18 (6) ◽  
pp. 4019-4038 ◽  
Author(s):  
Alejandro Marti ◽  
Arnau Folch
Keyword(s):  
Volcanic Ash ◽  
Source Parameters ◽  
Atmospheric Dispersion ◽  
Volcanic Eruptions ◽  
Systematic Errors ◽  
Cloud Detection ◽  
Coupled Modeling ◽  
Evaluation Scores ◽  
Modeling Systems ◽  
Ash Cloud

Abstract. Volcanic ash modeling systems are used to simulate the atmospheric dispersion of volcanic ash and to generate forecasts that quantify the impacts from volcanic eruptions on infrastructures, air quality, aviation, and climate. The efficiency of response and mitigation actions is directly associated with the accuracy of the volcanic ash cloud detection and modeling systems. Operational forecasts build on offline coupled modeling systems in which meteorological variables are updated at the specified coupling intervals. Despite the concerns from other communities regarding the accuracy of this strategy, the quantification of the systematic errors and shortcomings associated with the offline modeling systems has received no attention. This paper employs the NMMB-MONARCH-ASH model to quantify these errors by employing different quantitative and categorical evaluation scores. The skills of the offline coupling strategy are compared against those from an online forecast considered to be the best estimate of the true outcome. Case studies are considered for a synthetic eruption with constant eruption source parameters and for two historical events, which suitably illustrate the severe aviation disruptive effects of European (2010 Eyjafjallajökull) and South American (2011 Cordón Caulle) volcanic eruptions. Evaluation scores indicate that systematic errors due to the offline modeling are of the same order of magnitude as those associated with the source term uncertainties. In particular, traditional offline forecasts employed in operational model setups can result in significant uncertainties, failing to reproduce, in the worst cases, up to 45–70 % of the ash cloud of an online forecast. These inconsistencies are anticipated to be even more relevant in scenarios in which the meteorological conditions change rapidly in time. The outcome of this paper encourages operational groups responsible for real-time advisories for aviation to consider employing computationally efficient online dispersal models.

scholarly journals Multilayer Cloud Detection with the MODIS Near-Infrared Water Vapor Absorption Band

2010 ◽  
Vol 49 (11) ◽  
pp. 2315-2333 ◽  
Author(s):  
Galina Wind ◽  
Steven Platnick ◽  
Michael D. King ◽  
Paul A. Hubanks ◽  
Michael J. Pavolonis ◽  
...  
Keyword(s):  
Water Vapor ◽  
Near Infrared ◽  
Single Layer ◽  
Precipitable Water ◽  
Detection Algorithm ◽  
Cloud Detection ◽  
Water Vapor Absorption ◽  
Cloud Properties ◽  
Cloud Models ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Data Collection 5 processing for the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the NASA Earth Observing System (EOS) Terra and Aqua spacecraft includes an algorithm for detecting multilayered clouds in daytime. The main objective of this algorithm is to detect multilayered cloud scenes, specifically optically thin ice cloud overlying a lower-level water cloud, that present difficulties for retrieving cloud effective radius using single-layer plane-parallel cloud models. The algorithm uses the MODIS 0.94-μm water vapor band along with CO2 bands to obtain two above-cloud precipitable water retrievals, the difference of which, in conjunction with additional tests, provides a map of where multilayered clouds might potentially exist. The presence of a multilayered cloud results in a large difference in retrievals of above-cloud properties between the CO2 and the 0.94-μm methods. In this paper the MODIS multilayered cloud algorithm is described, results of using the algorithm over example scenes are shown, and global statistics for multilayered clouds as observed by MODIS are discussed. A theoretical study of the algorithm behavior for simulated multilayered clouds is also given. Results are compared to two other comparable passive imager methods. A set of standard cloudy atmospheric profiles developed during the course of this investigation is also presented. The results lead to the conclusion that the MODIS multilayer cloud detection algorithm has some skill in identifying multilayered clouds with different thermodynamic phases.

Near-real-time volcanic ash cloud detection: Experiences from the Alaska Volcano Observatory

2009 ◽  
Vol 186 (1-2) ◽  
pp. 79-90 ◽  
Author(s):  
P.W. Webley ◽  
J. Dehn ◽  
J. Lovick ◽  
K.G. Dean ◽  
J.E. Bailey ◽  
...  
Keyword(s):  
Real Time ◽  
Volcanic Ash ◽  
Cloud Detection ◽  
Volcano Observatory ◽  
Volcanic Ash Cloud ◽  
Ash Cloud

scholarly journals Mount Semeru Eruption on December 1, 2020: A Volcanic Ash Cloud Assessment Using TIR Imagery with 8.5-12.0 µm Spectral Bands

2020 ◽  
Author(s):  
Andri Wibowo
Keyword(s):  
Brightness Temperature ◽  
Volcanic Ash ◽  
Satellite Images ◽  
Cloud Detection ◽  
Lower Altitude ◽  
Spectral Bands ◽  
Volcanic Ash Cloud ◽  
Ir Bands ◽  
Ash Cloud ◽  
Active Volcanoes

Mount Semeru is one of the most active volcanoes in the Java Island. This article presents the results of observations and detections of volcanic ash cloud after Mt Semeru eruptions on 1 December 2020 at 01:23 AM. Volcanic ash cloud detection was conducted by analyzing thermal infrared (TIR) satellite images acquired by the NOAA-20 and SNPP with MODIS and VIIRS instruments. The TIR instruments have detected the presence of volcanic ash cloud. The results show increasing ash cloud brightness temperature (BT) from 240 to 270 Kelvin (K) several hours after eruptions. Increasing BT indicated the development of volcanic Cumulonimbus (Cb) at lower altitude. Northeast movements of 270 K BT clouds were observed at 06:12 AM. Presences of volcanic Cb and SO2 were confirmed using IR bands of 12.0-10.8 µm, 11.0-8.5µm and 11.0 µm. This Cb cloud was observed moving northeast directions. The data acquired from the TIR imagery resulted from this study is thought be used in future to support and complement ground-based observations and detections of active volcanoes mainly in Java Island.

scholarly journals FALL3D-8.0: a computational model for atmospheric transport and deposition of particles, aerosols and radionuclides – Part 2: Model validation

2021 ◽  
Vol 14 (1) ◽  
pp. 409-436
Author(s):  
Andrew T. Prata ◽  
Leonardo Mingari ◽  
Arnau Folch ◽  
Giovanni Macedonio ◽  
Antonio Costa
Keyword(s):  
Case Studies ◽  
Model Validation ◽  
Volcanic Ash ◽  
Atmospheric Transport ◽  
Test Suite ◽  
Structure Amplitude ◽  
Satellite Retrievals ◽  
Mt Etna ◽  
Ash Cloud ◽  
Tephra Fallout

Abstract. This paper presents model validation results for the latest version release of the FALL3D atmospheric transport model. The code has been redesigned from scratch to incorporate different categories of species and to overcome legacy issues that precluded its preparation towards extreme-scale computing. The model validation is based on the new FALL3D-8.0 test suite, which comprises a set of four real case studies that encapsulate the major features of the model; namely, the simulation of long-range fine volcanic ash dispersal, volcanic SO2 dispersal, tephra fallout deposits and the dispersal and deposition of radionuclides. The first two test suite cases (i.e. the June 2011 Puyehue-Cordón Caulle ash cloud and the June 2019 Raikoke SO2 cloud) are validated against geostationary satellite retrievals and demonstrate the new FALL3D data insertion scheme. The metrics used to validate the volcanic ash and SO2 simulations are the structure, amplitude and location (SAL) metric and the figure of merit in space (FMS). The other two test suite cases (i.e. the February 2013 Mt. Etna ash cloud and associated tephra fallout deposit, and the dispersal of radionuclides resulting from the 1986 Chernobyl nuclear accident) are validated with scattered ground-based observations of deposit load and local particle grain size distributions and with measurements from the Radioactivity Environmental Monitoring database. For validation of tephra deposit loads and radionuclides, we use two variants of the normalised root-mean-square error metric. We find that FALL3D-8.0 simulations initialised with data insertion consistently improve agreement with satellite retrievals at all lead times up to 48 h for both volcanic ash and SO2 simulations. In general, SAL scores lower than 1.5 and FMS scores greater than 0.40 indicate acceptable agreement with satellite retrievals of volcanic ash and SO2. In addition, we show very good agreement, across several orders of magnitude, between the model and observations for the 2013 Mt. Etna and 1986 Chernobyl case studies. Our results, along with the validation datasets provided in the publicly available test suite, form the basis for future improvements to FALL3D (version 8 or later) and also allow for model intercomparison studies.

Improving volcanic ash cloud detection by a robust satellite technique

2004 ◽  
Vol 90 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Nicola Pergola ◽  
Valerio Tramutoli ◽  
Francesco Marchese ◽  
Irene Scaffidi ◽  
Teodosio Lacava
Keyword(s):  
Volcanic Ash ◽  
Cloud Detection ◽  
Volcanic Ash Cloud ◽  
Ash Cloud

scholarly journals Improving volcanic ash predictions with the HYSPLIT dispersion model by assimilating MODIS satellite retrievals

2016 ◽  
Author(s):  
Tianfeng Chai ◽  
Alice Crawford ◽  
Barbara Stunder ◽  
Michael Pavolonis ◽  
Roland Draxler ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Dispersion Model ◽  
Temporal Variations ◽  
Current Case ◽  
Cost Functional ◽  
Free Region ◽  
Model Predictions ◽  
Satellite Retrievals ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Ash Cloud

Abstract. Currently NOAA's National Weather Service (NWS) runs the HYSPLIT dispersion model with a unit mass release rate to predict the transport and dispersion of volcanic ash. The model predictions provide information for the Volcanic Ash Advisory Centers (VAAC) to issue advisories to meteorological watch offices, area control centers, flight information centers, and others. This research aims provide quantitative forecasts of ash distributions generated by objectively and optimally estimating the volcanic ash source strengths, vertical distribution and temporal variations using an observation-modeling inversion technique. In this top-down approach, a cost functional is defined to mainly quantify the differences between model predictions and the satellite measurements of column integrated ash concentrations, weighted by the model and observation uncertainties. Minimizing this cost functional by adjusting the sources provides the volcanic ash emission estimates. As an example, MODIS (MOderate Resolution Imaging Spectroradiometer) satellite retrievals of the 2008 Kasatochi volcanic ash clouds are used to test the HYSPLIT volcanic ash inverse system. Because the satellite retrievals include the ash cloud top height but not the bottom height, three options for matching the model concentrations to the observed mass loadings are tested. Although the emission estimates vary significantly with different options the subsequent model predictions with the different release estimates all show decent skill when evaluated against the unassimilated satellite observations at later times. Among the three options, integrating over three model layers yields slightly better results than integrating from the surface up to the volcanic ash cloud top or using a single model layer. Inverse tests also show that including the ash-free region to constrain the model is not beneficial for the current case. In addition, extra constraints to the source terms can be given by explicitly enforcing ``no-ash'' for the atmosphere columns above or below the observed ash cloud top height. However, in this case such extra constraints are not helpful for the inverse modeling. It is also found that simultaneously assimilating observations at different times produces better hindcasts than only assimilating the most recent observations.

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