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 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

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

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

Volcanic ash cloud detection from MODIS image based on CPIWS method

2017 ◽  
Vol 65 (1) ◽  
pp. 151-163 ◽  
Author(s):  
Lan Liu ◽  
Chengfan Li ◽  
Yongmei Lei ◽  
Jingyuan Yin ◽  
Junjuan Zhao
Keyword(s):  
Volcanic Ash ◽  
Cloud Detection ◽  
Modis Image ◽  
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.

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