scholarly journals Characterisation of Absorbing Aerosols Using Ground and Satellite Data at an Urban Location, Hyderabad

2016 ◽  
Vol 16 (6) ◽  
pp. 1427-1440 ◽  
Author(s):  
Subin Jose ◽  
Kaundala Niranjan ◽  
Biswadip Gharai ◽  
Pamaraju Venkata Narasimha Rao ◽  
Vijayakumar S. Nair
Keyword(s):  
Satellite Data ◽  
Urban Location ◽  
Absorbing Aerosols

scholarly journals Analysis of properties of the 19 February 2018 volcanic eruption of Mount Sinabung in S5P/TROPOMI and Himawari satellite data

2019 ◽  
Author(s):  
Adrianus de Laat ◽  
Margarita Vazquez-Navarro ◽  
Nicolas Theys ◽  
Piet Stammes
Keyword(s):  
Satellite Data ◽  
Volcanic Ash ◽  
Geostationary Satellite ◽  
Oxygen Absorption ◽  
Volcanic Plume ◽  
Accurate Information ◽  
Ice Crystal ◽  
Volcanic Ash Cloud ◽  
Absorbing Aerosols ◽  
Eruption Plume

Abstract. This study presents an analysis of TROPOMI cloud heights as a proxy for volcanic plume heights in the presence of absorbing aerosols and sulfur dioxide for the 19 February 2018 eruption plume of the Sinabung volcano on Sumatra, Indonesia. Comparison with CALIPSO satellite data shows that all three TROPOMI cloud height data products based on oxygen absorption which are considered here (FRESCO, ROCINN, O22CLD) provide volcanic ash heights comparable to heights measured by CALIPSO for optically thick volcanic ash clouds. FRESCO and ROCINN heights are very similar with only differences for FRESCO cloud top heights above 14 km altitude. O22CLD cloud top heights unsurprisingly fall below those of FRESCO and ROCINN, as the O22CLD retrieval is less sensitive to cloud top heights above 10 km altitude. For optically thin volcanic ash clouds, i.e. when Earth’s surface or clouds at lower altitudes shine through the volcanic ash cloud, retrieved heights fall below the volcanic ash heights derived from CALIPSO data. Evaluation of corresponding Himawari geostationary volcanic ash height retrievals based on InfraRed (IR) brightness temperature differences (ΔBT) reveals that for this particular eruption the ΔBT volcanic ash signature – widely used for detection of volcanic ash in geostationary satellite data – changes to a ΔBT ice crystal signature for the part of the ash plume reaching the upper troposphere beyond 10 km altitude several hours after the start of the eruption and which TROPOMI clearly characterizes as volcanic (SO2 > 1 DU and AAI > 4 or more conservatively SO2 > 10). The presence of ice in volcanic ash clouds is known to prevent the detection of volcanic ash based on broadband geostationary satellite data. TROPOMI does not suffer from this effect, and can provide valuable and accurate information about volcanic ash clouds and ash top heights in cases where commonly used geostationary IR measurements of volcanic ash fail.

scholarly journals Analysis of properties of the 19 February 2018 volcanic eruption of Mount Sinabung in S5P/TROPOMI and Himawari-8 satellite data

2020 ◽  
Vol 20 (5) ◽  
pp. 1203-1217 ◽  
Author(s):  
Adrianus de Laat ◽  
Margarita Vazquez-Navarro ◽  
Nicolas Theys ◽  
Piet Stammes
Keyword(s):  
Satellite Data ◽  
Volcanic Ash ◽  
Geostationary Satellite ◽  
Oxygen Absorption ◽  
Volcanic Plume ◽  
Accurate Information ◽  
Ice Crystal ◽  
Volcanic Ash Cloud ◽  
Absorbing Aerosols ◽  
Ash Cloud

Abstract. This study presents an analysis of TROPOMI cloud heights as a proxy for volcanic plume heights in the presence of absorbing aerosols and sulfur dioxide for the 19 February 2018 eruption plume of the Sinabung volcano on Sumatra, Indonesia. Comparison with CALIPSO satellite data shows that all three TROPOMI cloud height data products based on oxygen absorption which are considered here (FRESCO, ROCINN, O22CLD) provide volcanic ash cloud heights comparable to heights measured by CALIPSO for optically thick volcanic ash clouds. FRESCO and ROCINN heights are very similar, with the only differences for FRESCO cloud top heights above 14 km altitude. O22CLD cloud top heights unsurprisingly fall below those of FRESCO and ROCINN, as the O22CLD retrieval is less sensitive to cloud top heights above 10 km altitude. For optically thin volcanic ash clouds, i.e., when Earth's surface or clouds at lower altitudes shine through the volcanic ash cloud, retrieved heights fall below the volcanic ash cloud heights derived from CALIPSO data. Evaluation of corresponding Himawari-8 geostationary infrared (IR) brightness temperature differences (ΔBTs) – a signature for detection of volcanic ash clouds in geostationary satellite data and widely used as input for quantitative volcanic ash cloud retrievals – reveals that for this particular eruption the ΔBT volcanic ash signature changes to a ΔBT ice crystal signature for the part of the ash plume reaching the upper troposphere beyond 10 km altitude several hours after the start of the eruption and which TROPOMI clearly characterizes as volcanic (SO2 > 1 DU – Dobson units – and AAI > 4 – absorbing aerosol index – or, more conservatively, SO2 > 10). The presence of ice in volcanic ash clouds is known to prevent the detection of volcanic ash clouds based on broadband geostationary satellite data. TROPOMI does not suffer from this effect and can provide valuable and accurate information about volcanic ash clouds and ash top heights in cases where commonly used geostationary IR measurements of volcanic ash clouds fail.

Generation of DEM and Ortho-image from High Resolution Stereo Satellite Data for a selected part of Southeast Bangladesh

2011 ◽  
Vol 4 (1) ◽  
pp. 500-502
Author(s):  
Md. Fazlul Haque ◽  
◽  
Md. Mostafizur Rahman Akhand ◽  
Dr. Dewan Abdul Quadir
Keyword(s):  
High Resolution ◽  
Satellite Data

Distributed access and search for the satellite data of remote sensing

2007 ◽  
Vol 13 (1s) ◽  
pp. 80-85
Author(s):  
E.B. Kudashev ◽  
◽  
A.N. Filonov ◽  
Keyword(s):  
Remote Sensing ◽  
Satellite Data

Delay-throughput tradeoff in satellite data relay networks with prioritized user satellites

2020 ◽  
Vol 17 (11) ◽  
pp. 219-230
Author(s):  
Yan Zhu ◽  
Min Sheng ◽  
Jiandong Li ◽  
Di Zhou
Keyword(s):  
Satellite Data ◽  
Relay Networks

Evaluation of Capacity of Salandi Reservoir Using Remote Sensing Data Along With Qgis Software

2018 ◽  
Vol 6 (8) ◽  
pp. 339
Author(s):  
Rupali Dhal ◽  
D. P. Satapathy
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Remote Sensing Data ◽  
Project Work ◽  
Space Technology ◽  
Reservoir Water ◽  
Sediment Distribution ◽  
Sensing Data ◽  
Spread Area ◽  
Water Spread

The dynamic aspects of the reservoir which are water spread, suspended sediment distribution and concentration requires regular and periodical mapping and monitoring. Sedimentation in a reservoir affects the capacity of the reservoir by affecting both life and dead storages. The life of a reservoir depends on the rate of siltation. The various aspects and behavior of the reservoir sedimentation, like the process of sedimentation in the reservoir, sources of sediments, measures to check the sediment and limitations of space technology have been discussed in this report. Multi satellite remote sensing data provide information on elevation contours in the form of water spread area. Any reduction in reservoir water spread area at a specified elevation corresponding to the date of satellite data is an indication of sediment deposition. Thus the quality of sediment load that is settled down over a period of time can be determined by evaluating the change in the aerial spread of the reservoir at various elevations. Salandi reservoir project work was completed in 1982 and the same is taken as the year of first impounding. The original gross and live storages capacities were 565 MCM& 556.50 MCM respectively. In SRS CWC (2009), they found that live storage capacity of the Salandi reservoir is 518.61 MCM witnessing a loss of 37.89 MCM (i.e. 6.81%) in a period of 27 years.The data obtained through satellite enables us to study the aspects on various scales and at different stages. This report comprises of the use of satellite to obtain data for the years 2009-2013 through remote sensing in the sedimentation study of Salandi reservoir. After analysis of the satellite data in the present study(2017), it is found that live capacity of the reservoir of the Salandi reservoir in 2017 is 524.19MCM witnessing a loss of 32.31 MCM (i.e. 5.80%)in a period of 35 years. This accounts for live capacity loss of 0.16 % per annum since 1982. The trap efficiencies of this reservoir evaluated by using Brown’s, Brune’s and Gill’s methods are 94.03%, 98.01and 99.94% respectively. Thus, the average trap efficiency of the Salandi Reservoir is obtained as 97.32%.

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