Day–Night Monitoring of Volcanic SO2 and Ash Clouds for Aviation Avoidance at Northern Polar Latitudes

We describe NASA’s Applied Sciences Disasters Program, which is a collaborative project between the Direct Readout Laboratory (DRL), ozone processing team, Jet Propulsion Laboratory, Geographic Information Network of Alaska (GINA), and Finnish Meteorological Institute (FMI), to expedite the processing and delivery of direct readout (DR) volcanic ash and sulfur dioxide (SO2) satellite data. We developed low-latency quantitative retrievals of SO2 column density from the solar backscattered ultraviolet (UV) measurements using the Ozone Mapping and Profiler Suite (OMPS) spectrometers as well as the thermal infrared (TIR) SO2 and ash indices using Visible Infrared Imaging Radiometer Suite (VIIRS) instruments, all flying aboard US polar-orbiting meteorological satellites. The VIIRS TIR indices were developed to address the critical need for nighttime coverage over northern polar regions. Our UV and TIR SO2 and ash software packages were designed for the DRL’s International Planetary Observation Processing Package (IPOPP); IPOPP runs operationally at GINA and FMI stations in Fairbanks, Alaska, and Sodankylä, Finland. The data are produced within 30 min of satellite overpasses and are distributed to the Alaska Volcano Observatory and Anchorage Volcanic Ash Advisory Center. FMI receives DR data from GINA and posts composite Arctic maps for ozone, volcanic SO2, and UV aerosol index (UVAI, proxy for ash or smoke) on its public website and provides DR data to EUMETCast users. The IPOPP-based software packages are available through DRL to a broad DR user community worldwide.

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Detection of Fire Smoke Plumes Based on Aerosol Scattering Using VIIRS Data over Global Fire-Prone Regions

Remote Sensing ◽

10.3390/rs13020196 ◽

2021 ◽

Vol 13 (2) ◽

pp. 196

Author(s):

Xiaoman Lu ◽

Xiaoyang Zhang ◽

Fangjun Li ◽

Mark A. Cochrane ◽

Pubu Ciren

Keyword(s):

Infrared Imaging ◽

Scattered Light ◽

Detection Algorithm ◽

Mie Scattering ◽

Spectral Contrast ◽

Aerosol Scattering ◽

Smoke Plumes ◽

Fire Smoke ◽

Weather And Human Health ◽

Aerosol Index

Smoke from fires significantly influences climate, weather, and human health. Fire smoke is traditionally detected using an aerosol index calculated from spectral contrast changes. However, such methods usually miss thin smoke plumes. It also remains challenging to accurately separate smoke plumes from dust, clouds, and bright surfaces. To improve smoke plume detections, this paper presents a new scattering-based smoke detection algorithm (SSDA) depending mainly on visible and infrared imaging radiometer suite (VIIRS) blue and green bands. The SSDA is established based on the theory of Mie scattering that occurs when the diameter of an atmospheric particulate is similar to the wavelength of the scattered light. Thus, smoke commonly causes Mie scattering in VIIRS blue and green bands because of the close correspondence between smoke particulate diameters and the blue/green band wavelengths. For developing the SSDA, training samples were selected from global fire-prone regions in North America, South America, Africa, Indonesia, Siberia, and Australia. The SSDA performance was evaluated against the VIIRS aerosol detection product and smoke detections from the ultraviolet aerosol index using manually labeled fire smoke plumes as a benchmark. Results show that the SSDA smoke detections are superior to existing products due chiefly to the improved ability of the algorithm to detect thin smoke and separate fire smoke from other surface types. Moreover, the SSDA smoke distribution pattern exhibits a high spatial correlation with the global fire density map, suggesting that SSDA is capable of detecting smoke plumes of fires in near real-time across the globe.

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Detection and Avoidance of Atmospheric Aviation Hazards Using Infrared Spectroscopic Imaging

Remote Sensing ◽

10.3390/rs12142309 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2309

Author(s):

Fred Prata

Keyword(s):

High Altitude ◽

Volcanic Ash ◽

Infrared Imaging ◽

Weather Forecasting ◽

Atmospheric Condition ◽

Ice Crystals ◽

Economic Damage ◽

Spectroscopic Techniques ◽

Infrared Spectroscopic ◽

Aviation Hazards

Atmospheric aviation hazards due to turbulence, poor visibility, high-altitude ice crystals and volcanic ash and gases are known problems for aviation and can cause both economic damage to engines and airframes as well as having the potential to cause the engines to stall in flight with possible loss of the aircraft. Current space- and ground-based assets allow observations of some of these hazards and their detection and movement can be forecast using modern computer weather forecasting and dispersion models. These largely strategic resources have proved very valuable but somewhat limited in the tactical sense, where commercial aviation must make rapid decisions in order to avoid an undetected or un-forecast hazardous cloud or atmospheric condition. Here we investigate the use of multi-spectral (two channels or more) infrared imaging from an aircraft perspective, and show that it is possible to use this information to provide tactical awareness tools for use by aviators and other stakeholders. This study has a strong focus on volcanic ash as an aviation hazard but also includes applications to some forms of clear air turbulence (CAT), to high-altitude ice crystals (HAIC) and windblown desert dust. For volcanic ash detection, the research shows that current two-channel satellite-based infrared techniques provide acceptable discrimination and quantification, but two-channel infrared imaging airborne solutions have significant drawbacks. Because of the limitation of two-channel methods, infrared spectroscopic techniques are investigated and it is shown they can significantly reduce the confusion caused by meteorological hydrometeors and potentially provide information on other atmospheric hazards to aviation, such as HAIC and some forms of turbulence. Not only are these findings important for on-going efforts to incorporate IR imaging onto commercial aircraft, but they also have relevance to the increasing use of drones for hazard detection, research and monitoring. Uncooled infrared bolometric imaging cameras with spectroscopic capabilities are available and we describe one such system for use on airborne platforms.

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

Journal of Volcanology and Geothermal Research ◽

10.1016/j.jvolgeores.2009.02.010 ◽

2009 ◽

Vol 186 (1-2) ◽

pp. 79-90 ◽

Cited By ~ 23

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

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Sources: Antarctica and the Arctic Circle: A Geographic Encyclopedia of the Earth's Polar Regions

Reference & User Services Quarterly ◽

10.5860/rusq.54n4.79a ◽

2015 ◽

Vol 54 (4) ◽

pp. 79

Author(s):

Lisa Euster

Keyword(s):

Social Sciences ◽

Applied Sciences ◽

Large Range ◽

Arctic Region ◽

Natural Sciences ◽

The Arctic ◽

Central Feature ◽

Polar Regions ◽

Vast Area ◽

The Arctic Region

Although calling itself a geographic encyclopedia, the scope of this two-volume set is broader than such a designation suggests. Hund has attempted to encompass a large range of information about a vast area, perhaps a bit much for a modest two-volume set. Attempting to address in a meaningful way topics in the humanities, social sciences, natural sciences, and applied sciences for both poles in approximately 350 entries and fewer than 800 pages is ambitious. His stated "central feature . . . the original inhabitants of the Arctic region" (xi) would, alone, merit a work of this size. John Stewart's larger, two-volume Antarctica: An Encyclopedia, 2nd ed. (McFarland, 2011) is more limited in both geographical and topical scope.

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TROPOMI Aerosol Index: detailed aerosol plume tracking and plans for future development

10.5194/egusphere-egu2020-20883 ◽

2020 ◽

Author(s):

Deborah Stein Zweers ◽

Maarten Sneep ◽

Maurits Kooreman ◽

Piet Stammes ◽

Gijsbert Tilstra ◽

...

Keyword(s):

Volcanic Ash ◽

High Spatial Resolution ◽

Absolute Calibration ◽

Pixel Size ◽

Plume Tracking ◽

Using Data ◽

Aerosol Index ◽

Aerosol Plume ◽

Global Mean

<p>The aerosol index (AER_AI) as calculated using data from the Tropospheric Monitoring Instrument (TROPOMI) onboard the ESA Sentinel 5 Precursor (S5P) platform was publically released in July 2018. The operational AER_AI dataset is available from May 2018 through the present. It is a useful data product not only for tracking ultraviolet (UV) absorbing aerosol plumes of desert dust, volcanic ash, and smoke from biomass burning but also for monitoring the quality of the TROPOMI Level 1b (L1b) data since the AER_AI calculation is very sensitive to the absolute calibration of irradiance and radiance. The aim of this work is first to highlight the new level of detail seen in aerosol plume events based on the recent switch to a reduced pixel size of 3.5 x 5.5 km. Such high spatial resolution also presents specific challenges as non-Lambertian cloud features and 3-D effects of clouds are now visible in the TROPOMI AER_AI data. Plans for an approach to flag and correct these features in future AER_AI updates will be given. Secondly this work will include an overview of the impacts on AER_AI due to observed degradation in the TROPOMI measured irradiance and wavelength-dependent features in the radiance. As a result of these L1b effects, there is a steadily increasing negative bias in the global mean AER_AI value. Examples are given how the new version of the L1b data (2.0.0) will be used to correct for this degradation-driven bias. Recommendations are also given to guide data users looking to perform trend analysis or those using AER_AI as a filter for aerosol removal or detection in other L2 data products. &#160;</p>

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Circuit simulators for circuit analysis in graduate engineering courses

Proceedings of the 4th International Conference on Higher Education Advances (HEAd'18) ◽

10.4995/head18.2018.8030 ◽

2018 ◽

Author(s):

Càndid Reig ◽

María-Dolores Cubells-Beltrán

Keyword(s):

Applied Sciences ◽

Integrated Circuits ◽

Spice Simulation ◽

University System ◽

Pass Filter ◽

Low Pass Filter ◽

Source Codes ◽

Software Packages ◽

Traditional Market ◽

Low Pass

Circuit simulators are extensively used as an aid in many courses at the graduate level in many different engineering and applied sciences programs. SPICE (Simulation Program with Integrated Circuits Emphasis) based software programs have been used for long due to their traditional market position. If we focus on circuits analysis and linear systems subjects, the features that are required from a given simulator can be found in student/limited versions of commercial EDA (Electronic Design Automation) suites or in freeware/open source codes. In this contribution, we analyse and compare the most revelant characteristics of a representative set of the software packages that are commonly adopted in these courses, focusing on the Spanish University system. For this purpose, the analysis (transient, DC and AC) of a typical second order passive low-pass filter is approached making use of each one. Then, we give some comments and recommendations, based on our own expertise, always taking into account the particular circumstances within a given academic scenario.

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Interpretation of FRESCO cloud retrievals in case of absorbing aerosol events

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-11-32685-2011 ◽

2011 ◽

Vol 11 (12) ◽

pp. 32685-32721 ◽

Cited By ~ 1

Author(s):

P. Wang ◽

O. N. E. Tuinder ◽

L. G. Tilstra ◽

P. Stammes

Keyword(s):

Volcanic Ash ◽

Trace Gas ◽

Aerosol Layer ◽

Satellite Measurements ◽

Cloud Data ◽

Retrieval Scheme ◽

Absorbing Aerosols ◽

Aerosol Effects ◽

Aerosol Index ◽

Aerosol Types

Abstract. Cloud and aerosol information is needed in trace gas retrievals from satellite measurements. The Fast REtrieval Scheme for Clouds from the Oxygen A band (FRESCO) cloud algorithm employs reflectance spectra of the O2 A band around 760 nm to derive cloud pressure and effective cloud fraction. In general, clouds contribute more to the O2 A band reflectance than aerosols. Therefore, the FRESCO algorithm does not correct for aerosol effects in the retrievals and attributes the retrieved cloud information entirely to the presence of clouds, and not to aerosols. For events with high aerosol loading, aerosols may have a dominant effect, especially for almost cloud-free scenes. We have analysed FRESCO cloud data and Absorbing Aerosol Index (AAI) data from the Global Ozone Monitoring Experiment (GOME-2) instrument on the Metop-A satellite for events with typical absorbing aerosol types, such as volcanic ash, desert dust and smoke. We find that the FRESCO effective cloud fractions are correlated with the AAI data for these absorbing aerosol events and that the FRESCO cloud pressures contain information on aerosol layer pressure. For cloud-free scenes, the derived FRESCO cloud pressures are close to those of the aerosol layer for optically thick aerosols. For cloudy scenes, if the strongly absorbing aerosols are located above the clouds, then the retrieved FRESCO cloud pressures may represent the height of the aerosol layer rather than the height of the clouds. Combining FRESCO cloud data and AAI, an estimate for the aerosol layer pressure can be given, which can be beneficial for aviation safety and operations in case of e.g. volcanic ash plumes.

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