Operational Use of Satellite Measurements in Volcano Observatory

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Plume height UV remote sensing UV remote sensing Plume height Challenges Missions Overview Missions Plume detection Nasa API Plume detection satellite SO Nasa API Framework Framework Computing Data Data Unwrap everything

Beer-Lambert law 170 - 230 nm Plume height Assumption Plume height Assumption UV SO absorption 240 - 340 nm Temperature dependant UV remote sensing Concentration dependant UV radiance / irradiance UV radiance / irradiance Return to root tree Return to root tree

Irradiance Radiance = backscattering + reflected scattering Radiance / Irradiance absorption SO deduction Back to UV rem. Sens. Back to UV rem. Sens.

Atmosphere is heterogenous Multifactorial: O , pressure, temperature, thickness, etc. Assumption about plume height Concentration / Thickness Concentration / thickness Back to UV rem. Sens. Back to UV rem. Sens.

TOMS OMI Start: 10-24-1978 Nimbus-7 Start: 07-15-2004 Aura 15 y. of data Still in service Missions OMPS TROPOMI Start: 10-28-2011 Start: 10-13-2017 S5P SNPP Still in service Still in service Return to root tree Go to glossary Return to root tree Go to glossary

Python Multiprocessed Specs As software As framework Software Demo Demo Return to root tree Return to root tree

You can try it online at https://nasaframework. mastiny.fr This presentation is available too Return to root tree Return Software Return to root tree Return Software

Pacaya (Guatemala), March-April 2021 Date 03-27-2021 03-28-2021 03-29-2021 03-30-2021 03-31-2021 04-01-2021 04-02-2021 04-03-2021 SO2 amount (tons) SO2 uncertainty (tons) 56.82* 44.34* 8.64* 2.61* 242**** 1.93* 344**** 56.6** 2.59* 0.924 1 190**** 6.263 1 892**** 200.9** 28.83* 2.355 6084**** 1074****

Pacaya (Guatemala), March 31st, 2021 Wind field (m/sec) 0.0 10 SO amount (tons) 200 km 0.012 100 km 0 km 0.000

Etna (Sicily), September 29th, 2019 Wind field (m/sec) 0.0 10 SO amount (tons) 200 km 0.012 100 km 0.000 Return to root tree 0 km Return to root tree

Challenges Community built database Text mining on reports Plume Height Variability of plume height Ideas Assumption on plume height Manual input is tedious Only low clouds supported Return to root tree Return to root tree

Plume mixing challenges Deep learning / Neural network Simulations for creating fake training data Plume Detection Ideas Return to root tree Return to root tree

_____________________________________ | studied plume | | | | | | | | | | foreign | plume | | | | | | |____________________________________| Raster limits _____________________________________ | studied plume | | | | | foreign | | | | |____________________________________| Raster limits | | | | | | | | | | | plume | Return to root tree Return to plume detection Return to root tree Return to plume detection

Glossary OMI: Ozone Monitoring Instrument S5P: Sentinel-5 Precursor OMPS: Ozone Mapping and Profiler Suite TOMS : Total Ozone Monitoring Instrument SNPP: Suomi National Polar-orbiting Partnership TROPOMI: TROPOspheric Monitoring Instrument Return to root tree Return to missions Return to root tree Return to missions