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SUMMARY:EESS talk on "Snowflakes of different size\, shape\, type and mass
 : the processes behind this variability\, measurement techniques and retri
 evals."
DTSTART:20211012T121500
DTEND:20211012T131500
DTSTAMP:20260928T190013Z
UID:24ce87a9c36d5684192ce6d6a5906dde04d490f171b6e901ea8556d9
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr Jacopo Grazioli\, Scientist\, Environmental Remote Sensing 
 Laboratory (LTE)\nAbstract:\nIndividual snow crystals or larger snowflakes
  naturally occur in an incredible variety of shapes\, sizes and types. The
  appearance of a snowflake\, as we observe it just before it deposits on t
 he ground\, is in fact the combined result of all the environmental condit
 ions encountered during its descent\, from the cloud to the ground\, and o
 f the mechanical interactions with other snowflakes or with liquid water d
 roplets.\nThere are several open questions regarding the small-scale micro
 physical properties of snowflakes\, many of them revolving around the esti
 mation of their mass or density and how it is varying or scaling with othe
 r quantities\, for example with size. This aspect is crucial for the param
 etrization of numerical weather models or climate models that cannot resol
 ve the processes occurring at the scale of individual snow particles. It i
 s of paramount importance also to appropriately interpret remote sensing d
 ata of snowfall collected by ground-based radars or satellites\, which are
  representative of large physical volumes and thus large populations of pa
 rticles.\n\nKnowledge often starts from observations. In the last years a 
 significant momentum for research in the field of snowfall microphysics ha
 s been promoted by the development of instruments designed to continuously
  collect high resolution images of (individual) snowflakes in free fall. T
 his talk focuses in particular on one of those instruments\, the Multi-Ang
 le Snowflake Camera (MASC).  It will be shown how high-resolution picture
 s can be used to automatically classify snowflakes\, to infer their physic
 al properties and\, in combination with simulations and machine learning t
 echniques\, even to reconstruct their mass and three-dimensional structure
 .\n\n\nShort biography:\nDr Jacopo Grazioli holds a PhD in Environmental S
 ciences and Engineering from the EPFL of Lausanne. He is working for the E
 nvironmental Remote Sensing (LTE) laboratory at EPFL in the field of remot
 e sensing applied to Alpine and Antarctic meteorology as well as for the S
 wiss Federal Office for Meteorology and Climatology (MeteoSwiss) to develo
 p meteorological services for the civil and military aviation.
LOCATION:https://epfl.zoom.us/j/63900222242?pwd=OXluejhzTklCbkdWakkvaUFCSG
 Vndz09
STATUS:CONFIRMED
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