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DEPARTMENT OF ATMOSPHERIC PHYSICS C HARLES UNIVERSITY. REMOTE SENSING: DATA COMBINATION AS A KEY FOR STORM NOWCASTING. Michaela Valachov á, Hana Kyznarov á , Petr Nov á k, Martin Setv á k 9 th European Conference on Severe Storms Pula, Croatia, 20 September 201 7.
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DEPARTMENT OF ATMOSPHERIC PHYSICS CHARLES UNIVERSITY REMOTE SENSING:DATA COMBINATION AS A KEY FOR STORM NOWCASTING Michaela Valachová, Hana Kyznarová, Petr Novák, Martin Setvák 9th European Conference on Severe Storms Pula, Croatia, 20 September 2017 Central Forecasting Office, Prague michaela.valachova@chmi.cz
motivation of this work • data used in this study (source & software) • lightning detection network (CELDN & R) • satellites (EUMETSAT & McIDAS-V, Python) • radars (CHMI & CELLTRACK, R) • ESWD (ESSL & R) • example: severe vs. non-severe storm • summary, acknowledgements outline Suomi-NPP/VIIRS sandwich 2013-06-20 11:05 UTC
convective storms are challenging • where and when will storm evolve ? • how dangerous will it be ? • how long will it last ? • remote sensing is available • information every 5 min • years of experience and data • independent sources Forecaster has the confidence to issue warning in time !!! forecaster’s point of view
electrification, dynamics and microphysics connected → changes visible in all remote sensing data → NOWCASTING possible utilization
microphysical properties and dynamics Polarimetric Doppler radars (upgrade in 2015) • C band (λ ~ 5 cm), 12 elevations • resolution 1×1 km (whole domain) • many useful applications: • CELLTRACK, COTRACK • CELDN strokes • PrecipView, WarnView RADARS
RADARS - celltrack • reflectivity cores tracking algorithm • developed in CHMI (Hana Kyznarová) • For the presented study: • no tracking, just identification of cores • characteristics of cells: • threshold of 44 → 20 dBZ (isolated storms) • parameters: • AREA, VOL, VOL44 • HP, POSH, MESH, VIL Operational output of CELLTRACK (JSMeteoView) Kyznarová H., Novák P. (2009): CELLTRACK – Convective cell tracking algorithm and its use for deriving lifecycle characteristics, Atmospheric Research, vol. 93
CELDN (Central European Lightning Detection Network) • part of EUCLID, operated by Siemens AG • operatively used in CHMI lightning detection
microphysical properties, strength of updraft every stroke: type (CC, CG), time[ms], location, current amplitude estimation[kA] and polarity • detection efficiency: about 90 % or higher for CG • location accuracy: about 1 km for CG • uncertain estimate of current amplitude ~ tens of % • no stroke clustering into flashes lightning detection
microphysical properties and dynamics geostationary: • Meteosat/SEVIRI RSS polar orbiting: • Suomi-NPP/VIIRS • Aqua/MODIS • CloudSat/radar and CALIPSO/lidar, imagers satellites Aqua/MODIS 2013-06-20 12:25 UTC; hail occurrence at 12:26 UTC
reports from ESWD operated by ESSL • quality control: QC0+, QC1, QC2 • time uncertainty up to 15 min • only “positive events” severe weather reports
indicators showing storm development • rapid anvil spread • rapid cooling of the cloud-top • features on the cloud-top • distinctive overshooting top • ice plume • small ice particles • cold-U or cold-ring shape in IR-BT summary - satellites
lifetime and track of radar cell • radar derived parameters: • radar reflectivity, height of the lowest reflectivity • HP, POSH, MESH, VIL summary - radars
pulsation in number of strokes • multimodal histograms • two or more processes, spacing ~ 20 - 50 min • the first OT ~ the first peak in all strokes • severe weather occurrence • abrupt increase of CC/TL as a precursor • amplitude of strokes • non-severe storms mostly low amplitudes (< 15 kA) • the first CG+ amplitude > 20 kA when a significant change inside the storm • CC increase, plume formation, OT more frequent, radar reflectivity increase summary - lightning