000005678 001__ 5678
000005678 005__ 20220202105735.0
000005678 037__ $$aPOSTER-2022-0023
000005678 100__ $$aBhattacharya, S
000005678 245__ $$aQuality Assessment of Sunspot data using various catalogs
000005678 260__ $$c2021
000005678 269__ $$c2021-05-21
000005678 520__ $$aThe SN series is one of the longest and most detailed available series in astrophysics. The series was first constructed in 1849 by Prof. Rudolf Wolf and a time series is built in real time since then, involving a lot of observers who differ from each other in terms of their way of counting sunspots, different telescopes and eye sights, which demands proper calibration. We present a novel time-dependent error determination on the sunspot number (SN) based on nonparametric statistical techniques in smoothing. In particular we propose a generalized linear regression model with overdispersed count data as response variables in the estimation of a time varying calibration of different sunspot time series with overlapping periods. The nonparametric regression takes place through a local polynomial smoothing procedure. This data assimilation model does not restrict to SN only but is applicable to most sunspots’ parameters such as area covered on the Sun, position etc... Many catalogs such as the one from Catania Observatory, the Royal Greenwich Observatory, the US Air Force solar observations and others, include the above mentioned parameters whose overall quality assessment is lacking as of yet. The time dependence criteria of our model allows us to access the quality of daily observations with respect to other catalogs thus adding an error bar. We focus this study on one of the stable stations (Mathieu et al,2019) of the World Data Center SILSO network, the Uccle Solar Equatorial table station in Brussels (USET). The study we present aims at a further refinement of earlier work in Mandal et al. (2020), adding a realistic statistical model and a time dependent calibration factor. Applying regression before computing the calibration avoids the presence of outliers and biased estimation.
000005678 536__ $$a3FULLCOST/$$c3FULLCOST/$$f3FULLCOST
000005678 594__ $$aNO
000005678 700__ $$aJansen, M
000005678 700__ $$aLef`evre, L
000005678 700__ $$aClette, F
000005678 773__ $$tApplications of Statistical Methods and Machine Learning in the Space Sciences hosted by Space Science Institute, Boulder, Colorado (virtual)
000005678 8560_ $$fshreya.bhattacharya@observatoire.be
000005678 85642 $$ahttp://spacescience.org/workshops/mlconference2021/AllEposters_F.pdf
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