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VERSION:2.0
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SUMMARY:Virtual Event: ECMWF/EUMETSAT NWP SAF Workshop on the treatment of
  random and systematic errors in satellite data assimilation for NWP
DTSTART:20201102T090000Z
DTEND:20201105T173000Z
DTSTAMP:20260518T191900Z
UID:indico-event-170@events.ecmwf.int
CONTACT:events@ecmwf.int
DESCRIPTION:\n\nWorkshop description\n\nDealing with random and systematic
  errors in observations and models is at the heart of satellite data assim
 ilation for Numerical Weather Prediction (NWP). This workshop captured rec
 ent progress and the state of the art in this area\, identified key issues
  and limitations\, and discussed avenues for further developments. It was 
 organised in collaboration with the EUMETSAT NWP SAF.\n\nThe workshop cove
 red the following main themes:\n\n1) Estimating uncertainties\n\nKnowledge
  about observational and model errors and uncertainties informs our treatm
 ent of these in the assimilation. This theme covered recent activities ran
 ging from thorough characterisation of satellite instruments\, better esti
 mation and modelling of other error contributions (such as radiative trans
 fer/observation operator errors or other representation errors)\, the use 
 of reference observations\, to advances in diagnostic methods.\n\n2) Corre
 ction of observational and model biases in data assimilation\n\nCorrection
  of observational biases is essential for the successful assimilation of m
 any satellite observations. Adaptive bias correction methods are now commo
 nly used\, but separation of observation and forecast model biases continu
 es to be challenging. This is particularly true in our present era with an
  ever increasing number of observations needing bias correction\, but also
  in systems with highly variable observational coverage such as reanalysis
 . In addition\, estimation of model bias during the assimilation is also b
 ecoming a reality\, further probing our ability to separate different sour
 ces of errors and biases. This theme covered practical experiences with th
 ese issues\, including approaches to introduce further prior knowledge to 
 constrain or aid the separation of different sources of bias.\n\n3) Repres
 enting observation errors in data assimilation\n\nThe description of the r
 andom component of observation error is key to optimising the impact of an
 y observation\, and this area has seen considerable progress in recent yea
 rs. Many satellite observations are strongly affected by representation er
 ror\, and this is often situation-dependent and correlated between differe
 nt observations. This is now increasingly taken explicitly into account\, 
 with the treatment of inter-channel error correlations for satellite radia
 nces\, for instance\, becoming wide-spread. The treatment of spatially and
  temporally correlated observation errors is also emerging. This poses que
 stions not only in terms of how to estimate and specify such correlations\
 , but also how to account for them in a computationally affordable way. Th
 is theme covered recent advances in the more complete specification of obs
 ervation errors\, including technical as well as conceptual challenges.\n\
 nWhile the primary application focus of the workshop was Numerical Weather
  Prediction (NWP)\, the way in which these issues impact climate re-analys
 es based on NWP systems were also be examined.\n\nWorkshop aims\n\nThe aim
  of this workshop was to capture the state of the art in the treatment of 
 random and systematic errors in satellite data assimilation for NWP\, to i
 dentify key issues and limitations\, and to identify avenues for further p
 rogress. Recent developments and open issues were covered through oral and
  poster presentations\, and targeted working groups were established to id
 entify and recommend future research needs. The output will be in the form
  of working group reports\, identifying key recommendations for NWP centre
 s.\n\n \n\n\n\nhttps://events.ecmwf.int/event/170/
IMAGE;VALUE=URI:https://events.ecmwf.int/event/170/logo-3953247138.png
LOCATION:ECMWF
URL:https://events.ecmwf.int/event/170/
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