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SUMMARY:Virtual Workshop: Warm Conveyor Belts – a challenge to forecasti
 ng
DTSTART:20200310T080000Z
DTEND:20200312T170000Z
DTSTAMP:20260315T175000Z
UID:indico-event-165@events.ecmwf.int
CONTACT:events@ecmwf.int
DESCRIPTION:\n\n#WCBWS\n\nDue to the measures associated with the COVID-19
  virus\, this workshop was held virtually for all external participants. \
 n\nWorkshop description\n\nWarm Conveyor Belts (WCBs) are cloudy regions o
 f ascent and strong diabatic forcing along the cold front of a synoptic de
 pression. They can lead to heavy local precipitation and can have downstre
 am impacts such as the onset and maintenance of blocking. They are therefo
 re important in both weather and climate prediction. However\, WCBs are al
 so associated with inherently increased error growth-rates\, and are diffi
 cult to constrain in analyses - due partly to clouds having a strong non-l
 inear impact on satellite observations and moist processes leading to larg
 er model errors. Indeed\, forecast “busts” are often associated with t
 he existence of WCBs. In the climate text\, model deficiencies in WCBs and
  in their large-scale drivers are likely to be particularly relevant\; wit
 h implications for the future statistics of precipitation\, heatwaves and 
 droughts for example. This workshop brought together observation\, assimil
 ation\, model\, forecast and research communities to explore these aspects
 .  The aim was to improve understanding and to help develop optimal strat
 egies to improve weather and climate prediction - a goal which would be ve
 ry difficult for a single such community to achieve on its own. The worksh
 op included invited and submitted talks\, and had a strong focus on poster
 s.\n\nProgramme and key questions\n\n1.  WCBs and downstream impacts\n\nW
 hat do we know about the formation\, dynamics and physics of WCBs and thei
 r downstream impacts? Is convection and upscale error growth important for
  the evolution and predictability of WCBs – with implications for the sc
 ales we need to represent in the model and constrain in data assimilation?
  Relationship to Atmospheric Rivers. Conceptual models.\n\n2.  Observatio
 ns\n\nNumerical weather prediction assimilates a wealth of observations\; 
 some sensitive to cloud and precipitation through the use of "all sky" met
 hods. What are the key observations which currently constrain WCBs? What a
 re the limits to how well they could constrain the relevant scales and par
 ameters?  Do WCBs strengthen the case for additional observations in futu
 re? EarthCARE\, Aeolus. Learn from NAWDEX and AR campaigns.\n\n3.  Models
  and model uncertainty\n\nHow well do model climates represent the dynamic
 s and physics of WCBs? What are the key sensitivities in model formulation
  and resolution (in the absence of initialisation)? Comparison with observ
 ations and reanalyses. Multi-model comparisons. Formulation and impact of 
 model uncertainty.\n\n4.  Data assimilation\n\nWhile WCBs might not highl
 ight useful developments in DA methodology per se\, there is a lot that di
 agnostics of data assimilation can tell us. How well do current assimilati
 on schemes constrain WCBs? Where might the largest achievable improvements
  be made amongst the prior (background)\, model (non-linear\, tangent line
 ar and model uncertainty) and observational components? Ensemble data assi
 milation. Adjoint sensitivity. Forecast Sensitivity - Observation Impact (
 FSOI). Initial process tendencies and analysis increments. Multi-analysis 
 comparisons.\n\n5.  Weather forecasting\n\nHow well are the dynamical evo
 lution (including downstream impacts) and physical aspects predicted at pr
 esent? Comparison with observational campaign data. Evaluation of ensemble
  forecast reliability\, refinement and sharpness. Role of model uncertaint
 y. What are the limits and challenges? Multi-model comparisons of ensemble
  forecasts (including TIGGE).\n\n6.  Climate variability and change\n\nFr
 om observations/reanalyses what broader-scale features are associated with
  variations in WCB statistics? How well do models at seasonal/climate reso
 lution represent these links?  What can we infer about the statistics of 
 WCBs (and their downstream impacts) in seasonal/climate predictions?\n\n7.
   Break out groups and plenary\n\nFurther consideration of the above ques
 tions (in the light of talks and posters) and report back.\n\nOrganising c
 ommittee\n\nStephen English\, Laura Ferranti\, Richard Forbes\, Christian 
 Grams\, David Lavers\, Linus Magnusson\, Mark Rodwell\, Irina Sandu\, Hein
 i Wernli\n\nhttps://events.ecmwf.int/event/165/
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URL:https://events.ecmwf.int/event/165/
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