Impact of WRF-3Dvar data assimilation on the prediction of rainfall over southern Brazil

Yoshihiro Yamasaki, Fabricio Pereira Harter, Vinicius Carvalho Beck


The  procedure  to  combine  mathematical  models  with  noise  data,  in  order  to  improve
numerical  weather  forecasting  by  statistical  methods,  is  an  important  and  challenging
meteorology research field, known as data assimilation. The 3DVAR approach, state of the art in
data  assimilation  technique,  is  applied  in  this  study.  The  aim  of  present  development  is  to
evaluate  the  results  of  the  data  assimilation  from  INMET  automatic  weather  stations  and
atmospheric soundings in the weather forecast produced by WRF model. The region of interest is
the South of Brazil. The specific aim is to evaluate the assimilation procedure of two precipitation
events  occurred  in  2012.  This  study  is  especially  important,  because  the  INMET  automatic
weather stations data are not transmitted by GTS. Therefore, these data were not assimilated by
prediction systems generated by global models, suchas GFS, which provides initial and boundary
conditions  for  regional  models,  such  as  WRF.  The  results  showed  that  the  WRF  with  data
assimilation procedure, reproduces satisfactorily the true synoptic scenario given by GFS model in
the two cases evaluated, and produces better forecasts then WRF without data assimilation. The
thermodynamic analysis showed that the WRF with data assimilation producing vertical profiles
of  air  temperature  and  dew  point  temperature  very  close  to  the  observed  profiles.  Additional
experiments  indicate  that  data  assimilated  from  other  sources,  in  addition  to  the  INMET
automatic  weather  stations  and  atmospheric  soundings,  as  well  as  the  increases  of  horizontal
resolution  in  the  integration  of  the  WRF  with  inclusion  of  subset,  provide  significant
improvements in weather forecasting fields.

Ключевые слова

data assimilation; 3DVAR; WRF

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