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Archive
by Martinet, P.
Abstract:
Nowadays, most data assimilated in numerical weather prediction come from satellite observations. However, the exploitation of satellite data is still sub-optimal with only 10 to 15% of these data assimilated operationally. Keeping in mind that about 80% of infrared data are affected by clouds, it is a priority to develop the assimilation of cloud-affected satellite data. The hyperspectral infrared sounder IASI has already contributed to the improvement of weather forecasts thanks to its far better spectral resolution and information content compared to previous instruments. The use of cloud-affected IASI radiances is still very complicated due to the high non-linearity of clouds in the infrared. This PhD work suggests an innovative way to take advantage of cloud-affected radiances observed by IASI. An advanced radiative transfer model using cloud microphysical properties has been evaluated. This method has the advantage of using cloud water content profiles directly produced by numerical weather prediction models. Thanks to this new scheme, profiles of cloud water contents have been successfully retrieved from IASI cloud-affected radiances with a one dimensional variational assimilation scheme (1D-Var). The impact of these data in terms of analysis and evolution of cloud variables has been evaluated in a numerical weather prediction model. This study is the first step in evaluating the choice that has been made for the control variables used during the retrievals. A simplified one-dimensional version of the AROME model was used to run three-hour forecasts from the 1D-Var analysed profiles. Promising results have shown a good maintenance of the analysis increment during more than one hour and a half of forecast. In regard to these encouraging results, a positive impact on nearcasting applications and forecasts of heavy rainfall events, which are highly coupled to cloud variables, can be expected in the future.
Reference:
Martinet, P., 2013: Apport des observations IASI pour la description des variables nuageuses du modèle AROME dans le cadre de la campagne HyMeXPhD thesis, Institut National Polytechnique de Toulouse.
Bibtex Entry:
@Phdthesis{Martinet2013a,
  Title                    = {Apport des observations IASI pour la description des variables nuageuses du modèle AROME dans le cadre de la campagne HyMeX},
  Author                   = {Martinet, P.},
  Beginningdate            = {2010},
  Country                  = {France},
  Enddate                  = {2013},
  Funding                  = {FCPLR},
  Laboratory               = {CNRM-GAME},
  Location                 = {Toulouse},
  School                   = {Institut National Polytechnique de Toulouse},
  Supervisors              = {N. Fourrié, F. Rabier (CNRM)},
  Supervisorsaffiliations  = {CNRM},
  Year                     = {2013},

  Address                  = {pauline.martinet@meteo.fr;},
  Jointdegree              = {No},

  Abstract                 = {Nowadays, most data assimilated in numerical weather prediction come from satellite observations. However, the exploitation of satellite data is still sub-optimal with only 10 to 15% of these data assimilated operationally. Keeping in mind that about 80% of infrared data are affected by clouds, it is a priority to develop the assimilation of cloud-affected satellite data. The hyperspectral infrared sounder IASI has already contributed to the improvement of weather forecasts thanks to its far better spectral resolution and information content compared to previous instruments. The use of cloud-affected IASI radiances is still very complicated due to the high non-linearity of clouds in the infrared. This PhD work suggests an innovative way to take advantage of cloud-affected radiances observed by IASI. An advanced radiative transfer model using cloud microphysical properties has been evaluated. This method has the advantage of using cloud water content profiles directly produced by numerical weather prediction models. Thanks to this new scheme, profiles of cloud water contents have been successfully retrieved from IASI cloud-affected radiances with a one dimensional variational assimilation scheme (1D-Var). The impact of these data in terms of analysis and evolution of cloud variables has been evaluated in a numerical weather prediction model. This study is the first step in evaluating the choice that has been made for the control variables used during the retrievals. A simplified one-dimensional version of the AROME model was used to run three-hour forecasts from the 1D-Var analysed profiles. Promising results have shown a good maintenance of the analysis increment during more than one hour and a half of forecast. In regard to these encouraging results, a positive impact on nearcasting applications and forecasts of heavy rainfall events, which are highly coupled to cloud variables, can be expected in the future.},
  Owner                    = {hymexw},
  Timestamp                = {2016.01.08}
}