METEOROLOGICAL DATA PERSPECTIVE
359
per data type, of by geographical areas, or even by parameters (Temperature,
humidity, wind…). Such a diagnostic can also be used to perform optimal
observation selection and error tuning. Looking forward to the future, we
are now in a position to further optimise the use of observations, including
more flow-dependency and a more interactive forecast system through the
WMO programme THORPEX. The basic idea beyond this programme is to
use the forecast system itself to predict where and when additional
observations or a better treatment of planned observations would bring a
major improvement in the forecasting of high-impact weather likely to have
a high economic and societal impact. The NWP community has achieved a
major improvement in average forecast scores in the last decade and is now
mature enough to concentrate some of its efforts on the challenge of
improving the forecast of rare events such as storms and floods.
Acknowledgements
This paper has benefited from contributions from Zhiquan Liu (Chinese
Meteorological Administration), Nadia Fourrié (CNRS), Bernard Chapnik
(CNRM), Adrian Simmons, Jean-Noël Thépaut and Carla Cardinali
(ECMWF). Students and Gérald Desroziers (CNRM) are acknowledged for
their review of the manuscript.
References
Aires, F., A. Chédin and N. A. Scott, 2002: A regularized neural net approach for retrieval of
Andersson, E., M. Fisher, R. Munro and A. McNally, 2000: Diagnosis of background errors
for radiances and other observable quantities in a variational data assimilation scheme, and
the explanation of a case of poor convergence. Quart. J. Roy. Meteor. Soc., 126, pp 14551472.
Baker, N. L, and Daley, R., 2000: Observation and background adjoint sensitivity in the
adaptive observation targeting problem. Q. J. R. Meteor. Soc., 126, 1431-1454.
Bouttier, F. and G. Kelly, 2001: Observing-system experiments in the ECMWF 4D-Var data
assimilation system. Quart. J. Roy. Meteor. Soc., 127, pp 1469-1488.
Cardinali, C., S. Pezzulli and E. Andersson, 2003: Influence matrix diagnostic of a data
assimilation system. ECMWF Seminar on Recent developments in data assimilation for
atmosphere and ocean.
Cardinali, C., S. Pezzulli and E. Andersson, 2005: Influence matrix diagnostic of a data
assimilation system. Quart. J. Roy. Meteor. Soc., to appear.
Chapnik, B., G. Desroziers, F.Rabier, and O.Talagrand. 2004: Properties and first applications
of an error statistic tuning method in variationnal assimilation. Quart. J. Roy. Meteor. Soc.
130, pp 2253-2276.
Chapnik, B., G. Desroziers, F.Rabier and O.Talagrand. 2005: Diagnosis and tuning of
observational error statistics in a quasi operational data assimilation setting. Quart. J. Roy.
Meteor. Soc. Accepted for publication.
Dee, D., L. Rukhovets, R. Todling, A. Da Silva and J. Larson, 2001: An adaptive buddy
check for observational quality control. Quart. J. Roy. Meteor. Soc., 127, pp 2451-2471.
atmospheric and surface temperature with the IASI instrument. J. Appl. Meteorol, 41,
pp 144-159.
359
per data type, of by geographical areas, or even by parameters (Temperature,
humidity, wind…). Such a diagnostic can also be used to perform optimal
observation selection and error tuning. Looking forward to the future, we
are now in a position to further optimise the use of observations, including
more flow-dependency and a more interactive forecast system through the
WMO programme THORPEX. The basic idea beyond this programme is to
use the forecast system itself to predict where and when additional
observations or a better treatment of planned observations would bring a
major improvement in the forecasting of high-impact weather likely to have
a high economic and societal impact. The NWP community has achieved a
major improvement in average forecast scores in the last decade and is now
mature enough to concentrate some of its efforts on the challenge of
improving the forecast of rare events such as storms and floods.
Acknowledgements
This paper has benefited from contributions from Zhiquan Liu (Chinese
Meteorological Administration), Nadia Fourrié (CNRS), Bernard Chapnik
(CNRM), Adrian Simmons, Jean-Noël Thépaut and Carla Cardinali
(ECMWF). Students and Gérald Desroziers (CNRM) are acknowledged for
their review of the manuscript.
References
Aires, F., A. Chédin and N. A. Scott, 2002: A regularized neural net approach for retrieval of
Andersson, E., M. Fisher, R. Munro and A. McNally, 2000: Diagnosis of background errors
for radiances and other observable quantities in a variational data assimilation scheme, and
the explanation of a case of poor convergence. Quart. J. Roy. Meteor. Soc., 126, pp 14551472.
Baker, N. L, and Daley, R., 2000: Observation and background adjoint sensitivity in the
adaptive observation targeting problem. Q. J. R. Meteor. Soc., 126, 1431-1454.
Bouttier, F. and G. Kelly, 2001: Observing-system experiments in the ECMWF 4D-Var data
assimilation system. Quart. J. Roy. Meteor. Soc., 127, pp 1469-1488.
Cardinali, C., S. Pezzulli and E. Andersson, 2003: Influence matrix diagnostic of a data
assimilation system. ECMWF Seminar on Recent developments in data assimilation for
atmosphere and ocean.
Cardinali, C., S. Pezzulli and E. Andersson, 2005: Influence matrix diagnostic of a data
assimilation system. Quart. J. Roy. Meteor. Soc., to appear.
Chapnik, B., G. Desroziers, F.Rabier, and O.Talagrand. 2004: Properties and first applications
of an error statistic tuning method in variationnal assimilation. Quart. J. Roy. Meteor. Soc.
130, pp 2253-2276.
Chapnik, B., G. Desroziers, F.Rabier and O.Talagrand. 2005: Diagnosis and tuning of
observational error statistics in a quasi operational data assimilation setting. Quart. J. Roy.
Meteor. Soc. Accepted for publication.
Dee, D., L. Rukhovets, R. Todling, A. Da Silva and J. Larson, 2001: An adaptive buddy
check for observational quality control. Quart. J. Roy. Meteor. Soc., 127, pp 2451-2471.
atmospheric and surface temperature with the IASI instrument. J. Appl. Meteorol, 41,
pp 144-159.