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in the development of Mediterranean severe storms is detailed by Miglietta and Regano (2008) who investigated a flash flood event via numerical simulations using the Weather Research and Forecasting Model (WRF; Michalakes et al. 2004 ) developed at the National Center for Atmospheric Research (NCAR). Observations reported in Moscatello et al. (2008b) where a more intense phenomenon was described, point to the orographic flow deflection as the baroclinic origin of cyclones in the
in the development of Mediterranean severe storms is detailed by Miglietta and Regano (2008) who investigated a flash flood event via numerical simulations using the Weather Research and Forecasting Model (WRF; Michalakes et al. 2004 ) developed at the National Center for Atmospheric Research (NCAR). Observations reported in Moscatello et al. (2008b) where a more intense phenomenon was described, point to the orographic flow deflection as the baroclinic origin of cyclones in the
archive of the European Centre for Medium-Range Weather Forecasts (ECMWF). However, precipitation and radiation fields were produced by carefully combining low-time-resolution observationally based data with 6-hourly model diagnostic fields from reanalyses. Convective and large-scale precipitation fields from NCEP–NCAR reanalysis were rescaled so that the total precipitation for each month agreed with the monthly gridded observed precipitation from the Global Precipitation Climatology Project (GPCP
archive of the European Centre for Medium-Range Weather Forecasts (ECMWF). However, precipitation and radiation fields were produced by carefully combining low-time-resolution observationally based data with 6-hourly model diagnostic fields from reanalyses. Convective and large-scale precipitation fields from NCEP–NCAR reanalysis were rescaled so that the total precipitation for each month agreed with the monthly gridded observed precipitation from the Global Precipitation Climatology Project (GPCP
improvements documented above suggest that our system is adequately calibrated, although not necessarily optimal. We can shed more light on this issue by examining internal diagnostics from the data assimilation system (see Reichle et al. 2002 , 2010 for details). Here, we analyze two diagnostics based on the sequence of innovations, or observation-minus-forecast residuals. For a filter that operates according to its underlying assumptions (that various linearizations hold, and that model and
improvements documented above suggest that our system is adequately calibrated, although not necessarily optimal. We can shed more light on this issue by examining internal diagnostics from the data assimilation system (see Reichle et al. 2002 , 2010 for details). Here, we analyze two diagnostics based on the sequence of innovations, or observation-minus-forecast residuals. For a filter that operates according to its underlying assumptions (that various linearizations hold, and that model and
. , Nathan R. , Midgley G. F. , Fragoso J. M. , Lischke H. , and Thompson K. , 2005 : Forecasting regional to global plant migration in response to climate change . Bioscience , 55 , 749 – 759 . Nijssen, B. , O’Donnell G. M. , Lettenmaier D. P. , Lohmann D. , and Wood E. F. , 2001 : Predicting the discharge of global rivers . J. Climate , 14 , 3307 – 3323 . Norby, R. J. , and Luo Y. , 2004 : Evaluating ecosystem responses to rising atmospheric CO 2 and global warming in
. , Nathan R. , Midgley G. F. , Fragoso J. M. , Lischke H. , and Thompson K. , 2005 : Forecasting regional to global plant migration in response to climate change . Bioscience , 55 , 749 – 759 . Nijssen, B. , O’Donnell G. M. , Lettenmaier D. P. , Lohmann D. , and Wood E. F. , 2001 : Predicting the discharge of global rivers . J. Climate , 14 , 3307 – 3323 . Norby, R. J. , and Luo Y. , 2004 : Evaluating ecosystem responses to rising atmospheric CO 2 and global warming in
remaining land cover types in the X dataset but to the specific water-related land cover type in the Y dataset, and d is the area-weighted number of pixels classified to the remaining land cover types in both datasets. This index is called the threat score in meteorology and is widely used for categorical weather forecast evaluation ( Wilks 2006 ). The index correctly provides the degree of the per-pixel agreement even for the case where a ≪ d . In Eq. (1) , d does not appear because the
remaining land cover types in the X dataset but to the specific water-related land cover type in the Y dataset, and d is the area-weighted number of pixels classified to the remaining land cover types in both datasets. This index is called the threat score in meteorology and is widely used for categorical weather forecast evaluation ( Wilks 2006 ). The index correctly provides the degree of the per-pixel agreement even for the case where a ≪ d . In Eq. (1) , d does not appear because the
