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being designed. b. The numerical model WRF The numerical system used for this study is the Advanced Research WRF (ARW-WRF), version 2.2 ( Skamarock et al. 2005 ; Michalakes et al. 2004 ; more information is available online at http://www.wrf-model.org ), which is a mesoscale numerical weather prediction (NWP) model for operational forecasting and atmospheric research needs. The simulations are carried out using a two-way nesting technique; two grid domains, the outer with a horizontal resolution
being designed. b. The numerical model WRF The numerical system used for this study is the Advanced Research WRF (ARW-WRF), version 2.2 ( Skamarock et al. 2005 ; Michalakes et al. 2004 ; more information is available online at http://www.wrf-model.org ), which is a mesoscale numerical weather prediction (NWP) model for operational forecasting and atmospheric research needs. The simulations are carried out using a two-way nesting technique; two grid domains, the outer with a horizontal resolution
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
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
