Search Results
and Kalnay 1993 ; Mullen and Baumhefner 1988 ; Houtekamer and Derome 1995 ; Du et al. 1997 ). Indeed, errors of any origin can grow rapidly during the quantitative precipitation forecasting and steer toward misleading predictions, especially when fast-growing modes, such as those leading mesoscale convective developments, are dominant for the predicted field. Therefore, QPF is highly sensitive to errors in the initial conditions (ICs), lateral boundary conditions (LBCs), and model physical
and Kalnay 1993 ; Mullen and Baumhefner 1988 ; Houtekamer and Derome 1995 ; Du et al. 1997 ). Indeed, errors of any origin can grow rapidly during the quantitative precipitation forecasting and steer toward misleading predictions, especially when fast-growing modes, such as those leading mesoscale convective developments, are dominant for the predicted field. Therefore, QPF is highly sensitive to errors in the initial conditions (ICs), lateral boundary conditions (LBCs), and model physical
assimilated were sparse, a result first discussed in Hamill and Snyder (2000) . At finer grid spacing (30 km), Meng and Zhang (2008a) and Meng and Zhang (2008b) showed improved forecast performance with an EnKF over that of 3DVAR with the Weather Research and Forecasting (WRF) Model for a mesoscale convective vortex and a month-long experiment verifying against radiosonde data. Meng and Zhang (2008b) assimilated only radiosonde data (relatively sparse), whereas Meng and Zhang (2008a) assimilated
assimilated were sparse, a result first discussed in Hamill and Snyder (2000) . At finer grid spacing (30 km), Meng and Zhang (2008a) and Meng and Zhang (2008b) showed improved forecast performance with an EnKF over that of 3DVAR with the Weather Research and Forecasting (WRF) Model for a mesoscale convective vortex and a month-long experiment verifying against radiosonde data. Meng and Zhang (2008b) assimilated only radiosonde data (relatively sparse), whereas Meng and Zhang (2008a) assimilated
framework mimicking that of the operational North American Mesoscale Forecast System (NAM), McCarty et al. (2009) showed at 48 h a forecast improvement in geopotential height at 500 hPa, defined as the time difference in hours at which the forecasts fall below two points of equal anomaly correction, is 2.3 h. They also showed improvement of 8% and 7% in equitable threat and bias scores of precipitation forecasts of 25 mm (6 h) −1 . Using a similar framework, Lim et al. (2014) showed improvement in
framework mimicking that of the operational North American Mesoscale Forecast System (NAM), McCarty et al. (2009) showed at 48 h a forecast improvement in geopotential height at 500 hPa, defined as the time difference in hours at which the forecasts fall below two points of equal anomaly correction, is 2.3 h. They also showed improvement of 8% and 7% in equitable threat and bias scores of precipitation forecasts of 25 mm (6 h) −1 . Using a similar framework, Lim et al. (2014) showed improvement in
of this paper. In the few studies that exist in the literature, the quality of the wind radii estimates provided in the TC vitals has been found to have an impact on TC-focused NWP forecasts. For example, Kunii (2015) found that the inclusion of wind radii data helped improve TC track forecasts in the Japan Meteorological Agency’s (JMA) operational mesoscale model. Also, Marchok et al. (2012) showed that modifying the observed 34- and 50-kt wind radii used to initialize the GFDL hurricane
of this paper. In the few studies that exist in the literature, the quality of the wind radii estimates provided in the TC vitals has been found to have an impact on TC-focused NWP forecasts. For example, Kunii (2015) found that the inclusion of wind radii data helped improve TC track forecasts in the Japan Meteorological Agency’s (JMA) operational mesoscale model. Also, Marchok et al. (2012) showed that modifying the observed 34- and 50-kt wind radii used to initialize the GFDL hurricane
; Thorarinsdottir and Gneiting 2010 ; Thorarinsdottir and Johnson 2012 ). However, in many of the aforementioned applications it is important to honor the full information about the bivariate structure of the future wind vector that is provided by the ensemble. Thus, our EMOS postprocessed forecasts take the form of elliptically symmetric bivariate normal densities, as illustrated in Fig. 1 in an application to the University of Washington Mesoscale Ensemble (UWME; Eckel and Mass 2005 ). A description of
; Thorarinsdottir and Gneiting 2010 ; Thorarinsdottir and Johnson 2012 ). However, in many of the aforementioned applications it is important to honor the full information about the bivariate structure of the future wind vector that is provided by the ensemble. Thus, our EMOS postprocessed forecasts take the form of elliptically symmetric bivariate normal densities, as illustrated in Fig. 1 in an application to the University of Washington Mesoscale Ensemble (UWME; Eckel and Mass 2005 ). A description of
frontogenesis northwest of the surface cyclone ( Fig. 1b ). These results are also consistent with the case study work of Martin (1998a , b ), Banacos (2003) , and Moore et al. (2005) , who have documented similar synoptic and mesoscale flow evolutions in case studies of mesoscale banding in the central and eastern United States. These emerging conceptual models of the synoptic and mesoscale flow environments conducive to band formation are providing forecasters with an awareness of the potential for
frontogenesis northwest of the surface cyclone ( Fig. 1b ). These results are also consistent with the case study work of Martin (1998a , b ), Banacos (2003) , and Moore et al. (2005) , who have documented similar synoptic and mesoscale flow evolutions in case studies of mesoscale banding in the central and eastern United States. These emerging conceptual models of the synoptic and mesoscale flow environments conducive to band formation are providing forecasters with an awareness of the potential for
that can be resolved at convection-allowing model resolutions. For example, the number of mesoscale surface observation networks (i.e., mesonets) have increased during the past several years, and methods to gather, quality control, and distribute these observations in real time have matured. These networks often provide data at higher spatial and temporal resolution than conventional observing systems (e.g., Automated Surface Observing Systems), and are routinely used by forecasters in real time to
that can be resolved at convection-allowing model resolutions. For example, the number of mesoscale surface observation networks (i.e., mesonets) have increased during the past several years, and methods to gather, quality control, and distribute these observations in real time have matured. These networks often provide data at higher spatial and temporal resolution than conventional observing systems (e.g., Automated Surface Observing Systems), and are routinely used by forecasters in real time to
the Advanced Research version (ARW) of the next-generation mesoscale Weather Research and Forecast Model (WRF) currently being developed and employed at the National Center for Atmospheric Research ( Skamarock et al. 2005 ). Two model domains coupled through two-way nesting are employed; the fine (coarse) domain has a horizontal grid spacing of 4.5 (13.5) km covering areas of 2700 × 2400 (8100 × 7200) km 2 . The model has 35 vertical levels with physics configurations that are the same as those
the Advanced Research version (ARW) of the next-generation mesoscale Weather Research and Forecast Model (WRF) currently being developed and employed at the National Center for Atmospheric Research ( Skamarock et al. 2005 ). Two model domains coupled through two-way nesting are employed; the fine (coarse) domain has a horizontal grid spacing of 4.5 (13.5) km covering areas of 2700 × 2400 (8100 × 7200) km 2 . The model has 35 vertical levels with physics configurations that are the same as those
complicated in complex terrain. Liu et al. (2008) conducted an interrange comparison of the model analyses and forecasts of five U.S. Army test and evaluation command ranges over a 5-yr period. They concluded that forecast errors vary from range to range and season to season. They also found that larger errors are typically associated with complex terrain. Zhong and Fast (2003) compared three mesoscale numerical models and evaluated the simulations over the Salt Lake Valley for cases influenced by
complicated in complex terrain. Liu et al. (2008) conducted an interrange comparison of the model analyses and forecasts of five U.S. Army test and evaluation command ranges over a 5-yr period. They concluded that forecast errors vary from range to range and season to season. They also found that larger errors are typically associated with complex terrain. Zhong and Fast (2003) compared three mesoscale numerical models and evaluated the simulations over the Salt Lake Valley for cases influenced by
development. As the temporal and spatial scales of interest become smaller, many more instabilities and processes are known to play a significant role, at least intermittently, in the evolution of the atmosphere at a given location. The skill of numerical models in predicting the variety of mesoscale and small-scale phenomena is not well known, since our ability to observe these phenomena is limited. However, if one examines summertime quantitative precipitation forecasts associated with mesoscale
development. As the temporal and spatial scales of interest become smaller, many more instabilities and processes are known to play a significant role, at least intermittently, in the evolution of the atmosphere at a given location. The skill of numerical models in predicting the variety of mesoscale and small-scale phenomena is not well known, since our ability to observe these phenomena is limited. However, if one examines summertime quantitative precipitation forecasts associated with mesoscale
