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1. Introduction Mesoscale convective systems (MCSs) are well known for their potential hazards, including flooding, severe winds, hail, and even tornadoes ( Maddox 1983 ; Johns and Hirt 1987 ; Houze et al. 1990 ; Doswell et al. 1996 ; Fritsch and Forbes 2001 ). To better anticipate and forecast these impacts, past studies have emphasized understanding the most fundamental dynamics of these systems, often neglecting other higher-order complications such as environmental heterogeneity or
1. Introduction Mesoscale convective systems (MCSs) are well known for their potential hazards, including flooding, severe winds, hail, and even tornadoes ( Maddox 1983 ; Johns and Hirt 1987 ; Houze et al. 1990 ; Doswell et al. 1996 ; Fritsch and Forbes 2001 ). To better anticipate and forecast these impacts, past studies have emphasized understanding the most fundamental dynamics of these systems, often neglecting other higher-order complications such as environmental heterogeneity or
evaluated either in specific case studies (e.g., Gao et al. 1999 ; Montmerle et al. 2002 ; Sun 2005 ) or in simulations of the usefulness of observing systems (e.g., Sokolovskiy et al. 2005 ; Tong and Xue 2005 ). But to the author’s knowledge, nothing has been done to systematically study the occurrence of a measurable and usable signal in the data itself, at least in the context of the mesoscale forecasting of convection. Both measurability and usability are important. Measurability refers
evaluated either in specific case studies (e.g., Gao et al. 1999 ; Montmerle et al. 2002 ; Sun 2005 ) or in simulations of the usefulness of observing systems (e.g., Sokolovskiy et al. 2005 ; Tong and Xue 2005 ). But to the author’s knowledge, nothing has been done to systematically study the occurrence of a measurable and usable signal in the data itself, at least in the context of the mesoscale forecasting of convection. Both measurability and usability are important. Measurability refers
1. Introduction Regional operational numerical weather prediction (NWP) systems such as the Weather Research and Forecasting (WRF) Model and the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) provide forecasters and local weather consumers with high-resolution gridded mesoscale forecasts on a regular basis. However, various substantial systematic errors, or biases, which are present in all numerical weather prediction systems
1. Introduction Regional operational numerical weather prediction (NWP) systems such as the Weather Research and Forecasting (WRF) Model and the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) provide forecasters and local weather consumers with high-resolution gridded mesoscale forecasts on a regular basis. However, various substantial systematic errors, or biases, which are present in all numerical weather prediction systems
Fritsch 1993 ), 2) Betts–Miller–Janjić (BMJ; Betts 1986 ; Betts and Miller 1986 ; Janjić 1994 ), and 3) Grell–Devenyi (GD; Grell and Devenyi 2002 ). ENS20 and ENS20 phys ensemble member specifications are provided in Tables 3 and 4 , respectively. For the SSEF control member, the 2100 UTC analyses from NCEP’s operational North American Mesoscale (NAM; Janjić 2003 ) model (at 12-km grid spacing) were used for the ICs and the 1800 UTC NAM 12-km forecasts were used for the LBCs. For the members
Fritsch 1993 ), 2) Betts–Miller–Janjić (BMJ; Betts 1986 ; Betts and Miller 1986 ; Janjić 1994 ), and 3) Grell–Devenyi (GD; Grell and Devenyi 2002 ). ENS20 and ENS20 phys ensemble member specifications are provided in Tables 3 and 4 , respectively. For the SSEF control member, the 2100 UTC analyses from NCEP’s operational North American Mesoscale (NAM; Janjić 2003 ) model (at 12-km grid spacing) were used for the ICs and the 1800 UTC NAM 12-km forecasts were used for the LBCs. For the members
weather forecasts of ARs has been documented, primarily at the synoptic scales where ARs are a recognizable feature of the extratropical circulation ( Wick et al. 2013 ; Nayak et al. 2014 ; Guan and Waliser 2015 ; DeFlorio et al. 2018 ). Mesoscale phenomena also create challenges when forecasting AR impacts, including precipitation ( Leung and Qian 2009 ; Neiman et al. 2009 ; Ralph et al. 2013 ; Martin et al. 2018 ). One such mesoscale phenomena is the mesoscale frontal wave ( Parker 1998
weather forecasts of ARs has been documented, primarily at the synoptic scales where ARs are a recognizable feature of the extratropical circulation ( Wick et al. 2013 ; Nayak et al. 2014 ; Guan and Waliser 2015 ; DeFlorio et al. 2018 ). Mesoscale phenomena also create challenges when forecasting AR impacts, including precipitation ( Leung and Qian 2009 ; Neiman et al. 2009 ; Ralph et al. 2013 ; Martin et al. 2018 ). One such mesoscale phenomena is the mesoscale frontal wave ( Parker 1998
model were necessary in order to accurately predict some of the other atmospheric variables, such as wind speed. Similarly, Bromwich et al. (2005) analyzed the performance of the polar-modified version of the MM5 model used in the Antarctic Mesoscale Prediction System (AMPS). The analysis looked at the spatial variability, seasonal variability, and forecast hour variability associated with the model performance. The study found that the surface temperature predictions are most accurate during the
model were necessary in order to accurately predict some of the other atmospheric variables, such as wind speed. Similarly, Bromwich et al. (2005) analyzed the performance of the polar-modified version of the MM5 model used in the Antarctic Mesoscale Prediction System (AMPS). The analysis looked at the spatial variability, seasonal variability, and forecast hour variability associated with the model performance. The study found that the surface temperature predictions are most accurate during the
forecasting. In this study, we are focusing on the determination of initial fields for mesoscale atmospheric modeling. Here, mathematical problems are indeed still an issue, as pointed out (e.g., in Rosatti et al. 2005 ; Steppeler et al. 2006 ). Furthermore, mesoscale forecasts are highly sensitive to the quality of model physics including land surface exchange ( Cheng and Cotton 2004 ; Trier et al. 2004 ; Holt et al. 2006 ), boundary layer properties ( Bright and Mullen 2002 ; Berg and Zhong 2005
forecasting. In this study, we are focusing on the determination of initial fields for mesoscale atmospheric modeling. Here, mathematical problems are indeed still an issue, as pointed out (e.g., in Rosatti et al. 2005 ; Steppeler et al. 2006 ). Furthermore, mesoscale forecasts are highly sensitive to the quality of model physics including land surface exchange ( Cheng and Cotton 2004 ; Trier et al. 2004 ; Holt et al. 2006 ), boundary layer properties ( Bright and Mullen 2002 ; Berg and Zhong 2005
1. Introduction For more than a decade, there has been a great demand to understand the significance of predictability in numerical weather prediction models, as the predictability issues are related to the forecast skill. Because of the complex interactions of dynamical, radiative, and microphysical processes that occur on small spatial and temporal scales, realistic simulation of the cloud-capped marine boundary layer remains a potential challenge for most mesoscale models and even more so
1. Introduction For more than a decade, there has been a great demand to understand the significance of predictability in numerical weather prediction models, as the predictability issues are related to the forecast skill. Because of the complex interactions of dynamical, radiative, and microphysical processes that occur on small spatial and temporal scales, realistic simulation of the cloud-capped marine boundary layer remains a potential challenge for most mesoscale models and even more so
, and energy exchange ( Farquhar et al. 1980 ; Collatz et al. 1991 , 1992 ; Anderson et al. 2000 ; see also Niyogi and Raman 1997 ). These schemes generally have been applied in leaf/canopy scale models (e.g., Baldocchi 1992 ), or in global/regional climate studies ( Sellers et al. 1996 ; Calvet et al. 1998 ; Cox et al. 1999 ; Dai et al. 2003 ). Mesoscale or weather forecast models continue to be predominantly Jarvis-based (e.g., Chen and Dudhia 2001 ; Ek et al. 2003 ), but several reasons
, and energy exchange ( Farquhar et al. 1980 ; Collatz et al. 1991 , 1992 ; Anderson et al. 2000 ; see also Niyogi and Raman 1997 ). These schemes generally have been applied in leaf/canopy scale models (e.g., Baldocchi 1992 ), or in global/regional climate studies ( Sellers et al. 1996 ; Calvet et al. 1998 ; Cox et al. 1999 ; Dai et al. 2003 ). Mesoscale or weather forecast models continue to be predominantly Jarvis-based (e.g., Chen and Dudhia 2001 ; Ek et al. 2003 ), but several reasons
) use an idealized two-dimensional model to investigate the predictability of ensemble forecasts of mesoscale convective systems (MCSs), while Aksoy et al. (2010) used the Weather Research and Forecasting Model (WRF) to assimilate Doppler radar observations and perform idealized ensemble predictions of storms with both supercellular and linear convective modes. Stensrud and Gao (2010) assimilate radar data using 3DVAR and perform short-range ensemble forecasts of a supercell case. All three of
) use an idealized two-dimensional model to investigate the predictability of ensemble forecasts of mesoscale convective systems (MCSs), while Aksoy et al. (2010) used the Weather Research and Forecasting Model (WRF) to assimilate Doppler radar observations and perform idealized ensemble predictions of storms with both supercellular and linear convective modes. Stensrud and Gao (2010) assimilate radar data using 3DVAR and perform short-range ensemble forecasts of a supercell case. All three of
