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parameterized; these include unresolved waves, local turbulence, and coherent structures such as convective thermals or plumes. These physical processes are qualitatively quite different from each other and lead to subgrid models that are structurally quite different, for example, eddy diffusivity schemes for local turbulence compared with mass flux schemes for cumulus convection. The usual LES filtering approach does not, itself, make any distinction between these different types of subgrid process. Recent
parameterized; these include unresolved waves, local turbulence, and coherent structures such as convective thermals or plumes. These physical processes are qualitatively quite different from each other and lead to subgrid models that are structurally quite different, for example, eddy diffusivity schemes for local turbulence compared with mass flux schemes for cumulus convection. The usual LES filtering approach does not, itself, make any distinction between these different types of subgrid process. Recent
order of 100 m is necessary to resolve individual cumulus clouds explicitly ( Yamasaki 1975 ; Bryan et al. 2003 ). Thus, cumulus parameterizations are still important even now as the resolution of atmospheric models improves. Various cumulus parameterization schemes have been developed, such as convective-adjustment schemes (e.g., Manabe and Strickler 1964 ; Betts and Miller 1986 ), Kuo schemes ( Kuo 1965 , 1974 ), and mass-flux schemes. Mass-flux schemes are widely used because they explicitly
order of 100 m is necessary to resolve individual cumulus clouds explicitly ( Yamasaki 1975 ; Bryan et al. 2003 ). Thus, cumulus parameterizations are still important even now as the resolution of atmospheric models improves. Various cumulus parameterization schemes have been developed, such as convective-adjustment schemes (e.g., Manabe and Strickler 1964 ; Betts and Miller 1986 ), Kuo schemes ( Kuo 1965 , 1974 ), and mass-flux schemes. Mass-flux schemes are widely used because they explicitly
1. Introduction Coarse grid spacing weather and climate simulations require the use of convective parameterization schemes (CPS) since these models cannot explicitly resolve cloud processes. Improving these models requires clarification of how and why convective parameterizations fail. Molinari and Dudek (1992) ask that more studies document why cumulus parameterization schemes (CPSs) succeed and fail. Cohen (2002) explored CPSs in an idealized sea-breeze experiment with the fifth
1. Introduction Coarse grid spacing weather and climate simulations require the use of convective parameterization schemes (CPS) since these models cannot explicitly resolve cloud processes. Improving these models requires clarification of how and why convective parameterizations fail. Molinari and Dudek (1992) ask that more studies document why cumulus parameterization schemes (CPSs) succeed and fail. Cohen (2002) explored CPSs in an idealized sea-breeze experiment with the fifth
pools generated by convective downdrafts (e.g., Charba 1974 ; Simpson 1980 ; Thorpe et al. 1982 ; Fovell and Tan 1998 ). Convective organization has a significant effect on the vertical transport of heat, moisture, and momentum ( Moncrieff and Klinker 1997 ). The resolution of most current global models is still too coarse to resolve convective clouds (or even cloud systems), especially in climate rather than weather simulations, and thus convection is parameterized in such models. It is widely
pools generated by convective downdrafts (e.g., Charba 1974 ; Simpson 1980 ; Thorpe et al. 1982 ; Fovell and Tan 1998 ). Convective organization has a significant effect on the vertical transport of heat, moisture, and momentum ( Moncrieff and Klinker 1997 ). The resolution of most current global models is still too coarse to resolve convective clouds (or even cloud systems), especially in climate rather than weather simulations, and thus convection is parameterized in such models. It is widely
1. Introduction The representation of moist convection in atmospheric numerical models is an important source of uncertainty and potential error ( Molinari and Dudek 1992 ; Holloway et al. 2014 ). Although the march toward finer grid spacings that are capable of resolving convective processes proceeds apace, many large-scale regional models, global NWP systems, and climate models will be forced to parameterize convection for the foreseeable future ( Arakawa et al. 2016 ). Many of these systems
1. Introduction The representation of moist convection in atmospheric numerical models is an important source of uncertainty and potential error ( Molinari and Dudek 1992 ; Holloway et al. 2014 ). Although the march toward finer grid spacings that are capable of resolving convective processes proceeds apace, many large-scale regional models, global NWP systems, and climate models will be forced to parameterize convection for the foreseeable future ( Arakawa et al. 2016 ). Many of these systems
errors in depiction of the resolved-scale flow. One of the first gray zone problems to be encountered by operational NWP systems is associated with the parameterization of deep convection, for which grid spacings on the order of 1–10 km pose a significant challenge ( Molinari and Dudek 1992 ; Arakawa 2004 ). At such scales, it is impossible to cleanly separate cloud properties from the environmental conditions without an explicit estimate of the updraft area fraction of the kind proposed by Gerard
errors in depiction of the resolved-scale flow. One of the first gray zone problems to be encountered by operational NWP systems is associated with the parameterization of deep convection, for which grid spacings on the order of 1–10 km pose a significant challenge ( Molinari and Dudek 1992 ; Arakawa 2004 ). At such scales, it is impossible to cleanly separate cloud properties from the environmental conditions without an explicit estimate of the updraft area fraction of the kind proposed by Gerard
significantly impact the resulting circulation (e.g., Tiedtke 1989 ). More recently, it has been further suggested that, because of its abundance, the shallow cumuli are a leading factor in determining the cloud–climate feedback ( Bony et al. 2004 ; Bony and Dufresne 2005 ). It is therefore important to understand the dynamics of shallow cumulus convection and to better parameterize it in global climate models or general circulation models (GCMs). Our strategy to better understand shallow cumuli is to
significantly impact the resulting circulation (e.g., Tiedtke 1989 ). More recently, it has been further suggested that, because of its abundance, the shallow cumuli are a leading factor in determining the cloud–climate feedback ( Bony et al. 2004 ; Bony and Dufresne 2005 ). It is therefore important to understand the dynamics of shallow cumulus convection and to better parameterize it in global climate models or general circulation models (GCMs). Our strategy to better understand shallow cumuli is to
transport by GWs is currently a subgrid process in most general circulation models (GCMs), the parameterization of GW drag is crucial for the accurate reproduction of observed middle atmosphere circulations in GCMs. Among the various sources of GWs, cumulus convection has been widely accepted as a major source, which has led to the parameterization of the convective gravity wave drag (GWDC). The momentum flux spectrum for the GWDC parameterization was first formulated analytically under uniform basic
transport by GWs is currently a subgrid process in most general circulation models (GCMs), the parameterization of GW drag is crucial for the accurate reproduction of observed middle atmosphere circulations in GCMs. Among the various sources of GWs, cumulus convection has been widely accepted as a major source, which has led to the parameterization of the convective gravity wave drag (GWDC). The momentum flux spectrum for the GWDC parameterization was first formulated analytically under uniform basic
) have been found to more accurately depict the diurnal precipitation cycle ( Clark et al. 2007 ; Weisman et al. 2008 ), as well as MCS frequency and the convective system mode ( Done et al. 2004 ; Weisman et al. 2008 ) relative to simulations using parameterized-convection resolution (PCR). Although increasing to CAR may not necessarily increase the forecast skill for deterministic forecasts as measured by traditional “grid based” metrics [e.g., equitable threat score ( Schaefer 1990 ) and bias
) have been found to more accurately depict the diurnal precipitation cycle ( Clark et al. 2007 ; Weisman et al. 2008 ), as well as MCS frequency and the convective system mode ( Done et al. 2004 ; Weisman et al. 2008 ) relative to simulations using parameterized-convection resolution (PCR). Although increasing to CAR may not necessarily increase the forecast skill for deterministic forecasts as measured by traditional “grid based” metrics [e.g., equitable threat score ( Schaefer 1990 ) and bias
1. Introduction With advances in computing power, present-day numerical weather prediction models can run at computational grid spacings of 1–10 km, where convective clouds may be resolved as well as unresolved ( Lean et al. 2008 ). For models with such a high resolution, further improvements on both physical parameterizations and numerical techniques are required ( Steppeler et al. 2003 ). Gerard (2007) defined the resolution in the range of 1–10 km as “grey-zone resolution,” with which
1. Introduction With advances in computing power, present-day numerical weather prediction models can run at computational grid spacings of 1–10 km, where convective clouds may be resolved as well as unresolved ( Lean et al. 2008 ). For models with such a high resolution, further improvements on both physical parameterizations and numerical techniques are required ( Steppeler et al. 2003 ). Gerard (2007) defined the resolution in the range of 1–10 km as “grey-zone resolution,” with which
