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Abstract
The statistical theory of convective variability developed by Craig and Cohen in 2006 has provided a promising foundation for the design of stochastic parameterizations. The simplifying assumptions of this theory, however, were made with tropical equilibrium convection in mind. This study investigates the predictions of the statistical theory in real-weather case studies of nonequilibrium summertime convection over land. For this purpose, a convection-permitting ensemble is used in which all members share the same large-scale weather conditions but the convection is displaced using stochastic boundary layer perturbations. The results show that the standard deviation of the domain-integrated mass flux is proportional to the square root of its mean over a wide range of scales. This confirms the general applicability and scale adaptivity of the Craig and Cohen theory for complex weather. However, clouds tend to cluster on scales of around 100 km, particularly in the morning and evening. This strongly impacts the theoretical predictions of the variability, which does not include clustering. Furthermore, the mass flux per cloud closely follows an exponential distribution if all clouds are considered together and if overlapping cloud objects are separated. The nonseparated cloud mass flux distribution resembles a power law. These findings support the use of the theory for stochastic parameterizations but also highlight areas for improvement.
Abstract
The statistical theory of convective variability developed by Craig and Cohen in 2006 has provided a promising foundation for the design of stochastic parameterizations. The simplifying assumptions of this theory, however, were made with tropical equilibrium convection in mind. This study investigates the predictions of the statistical theory in real-weather case studies of nonequilibrium summertime convection over land. For this purpose, a convection-permitting ensemble is used in which all members share the same large-scale weather conditions but the convection is displaced using stochastic boundary layer perturbations. The results show that the standard deviation of the domain-integrated mass flux is proportional to the square root of its mean over a wide range of scales. This confirms the general applicability and scale adaptivity of the Craig and Cohen theory for complex weather. However, clouds tend to cluster on scales of around 100 km, particularly in the morning and evening. This strongly impacts the theoretical predictions of the variability, which does not include clustering. Furthermore, the mass flux per cloud closely follows an exponential distribution if all clouds are considered together and if overlapping cloud objects are separated. The nonseparated cloud mass flux distribution resembles a power law. These findings support the use of the theory for stochastic parameterizations but also highlight areas for improvement.
Abstract
The spatial scale dependence of midlatitude water vapor variability in the high-resolution limited-area model COSMO is evaluated using diagnostics of scaling behavior. Past analysis of airborne lidar measurements showed that structure function scaling exponents depend on the corresponding airmass characteristics, and that a classification of the troposphere into convective and nonconvective layers led to significantly different power-law behaviors for each of these two regimes. In particular, scaling properties in the convective air mass were characterized by rough and highly intermittent data series, whereas the nonconvective regime was dominated by smoother structures with weaker small-scale variability. This study finds similar results in a model simulation with an even more pronounced distinction between the two air masses. Quantitative scaling diagnostics agree well with measurements in the nonconvective air mass, whereas in the convective air mass the simulation shows a much higher intermittency. Sensitivity analyses were performed using the model data to assess the impact of limitations of the observational dataset, which indicate that analyses of lidar data most likely underestimated the intermittency in convective air masses due to the small samples from single flight tracks, which led to a bias when data with poor fits were rejected. Though the quantitative estimation of intermittency remains uncertain for convective air masses, the ability of the model to capture the dominant weather regime dependence of water vapor scaling properties is encouraging.
Abstract
The spatial scale dependence of midlatitude water vapor variability in the high-resolution limited-area model COSMO is evaluated using diagnostics of scaling behavior. Past analysis of airborne lidar measurements showed that structure function scaling exponents depend on the corresponding airmass characteristics, and that a classification of the troposphere into convective and nonconvective layers led to significantly different power-law behaviors for each of these two regimes. In particular, scaling properties in the convective air mass were characterized by rough and highly intermittent data series, whereas the nonconvective regime was dominated by smoother structures with weaker small-scale variability. This study finds similar results in a model simulation with an even more pronounced distinction between the two air masses. Quantitative scaling diagnostics agree well with measurements in the nonconvective air mass, whereas in the convective air mass the simulation shows a much higher intermittency. Sensitivity analyses were performed using the model data to assess the impact of limitations of the observational dataset, which indicate that analyses of lidar data most likely underestimated the intermittency in convective air masses due to the small samples from single flight tracks, which led to a bias when data with poor fits were rejected. Though the quantitative estimation of intermittency remains uncertain for convective air masses, the ability of the model to capture the dominant weather regime dependence of water vapor scaling properties is encouraging.
Abstract
Stochastic perturbations allow for the representation of small-scale variability due to unresolved physical processes. However, the properties of this variability depend on model resolution and weather regime. A physically based method is presented for introducing stochastic perturbations into kilometer-scale atmospheric models that explicitly account for these dependencies. The amplitude of the perturbations is based on information obtained from the model’s subgrid turbulence parameterization, while the spatial and temporal correlations are based on physical length and time scales of the turbulent motions. The stochastic perturbations lead to triggering of additional convective cells and improved precipitation amounts in simulations of two days with weak synoptic forcing of convection but different amounts of precipitation. The perturbations had little impact in a third case study, where precipitation was mainly associated with a cold front. In contrast, an unphysical version of the scheme with constant perturbation amplitude performed poorly since there was no perturbation amplitude that would give improved amounts of precipitation during the day without generating spurious convection at other times.
Abstract
Stochastic perturbations allow for the representation of small-scale variability due to unresolved physical processes. However, the properties of this variability depend on model resolution and weather regime. A physically based method is presented for introducing stochastic perturbations into kilometer-scale atmospheric models that explicitly account for these dependencies. The amplitude of the perturbations is based on information obtained from the model’s subgrid turbulence parameterization, while the spatial and temporal correlations are based on physical length and time scales of the turbulent motions. The stochastic perturbations lead to triggering of additional convective cells and improved precipitation amounts in simulations of two days with weak synoptic forcing of convection but different amounts of precipitation. The perturbations had little impact in a third case study, where precipitation was mainly associated with a cold front. In contrast, an unphysical version of the scheme with constant perturbation amplitude performed poorly since there was no perturbation amplitude that would give improved amounts of precipitation during the day without generating spurious convection at other times.
