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- Author or Editor: Pedro M. M. Soares x
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Abstract
A better conceptual understanding and more realistic parameterizations of convective boundary layers in climate and weather prediction models have been major challenges in meteorological research. In particular, parameterizations of the dry convective boundary layer, in spite of the absence of water phase-changes and its consequent simplicity as compared to moist convection, typically suffer from problems in attempting to represent realistically the boundary layer growth and what is often referred to as countergradient fluxes. The eddy-diffusivity (ED) approach has been relatively successful in representing some characteristics of neutral boundary layers and surface layers in general. The mass-flux (MF) approach, on the other hand, has been used for the parameterization of shallow and deep moist convection. In this paper, a new approach that relies on a combination of the ED and MF parameterizations (EDMF) is proposed for the dry convective boundary layer. It is shown that the EDMF approach follows naturally from a decomposition of the turbulent fluxes into 1) a part that includes strong organized updrafts, and 2) a remaining turbulent field. At the basis of the EDMF approach is the concept that nonlocal subgrid transport due to the strong updrafts is taken into account by the MF approach, while the remaining transport is taken into account by an ED closure. Large-eddy simulation (LES) results of the dry convective boundary layer are used to support the theoretical framework of this new approach and to determine the parameters of the EDMF model. The performance of the new formulation is evaluated against LES results, and it is shown that the EDMF closure is able to reproduce the main properties of dry convective boundary layers in a realistic manner. Furthermore, it will be shown that this approach has strong advantages over the more traditional countergradient approach, especially in the entrainment layer. As a result, this EDMF approach opens the way to parameterize the clear and cumulus-topped boundary layer in a simple and unified way.
Abstract
A better conceptual understanding and more realistic parameterizations of convective boundary layers in climate and weather prediction models have been major challenges in meteorological research. In particular, parameterizations of the dry convective boundary layer, in spite of the absence of water phase-changes and its consequent simplicity as compared to moist convection, typically suffer from problems in attempting to represent realistically the boundary layer growth and what is often referred to as countergradient fluxes. The eddy-diffusivity (ED) approach has been relatively successful in representing some characteristics of neutral boundary layers and surface layers in general. The mass-flux (MF) approach, on the other hand, has been used for the parameterization of shallow and deep moist convection. In this paper, a new approach that relies on a combination of the ED and MF parameterizations (EDMF) is proposed for the dry convective boundary layer. It is shown that the EDMF approach follows naturally from a decomposition of the turbulent fluxes into 1) a part that includes strong organized updrafts, and 2) a remaining turbulent field. At the basis of the EDMF approach is the concept that nonlocal subgrid transport due to the strong updrafts is taken into account by the MF approach, while the remaining transport is taken into account by an ED closure. Large-eddy simulation (LES) results of the dry convective boundary layer are used to support the theoretical framework of this new approach and to determine the parameters of the EDMF model. The performance of the new formulation is evaluated against LES results, and it is shown that the EDMF closure is able to reproduce the main properties of dry convective boundary layers in a realistic manner. Furthermore, it will be shown that this approach has strong advantages over the more traditional countergradient approach, especially in the entrainment layer. As a result, this EDMF approach opens the way to parameterize the clear and cumulus-topped boundary layer in a simple and unified way.
Abstract
Global reanalyses are powerful tools to study the recent climate. They are built by combining forecast models with observations through data assimilation, which provide complete spatial and temporal information of observable and unobservable parameters. The reanalyses constitute very valuable three-dimensional data of the atmosphere, which make it possible to investigate a panoply of atmospheric processes, such as coastal low-level jets (CLLJs). In the present study, three global reanalyses, the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim), the Japanese 55-year Reanalysis (JRA-55), and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), are used to build an ensemble of reanalyses for a period encompassing 1980–2016 with 6-hourly output. A detailed global climatology of CLLJs is presented based on this ensemble of reanalyses. This reanalysis ensemble makes it possible to explore the ability of reanalysis to represent the CLLJs mitigating its uncertainty and adding robustness. The annual and diurnal cycle as well as the interannual variability are analyzed in order to evaluate the temporal variability of frequency of occurrence of CLLJ. The ensemble mean displays a good representation of the seasonal spatial variability of frequency of occurrence of coastal jets. The Oman and Benguela CLLJs show, respectively, a decrease and increase of frequency of occurrence in the studied period, which are statistically significant during boreal summer and austral spring. The coastal jets have higher mean frequencies of occurrences during late afternoon and early evening. During the season where each CLLJ has higher mean frequency of occurrence, the Oman CLLJ is the most intense and occurs at higher altitudes.
Abstract
Global reanalyses are powerful tools to study the recent climate. They are built by combining forecast models with observations through data assimilation, which provide complete spatial and temporal information of observable and unobservable parameters. The reanalyses constitute very valuable three-dimensional data of the atmosphere, which make it possible to investigate a panoply of atmospheric processes, such as coastal low-level jets (CLLJs). In the present study, three global reanalyses, the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim), the Japanese 55-year Reanalysis (JRA-55), and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), are used to build an ensemble of reanalyses for a period encompassing 1980–2016 with 6-hourly output. A detailed global climatology of CLLJs is presented based on this ensemble of reanalyses. This reanalysis ensemble makes it possible to explore the ability of reanalysis to represent the CLLJs mitigating its uncertainty and adding robustness. The annual and diurnal cycle as well as the interannual variability are analyzed in order to evaluate the temporal variability of frequency of occurrence of CLLJ. The ensemble mean displays a good representation of the seasonal spatial variability of frequency of occurrence of coastal jets. The Oman and Benguela CLLJs show, respectively, a decrease and increase of frequency of occurrence in the studied period, which are statistically significant during boreal summer and austral spring. The coastal jets have higher mean frequencies of occurrences during late afternoon and early evening. During the season where each CLLJ has higher mean frequency of occurrence, the Oman CLLJ is the most intense and occurs at higher altitudes.
Abstract
After extensive efforts over the course of a decade, convective-scale weather forecasts with horizontal grid spacings of 1–5 km are now operational at national weather services around the world, accompanied by ensemble prediction systems (EPSs). However, though already operational, the capacity of forecasts for this scale is still to be fully exploited by overcoming the fundamental difficulty in prediction: the fully three-dimensional and turbulent nature of the atmosphere. The prediction of this scale is totally different from that of the synoptic scale (103 km), with slowly evolving semigeostrophic dynamics and relatively long predictability on the order of a few days.
Even theoretically, very little is understood about the convective scale compared to our extensive knowledge of the synoptic-scale weather regime as a partial differential equation system, as well as in terms of the fluid mechanics, predictability, uncertainties, and stochasticity. Furthermore, there is a requirement for a drastic modification of data assimilation methodologies, physics (e.g., microphysics), and parameterizations, as well as the numerics for use at the convective scale. We need to focus on more fundamental theoretical issues—the Liouville principle and Bayesian probability for probabilistic forecasts—and more fundamental turbulence research to provide robust numerics for the full variety of turbulent flows.
The present essay reviews those basic theoretical challenges as comprehensibly as possible. The breadth of the problems that we face is a challenge in itself: an attempt to reduce these into a single critical agenda should be avoided.
Abstract
After extensive efforts over the course of a decade, convective-scale weather forecasts with horizontal grid spacings of 1–5 km are now operational at national weather services around the world, accompanied by ensemble prediction systems (EPSs). However, though already operational, the capacity of forecasts for this scale is still to be fully exploited by overcoming the fundamental difficulty in prediction: the fully three-dimensional and turbulent nature of the atmosphere. The prediction of this scale is totally different from that of the synoptic scale (103 km), with slowly evolving semigeostrophic dynamics and relatively long predictability on the order of a few days.
Even theoretically, very little is understood about the convective scale compared to our extensive knowledge of the synoptic-scale weather regime as a partial differential equation system, as well as in terms of the fluid mechanics, predictability, uncertainties, and stochasticity. Furthermore, there is a requirement for a drastic modification of data assimilation methodologies, physics (e.g., microphysics), and parameterizations, as well as the numerics for use at the convective scale. We need to focus on more fundamental theoretical issues—the Liouville principle and Bayesian probability for probabilistic forecasts—and more fundamental turbulence research to provide robust numerics for the full variety of turbulent flows.
The present essay reviews those basic theoretical challenges as comprehensibly as possible. The breadth of the problems that we face is a challenge in itself: an attempt to reduce these into a single critical agenda should be avoided.