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- Author or Editor: Kevin C. Vining x
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Abstract
The use of Klemp–Wilhelmson (KW) time splitting for large-scale and global modeling is assessed through a series of von Neumann accuracy and stability analyses. Two variations of the KW splitting are evaluated in particular: the original acoustic-mode splitting of Klemp and Wilhelmson (KW78) and a modified splitting due to Skamarock and Klemp (SK92) in which the buoyancy and vertical stratification terms are treated as fast-mode terms. The large-scale case of interest is the problem of Rossby wave propagation on a resting background state. The results show that the original KW78 splitting is surprisingly inaccurate when applied to large-scale wave modes. The source of this inaccuracy is traced to the compressible vertical adjustment—and more precisely, to the splitting of the hydrostatic balance terms between the small and large time steps. The splitting errors can be reduced somewhat through implicit biasing, but large biasing coefficients are needed for acceptable error levels—and even then the time steps are limited to moderate values. The errors in the KW78 splitting are shown to be largely absent from the SK92 scheme. Two versions of the SK92 splitting are considered in particular: the original leapfrog splitting (SK92-LF) of Skamarock and Klemp and the third-order Runge–Kutta splitting (SK92-RK) proposed by Wicker and Skamarock. The mixed cubic (on the large time step) and quadratic (on the small step) behavior of the SK92-RK scheme is described in detail and is compared with the strictly quadratic behavior of the SK92-LF method.
Abstract
The use of Klemp–Wilhelmson (KW) time splitting for large-scale and global modeling is assessed through a series of von Neumann accuracy and stability analyses. Two variations of the KW splitting are evaluated in particular: the original acoustic-mode splitting of Klemp and Wilhelmson (KW78) and a modified splitting due to Skamarock and Klemp (SK92) in which the buoyancy and vertical stratification terms are treated as fast-mode terms. The large-scale case of interest is the problem of Rossby wave propagation on a resting background state. The results show that the original KW78 splitting is surprisingly inaccurate when applied to large-scale wave modes. The source of this inaccuracy is traced to the compressible vertical adjustment—and more precisely, to the splitting of the hydrostatic balance terms between the small and large time steps. The splitting errors can be reduced somewhat through implicit biasing, but large biasing coefficients are needed for acceptable error levels—and even then the time steps are limited to moderate values. The errors in the KW78 splitting are shown to be largely absent from the SK92 scheme. Two versions of the SK92 splitting are considered in particular: the original leapfrog splitting (SK92-LF) of Skamarock and Klemp and the third-order Runge–Kutta splitting (SK92-RK) proposed by Wicker and Skamarock. The mixed cubic (on the large time step) and quadratic (on the small step) behavior of the SK92-RK scheme is described in detail and is compared with the strictly quadratic behavior of the SK92-LF method.
Abstract
Ten stations are chosen for a study of climatic variability in the continental United States, using as the main criteria good geographical distribution, long-period records (since before 1900), and available daily, monthly and annual values of maximum and minimum temperatures and precipitation. Regression lines of decadal variances calculated from annual temperature indicate significant trends of variances, and thus variability, at several stations. A slight increase in precipitation variability is evident across the entire region.
Analyses of ranked monthly maximum and minimum temperatures reveal several distinct discontinuities in the data. In most instances, station relocations coincide with the discontinuities.
Abstract
Ten stations are chosen for a study of climatic variability in the continental United States, using as the main criteria good geographical distribution, long-period records (since before 1900), and available daily, monthly and annual values of maximum and minimum temperatures and precipitation. Regression lines of decadal variances calculated from annual temperature indicate significant trends of variances, and thus variability, at several stations. A slight increase in precipitation variability is evident across the entire region.
Analyses of ranked monthly maximum and minimum temperatures reveal several distinct discontinuities in the data. In most instances, station relocations coincide with the discontinuities.
Abstract
The coupling of the dynamical core of a numerical weather prediction model to the physical parameterizations is an important component of model design. This coupling between the physics and the dynamics is explored here from the perspective of stochastic differential equations (SDEs). It will be shown that the basic properties of the impact of noisy physics on the stability and accuracy of common numerical methods may be obtained through the application of the basic principles of SDEs. A conceptual model setting is used that allows the study of the impact of noise whose character may be tuned to be either very red (smooth) or white (noisy). The change in the stability and accuracy of common numerical methods as the character of the noise changes is then studied. Distinct differences are found between the ability of multistage (Runge–Kutta) schemes as compared with multistep (Adams–Bashforth/leapfrog) schemes to handle noise of various characters. These differences will be shown to be attributable to the basic philosophy used to design the scheme. Additional experiments using the decentering of the noisy physics will also be shown to lead to strong sensitivity to the quality of the noise. As an example, the authors find the novel result that noise of a diffusive character may lead to instability when the scheme is decentered toward greater implicitness. These results are confirmed in a nonlinear shear layer simulation using a subgrid-scale mixing parameterization. This subgrid-scale mixing parameterization is modified stochastically and shown to reproduce the basic principles found here, including the notion that decentering toward implicitness may lead to instability.
Abstract
The coupling of the dynamical core of a numerical weather prediction model to the physical parameterizations is an important component of model design. This coupling between the physics and the dynamics is explored here from the perspective of stochastic differential equations (SDEs). It will be shown that the basic properties of the impact of noisy physics on the stability and accuracy of common numerical methods may be obtained through the application of the basic principles of SDEs. A conceptual model setting is used that allows the study of the impact of noise whose character may be tuned to be either very red (smooth) or white (noisy). The change in the stability and accuracy of common numerical methods as the character of the noise changes is then studied. Distinct differences are found between the ability of multistage (Runge–Kutta) schemes as compared with multistep (Adams–Bashforth/leapfrog) schemes to handle noise of various characters. These differences will be shown to be attributable to the basic philosophy used to design the scheme. Additional experiments using the decentering of the noisy physics will also be shown to lead to strong sensitivity to the quality of the noise. As an example, the authors find the novel result that noise of a diffusive character may lead to instability when the scheme is decentered toward greater implicitness. These results are confirmed in a nonlinear shear layer simulation using a subgrid-scale mixing parameterization. This subgrid-scale mixing parameterization is modified stochastically and shown to reproduce the basic principles found here, including the notion that decentering toward implicitness may lead to instability.
A mail survey was sent to 900 Texas farmers and ranchers asking them to rank the importance of various agrometeorological information types to their operations, and querying their willingness to pay for weather information. Most producers ranked as important those information types commonly broadcast over public media. Few producers would be willing to pay for weather information. Comments indicated a distrust of weather information, especially forecasts.
A mail survey was sent to 900 Texas farmers and ranchers asking them to rank the importance of various agrometeorological information types to their operations, and querying their willingness to pay for weather information. Most producers ranked as important those information types commonly broadcast over public media. Few producers would be willing to pay for weather information. Comments indicated a distrust of weather information, especially forecasts.
Abstract
Interactions between the upper-level outflow of a sheared, rapidly intensifying tropical cyclone (TC) and the background environmental flow in an idealized model are presented. The most important finding is that the divergent outflow from convection localized by the tilt of the vortex serves to divert the background environmental flow around the TC, thus reducing the local vertical wind shear. We show that this effect can be understood from basic theoretical arguments related to Bernoulli flow around an obstacle. In the simulation discussed, the environmental flow diversion by the outflow is limited to 2 km below the tropopause in the 12–14-km (250–150 hPa) layer. Synthetic water vapor satellite imagery confirms the presence of upshear arcs in the cloud field, matching satellite observations. These arcs, which exist in the same layer as the outflow, are caused by slow-moving wave features and serve as visual markers of the outflow–environment interface. The blocking effect where the outflow and the environmental winds meet creates a dynamic high pressure whose pressure gradient extends nearly 1000 km upwind, thus causing the environmental winds to slow down, to converge, and to sink. We discuss these results with respect to the first part of this three-part study, and apply them to another atypical rapid intensification hurricane: Matthew (2016).
Abstract
Interactions between the upper-level outflow of a sheared, rapidly intensifying tropical cyclone (TC) and the background environmental flow in an idealized model are presented. The most important finding is that the divergent outflow from convection localized by the tilt of the vortex serves to divert the background environmental flow around the TC, thus reducing the local vertical wind shear. We show that this effect can be understood from basic theoretical arguments related to Bernoulli flow around an obstacle. In the simulation discussed, the environmental flow diversion by the outflow is limited to 2 km below the tropopause in the 12–14-km (250–150 hPa) layer. Synthetic water vapor satellite imagery confirms the presence of upshear arcs in the cloud field, matching satellite observations. These arcs, which exist in the same layer as the outflow, are caused by slow-moving wave features and serve as visual markers of the outflow–environment interface. The blocking effect where the outflow and the environmental winds meet creates a dynamic high pressure whose pressure gradient extends nearly 1000 km upwind, thus causing the environmental winds to slow down, to converge, and to sink. We discuss these results with respect to the first part of this three-part study, and apply them to another atypical rapid intensification hurricane: Matthew (2016).
Abstract
A data assimilation system (DAS) is described for global atmospheric reanalysis from 0- to 100-km altitude. We apply it to the 2014 austral winter of the Deep Propagating Gravity Wave Experiment (DEEPWAVE), an international field campaign focused on gravity wave dynamics from 0 to 100 km, where an absence of reanalysis above 60 km inhibits research. Four experiments were performed from April to September 2014 and assessed for reanalysis skill above 50 km. A four-dimensional variational (4DVAR) run specified initial background error covariances statically. A hybrid-4DVAR (HYBRID) run formed background error covariances from an 80-member forecast ensemble blended with a static estimate. Each configuration was run at low and high horizontal resolution. In addition to operational observations below 50 km, each experiment assimilated 
Abstract
A data assimilation system (DAS) is described for global atmospheric reanalysis from 0- to 100-km altitude. We apply it to the 2014 austral winter of the Deep Propagating Gravity Wave Experiment (DEEPWAVE), an international field campaign focused on gravity wave dynamics from 0 to 100 km, where an absence of reanalysis above 60 km inhibits research. Four experiments were performed from April to September 2014 and assessed for reanalysis skill above 50 km. A four-dimensional variational (4DVAR) run specified initial background error covariances statically. A hybrid-4DVAR (HYBRID) run formed background error covariances from an 80-member forecast ensemble blended with a static estimate. Each configuration was run at low and high horizontal resolution. In addition to operational observations below 50 km, each experiment assimilated
