Clouds associated with extratropical cyclones complicate the well-developed theory of dry baroclinic waves through feedback on their dynamics by precipitation and cloud-altered radiative heating. The relationships between cyclone characteristics and the diabatic heating associated with cloud radiative effects (CREs) and latent heat release remain unclear. A cyclone tracking algorithm [NASA’s Modeling, Analysis, and Prediction (MAP) Climatology of Midlatitude Storminess (MCMS)] is used to identify over 106 cyclones in 33 years of the ERA-Interim and collect the properties of each disturbance. Considering storm intensity as related to wind speeds, which depend on the pressure gradient, the distribution of cyclone properties is investigated using groups defined by their depth (local pressure anomaly) and the radius of the region within closed pressure contours to investigate variations with longitude (especially ocean and land), hemisphere, and season. Using global data products of cloud radiative effects on in-atmosphere net radiation [the ISCCP radiative flux profile dataset (ISCCP-FD)] and precipitation (GPCP), composites are assembled for each cyclone group and for “nonstormy” locations. On average, the precipitation rate and the CRE are approximately the same among all cyclone groups and do not strongly differ from nonstormy conditions. The variance of both precipitation and CRE increases with cyclone size and depth. In larger, deeper storms, maximum precipitation and CRE increase, but so do the amounts of nonprecipitating and clear-sky conditions.

No abstract

Control of fluid flow is an important, underutilized process possessing potential benefits ranging from avoidance of separation and stall on aircraft wings to reduction of friction in oil and gas pipelines to mitigation of noise from wind turbines. But the Navier-Stokes N.-S. equations, whose solutions describe such flows, consist of a system of time-dependent, multidimensional, nonlinear partial differential equations PDEs which cannot be solved in real time using current computing hardware. The poor man's Navier-Stokes PMNS equations comprise a discrete dynamical system that is algebraic-hence, easily and rapidly solved-and yet which retains many possibly all of the temporal behaviors of the PDE N.-S. system at specific spatial locations. Herein, we outline derivation of these equations and discuss their basic properties. We consider application of these equations to the control problem by adding a control force. We examine the range of behaviors that can be achieved by changing this control force and, in particular, consider controllability of this nonlinear system via numerical experiments. Moreover, we observe that the derivation leading to the PMNS equations is very general and may be applied to a wide variety of problems governed by PDEs and possibly time-delay ordinary differential equations such as, for example, models of machining processes.

Control of fluid flow is an important, underutilized process possessing potential benefits ranging from avoidance of separation and stall on aircraft wings to reduction of friction in oil and gas pipelines to mitigation of noise from wind turbines. But the Navier-Stokes (N.-S.) equations, whose solutions describe such flows, consist of a system of time-dependent, multidimensional, nonlinear partial differential equations (PDEs) which cannot be solved in real time using current computing hardware. The poor man's Navier-Stokes (PMNS) equations comprise a discrete dynamical system that is algebraic—hence, easily (and rapidly) solved—and yet which retains many (possibly all) of the temporal behaviors of the PDE N.-S. system at specific spatial locations. Herein, we outline derivation of these equations and discuss their basic properties. We consider application of these equations to the control problem by adding a control force. We examine the range of behaviors that can be achieved by changing this control force and, in particular, consider controllability of this (nonlinear) systemvianumerical experiments. Moreover, we observe that the derivation leading to the PMNS equations is very general and may be applied to a wide variety of problems governed by PDEs and (possibly) time-delay ordinary differential equations such as, for example, models of machining processes.

No abstract