Why is the tropical cyclone boundary layer not "well-mixed"?

Plausible diagnostics for the top of the tropical cyclone boundary layer include (i) the top of the layer of strong frictional inflow and (ii) the top of the “well-mixed” layer; that is, the layer over which potential temperature θ is approximately constant. Observations show that these two candidate definitions give markedly different results in practice, with the inflow layer being roughly twice the depth of the layer of nearly constant θ. Here, we will present an analysis of the thermodynamics of the tropical cyclone boundary layer derived from an axisymmetric model. We show that the marked dry static stability in the upper part of the inflow layer is due largely to diabatic effects. The radial wind varies strongly with height, and therefore so does radial advection of θ. This process also stabilizes the boundary layer, but to a lesser degree than diabatic effects. We also show that this differential vertical advection contributes to the observed superadiabatic layer adjacent to the ocean surface, where the vertical gradient of the radial wind is reversed, but that the main cause of this unstable layer is heating from turbulent dissipation. The top of the “well-mixed” layer is thus distinct from the top of the boundary layer in tropical cyclones. The top of the inflow layer is a better proxy for the top of the boundary layer, but is not without limitations. These results may have implications for boundary-layer parameterisations that diagnose the boundary layer depth from thermodynamic, or partly thermodynamic, criteria

Oxygen Chemisorption/Desorption in a Reversible Single-Crystal-To-Single-Crystal Transformation

A compound that can reversibly chemisorb O2 in the solid phase undergoes several consecutive single-crystal-to-single-crystal transformations involving the cleavage of four bonds and the creation of four new bonds, in one single molecule.</p

Ensemble prediction for nowcasting with a convection-permitting model - II: forecast error statistics

A 24-member ensemble of 1-h high-resolution forecasts over the Southern United Kingdom is used to study short-range forecast error statistics. The initial conditions are found from perturbations from an ensemble transform Kalman filter. Forecasts from this system are assumed to lie within the bounds of forecast error of an operational forecast system. Although noisy, this system is capable of producing physically reasonable statistics which are analysed and compared to statistics implied from a variational assimilation system. The variances for temperature errors for instance show structures that reflect convective activity. Some variables, notably potential temperature and specific humidity perturbations, have autocorrelation functions that deviate from 3-D isotropy at the convective-scale (horizontal scales less than 10 km). Other variables, notably the velocity potential for horizontal divergence perturbations, maintain 3-D isotropy at all scales. Geostrophic and hydrostatic balances are studied by examining correlations between terms in the divergence and vertical momentum equations respectively. Both balances are found to decay as the horizontal scale decreases. It is estimated that geostrophic balance becomes less important at scales smaller than 75 km, and hydrostatic balance becomes less important at scales smaller than 35 km, although more work is required to validate these findings. The implications of these results for high-resolution data assimilation are discussed

The TIGGE project and its achievements

TIGGE was a major component of the THORPEX (The Observing System Research and Predictability Experiment) research program, whose aim is to accelerate improvements in forecasting high-impact weather. By providing ensemble prediction data from leading operational forecast centers, TIGGE has enhanced collaboration between the research and operational meteorological communities and enabled research studies on a wide range of topics. The paper covers the objective evaluation of the TIGGE data. For a range of forecast parameters, it is shown to be beneficial to combine ensembles from several data providers in a Multi-model Grand Ensemble. Alternative methods to correct systematic errors, including the use of reforecast data, are also discussed. TIGGE data have been used for a range of research studies on predictability and dynamical processes. Tropical cyclones are the most destructive weather systems in the world, and are a focus of multi-model ensemble research. Their extra-tropical transition also has a major impact on skill of mid-latitude forecasts. We also review how TIGGE has added to our understanding of the dynamics of extra-tropical cyclones and storm tracks. Although TIGGE is a research project, it has proved invaluable for the development of products for future operational forecasting. Examples include the forecasting of tropical cyclone tracks, heavy rainfall, strong winds, and flood prediction through coupling hydrological models to ensembles. Finally the paper considers the legacy of TIGGE. We discuss the priorities and key issues in predictability and ensemble forecasting, including the new opportunities of convective-scale ensembles, links with ensemble data assimilation methods, and extension of the range of useful forecast skill

Reticular synthesis and the design of new materials

The long-standing challenge of designing and constructing new crystalline solid-state materials from molecular building blocks is just beginning to be addressed with success. A conceptual approach that requires the use of secondary building units to direct the assembly of ordered frameworks epitomizes this process: we call this approach reticular synthesis. This chemistry has yielded materials designed to have predetermined structures, compositions and properties. In particular, highly porous frameworks held together by strong metal-oxygen-carbon bonds and with exceptionally large surface area and capacity for gas storage have been prepared and their pore metrics systematically varied and functionalized.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62718/1/nature01650.pd