Potential energy for slantwise parcel motion

Two formulations for the potential energy for slantwise motion are compared: one which applies strictly only to two-dimensional flows (SCAPE) and a three-dimensional formulation based on a Bernoulli equation. The two formulations share an identical contribution from the vertically integrated buoyancy anomaly and a contribution from different Coriolis terms. The latter arise from the neglect of (different) components of the total change in kinetic energy along a trajectory in the two formulations. This neglect is necessary in order to quantify the potential energy available for slantwise motion relative to a defined steady environment. Copyright © 2000 Royal Meteorological Society.

Variability of the North Atlantic eddy-driven jet stream

Much of the atmospheric variability in the North Atlantic sector is associated with variations in the eddy-driven component of the zonal flow. Here we present a simple method to specifically diagnose this component of the flow using the low-level wind field (925–700 hpa ). We focus on the North Atlantic winter season in the ERA-40 reanalysis. Diagnostics of the latitude and speed of the eddy-driven jet stream are compared with conventional diagnostics of the North Atlantic Oscillation (NAO) and the East Atlantic (EA) pattern. This shows that the NAO and the EA both describe combined changes in the latitude and speed of the jet stream. It is therefore necessary, but not always sufficient, to consider both the NAO and the EA in identifying changes in the jet stream. The jet stream analysis suggests that there are three preferred latitudinal positions of the North Atlantic eddy-driven jet stream in winter. This result is in very good agreement with the application of a statistical mixture model to the two-dimensional state space defined by the NAO and the EA. These results are consistent with several other studies which identify four European/Atlantic regimes, comprising three jet stream patterns plus European blocking events.

Influence-matrix diagnostic of a data assimilation system

The influence matrix is used in ordinary least-squares applications for monitoring statistical multiple-regression analyses. Concepts related to the influence matrix provide diagnostics on the influence of individual data on the analysis - the analysis change that would occur by leaving one observation out, and the effective information content (degrees of freedom for signal) in any sub-set of the analysed data. In this paper, the corresponding concepts have been derived in the context of linear statistical data assimilation in numerical weather prediction. An approximate method to compute the diagonal elements of the influence matrix (the self-sensitivities) has been developed for a large-dimension variational data assimilation system (the four-dimensional variational system of the European Centre for Medium-Range Weather Forecasts). Results show that, in the boreal spring 2003 operational system, 15% of the global influence is due to the assimilated observations in any one analysis, and the complementary 85% is the influence of the prior (background) information, a short-range forecast containing information from earlier assimilated observations. About 25% of the observational information is currently provided by surface-based observing systems, and 75% by satellite systems. Low-influence data points usually occur in data-rich areas, while high-influence data points are in data-sparse areas or in dynamically active regions. Background-error correlations also play an important role: high correlation diminishes the observation influence and amplifies the importance of the surrounding real and pseudo observations (prior information in observation space). Incorrect specifications of background and observation-error covariance matrices can be identified, interpreted and better understood by the use of influence-matrix diagnostics for the variety of observation types and observed variables used in the data assimilation system. Copyright © 2004 Royal Meteorological Society

A jackknife variance estimator for unequal probability sampling

The jackknife method is often used for variance estimation in sample surveys but has only been developed for a limited class of sampling designs.We propose a jackknife variance estimator which is defined for any without-replacement unequal probability sampling design. We demonstrate design consistency of this estimator for a broad class of point estimators. A Monte Carlo study shows how the proposed estimator may improve on existing estimators.

Monitoring and evaluation in Malawi

When Ian Wilson and Carlos Barahona of the Statistical Services Centre at the University of Reading were asked to review an evaluation of the effectiveness of an aid package in Malawi, they expected a simple enough task. But few things in the developing world are simple. Where aid for the poorest is concerned, is evidence collected and analysed with enough rigour to enable well-informed decisions to be made?

Adjusted jackknife for imputation under unequal probability sampling without replacement

Imputation is commonly used to compensate for item non-response in sample surveys. If we treat the imputed values as if they are true values, and then compute the variance estimates by using standard methods, such as the jackknife, we can seriously underestimate the true variances. We propose a modified jackknife variance estimator which is defined for any without-replacement unequal probability sampling design in the presence of imputation and non-negligible sampling fraction. Mean, ratio and random-imputation methods will be considered. The practical advantage of the method proposed is its breadth of applicability.

Targeting the cell cycle machinery for the treatment of cardiovascular disease

Cardiovascular disease represents a major clinical problem affecting a significant proportion of the world's population and remains the main cause of death in the UK. The majority of therapies currently available for the treatment of cardiovascular disease do not cure the problem but merely treat the symptoms. Furthermore, many cardioactive drugs have serious side effects and have narrow therapeutic windows that can limit their usefulness in the clinic. Thus, the development of more selective and highly effective therapeutic strategies that could cure specific cardiovascular diseases would be of enormous benefit both to the patient and to those countries where healthcare systems are responsible for an increasing number of patients. In this review, we discuss the evidence that suggests that targeting the cell cycle machinery in cardiovascular cells provides a novel strategy for the treatment of certain cardiovascular diseases. Those cell cycle molecules that are important for regulating terminal differentiation of cardiac myocytes and whether they can be targeted to reinitiate cell division and myocardial repair will be discussed as will the molecules that control vascular smooth muscle cell (VSMC) and endothelial cell proliferation in disorders such as atherosclerosis and restenosis. The main approaches currently used to target the cell cycle machinery in cardiovascular disease have employed gene therapy techniques. We will overview the different methods and routes of gene delivery to the cardiovascular system and describe possible future drug therapies for these disorders. Although the majority of the published data comes from animal studies, there are several instances where potential therapies have moved into the clinical setting with promising results.

Adapting our cities for future climates: 17 February 2010

Over 50% of humans live in urban areas, occupying less than 3% of land area. Heat, flooding, drought and wind in urban areas were discussed in this meeting at Imperial College organised by Dr Janet Barlow (jointly held between the Royal Meteorological Society and the Wind Engineering Society).

Effect of improving representation of horizontal and vertical cloud structure on the Earth's global radiation budget. Part I: review and parametrization

A poor representation of cloud structure in a general circulation model (GCM) is widely recognised as a potential source of error in the radiation budget. Here, we develop a new way of representing both horizontal and vertical cloud structure in a radiation scheme. This combines the ‘Tripleclouds’ parametrization, which introduces inhomogeneity by using two cloudy regions in each layer as opposed to one, each with different water content values, with ‘exponential-random’ overlap, in which clouds in adjacent layers are not overlapped maximally, but according to a vertical decorrelation scale. This paper, Part I of two, aims to parametrize the two effects such that they can be used in a GCM. To achieve this, we first review a number of studies for a globally applicable value of fractional standard deviation of water content for use in Tripleclouds. We obtain a value of 0.75 ± 0.18 from a variety of different types of observations, with no apparent dependence on cloud type or gridbox size. Then, through a second short review, we create a parametrization of decorrelation scale for use in exponential-random overlap, which varies the scale linearly with latitude from 2.9 km at the Equator to 0.4 km at the poles. When applied to radar data, both components are found to have radiative impacts capable of offsetting biases caused by cloud misrepresentation. Part II of this paper implements Tripleclouds and exponential-random overlap into a radiation code and examines both their individual and combined impacts on the global radiation budget using re-analysis data.

Effect of improving representation of horizontal and vertical cloud structure on the Earth's global radiation budget. Part II: The global effects

Reliably representing both horizontal cloud inhomogeneity and vertical cloud overlap is fundamentally important for the radiation budget of a general circulation model. Here, we build on the work of Part One of this two-part paper by applying a pair of parameterisations that account for horizontal inhomogeneity and vertical overlap to global re-analysis data. These are applied both together and separately in an attempt to quantify the effects of poor representation of the two components on radiation budget. Horizontal inhomogeneity is accounted for using the “Tripleclouds” scheme, which uses two regions of cloud in each layer of a gridbox as opposed to one; vertical overlap is accounted for using “exponential-random” overlap, which aligns vertically continuous cloud according to a decorrelation height. These are applied to a sample of scenes from a year of ERA-40 data. The largest radiative effect of horizontal inhomogeneity is found to be in areas of marine stratocumulus; the effect of vertical overlap is found to be fairly uniform, but with larger individual short-wave and long-wave effects in areas of deep, tropical convection. The combined effect of the two parameterisations is found to reduce the magnitude of the net top-of-atmosphere cloud radiative forcing (CRF) by 2.25 W m−2, with shifts of up to 10 W m−2 in areas of marine stratocumulus. The effects of the uncertainty in our parameterisations on radiation budget is also investigated. It is found that the uncertainty in the impact of horizontal inhomogeneity is of order ±60%, while the uncertainty in the impact of vertical overlap is much smaller. This suggests an insensitivity of the radiation budget to the exact nature of the global decorrelation height distribution derived in Part One.