The development of a new dust uplift scheme in the Met Office Unified Model

Aeolian mineral dust aerosol is an important consideration in the Earth's radiation budget as well as a source of nutrients to oceanic and land biota. The modelling of aeolian mineral dust has been improving consistently despite the relatively sparse observations to constrain them. This study documents the development of a new dust emissions scheme in the Met Office Unified ModelTM (MetUM) based on the Dust Entrainment and Deposition (DEAD) module. Four separate case studies are used to test and constrain the model output. Initial testing was undertaken on a large dust event over North Africa in March 2006 with the model constrained using AERONET data. The second case study involved testing the capability of the model to represent dust events in the Middle East without being re-tuned from the March 2006 case in the Sahara. While the model is unable to capture some of the daytime variation in AERONET AOD there is good agreement between the model and observed dust events. In the final two case studies new observations from in situ aircraft data during the Dust Outflow and Deposition to the Ocean (DODO) campaigns in February and August 2006 were used. These recent observations provided further data on dust size distributions and vertical profiles to constrain the model. The modelled DODO cases were also compared to AERONET data to make sure the radiative properties of the dust were comparable to observations. Copyright © 2009 Royal Meteorological Society and Crown Copyright

The postfusion structure of baculovirus gp64 supports a unified view of viral fusion machines

Viral fusion proteins mediate the merger of host and viral membranes during cell entry for all enveloped viruses. Baculovirus glycoprotein gp64 (gp64) is unusual in promoting entry into both insect and mammalian cells and is distinct from established class I and class II fusion proteins. We report the crystal structure of its postfusion form, which explains a number of gp64's biological properties including its cellular promiscuity, identifies the fusion peptides and shows it to be the third representative of a new class (III) of fusion proteins with unexpected structural homology with vesicular stomatitis virus G and herpes simplex virus type 1 gB proteins. We show that domains of class III proteins have counterparts in both class I and II proteins, suggesting that all these viral fusion machines are structurally more related than previously thought.

Sparse kernel modelling: a unified approach

A unified approach is proposed for sparse kernel data modelling that includes regression and classification as well as probability density function estimation. The orthogonal-least-squares forward selection method based on the leave-one-out test criteria is presented within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic sparse kernel data modelling approach.

Orthogonal-least-squares regression: A unified approach for data modelling

A unified approach is proposed for data modelling that includes supervised regression and classification applications as well as unsupervised probability density function estimation. The orthogonal-least-squares regression based on the leave-one-out test criteria is formulated within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic data-modelling approach for constructing parsimonious kernel models with excellent generalisation capability. (C) 2008 Elsevier B.V. All rights reserved.

Particle swarm optimization aided orthogonal forward regression for unified data modeling

We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave-one-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixed-node RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications.

A unified titration formula

In a number of standard titrations, a volume of a monoprotic base MOH at a specific concentration is added to a volume of a monoprotic acid HA at a specific concentrations. Four different types of titration are possible, depending on whether the acid and base are strong or weak. A single unifying formula covering all four cases has been determined.

Influences on secured lending property valuations in the UK

A significant part of bank lending in the UK is secured on commercial property and valuations play an important part in this process. They are an integral part of risk management within the banking sector. It is therefore important that valuations are independent and objective and are used properly to ensure that secured lending is soundly based from the perspective of both lender and borrower. The purpose of this research is to examine objectivity and transparency in the valuation process for bank lending and to identify any influences which may undermine the process. A detailed analysis of 31 valuation negligence cases has been followed by two focus groups of lenders and valuers and also questionnaire surveys of commercial lenders and valuers. Many stakeholders exist, for example lenders, borrowers and brokers, who are able to influence the process in various ways. The strongest evidence of overt influence in the process comes from the method of valuer selection with borrowers and brokers seen to be heavily involved. There is some also some evidence of influence during the draft valuation process. A significant minority of valuers feel that inappropriate pressure is applied by borrowers and brokers yet there is no apparent part of the process that leads to this. The panel system employed by lenders is found to be a significant part of the system and merits further examination. The pressure felt by valuers needs more investigation along with the question of if and how the process could dispel such feelings. This is seen as particularly important in the context of bank regulation.

The HadGEM2 family of Met Office Unified Model climate configurations

We describe the HadGEM2 family of climate configurations of the Met Office Unified Model, MetUM. The concept of a model "family" comprises a range of specific model configurations incorporating different levels of complexity but with a common physical framework. The HadGEM2 family of configurations includes atmosphere and ocean components, with and without a vertical extension to include a well-resolved stratosphere, and an Earth-System (ES) component which includes dynamic vegetation, ocean biology and atmospheric chemistry. The HadGEM2 physical model includes improvements designed to address specific systematic errors encountered in the previous climate configuration, HadGEM1, namely Northern Hemisphere continental temperature biases and tropical sea surface temperature biases and poor variability. Targeting these biases was crucial in order that the ES configuration could represent important biogeochemical climate feedbacks. Detailed descriptions and evaluations of particular HadGEM2 family members are included in a number of other publications, and the discussion here is limited to a summary of the overall performance using a set of model metrics which compare the way in which the various configurations simulate present-day climate and its variability.