Evaluation of ice cloud representation in the ECMWF and UK Met Office models using CloudSat and CALIPSO data

Ice cloud representation in general circulation models remains a challenging task, due to the lack of accurate observations and the complexity of microphysical processes. In this article, we evaluate the ice water content (IWC) and ice cloud fraction statistical distributions from the numerical weather prediction models of the European Centre for Medium-Range Weather Forecasts (ECMWF) and the UK Met Office, exploiting the synergy between the CloudSat radar and CALIPSO lidar. Using the last three weeks of July 2006, we analyse the global ice cloud occurrence as a function of temperature and latitude and show that the models capture the main geographical and temperature-dependent distributions, but overestimate the ice cloud occurrence in the Tropics in the temperature range from −60 °C to −20 °C and in the Antarctic for temperatures higher than −20 °C, but underestimate ice cloud occurrence at very low temperatures. A global statistical comparison of the occurrence of grid-box mean IWC at different temperatures shows that both the mean and range of IWC increases with increasing temperature. Globally, the models capture most of the IWC variability in the temperature range between −60 °C and −5 °C, and also reproduce the observed latitudinal dependencies in the IWC distribution due to different meteorological regimes. Two versions of the ECMWF model are assessed. The recent operational version with a diagnostic representation of precipitating snow and mixed-phase ice cloud fails to represent the IWC distribution in the −20 °C to 0 °C range, but a new version with prognostic variables for liquid water, ice and snow is much closer to the observed distribution. The comparison of models and observations provides a much-needed analysis of the vertical distribution of IWC across the globe, highlighting the ability of the models to reproduce much of the observed variability as well as the deficiencies where further improvements are required.

Connecting mean field models of neural activity to EEG and fMRI data

Progress in functional neuroimaging of the brain increasingly relies on the integration of data from complementary imaging modalities in order to improve spatiotemporal resolution and interpretability. However, the usefulness of merely statistical combinations is limited, since neural signal sources differ between modalities and are related non-trivially. We demonstrate here that a mean field model of brain activity can simultaneously predict EEG and fMRI BOLD with proper signal generation and expression. Simulations are shown using a realistic head model based on structural MRI, which includes both dense short-range background connectivity and long-range specific connectivity between brain regions. The distribution of modeled neural masses is comparable to the spatial resolution of fMRI BOLD, and the temporal resolution of the modeled dynamics, importantly including activity conduction, matches the fastest known EEG phenomena. The creation of a cortical mean field model with anatomically sound geometry, extensive connectivity, and proper signal expression is an important first step towards the model-based integration of multimodal neuroimages.

The relationship between community-based organisations and the effective management of coastal and marine resources in the Western Indian Ocean region

This study examines the relationship between community based organisations and marine and coastal resource management in the Western Indian Ocean Region.

An aggregated wind power generation model based on MERRA reanalysis data: MATLAB model and example data for the April 2014 wind farm distribution of Great Britain.

The MATLAB model is contained within the compressed folders (versions are available as .zip and .tgz). This model uses MERRA reanalysis data (>34 years available) to estimate the hourly aggregated wind power generation for a predefined (fixed) distribution of wind farms. A ready made example is included for the wind farm distribution of Great Britain, April 2014 ("CF.dat"). This consists of an hourly time series of GB-total capacity factor spanning the period 1980-2013 inclusive. Given the global nature of reanalysis data, the model can be applied to any specified distribution of wind farms in any region of the world. Users are, however, strongly advised to bear in mind the limitations of reanalysis data when using this model/data. This is discussed in our paper: Cannon, Brayshaw, Methven, Coker, Lenaghan. "Using reanalysis data to quantify extreme wind power generation statistics: a 33 year case study in Great Britain". Submitted to Renewable Energy in March, 2014. Additional information about the model is contained in the model code itself, in the accompanying ReadMe file, and on our website: http://www.met.reading.ac.uk/~energymet/data/Cannon2014/

Technology acceptance and care self-management: consideration in context of chronic care management

With an aging global population, the number of people living with a chronic illness is expected to increase significantly by 2050. If left unmanaged, chronic care leads to serious health complications, resulting in poor patient quality of life and a costly time bomb for care providers. If effectively managed, patients with chronic care tend to live a richer and more healthy life, resulting in a less costly total care solution. This chapter considers literature from the areas of technology acceptance and care self-management, which aims to alleviate symptoms and/or reason for non-acceptance of care, and thus minimise the risk of long-term complications, which in turn reduces the chance of spiralling health expenditure. By bringing together these areas, the chapter highlights areas where self-management is failing so that changes can be made in care in advance of health deterioration.

Representation errors and retrievals in linear and nonlinear data assimilation

This article shows how one can formulate the representation problem starting from Bayes’ theorem. The purpose of this article is to raise awareness of the formal solutions,so that approximations can be placed in a proper context. The representation errors appear in the likelihood, and the different possibilities for the representation of reality in model and observations are discussed, including nonlinear representation probability density functions. Specifically, the assumptions needed in the usual procedure to add a representation error covariance to the error covariance of the observations are discussed,and it is shown that, when several sub-grid observations are present, their mean still has a representation error ; socalled ‘superobbing’ does not resolve the issue. Connection is made to the off-line or on-line retrieval problem, providing a new simple proof of the equivalence of assimilating linear retrievals and original observations. Furthermore, it is shown how nonlinear retrievals can be assimilated without loss of information. Finally we discuss how errors in the observation operator model can be treated consistently in the Bayesian framework, connecting to previous work in this area.

The dependence of high-latitude dayside ionospheric flows on the North-South component of the IMF: A high time resolution correlation analysis using EISCAT “Polar” and AMPTE UKS and IRM data

In 1984 and 1985 a series of experiments was undertaken in which dayside ionospheric flows were measured by the EISCAT “Polar” experiment, while observations of the solar wind and interplanetary magnetic field (IMF) were made by the AMPTE UKS and IRM spacecraft upstream from the Earth's bow shock. As a result, 40 h of simultaneous data were acquired, which are analysed in this paper to investigate the relationship between the ionospheric flow and the North-South (Bz) component of the IMF. The ionospheric flow data have 2.5 min resolution, and cover the dayside local time sector from ∼ 09:30 to ∼ 18:30 M.L.T. and the latitude range from 70.8° to 74.3°. Using cross-correlation analysis it is shown that clear relationships do exist between the ionospheric flow and IMF Bz, but that the form of the relations depends strongly on latitude and local time. These dependencies are readily interpreted in terms of a twinvortex flow pattern in which the magnitude and latitudinal extent of the flows become successively larger as Bz becomes successively more negative. Detailed maps of the flow are derived for a range of Bz values (between ± 4 nT) which clearly demonstrate the presence of these effects in the data. The data also suggest that the morning reversal in the East-West component of flow moves to earlier local times as Bz, declines in value and becomes negative. The correlation analysis also provides information on the ionospheric response time to changes in IMF Bz, it being found that the response is very rapid indeed. The most rapid response occurs in the noon to mid-afternoon sector, where the westward flows of the dusk cell respond with a delay of 3.9 ± 2.2 min to changes in the North-South field at the subsolar magnetopause. The flows appear to evolve in form over the subsequent ~ 5 min interval, however, as indicated by the longer response times found for the northward component of flow in this sector (6.7 ±2.2 min), and in data from earlier and later local times. No evidence is found for a latitudinal gradient in response time; changes in flow take place coherently in time across the entire radar field-of-view.

Networks and plant disease management: concepts and applications

A network is a natural structure with which to describe many aspects of a plant pathosystem. The article seeks to set out in a nonmathematical way some of the network concepts that promise to be useful in managing plant disease. The field has been stimulated by developments designed to help understand and manage animal and human disease, as well as by technical infrastructures, such as the internet. It overlaps partly with landscape ecology. The study of networks has helped identify likely ways to reduce flow of disease in traded plants, to find the best sites to monitor as warning sites for annually reinvading disease, and to understand the fundamentals of how a pathogen spreads in different structures. A tension between the free flow of goods or species down communication channels and free flow of pathogens down the same pathways is highlighted.

A cross layer protocol for energy aware and critical data delivered applications using Wireless Sensor Networks

Environment monitoring applications using Wireless Sensor Networks (WSNs) have had a lot of attention in recent years. In much of this research tasks like sensor data processing, environment states and events decision making and emergency message sending are done by a remote server. A proposed cross layer protocol for two different applications where, reliability for delivered data, delay and life time of the network need to be considered, has been simulated and the results are presented in this paper. A WSN designed for the proposed applications needs efficient MAC and routing protocols to provide a guarantee for the reliability of the data delivered from source nodes to the sink. A cross layer based on the design given in [1] has been extended and simulated for the proposed applications, with new features, such as routes discovery algorithms added. Simulation results show that the proposed cross layer based protocol can conserve energy for nodes and provide the required performance such as life time of the network, delay and reliability.