Retrieval of effective ice crystal size in the infrared: sensitivity study and global measurements from TIROS-N Operational Vertical Sounder

The difference between cirrus emissivities at 8 and 11 μm is sensitive to the mean effective ice crystal size of the cirrus cloud, De. By using single scattering properties of ice crystals shaped as planar polycrystals, diameters of up to about 70 μm can be retrieved, instead of up to 45 μm assuming spheres or hexagonal columns. The method described in this article is used for a global determination of mean effective ice crystal sizes of cirrus clouds from TOVS satellite observations. A sensitivity study of the De retrieval to uncertainties in hypotheses on ice crystal shape, size distributions, and temperature profiles, as well as in vertical and horizontal cloud heterogeneities shows that uncertainties can be as large as 30%. However, the TOVS data set is one of few data sets which provides global and long-term coverage. Having analyzed the years 1987–1991, it was found that measured effective ice crystal diameters De are stable from year to year. For 1990 a global median De of 53.5 μm was determined. Averages distinguishing ocean/land, season, and latitude lie between 23 μm in winter over Northern Hemisphere midlatitude land and 64 μm in the tropics. In general, larger Des are found in regions with higher atmospheric water vapor and for cirrus with a smaller effective emissivity.

Data assimilation for marine monitoring and prediction: The MERCATOR operational assimilation systems and the MERSEA developments

During the past 15 years, a number of initiatives have been undertaken at national level to develop ocean forecasting systems operating at regional and/or global scales. The co-ordination between these efforts has been organized internationally through the Global Ocean Data Assimilation Experiment (GODAE). The French MERCATOR project is one of the leading participants in GODAE. The MERCATOR systems routinely assimilate a variety of observations such as multi-satellite altimeter data, sea-surface temperature and in situ temperature and salinity profiles, focusing on high-resolution scales of the ocean dynamics. The assimilation strategy in MERCATOR is based on a hierarchy of methods of increasing sophistication including optimal interpolation, Kalman filtering and variational methods, which are progressively deployed through the Syst`eme d’Assimilation MERCATOR (SAM) series. SAM-1 is based on a reduced-order optimal interpolation which can be operated using ‘altimetry-only’ or ‘multi-data’ set-ups; it relies on the concept of separability, assuming that the correlations can be separated into a product of horizontal and vertical contributions. The second release, SAM-2, is being developed to include new features from the singular evolutive extended Kalman (SEEK) filter, such as three-dimensional, multivariate error modes and adaptivity schemes. The third one, SAM-3, considers variational methods such as the incremental four-dimensional variational algorithm. Most operational forecasting systems evaluated during GODAE are based on least-squares statistical estimation assuming Gaussian errors. In the framework of the EU MERSEA (Marine EnviRonment and Security for the European Area) project, research is being conducted to prepare the next-generation operational ocean monitoring and forecasting systems. The research effort will explore nonlinear assimilation formulations to overcome limitations of the current systems. This paper provides an overview of the developments conducted in MERSEA with the SEEK filter, the Ensemble Kalman filter and the sequential importance re-sampling filter.

Building operational energy consumption of Chongqing and its exergy assessment

Building energy consumption(BEC) accounting and assessment is fundamental work for building energy efficiency(BEE) development. In existing Chinese statistical yearbook, there is no specific item for BEC accounting and relevant data are separated and mixed with other industry consumption. Approximate BEC data can be acquired from existing energy statistical yearbook. For BEC assessment, caloric values of different energy carriers are adopted in energy accounting and assessment field. This methodology obtained much useful conclusion for energy efficiency development. While the traditional methodology concerns only on the energy quantity, energy classification issue is omitted. Exergy methodology is put forward to assess BEC. With the new methodology, energy quantity and quality issues are both concerned in BEC assessment. To illustrate the BEC accounting and exergy assessment, a case of Chongqing in 2004 is shown. Based on the exergy analysis, BEC of Chongqing in 2004 accounts for 17.3% of the total energy consumption. This result is quite common to that of traditional methodology. As far as energy supply efficiency is concerned, the difference is highlighted by 0.417 of the exergy methodology to 0.645 of the traditional methodology.

Introducing assistive technology into the existing homes of older people: Feasibility, acceptability, costs and outcomes

We examined how far, and at what cost, the housing stock could be modified to accommodate the assistive technology (AT) necessary to enable older people to remain in their own homes. A multidisciplinary team devised seven hypothetical user profiles for 10 case study areas, with five local authorities and five housing associations in England and Wales. Each profile was considered at two times, five years apart, with the users' functional abilities deteriorating in between. In addition, in-depth interviews were carried out with a sample of 67 older people in the case study areas about their use and experience of a wide range of AT. The interviews showed the need to listen to older people and that they welcomed AT when it addressed a perceived need. The results showed that the extent of adaptation required of buildings to accommodate a user's needs varied greatly. It was also found that there was confusion about the terminology of AT, including the idea of the 'smart house'. The study shows that the adaptability of the housing depends on a range of factors and costs.

Optimal maintenance policies under different operational schedules

In the reliability literature, maintenance time is usually ignored during the optimization of maintenance policies. In some scenarios, costs due to system failures may vary with time, and the ignorance of maintenance time will lead to unrealistic results. This paper develops maintenance policies for such situations where the system under study operates iteratively at two successive states: up or down. The costs due to system failure at the up state consist of both business losses & maintenance costs, whereas those at the down state only include maintenance costs. We consider three models: Model A, B, and C: Model A makes only corrective maintenance (CM). Model B performs imperfect preventive maintenance (PM) sequentially, and CM. Model C executes PM periodically, and CM; this PM can restore the system as good as the state just after the latest CM. The CM in this paper is imperfect repair. Finally, the impact of these maintenance policies is illustrated through numerical examples.

Investigating the feasibility of a swarm-array computing approach

The work reported in this paper proposes a novel synergy between parallel computing and swarm robotics to offer a new computing paradigm, 'swarm-array computing' that can harness and apply autonomic computing for parallel computing systems. One approach among three proposed approaches in swarm-array computing based on landscapes of intelligent cores, in which the cores of a parallel computing system are abstracted to swarm agents, is investigated. A task is executed and transferred seamlessly between cores in the proposed approach thereby achieving self-ware properties that characterize autonomic computing. FPGAs are considered as an experimental platform taking into account its application in space robotics. The feasibility of the proposed approach is validated on the SeSAm multi-agent simulator.

Achieving intelligent agents and its feasibility in swarm-array computing

The work reported in this paper proposes ‘Intelligent Agents’, a Swarm-Array computing approach focused to apply autonomic computing concepts to parallel computing systems and build reliable systems for space applications. Swarm-array computing is a robotics a swarm robotics inspired novel computing approach considered as a path to achieve autonomy in parallel computing systems. In the intelligent agent approach, a task to be executed on parallel computing cores is considered as a swarm of autonomous agents. A task is carried to a computing core by carrier agents and can be seamlessly transferred between cores in the event of a predicted failure, thereby achieving self-* objectives of autonomic computing. The approach is validated on a multi-agent simulator.

Using continuous ground-based radar and lidar measurements for evaluating the representation of clouds in four operational models

The ability of four operational weather forecast models [ECMWF, Action de Recherche Petite Echelle Grande Echelle model (ARPEGE), Regional Atmospheric Climate Model (RACMO), and Met Office] to generate a cloud at the right location and time (the cloud frequency of occurrence) is assessed in the present paper using a two-year time series of observations collected by profiling ground-based active remote sensors (cloud radar and lidar) located at three different sites in western Europe (Cabauw. Netherlands; Chilbolton, United Kingdom; and Palaiseau, France). Particular attention is given to potential biases that may arise from instrumentation differences (especially sensitivity) from one site to another and intermittent sampling. In a second step the statistical properties of the cloud variables involved in most advanced cloud schemes of numerical weather forecast models (ice water content and cloud fraction) are characterized and compared with their counterparts in the models. The two years of observations are first considered as a whole in order to evaluate the accuracy of the statistical representation of the cloud variables in each model. It is shown that all models tend to produce too many high-level clouds, with too-high cloud fraction and ice water content. The midlevel and low-level cloud occurrence is also generally overestimated, with too-low cloud fraction but a correct ice water content. The dataset is then divided into seasons to evaluate the potential of the models to generate different cloud situations in response to different large-scale forcings. Strong variations in cloud occurrence are found in the observations from one season to the same season the following year as well as in the seasonal cycle. Overall, the model biases observed using the whole dataset are still found at seasonal scale, but the models generally manage to well reproduce the observed seasonal variations in cloud occurrence. Overall, models do not generate the same cloud fraction distributions and these distributions do not agree with the observations. Another general conclusion is that the use of continuous ground-based radar and lidar observations is definitely a powerful tool for evaluating model cloud schemes and for a responsive assessment of the benefit achieved by changing or tuning a model cloud

Feasibility of guided CBT self-help for childhood anxiety disorders in primary care

Anxiety disorders in childhood are common, disabling and run a chronic course. Cognitive Behaviour Therapy (CBT) effective but is expensive and trained therapists are scarce. Guided self-help treatments may be a means of widening access to treatment. This study aimed to examine the feasibility of guided CBT self-help for childhood anxiety disorders in Primary Care, specifically in terms of therapist adherence, patient and therapist satisfaction and clinical gain. Participants were children aged 5-12 years referred to two Primary Child and Adolescent Mental Health Services (PCAMHSs) in Oxfordshire, UK, who met diagnostic criteria for a primary anxiety disorder. Of the 52 eligible children, 41 anxious children were assessed for anxiety severity and interference before and after receiving CBT self-help, delivered via the parent (total therapy time= 5 hours) by Primary Mental Health Workers (PMHWs). Therapy sessions were rated for treatment adherence and patients and PMHWs completed satisfaction questionnaires after treatment completion. Over 80% of therapy sessions were rated at a high level of treatment adherence. Parents and PMHWs reported high satisfaction with the treatment. 61% of the children assessed no longer met criteria for their primary anxiety disorder diagnosis following treatment, and 76% were rated as ‘much’/’very much’ improved on the Clinician’s Global Impression-Improvement scale. There were significant reductions on parent and child report measures of anxiety symptoms, interference, and depression. Preliminary exploration indicated that parental anxiety was associated with child treatment outcome. The findings suggest that guided CBT self-help represents a promising treatment for childhood anxiety in primary care.