Stochastic generation of annual, monthly and daily climate data: A review

International audienceThe generation of rainfall and other climate data needs a range of models depending on the time and spatial scales involved. Most of the models used previously do not take into account year to year variations in the model parameters. Long periods of wet and dry years were observed in the past but were not taken into account. Recently, Thyer and Kuczera (1999) developed a hidden state Markov model to account for the wet and dry spells explicitly in annual rainfall. This review looks firstly at traditional time series models and then at the more complex models which take account of the pseudo-cycles in the data. Monthly rainfall data have been generated successfully by using the method of fragments. The main criticism of this approach is the repetitions of the same yearly pattern when only a limited number of years of historical data are available. This deficiency has been overcome by using synthetic fragments but this brings an additional problem of generating the right number of months with zero rainfall. Disaggregation schemes are effective in obtaining monthly data but the main problem is the large number of parameters to be estimated when dealing with many sites. Several simplifications have been proposed to overcome this problem. Models for generating daily rainfall are well developed. The transition probability matrix method preserves most of the characteristics of daily, monthly and annual characteristics and is shown to be the best performing model. The two-part model has been shown by many researchers to perform well across a range of climates at the daily level but has not been tested adequately at monthly or annual levels. A shortcoming of the existing models is the consistent underestimation of the variances of the simulated monthly and annual totals. As an alternative, conditioning model parameters on monthly amounts or perturbing the model parameters with the Southern Oscillation Index (SOI) result in better agreement between the variance of the simulated and observed annual rainfall and these approaches should be investigated further. As climate data are less variable than rainfall, but are correlated among themselves and with rainfall, multisite-type models have been used successfully to generate annual data. The monthly climate data can be obtained by disaggregating these annual data. On a daily time step at a site, climate data have been generated using a multisite type model conditional on the state of the present and previous days. The generation of daily climate data at a number of sites remains a challenging problem. If daily rainfall can be modelled successfully by a censored power of normal distribution then the model can be extended easily to generate daily climate data at several sites simultaneously. Most of the early work on the impacts of climate change used historical data adjusted for the climate change. In recent studies, stochastic daily weather generation models are used to compute climate data by adjusting the parameters appropriately for the future climates assumed

Efficient reconfigurable techniques for VLSI arrays with 6-port switches

This paper proposes an efficient techniques to reconfigure a two-dimensional degradable very large scale integration/wafer scale integration (VLSI/WSI) array under the row and column routing constraints, which has been shown to be NP-complete. The proposed VLSI/WSI array consists of identical processing elements such as processors or memory cells embedded in a 6-port switch lattice in the form of a rectangular grid. It has been shown that the proposed VLSI structure with 6-port switches eliminates the need to incorporate internal bypass within processing elements and leads to notable increase in the harvest when compared with the one using 4-port switches. A new greedy rerouting algorithm and compensation approaches are also proposed to maximize harvest through reconfiguration. Experimental results show that the proposed VLSI array with 6-port switches consistently outperforms the most efficient alternative, proposed in literature, toward maximizing the harvest in the presence of fault processing elements

A real time correlator architecture using distributed arithmetic principles

A real time correlator design based on the principles of Distributed Arithmetic (DA) is described. This design is shown to be more efficient in terms of memory requirement than the direct DA implementation, especially when the number of coefficients is large. Since the proposed architecture implements the sum of product evaluation, it can be easily extended to finite and infinite response filters. Methods to further reduce the memory requirements are also discussed. A brief comparison is made between the proposed method and different DA implementations

Bronchoscopic Targeted Lung Denervation in Patients with Severe Asthma:Preliminary Findings

Treatment options for severe asthma are limited, particularly in those patients who do not meet criteria for biologicals. Targeted lung denervation (TLD) is the bronchoscopic ablation of the peribronchial vagal nerve trunks to reduce cholinergic stimulation of airway smooth muscle and submucosal glands. This report describes the experience of the first 2 asthma patients treated with TLD worldwide. The participants were 54 and 51 years of age, and both had severe asthma (GINA 5) (FEV1: 53% and 113% of predicted; AQLQ scores: 5.3 and 4.4). Both participants were treated with TLD in a single day-case procedure under general anaesthesia. Lung function, health status, and adverse event data were collected at baseline and 12 months after TLD. No treatment-related serious adverse events were reported up to 12 months. Cough symptoms improved in both participants, and 1 participant reported a marked reduction in rescue medication use at 6 months. There were no significant changes in spirometry, lung volumes, or health status. In conclusion, TLD was performed safely in both participants, but more evidence is needed to clarify safety and efficacy of TLD in severe asthma. Therefore, further investigation of the treatment in severe asthma patients would be useful

Bronchial Thermoplasty Induced Airway Smooth Muscle Reduction and Clinical Response in Severe Asthma:The TASMA Randomized Trial

RATIONALE: Bronchial Thermoplasty (BT) is a bronchoscopic treatment for severe asthma targeting airway smooth muscle (ASM). Observational studies have shown ASM mass reduction after BT but appropriate control groups are lacking. Furthermore, as treatment response is variable, identifying optimal candidates for BT treatment is important. AIMS: First, to assess the effect of BT on ASM mass and second, to identify patient characteristics that correlate with BT-response. METHODS: Severe asthma patients (n=40) were randomized to immediate (n=20) or delayed (n=20) BT-treatment. Prior to randomization, clinical, functional, blood and airway biopsy data were collected. In the delayed control group, re-assessment, including biopsies, was performed after 6 months of standard clinical care, followed by BT. In both groups, post-BT data including biopsies were obtained after 6 months. ASM mass (% positive desmin or α-smooth muscle actin area in the total biopsy) was calculated with automated digital analyses software. Associations between baseline characteristics and Asthma Control and Asthma Quality of Life Questionnaire (ACQ/AQLQ) improvement were explored. RESULTS: Median ASM mass decreased by >50% in the immediate BT group (n=17) versus no change in the delayed control group (n=19) (p=0.0004). In the immediate group ACQ scores improved with -0.79 (-1.61;0.02 IQR) compared to 0.09 (-0.25;1.17 IQR) in the delayed group (p=0.006). AQLQ scores improved with 0.83 (-0.15;1.69 IQR) versus -0.02 (-0.77;0.75 IQR) (p=0.04). Treatment response in the total group (n=35) was positively associated with serum IgE and eosinophils, but not with baseline ASM mass. CONCLUSION: ASM mass significantly decreases after BT when compared to a randomized non-BT treated control group. Treatment response was associated with serum IgE and eosinophil levels but not with ASM mass. Clinical trial registration available at www.clinicaltrials.gov, ID:NCT0222539