Cloud bands over southern Africa: seasonality, contribution to rainfall variability, and modulation by the MJO

Tropical-extratropical cloud band systems over southern Africa, known as tropical temperate troughs (TTTs), are known to contribute substantially to South African summer rainfall. This study performs a comprehensive assessment of the seasonal cycle and rainfall contribution of TTTs by using a novel object-based strategy that explicitly tracks these systems for their full life cycle. The methodology incorporates a simple assignment of station rainfall data to each event, thereby creating a database containing detailed rainfall characteristics for each TTT. This is used to explore the importance of TTTs for rain days and climatological rainfall totals in October–March. Average contributions range from 30 to 60 % with substantial spatial heterogeneity observed. TTT rainfall contributions over the Highveld and eastern escarpment are lower than expected. A short analysis of TTT rainfall variability indicates TTTs provide substantial, but not dominant, intraseasonal and interannual variability in station rainfall totals. TTTs are however responsible for a high proportion of heavy rainfall days. Of 52 extreme rainfall events in the 1979–1999 period, 30 are associated with these tropical-extratropical interactions. Cut-off lows were included in the evolution of 6 of these TTTs. The study concludes with an analysis of the question: does the Madden-Julian Oscillation influence the intensity of TTT rainfall over South Africa? Results suggest a weak but significant suppression (enhancement) of intensity during phase 1(6).

Impacts of the Madden-Julian oscillation on intraseasonal precipitation over northeast Brazil

Este estudo tem como objetivo investigar os impactos da oscilação de Madden-Julian (OMJ) na precipitação da região Nordeste do Brasil (NEB). Para tanto foram utilizados dados diários de precipitação baseados em 492 pluviômetros distribuídos na região e cobrindo um período de 30 anos (1981 − 2010). As análises através de composições de anomalias de precipitação, radiação de onda longa e fluxo de umidade, foram obtidas com base no índice da OMJ desenvolvido por Jones-Carvalho. Para distinguir o sinal da OMJ de outros padrões de variabilidade climática, todos os dados diários foram filtrados na escala de 20 − 90 dias; portanto somente dias classificados como eventos da OMJ foram considerados nas composições. Uma análise preliminar baseada apenas nos dados de precipitação foi feita para uma pequena área localizada no interior semiárido do NEB, conhecida como Seridó. Essa microrregião é uma das áreas mais secas do NEB e foi reconhecida pela Convenção das Nações Unidas para o Combate à Desertificação e Mitigação dos Efeitos das Secas como particularmente vulnerável à desertificação. Composições de anomalias de precipitação foram feitas para cada uma das oito fases da OMJ durante Fevereiro-Maio (principal período chuvoso da microrregião). Os resultados mostraram a existência de variações significativas nos padrões de precipitação (de precipitação excessiva à deficiente) associados à propagação da OMJ. A combinação dos sinais de precipitação obtidos durantes as fases úmidas e secas da OMJ mostrou que a diferença corresponde cerca de 50 − 150% de modulação das chuvas na microrregião. Em seguida, uma investigação abrangente sobre o papel da OMJ sobre toda a região Nordeste foi feita considerando-se as quatro estações do ano. Os resultados mostraram que os impactos da OMJ na precipitação intrassazonal do NEB apresentam forte sazonalidade. A maior coerência espacial dos sinais de precipitação ocorreram durante o verão austral, quando cerca de 80% das estações pluviométricas apresentaram anomalias positivas de precipitação durante as fases 1 − 2 da OMJ e anomalias negativas de precipitação nas fases 5 − 6 da oscilação. Embora impactos da OMJ na precipitação intrassazonal tenham sido encontrados na maioria das localidades e em todas as estações do ano, eles apresentaram variações na magnitude dos sinais e dependem da fase da oscilação. As anomalias de precipitação do NEB observadas são explicadas através da interação existente entre as ondas de Kelvin-Rossby acopladas convectivamente e as características climáticas predominantes sobre a região em cada estação do ano. O aumento de precipitação observado sobre a maior parte do NEB durante o verão e primavera austrais encontra-se associado com o fluxo de umidade de oeste (regime de oeste), o qual favorece a atividade convectiva em amplas áreas da América do Sul tropical. Por outro lado, as anomalias de precipitação durante o inverno e outono austrais apresentaram uma variabilidade espacial mais complexa. Durante estas estações, as anomalias de precipitação observadas nas estações localizadas na costa leste do NEB dependem da intensidade do anticiclone do Atlântico Sul, o qual é modulado em grande parte por ondas de Rossby. As características topográficas do NEB parecem desempenhar um papel importante na variabilidade observada na precipitação, principalmente nestas áreas costeiras. A intensificação do anticiclone aumenta a convergência dos ventos alísios na costa contribuindo para a ocorrência de precipitação observada à barlavento do planalto da Borborema. Por outro lado, o aumento da subsidência parece ser responsável pelos déficits de precipitação observados à sotavento. Tais condições mostraram-se típicas durante o predomínio do regime de leste sobre a região tropical da América do Sul e o NEB, durante o qual ocorre uma diminuição no fluxo de umidade proveniente da Amazônia.

Weather variability, tides, and Barmah Forest Virus Disease in the Gladstone region, Australia

In this study we examined the impact of weather variability and tides on the transmission of Barmah Forest virus (BFV) disease and developed a weather-based forecasting model for BFV disease in the Gladstone region, Australia. We used seasonal autoregressive integrated moving-average (SARIMA) models to determine the contribution of weather variables to BFV transmission after the time-series data of response and explanatory variables were made stationary through seasonal differencing. We obtained data on the monthly counts of BFV cases, weather variables (e.g., mean minimum and maximum temperature, total rainfall, and mean relative humidity), high and low tides, and the population size in the Gladstone region between January 1992 and December 2001 from the Queensland Department of Health, Australian Bureau of Meteorology, Queensland Department of Transport, and Australian Bureau of Statistics, respectively. The SARIMA model shows that the 5-month moving average of minimum temperature (β = 0.15, p-value < 0.001) was statistically significantly and positively associated with BFV disease, whereas high tide in the current month (β = −1.03, p-value = 0.04) was statistically significantly and inversely associated with it. However, no significant association was found for other variables. These results may be applied to forecast the occurrence of BFV disease and to use public health resources in BFV control and prevention.

On analysing and interpreting variability in motor output

A recent article in the Journal of Science and Medicine in Sport by Chapman et al.1 reported data from an empirical investigation comparing lower extremity joint motions, joint coordination and muscle recruitment in expert and novice cyclists. 3D kinematic and intramuscular electromyographic (EMG) analyses revealed no differences between expert and novice cyclists for normalised joint angles and velocities of the pelvis, hip, knee and ankle. However, significant differences in the strength of sagittal plane kinematics for hip–ankle and knee–ankle joint couplings were reported, with expert cyclists displaying tighter coupling relationships than novice cyclists. Furthermore, significant differences between expert and novice cyclists for all muscle recruitment parameters, except timing of peak EMG amplitude, were also reported.

Magnitude and variability of loading on the osseointegrated implant of transfemoral amputees during walking

This study directly measured the load acting on the abutment of the osseointegrated implant system of transfemoral amputees during level walking, and studied the variability of the load within and among amputees. Twelve active transfemoral amputees (age: 54±12 years, mass:84.3±16.3 kg, height: 17.8±0.10 m) fitted with an osseointegrated implant for over 1 year participated in the study. The load applied on the abutment was measured during unimpeded, level walking in a straight line using a commercial six-channel transducer mounted between the abutment and the prosthetic knee. The pattern and the magnitude of the three-dimensional forces and moments were revealed. Results showed a low step-to-step variability of each subject, but a high subject-to-subject variability in local extrema of body-weight normalized forces and moments and impulse data. The high subject-to-subject variability suggests that the mechanical design of the implant system should be customized for each individual, or that a fit-all design should take into consideration the highest values of load within a broad range of amputees. It also suggests specific loading regime in rehabilitation training are necessary for a given subject. Thus the loading magnitude and variability demonstrated should be useful in designing an osseointegrated implant system better able to resist mechanical failure and in refining the rehabilitation protocol.

Within-session variability modelling for factor analysis speaker verification

This work presents an extended Joint Factor Analysis model including explicit modelling of unwanted within-session variability. The goals of the proposed extended JFA model are to improve verification performance with short utterances by compensating for the effects of limited or imbalanced phonetic coverage, and to produce a flexible JFA model that is effective over a wide range of utterance lengths without adjusting model parameters such as retraining session subspaces. Experimental results on the 2006 NIST SRE corpus demonstrate the flexibility of the proposed model by providing competitive results over a wide range of utterance lengths without retraining and also yielding modest improvements in a number of conditions over current state-of-the-art.

Within- and between-day repeatability and variability in children's physiological responses during submaximal treadmill exercise

The purpose of this study was to verify within- and between-day repeatability and variability in children's oxygen uptake (VO^sub 2^), gross economy (GE; VO^sub 2^ divided by speed) and heart rate (HR) during treadmill walking based on self-selected speed (SS). Fourteen children (10.1 ± 1.4 years) undertook three testing sessions over 2 days in which four walking speeds, including SS were tested. Within- and between-day repeatability were assessed using the Bland and Altman method, and coefficients of variability (CV) were determined for each child across exercise bouts and averaged to obtain a mean group CV value for VO^sub 2^, GE, and HR per speed. Repeated measures analysis of variance showed no statistically significant differences in within- or between-day CV for VO^sub 2^, GE, or HR at any speed. Repeatability within- and between-day for VO^sub 2^, GE, and HR for all speeds was verified. These results suggest that submaximal VO^sub 2^ during treadmill walking is stable and reproducible at a range of speeds based on children's SS.

Weather variability, sunspots, and the blooms of cyanobacteria

The roles of weather variability and sunspots in the occurrence of cyanobacteria blooms, were investigated using cyanobacteria cell data collected from the Fred Haigh Dam, Queensland, Australia. Time series generalized linear model and classification and regression (CART) model were used in the analysis. Data on notified cell numbers of cyanobacteria and weather variables over the periods 2001 and 2005 were provided by the Australian Department of Natural Resources and Water, and Australian Bureau of Meteorology, respectively. The results indicate that monthly minimum temperature (relative risk [RR]: 1.13, 95% confidence interval [CI]: 1.02-1.25) and rainfall (RR: 1.11; 95% CI: 1.03-1.20) had a positive association, but relative humidity (RR: 0.94; 95% CI: 0.91-0.98) and wind speed (RR:0.90; 95% CI: 0.82-0.98) were negatively associated with the cyanobacterial numbers, after adjustment for seasonality and auto-correlation. The CART model showed that the cyanobacteria numbers were best described by an interaction between minimum temperature, relative humidity, and sunspot numbers. When minimum temperature exceeded 18%C and relative humidity was under 66%, the number of cyanobacterial cells rose by 2.15-fold. We conclude that the weather variability and sunspot activity may affect cyanobacterial blooms in dams.

Cardiac state diagnosis using higher order spectra of heart rate variability

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart, by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are nonlinear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of nonlinear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and seven classes of arrhythmia. We present some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. We also extracted features from the HOS and performed an analysis of variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test.

Spatial variability of soil carbon in forested and cultivated sites: Implications for change detection

The potential to sequester atmospheric carbon in agricultural and forest soils to offset greenhouse gas emissions has generated interest in measuring changes in soil carbon resulting from changes in land management. However, inherent spatial variability of soil carbon limits the precision of measurement of changes in soil carbon and hence, the ability to detect changes. We analyzed variability of soil carbon by intensively sampling sites under different land management as a step toward developing efficient soil sampling designs. Sites were tilled crop-land and a mixed deciduous forest in Tennessee, and old-growth and second-growth coniferous forest in western Washington, USA. Six soil cores within each of three microplots were taken as an initial sample and an additional six cores were taken to simulate resampling. Soil C variability was greater in Washington than in Tennessee, and greater in less disturbed than in more disturbed sites. Using this protocol, our data suggest that differences on the order of 2.0 Mg C ha(-1) could be detected by collection and analysis of cores from at least five (tilled) or two (forest) microplots in Tennessee. More spatial variability in the forested sites in Washington increased the minimum detectable difference, but these systems, consisting of low C content sandy soil with irregularly distributed pockets of organic C in buried logs, are likely to rank among the most spatially heterogeneous of systems. Our results clearly indicate that consistent intramicroplot differences at all sites will enable detection of much more modest changes if the same microplots are resampled.

Spatial variability of soil organic carbon in grasslands: implications for detecting change at different scales

Extensive data used to quantify broad soil C changes (without information about causation), coupled with intensive data used for attribution of changes to specific management practices, could form the basis of an efficient national grassland soil C monitoring network. Based on variability of extensive (USDA/NRCS pedon database) and intensive field-level soil C data, we evaluated the efficacy of future sample collection to detect changes in soil C in grasslands. Potential soil C changes at a range of spatial scales related to changes in grassland management can be verified (alpha=0.1) after 5 years with collection of 34, 224, 501 samples at the county, state, or national scales, respectively. Farm-level analysis indicates that equivalent numbers of cores and distinct groups of cores (microplots) results in lowest soil C coefficients of variation for a variety of ecosystems. Our results suggest that grassland soil C changes can be precisely quantified using current technology at scales ranging from farms to the entire nation. (C) 2001 Elsevier Science Ltd. All rights reserved.

Climate variability and hemorrhagic fever with renal syndrome transmission in northeastern China

Background: The transmission of hemorrhagic fever with renal syndrome (HFRS) is influenced by climatic variables. However, few studies have examined the quantitative relationship between climate variation and HFRS transmission. ---------- Objective: We examined the potential impact of climate variability on HFRS transmission and developed climate-based forecasting models for HFRS in northeastern China. ---------- Methods: We obtained data on monthly counts of reported HFRS cases in Elunchun and Molidawahaner counties for 1997–2007 from the Inner Mongolia Center for Disease Control and Prevention and climate data from the Chinese Bureau of Meteorology. Cross-correlations assessed crude associations between climate variables, including rainfall, land surface temperature (LST), relative humidity (RH), and the multivariate El Niño Southern Oscillation (ENSO) index (MEI) and monthly HFRS cases over a range of lags. We used time-series Poisson regression models to examine the independent contribution of climatic variables to HFRS transmission. ----------- Results: Cross-correlation analyses showed that rainfall, LST, RH, and MEI were significantly associated with monthly HFRS cases with lags of 3–5 months in both study areas. The results of Poisson regression indicated that after controlling for the autocorrelation, seasonality, and long-term trend, rainfall, LST, RH, and MEI with lags of 3–5 months were associated with HFRS in both study areas. The final model had good accuracy in forecasting the occurrence of HFRS. ---------- Conclusions: Climate variability plays a significant role in HFRS transmission in northeastern China. The model developed in this study has implications for HFRS control and prevention.