Short-lead seasonal precipitation forecast in northeastern Brazil using an ensemble of artificial neural networks.

This study assesses the deterministic and probabilistic forecasting skill of a 1-month-lead ensemble of Artificial Neural Networks (EANN) based on low-frequency climate oscillation indices. The predictand is the February-April (FMA) rainfall in the Brazilian state of Ceará, which is a prominent subject in climate forecasting studies due to its high seasonal predictability. Additionally, the study proposes combining the EANN with dynamical models into a hybrid multi-model ensemble (MME). The forecast verification is carried out through a leave-one-out cross-validation based on 40 years of data. The EANN forecasting skill is compared with traditional statistical models and the dynamical models that compose Ceará's operational seasonal forecasting system. A spatial comparison showed that the EANN was among the models with the smallest Root Mean Squared Error (RMSE) and Ranked Probability Score (RPS) in most regions. Moreover, the analysis of the area-aggregated reliability showed that the EANN is better calibrated than the individual dynamical models and has better resolution than Multinomial Logistic Regression for above-normal (AN) and below-normal (BN) categories. It is also shown that combining the EANN and dynamical models into a hybrid MME reduces the overconfidence of the extreme categories observed in a dynamically-based MME, improving the reliability of the forecasting system.

Constrained groupwise additive index models.

In environmental epidemiology, there is wide interest in creating and using comprehensive indices that can summarize information from different environmental exposures while retaining strong predictive power on a target health outcome. In this context, the present article proposes a model called the constrained groupwise additive index model (CGAIM) to create easy-to-interpret indices predictive of a response variable, from a potentially large list of variables. The CGAIM considers groups of predictors that naturally belong together to yield meaningful indices. It also allows the addition of linear constraints on both the index weights and the form of their relationship with the response variable to represent prior assumptions or operational requirements. We propose an efficient algorithm to estimate the CGAIM, along with index selection and inference procedures. A simulation study shows that the proposed algorithm has good estimation performances, with low bias and variance and is applicable in complex situations with many correlated predictors. It also demonstrates important sensitivity and specificity in index selection, but non-negligible coverage error on constructed confidence intervals. The CGAIM is then illustrated in the construction of heat indices in a health warning system context. We believe the CGAIM could become useful in a wide variety of situations, such as warning systems establishment, and multipollutant or exposome studies.

Heat-related mortality prediction using low-frequency climate oscillation indices: Case studies of the cities of Montréal and Québec, Canada.

Heat-related mortality is an increasingly important public health burden that is expected to worsen with climate change. In addition to long-term trends, there are also interannual variations in heat-related mortality that are of interest for efficient planning of health services. Large-scale climate patterns have an important influence on summer weather and therefore constitute important tools to understand and predict the variations in heat-related mortality. Methods: In this article, we propose to model summer heat-related mortality using seven climate indices through a two-stage analysis using data covering the period 1981-2018 in two metropolitan areas of the province of Québec (Canada): Montréal and Québec. In the first stage, heat attributable fractions are estimated through a time series regression design and distributed lag nonlinear specification. We consider different definitions of heat. In the second stage, estimated attributable fractions are predicted using climate index curves through a functional linear regression model. Results: Results indicate that the Atlantic Multidecadal Oscillation is the best predictor of heat-related mortality in both Montréal and Québec and that it can predict up to 20% of the interannual variability. Conclusion: We found evidence that one climate index is predictive of summer heat-related mortality. More research is needed with longer time series and in different spatial contexts. The proposed analysis and the results may nonetheless help public health authorities plan for future mortality related to summer heat.

Data-Enhancement Strategies in Weather-Related Health Studies.

Although the relationship between weather and health is widely studied, there are still gaps in this knowledge. The present paper proposes data transformation as a way to address these gaps and discusses four different strategies designed to study particular aspects of a weather-health relationship, including (i) temporally aggregating the series, (ii) decomposing the different time scales of the data by empirical model decomposition, (iii) disaggregating the exposure series by considering the whole daily temperature curve as a single function, and (iv) considering the whole year of data as a single, continuous function. These four strategies allow studying non-conventional aspects of the mortality-temperature relationship by retrieving non-dominant time scale from data and allow to study the impact of the time of occurrence of particular event. A real-world case study of temperature-related cardiovascular mortality in the city of Montreal, Canada illustrates that these strategies can shed new lights on the relationship and outlines their strengths and weaknesses. A cross-validation comparison shows that the flexibility of functional regression used in strategies (iii) and (iv) allows a good fit of temperature-related mortality. These strategies can help understanding more accurately climate-related health.

A heat-health watch and warning system with extended season and evolving thresholds.

BACKGROUND: Many countries have developed heat-health watch and warning systems (HHWWS) or early-warning systems to mitigate the health consequences of extreme heat events. HHWWS usually focuses on the four hottest months of the year and imposes the same threshold over these months. However, according to climate projections, the warm season is expected to extend and/or shift. Some studies demonstrated that health impacts of heat waves are more severe when the human body is not acclimatized to the heat. In order to adapt those systems to potential heat waves occurring outside the hottest months of the season, this study proposes specific health-based monthly heat indicators and thresholds over an extended season from April to October in the northern hemisphere. METHODS: The proposed approach, an adoption and extension of the HHWWS methodology currently implemented in Quebec (Canada). The latter is developed and applied to the Greater Montreal area (current population 4.3 million) based on historical health and meteorological data over the years. This approach consists of determining excess mortality episodes and then choosing monthly indicators and thresholds that may involve excess mortality. RESULTS: We obtain thresholds for the maximum and minimum temperature couple (in °C) that range from (respectively, 23 and 12) in April, to (32 and 21) in July and back to (25 and 13) in October. The resulting HHWWS is flexible, with health-related thresholds taking into account the seasonality and the monthly variability of temperatures over an extended summer season. CONCLUSIONS: This adaptive and more realistic system has the potential to prevent, by data-driven health alerts, heat-related mortality outside the typical July-August months of heat waves. The proposed methodology is general and can be applied to other regions and situations based on their characteristics.

A cold-health watch and warning system, applied to the province of Quebec (Canada).

CONTEXT: A number of studies have shown that cold has an important impact on human health. However, almost no studies focused on cold warning systems to prevent those health effects. For Nordic regions, like the province of Quebec in Canada, winter is long and usually very cold with an observed increase in mortality and hospitalizations throughout the season. However, there is no existing system specifically designed to follow in real-time this mortality increase throughout the season and to alert public health authorities prior to cold waves. OBJECTIVE: The aim is to establish a watch and warning system specifically for health impacts of cold, applied to different climatic regions of the province of Quebec. METHODOLOGY: A methodology previously used to establish the health-heat warning system in Quebec is adapted to cold. The approach identifies cold weather indicators and establishes thresholds related to extreme over-mortality or over-hospitalization events in the province of Quebec, Canada. RESULTS AND CONCLUSION: The final health-related thresholds proposed are between (-15 °C, -23 °C) and (-20 °C, -29 °C) according to the climatic region for excesses of mortality, and between (-13 °C, -23 °C) and (-17 °C, -30 °C) for excesses of hospitalization. These results suggest that the system model has a high sensitivity and an acceptable number of false alarms. This could lead to the establishment of a cold-health watch and warning system with valid indicators and thresholds for each climatic region of Quebec. It can be seen as a complementary system to the existing one for heat warnings, in order to help the public health authorities to be well prepared during an extreme cold event.

Toward an Improved Air Pollution Warning System in Quebec.

The nature of pollutants involved in smog episodes can vary significantly in various cities and contexts and will impact local populations differently due to actual exposure and pre-existing sensitivities for cardiovascular or respiratory diseases. While regulated standards and guidance remain important, it is relevant for cities to have local warning systems related to air pollution. The present paper proposes indicators and thresholds for an air pollution warning system in the metropolitan areas of Montreal and Quebec City (Canada). It takes into account past and current local health impacts to launch its public health warnings for short-term episodes. This warning system considers fine particulate matter (PM2.5) as well as the combined oxidant capacity of ozone and nitrogen dioxide (Ox) as environmental exposures. The methodology used to determine indicators and thresholds consists in identifying extreme excess mortality episodes in the data and then choosing the indicators and thresholds to optimize the detection of these episodes. The thresholds found for the summer were 31 µg/m3 for PM2.5 and 43 ppb for Ox in Montreal, and 32 µg/m3 and 23 ppb in Quebec City. In winter, thresholds found were 25 µg/m3 and 26 ppb in Montreal, and 33 µg/m3 and 21 ppb in Quebec City. These results are in line with different guidelines existing concerning air quality, but more adapted to the cities examined. In addition, a sensitivity analysis is conducted which suggests that Ox is more determinant than PM2.5 in detecting excess mortality episodes.

Changes in the distribution of hydro-climatic extremes in a non-stationary framework.

Hydro-climatic extremes are influenced by climate change and climate variability associated to large-scale oscillations. Non-stationary frequency models integrate trends and climate variability by introducing covariates in the distribution parameters. These models often assume that the distribution function and shape of the distribution do not change. However, these assumptions are rarely verified in practice. We propose here an approach based on L-moment ratio diagrams to analyze changes in the distribution function and shape parameter of hydro-climate extremes. We found that important changes occur in the distribution of annual maximum streamflow and extreme temperatures. Eventual relations between the shapes of the distributions of extremes and climate indices are also identified. We provide an example of a non-stationary frequency model applied to flood flows. Results show that a model with a shape parameter dependent on climate indices in combination with a scale parameter dependent on time improves significantly the goodness-of-fit.

A new look at weather-related health impacts through functional regression.

A major challenge of climate change adaptation is to assess the effect of changing weather on human health. In spite of an increasing literature on the weather-related health subject, many aspect of the relationship are not known, limiting the predictive power of epidemiologic models. The present paper proposes new models to improve the performances of the currently used ones. The proposed models are based on functional data analysis (FDA), a statistical framework dealing with continuous curves instead of scalar time series. The models are applied to the temperature-related cardiovascular mortality issue in Montreal. By making use of the whole information available, the proposed models improve the prediction of cardiovascular mortality according to temperature. In addition, results shed new lights on the relationship by quantifying physiological adaptation effects. These results, not found with classical model, illustrate the potential of FDA approaches.

Nonstationary Temperature-Duration-Frequency curves.

Persistent extreme heat events are of growing concern in a climate change context. An increase in the intensity, frequency and duration of heat waves is observed in several regions. Temperature extremes are also influenced by global-scale modes of climate variability. Temperature-Duration-Frequency (TDF) curves, which relate the intensity of heat events of different durations to their frequencies, can be useful tools for the analysis of heat extremes. To account for climate external forcings, we develop a nonstationary approach to the TDF curves by introducing indices that account for the temporal trend and teleconnections. Nonstationary TDF modeling can find applications in adaptive management in the fields of health care, public safety and energy production. We present a one-step method, based on the maximization of the composite likelihood of observed heat extremes, to build the nonstationary TDF curves. We show the importance of integrating the information concerning climate change and climate oscillations. In an application to the province of Quebec, Canada, the influence of Atlantic Multidecadal Oscillations (AMO) on heat events is shown to be more important than the temporal trend.

Aggregating the response in time series regression models, applied to weather-related cardiovascular mortality.

In environmental epidemiology studies, health response data (e.g. hospitalization or mortality) are often noisy because of hospital organization and other social factors. The noise in the data can hide the true signal related to the exposure. The signal can be unveiled by performing a temporal aggregation on health data and then using it as the response in regression analysis. From aggregated series, a general methodology is introduced to account for the particularities of an aggregated response in a regression setting. This methodology can be used with usually applied regression models in weather-related health studies, such as generalized additive models (GAM) and distributed lag nonlinear models (DLNM). In particular, the residuals are modelled using an autoregressive-moving average (ARMA) model to account for the temporal dependence. The proposed methodology is illustrated by modelling the influence of temperature on cardiovascular mortality in Canada. A comparison with classical DLNMs is provided and several aggregation methods are compared. Results show that there is an increase in the fit quality when the response is aggregated, and that the estimated relationship focuses more on the outcome over several days than the classical DLNM. More precisely, among various investigated aggregation schemes, it was found that an aggregation with an asymmetric Epanechnikov kernel is more suited for studying the temperature-mortality relationship.

EMD-regression for modelling multi-scale relationships, and application to weather-related cardiovascular mortality.

In a number of environmental studies, relationships between nat4ural processes are often assessed through regression analyses, using time series data. Such data are often multi-scale and non-stationary, leading to a poor accuracy of the resulting regression models and therefore to results with moderate reliability. To deal with this issue, the present paper introduces the EMD-regression methodology consisting in applying the empirical mode decomposition (EMD) algorithm on data series and then using the resulting components in regression models. The proposed methodology presents a number of advantages. First, it accounts of the issues of non-stationarity associated to the data series. Second, this approach acts as a scan for the relationship between a response variable and the predictors at different time scales, providing new insights about this relationship. To illustrate the proposed methodology it is applied to study the relationship between weather and cardiovascular mortality in Montreal, Canada. The results shed new knowledge concerning the studied relationship. For instance, they show that the humidity can cause excess mortality at the monthly time scale, which is a scale not visible in classical models. A comparison is also conducted with state of the art methods which are the generalized additive models and distributed lag models, both widely used in weather-related health studies. The comparison shows that EMD-regression achieves better prediction performances and provides more details than classical models concerning the relationship.

North Atlantic controls on wintertime warm extremes and aridification trends in the Middle East.

The Middle East is one of the most water stressed regions in the world, receiving the majority of its hydrological input during the winter, in the form of highly variable and scattered precipitation. The persistence of wintertime anticyclonic conditions over the region can deflect storm tracks and result in extended spells of exceptionally hot weather, favoring prolonged droughts and posing a major threat to the already fragile hydrological equilibrium of the Middle East. Despite their potential impacts on water-security, winter warm spells (WWS's) have received far less attention than their summer counterparts, and the climatic drivers leading to WWS's onset are still largely unexplored. Here, we investigate their relationship with the internal modes of variability in the Atlantic Ocean, already known to influence winter circulation and extremes in Eurasia and Northern America. We show that the occurrence of WWS's is strongly correlated with Atlantic variability over decadal time scales. To explain this correlation, we propose a teleconnection mechanism linking Atlantic variability to WWS's via the propagation of Rossby waves from the North Atlantic pool, and the mediation of the Mediterranean circulation - thereby providing a basis to better predict future warming and aridification trends in the Middle East.

Historical and Projected Surface Temperature over India during the 20th and 21st century.

Surface Temperature (ST) over India has increased by ~0.055 K/decade during 1860-2005 and follows the global warming trend. Here, the natural and external forcings (e.g., natural and anthropogenic) responsible for ST variability are studied from Coupled Model Inter-comparison phase 5 (CMIP5) models during the 20th century and projections during the 21st century along with seasonal variability. Greenhouse Gases (GHG) and Land Use (LU) are the major factors that gave rise to warming during the 20th century. Anthropogenic Aerosols (AA) have slowed down the warming rate. The CMIP5 projection over India shows a sharp increase in ST under Representative Concentration Pathways (RCP) 8.5 where it reaches a maximum of 5 K by the end of the 21st century. Under RCP2.6 emission scenarios, ST increases up to the year 2050 and decreases afterwards. The seasonal variability of ST during the 21st century shows significant increase during summer. Analysis of rare heat and cold events for 2080-2099 relative to a base period of 1986-2006 under RCP8.5 scenarios reveals that both are likely to increase substantially. However, by controlling the regional AA and LU change in India, a reduction in further warming over India region might be achieved.

Technologically sensed social exposure related to slow-wave sleep in healthy adults.

OBJECTIVE: The aim of this study is to understand the relationship between automatically captured social exposure and detailed sleep parameters of healthy young adults. METHODS: This study was conducted in a real-world setting in a graduate-student housing community at a US university. Social exposure was measured using Bluetooth proximity sensing technology in mobile devices. Sleep was monitored in a naturalistic setting using a headband sleep monitoring device over a period of 2 weeks. The analysis included a total of 11 subjects (6 males and 5 females) aged 24-35 (149 subject nights). RESULTS: Slow-wave sleep showed a significant positive correlation (Spearman's rho = 0.51, p < 0.0001) with social exposure, whereas light non-REM (N1 + N2) sleep and wake time were found to be negatively correlated (rho = -0.25, p < 0.01; rho = -0.21, p < 0.01, respectively). The correlation of median slow-wave sleep with median social exposure per subject showed a strong positive significance (rho = 0.88, p < 0.001). On average, within subjects, following day's social exposure was higher when (slow-wave NREM + REM) percentage was high (Wilcoxon sign-ranked test, p < 0.05). CONCLUSIONS: Subjects with higher social exposure spent more time in slow-wave sleep. Following day's social exposure was found to be positively affected by previous night's (slow-wave NREM + REM) percentage. This suggests that sleep affects following day's social exposure and not vice versa. Capturing an individual's dynamic social behavior and sleep from their natural environment can provide novel insights into these relationships.

Modeling climate effects on hip fracture rate by the multivariate GARCH model in Montreal region, Canada.

Changes in extreme meteorological variables and the demographic shift towards an older population have made it important to investigate the association of climate variables and hip fracture by advanced methods in order to determine the climate variables that most affect hip fracture incidence. The nonlinear autoregressive moving average with exogenous variable-generalized autoregressive conditional heteroscedasticity (ARMAX-GARCH) and multivariate GARCH (MGARCH) time series approaches were applied to investigate the nonlinear association between hip fracture rate in female and male patients aged 40-74 and 75+ years and climate variables in the period of 1993-2004, in Montreal, Canada. The models describe 50-56% of daily variation in hip fracture rate and identify snow depth, air temperature, day length and air pressure as the influencing variables on the time-varying mean and variance of the hip fracture rate. The conditional covariance between climate variables and hip fracture rate is increasing exponentially, showing that the effect of climate variables on hip fracture rate is most acute when rates are high and climate conditions are at their worst. In Montreal, climate variables, particularly snow depth and air temperature, appear to be important predictors of hip fracture incidence. The association of climate variables and hip fracture does not seem to change linearly with time, but increases exponentially under harsh climate conditions. The results of this study can be used to provide an adaptive climate-related public health program and ti guide allocation of services for avoiding hip fracture risk.

Change in BMI accurately predicted by social exposure to acquaintances.

Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data. We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO) method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC) and R(2). This study found a model that explains 68% (p<0.0001) of the variation in change in BMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI. This is the first study taking into account both interactions with different levels of social interaction and personal health-related information. Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as close friends.

A general and flexible methodology to define thresholds for heat health watch and warning systems, applied to the province of Québec (Canada).

Several watch and warning systems have been established in the world in recent years to prevent the effects of heat waves. However, many of these approaches can be applied only in regions with perfect conditions (e.g., enough data, stationary series or homogeneous regions). Furthermore, a number of these approaches do not account for possible trend in mortality and/or temperature series, whereas others are generally not adapted to regions with low population densities or low daily mortality levels. In addition, prediction based on multiple days preceding the event can be less accurate if it attributes the same importance to each of these days, since the forecasting accuracy actually decreases with the period. The aim of the present study was to identify appropriate indicators as well as flexible and general thresholds that can be applied to a variety of regions and conditions. From a practical point of view, the province of Québec constitutes a typical case where a number of the above-mentioned constraints are present. On the other hand, until recently, the province's watch and warning system was based on a study conducted in 2005, covering only the city of Montreal and applied to the whole province. The proposed approach is applied to each one of the other health regions of the province often experiencing low daily counts of mortality and presenting trends. The first constraint led to grouping meteorologically homogeneous regions across the province in which the number of deaths is sufficient to carry out the appropriate data analyses. In each region, mortality trends are taken into account. In addition, the proposed indicators are defined by a 3-day weighted mean of maximal and minimal temperatures. The sensitivity of the results to the inclusion of traumatic deaths is also checked. The application shows that the proposed method improved the results in terms of sensitivity, specificity and number of yearly false alarms, compared to those of the existing and other classical approaches. An additional criterion based on the Humidex is applied in a second step and a local validation is applied to historical observations at reference forecasting stations. An integrated heat health watch and warning system with thresholds that are adapted to the regional climate has thus been established for each sub-region of the province of Quebec and became operational in June 2010.

Relationship between ambient temperature and humidity and visits to mental health emergency departments in Québec.

OBJECTIVE: This study examined whether the number of emergency department visits for "mental and psychosocial problems" varies with temperature or humidity. METHODS: The number of visits in three geographic areas of Québec were examined as a function of temperature and humidity by using routinely collected May-September data for 1995-2007 (N=347,552 visits). Data for two age groups (under age 65 and age 65 and older) were examined. Incidence rate ratios for mean temperature and humidity were estimated by using Poisson regression and generalized additive models. RESULTS: The number of visits tended to increase with increasing mean temperature. At 22.5 °C (72.5 °F) and 25 °C (77.0 °F), the number was usually significantly higher than average. Visits increased with humidity in the younger age group. CONCLUSIONS: Results suggest increased use of emergency departments for mental and psychosocial problems with higher mean temperature and humidity, especially in metropolitan areas and in southern Québec. Climate change may make this effect increasingly important.

Coupling geostatistical approaches with PCA and fuzzy optimal model (FOM) for the integrated assessment of sampling locations of water quality monitoring networks (WQMNs).

The assessment of the adequacy of sampling locations is an important aspect in the validation of an effective and efficient water quality monitoring network. Two geostatistical approaches (e.g., kriging and Moran's I) are presented to assess multiple sampling locations. A flexible and comprehensive framework was developed for the selection of multiple sampling locations of multiple variables which was accomplished by coupling geostatistical approaches with principal component analysis (PCA) and fuzzy optimal model (FOM). The FOM was used in the integrated assessment of both multiple principal components and multiple geostatistical approaches. These integrated methods were successfully applied to the assessment of two independent water quality monitoring networks (WQMNs) of Lake Winnipeg, Canada, which respectively included 14 and 30 stations from 2006 to 2010.