Characterizing the atmospheric conditions during the 2010 heatwave in Rio de Janeiro marked by excessive mortality rates.

Global temperatures have increased considerably over the last decades, directly impacting the number, intensity and duration of extreme events such as heat waves. Climate model projections accounting for anthropogenic factors indicate that deadly mega-heat waves are likely to become more frequent in the future. Although the atmospheric features and social-economic related impacts of heat waves have already been documented in various regions around the world, for other highly populated regions, such as the Metropolitan Region of Rio de Janeiro (MRRJ), a similar objective assessment is still needed. Heat waves directly impact the public health sector and particularly the less wealthy and elderly population groups. During February 2010, an elevated mortality peak occurred during a 8-day period (from 2 to 9 Feb 2010) characterized as a heat wave episode in MRRJ. A total excess of 737 deaths was recorded with the elderly group registering the highest mortality incidence. During this heat wave period, a quasi-stationary anticyclonic anomaly forced in altitude by a Rossby wave train was established over the south Brazilian coast. At the surface, the meteorological scenario from January 2010 to the heat wave period was marked by clear sky conditions, large precipitation deficits, and enhanced diabatic heating. During the heat wave period, warm and dry air masses were advected from interior regions towards the MRRJ, exacerbating temperature conditions by pronounced subsidence and adiabatic heating mechanisms. All these conditions contributed to pronounced positive temperature anomalies, reinforced by land-atmosphere feedbacks.

Dengue outlook for the World Cup in Brazil: an early warning model framework driven by real-time seasonal climate forecasts.

BACKGROUND: With more than a million spectators expected to travel among 12 different cities in Brazil during the football World Cup, June 12-July 13, 2014, the risk of the mosquito-transmitted disease dengue fever is a concern. We addressed the potential for a dengue epidemic during the tournament, using a probabilistic forecast of dengue risk for the 553 microregions of Brazil, with risk level warnings for the 12 cities where matches will be played. METHODS: We obtained real-time seasonal climate forecasts from several international sources (European Centre for Medium-Range Weather Forecasts [ECMWF], Met Office, Meteo-France and Centro de Previsão de Tempo e Estudos Climáticos [CPTEC]) and the observed dengue epidemiological situation in Brazil at the forecast issue date as provided by the Ministry of Health. Using this information we devised a spatiotemporal hierarchical Bayesian modelling framework that enabled dengue warnings to be made 3 months ahead. By assessing the past performance of the forecasting system using observed dengue incidence rates for June, 2000-2013, we identified optimum trigger alert thresholds for scenarios of medium-risk and high-risk of dengue. FINDINGS: Our forecasts for June, 2014, showed that dengue risk was likely to be low in the host cities Brasília, Cuiabá, Curitiba, Porto Alegre, and São Paulo. The risk was medium in Rio de Janeiro, Belo Horizonte, Salvador, and Manaus. High-risk alerts were triggered for the northeastern cities of Recife (p(high)=19%), Fortaleza (p(high)=46%), and Natal (p(high)=48%). For these high-risk areas, particularly Natal, the forecasting system did well for previous years (in June, 2000-13). INTERPRETATION: This timely dengue early warning permits the Ministry of Health and local authorities to implement appropriate, city-specific mitigation and control actions ahead of the World Cup. FUNDING: European Commission's Seventh Framework Research Programme projects DENFREE, EUPORIAS, and SPECS; Conselho Nacional de Desenvolvimento Científico e Tecnológico and Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro.

On the visualization, verification and recalibration of ternary probabilistic forecasts.

We develop a graphical interpretation of ternary probabilistic forecasts in which forecasts and observations are regarded as points inside a triangle. Within the triangle, we define a continuous colour palette in which hue and colour saturation are defined with reference to the observed climatology. In contrast to current methods, forecast maps created with this colour scheme convey all of the information present in each ternary forecast. The geometrical interpretation is then extended to verification under quadratic scoring rules (of which the Brier score and the ranked probability score are well-known examples). Each scoring rule defines an associated triangle in which the square roots of the score, the reliability, the uncertainty and the resolution all have natural interpretations as root mean square distances. This leads to our proposal for a ternary reliability diagram in which data relating to verification and calibration can be summarized. We illustrate these ideas with data relating to seasonal forecasting of precipitation in South America, including an example of nonlinear forecast calibration. Codes implementing these ideas have been produced using the statistical software package R and are available from the authors.