ABSTRACT:The Amazon has a well-defined wet austral summer monsoon and dry winter monsoon precipitation regime and experienced a sequence of drought events in the last 13 years. This study performs a comparative assessment of observed and predicted climate conditions during the three most recent drought events in the Amazon, in 1997Amazon, in -1998Amazon, in , 2004Amazon, in -2005Amazon, in and 2009Amazon, in -2010, with emphasis on how these events affected the regional monsoon-like precipitation regime. A century long Negro River level time series at Manaus is investigated, applying extreme values theory for estimating return periods of these major drought events. Possible teleconnections of river levels at Manaus and sea surface temperature at remote regions are explored. Large scale oceanic and atmospheric conditions are investigated to highlight the mechanisms associated with the observed drought conditions, particularly during the dry monsoon season. Satellite estimates are used for diagnosing biomass burning aerosol and discuss possible contributions to the observed precipitation deficits in the 2005 and 2010 drought events during the dry monsoon season. The study is concluded with an analysis of the performance of seasonal precipitation predictions for the dry monsoon seasons of July to September 1998September , 2005September and 2010 produced with the operational seasonal prediction system used at the Center for Weather Forecasts and Climate Studies (CPTEC) of the Brazilian National Institute for Space Research (INPE). This system was capable of producing 1 month in advance drought warning for the three investigated events, relevant for helping the government and local population make decisions for reducing drought impacts in the Amazon region.

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A model simulation of an intense rainfall associated with a case of South Atlantic Convergence Zone that occurred during 21-24 February 2004 using the Brazilian developments on the Regional Atmospheric Modelling System was performed. The convective parameterization scheme of Grell and De´ve´nyi was used to represent clouds of the sub-grid scale and their interaction with the large-scale environment. This method is a convective parameterization that can make use of a large variety of approaches previously introduced in earlier formulations, considering an ensemble of several hypotheses and closures. The rainfall was evaluated by six experiments, using different choices of rainfall parameterizations, providing six different simulated responses for the rainfall field. The sixth experiment ran with an average among five closures (ensemble mean). The purpose of this study was to generate a set of weights to compute a best combination of the ensemble members. This inverse problem of parameter estimation is solved as an optimization problem. The objective function was computed with the quadratic difference between five simulated precipitation fields and observation. The precipitation field estimated by the Tropical Rainfall Measuring Mission satellite was used as observed data. Weights were obtained using the firefly optimization algorithm and it was included in the cumulus parameterization code to simulate precipitation. The results indicated the better skill of the model with the new methodology compared with the old ensemble mean calculation.

Abstract. In this paper we consider an optimization problem applying the metaheuristic Firefly algorithm (FY) to weight an ensemble of rainfall forecasts from daily precipitation simulations with the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS) over South America during January 2006. The method is addressed as a parameter estimation problem to weight the ensemble of precipitation forecasts carried out using different options of the convective parameterization scheme. Ensemble simulations were performed using different choices of closures, representing different formulations of dynamic control (the modulation of convection by the environment) in a deep convection scheme. The optimization problem is solved as an inverse problem of parameter estimation. The application and validation of the methodology is carried out using daily precipitation fields, defined over South America and obtained by merging remote sensing estimations with rain gauge observations. The quadratic difference between the model and observed data was used as the objective function to determine the best combination of the ensemble members to reproduce the observations. To reduce the model rainfall biases, the set of weights determined by the algorithm is used to weight members of an ensemble of model simulations in order to compute a new precipitation field that represents the observed precipitation as closely as possible. The validation of the methodology is carried out using classical statistical scores. The algorithm has produced the best combination of the weights, resulting in a new precipitation field closest to the observations.

6th WGNE workshop on systematic errors in weather and climate models What: Scientists, ranging from early career to highly experienced, involved in the development of weather and climate models and in the diagnosis of model errors, held an international workshop to discuss the nature, causes and remedies of systematic errors across timescales and across Earth system modeling components. When: 31 Oct - 04 Nov 2022 Where: Reading, UK and online