Drought is a global problem that has far-reaching impacts, especially on vulnerable populations in developing regions. This paper highlights the need for a Global Drought Early Warning System (GDEWS), the elements that constitute its underlying framework (GDEWF), and the recent progress made toward its development. Many countries lack drought monitoring systems, as well as the capacity to respond via appropriate political, institutional, and technological frameworks, and these have inhibited the development of integrated drought management plans or early warning systems. The GDEWS will provide a source of drought tools and products via the GDEWF for countries and regions to develop tailored drought early warning systems for their own users. A key goal of a GDEWS is to maximize the lead time for early warning, allowing drought managers and disaster coordinators more time to put mitigation measures in place to reduce the vulnerability to drought. To address this, the GDEWF will take both a top-down approach to provide global realtime drought monitoring and seasonal forecasting, and a bottom-up approach that builds upon existing national and regional systems to provide continental-to-global coverage. A number of challenges must be overcome, however, before a GDEWS can become a reality, including the lack of in situ measurement networks and modest seasonal forecast skill in many regions, and the lack of infrastructure to translate data into useable information. A set of international partners, through a series of recent workshops and evolving collaborations, has made progress toward meeting these challenges and developing a global system.

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The definition and derivation of a 5-yr, 0.1258, 3-hourly atmospheric forcing dataset that is appropriate for use in a Land Data Assimilation System operating across South America is described. Because surface observations are limited in this region, many of the variables were taken from the South American Regional Reanalysis; however, remotely sensed data were merged with surface observations to calculate the precipitation and downward shortwave radiation fields. The quality of this dataset was evaluated against the surface observations available. There are regional differences in the biases for all variables in the dataset, with volumetric biases in precipitation of the order 0-1 mm day 21 and RMSE of 5-15 mm day 21 , biases in surface solar radiation of the order 10 W m 22 and RMSE of 20 W m 22 , positive biases in temperature typically between 0 and 4 K depending on the region, and positive biases in specific humidity around 2-3 g kg 21 in tropical regions and negative biases around 1-2 g kg 21 farther south.

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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.