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Peter J. Webster, Jun Jian, Thomas M. Hopson, Carlos D. Hoyos, Paula A. Agudelo, Hai-Ru Chang, Judith A. Curry, Robert L. Grossman, Timothy N. Palmer, and A. R. Subbiah

The authors have developed a new extended-range flood forecasting system for large river basins that uses satellite data and statistically rendered probabilistic weather and climate predictions to initialize basin-scale hydrological models. The forecasting system overcomes the absence of upstreamflow data, a problem that is prevalent in the developing world. Forecasts of the Ganges and Brahmaputra discharge into Bangladesh were made in real time on 1–10-day time horizons for the period 2003–08. Serious flooding of the Brahmaputra occurred in 2004, 2007, and 2008. Detailed forecasts of the flood onset and withdrawal were made 10 days in advance for each of the flooding events with correlations at 10 days ≥0.8 and Brier scores <0.05. Extensions to 15 days show useable skill. Based on the 1–10-day forecasts of the 2007 and 2008 floods, emergency managers in Bangladesh were able to act preemptively, arrange the evacuation of populations in peril along the Brahmaputra, and minimize financial loss. The particular application of this forecast scheme in Bangladesh represents a “world is f lat” approach to emergency management through the collaboration of scientists in Europe (generating global ensemble meteorological and climate forecasts), the United States (developing and producing the integrated flood forecasts), and the developing world (integrating the flood forecasts into their disaster management decision-making protocol), all enabled by high-speed Internet connections. We also make suggestions of how scientific and technical collaborations between more developed and developing nations can be improved to increase their prospects for sustaining the technology adoption and transfer.

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Judith Berner, Ulrich Achatz, Lauriane Batté, Lisa Bengtsson, Alvaro de la Cámara, Hannah M. Christensen, Matteo Colangeli, Danielle R. B. Coleman, Daan Crommelin, Stamen I. Dolaptchiev, Christian L. E. Franzke, Petra Friederichs, Peter Imkeller, Heikki Järvinen, Stephan Juricke, Vassili Kitsios, François Lott, Valerio Lucarini, Salil Mahajan, Timothy N. Palmer, Cécile Penland, Mirjana Sakradzija, Jin-Song von Storch, Antje Weisheimer, Michael Weniger, Paul D. Williams, and Jun-Ichi Yano


The last decade has seen the success of stochastic parameterizations in short-term, medium-range, and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to represent model inadequacy better and to improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing long-standing climate biases and is relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups that show that the stochastic representation of unresolved processes in the atmosphere, oceans, land surface, and cryosphere of comprehensive weather and climate models 1) gives rise to more reliable probabilistic forecasts of weather and climate and 2) reduces systematic model bias. We make a case that the use of mathematically stringent methods for the derivation of stochastic dynamic equations will lead to substantial improvements in our ability to accurately simulate weather and climate at all scales. Recent work in mathematics, statistical mechanics, and turbulence is reviewed; its relevance for the climate problem is demonstrated; and future research directions are outlined.

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