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2 History and Current Status of S2S Forecasting
Pages 25-42

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From page 25...
... in the 1960s, provided striking views of Earth's changing weather patterns and contributed to the understanding of weather systems and to the improvement of routine weather forecasts. With these improved data sources and modeling capabilities, purely subjective forecasts based on atmospheric synoptic maps, experience, and intuition gave way to a combination of computer-generated atmospheric and marine forecasts based on physics equations and a statistical interpretation of the forecast information.
From page 26...
... Moving into the 21st century, the combination of greatly improved atmospheric and oceanic observations and accelerating computer power has produced increasingly accu­ ate and reliable atmospheric forecasts. Computer-calculated forecasts of r global and regional weather patterns are now as accurate at 72 hours as they were at 36 hours in the 1990s (Figure 2.1)
From page 27...
... Today, the emphasis is on improvement and extension of lead times through probability forecasts, created by averaging over space and time and running multiple cases to create ensembles of forecasts that reflect probabilities of variables or events at future times. Along with probabilistic ensemble forecasts, recent advances in weather prediction accuracy have come from improved understanding of the underlying processes and more realistically incorporating them into the forecast models, in part by increasing model ­ patial s resolution and in part through better parameterization of unresolved processes.
From page 28...
... Dynamical seasonal predictions started in the early 1980s (Reeves and Gemmill, 2004) , using atmosphere-only models with prescribed surface conditions.
From page 29...
... This expectation arose from the perception that the subseasonal atmospheric forecast problem does not fit neatly into the simplistic paradigms of an initialvalue weather forecast problem (because the lead times are too large and initial-value information can be lost) or the so-called "boundary-value climate prediction problem," terminology associated with the early seasonal climate forecast systems that were driven by prescribed surface temperature anomalies.
From page 30...
... Seasonal Most operational centers have produced routine dynamical seasonal predictions for more than a decade. A majority of the centers utilize global atmosphere, ocean, land, and sea ice coupled models (one-tier systems)
From page 31...
... Almost all operational centers produce seasonal predictions at least once per month. Usually, deterministic and probabilistic forecasts of seasonal mean anomalies of surface temperature (atmosphere and ocean)
From page 32...
... provides routine seasonal MME forecasts to member countries, and the aligned Climate Prediction and its Application to Society (CliPAS) developed a database of retrospective forecasts for prediction and predictability research (Box 2.1)
From page 33...
... is an S2S research and prediction effort involving universities and laboratories in the United States, NOAA National Centers for Environmental Prediction (NCEP) , and the Canadian Meteorological Center (Kirtman, 2014)
From page 34...
... Subseasonal Building on a number of research and experimental efforts over the past decade, subseasonal predictions began in earnest with the establishment of an MJO prediction metric and its uptake by a number of forecast centers (e.g., Gottschalck et al., 2010; Vitart and Molteni, 2010; Waliser, 2011)
From page 35...
... Many of the same statistical considerations and associated trade-offs cited above for seasonal forecasting (e.g., forecast lengths and averages, ensemble sizes, MMEs, verification periods) are relevant for subseasonal forecasting, although the shorter lead times for subseasonal prediction allow for increased verification instances for a given size observation period.
From page 36...
... To do so, the project collects forecasts and retrospective forecasts from a number of operational modeling centers into a common database and disseminates them in delayed mode for research purposes to the science and applications communities. In addition to the seasonal forecasts discussed in the previous section, NMME-2 (Box 2.2)
From page 37...
... The central activity of the S2S Project is the establishment of a multi-model database consisting of ensembles of subseasonal (up to 60 days) forecasts and supplemented with an extensive set of retrospective forecasts following THORPEX Interactive Grand Global Ensemble (TIGGE)
From page 38...
... Similar progress in forecasting indices has also been made on subseasonal timescales. About 15 years ago, dynamical models had some MJO forecast skill out to 7-10 days (Waliser, 2011)
From page 39...
... . NOTES: The MJO skill scores are computed on the ensemble mean of the ECMWF retrospective forecasts produced during a complete year.
From page 40...
... As mentioned above, MME forecasts have improved forecast skill of such traditional atmospheric variables in some cases, but even for seasonal forecasts, large gaps persist across specific regions and seasons, especially for precipitation. For example, skill of ENSEMBLES multi-model forecasts of boreal winter conditions is good in the ­ ropicst and over oceans, particularly for temperature; however, skill over land, especially outside of the tropics, is limited (Figure 2.4)
From page 41...
... S2S forecasts for Earth system variables outside traditional weather and climate forecasts are less well developed, but have also been advanced by the development of coupled Earth system prediction systems. The growing interest by the science community and operational forecast centers to develop and implement many of the projects and experiments described above, in addition to recent progress in S2S predictability research and operational predictions, illustrates the research priority and expectations associated with S2S timescales.


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