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6 Recommendations and Remarks on Implementation
Pages 129-142

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From page 129...
... The Best Practices are largely focused on the activities of the operational forecast centers and aim to improve the delivery and dissemination of forecast information for both decision-makers and researchers. The Improvements to the Building Blocks of ISI Forecast Systems pertain to both the operational and research communities and focus on the continued development of observations, statistical and dynamical models, and data assimilations systems.
From page 130...
... Archives of forecast information are needed by national and international operational centers, researchers, and the private sector in their efforts to quantify and identify sources of forecast error, provide the baseline for forecast assessment and model fidelity, develop metrics and diagnostics for model assessment, calibrate model forecasts, quantify and document model and forecast improvement, such as those that results from changing resolution or
From page 131...
... Since it is not possible for operational centers to foresee or address all possible needs of these users, archives of forecast information will permit users to access the information that is most important to them and, in some cases, develop their own derivative products. Feedback from forecast users can also offer pathways to improving ISI forecast quality.
From page 132...
... Systematic errors in dynamical models should be reduced by expanding the understanding of underlying physical processes, with the goal of transferring improvements into operational ISI forecasts. This includes systematic errors in the mean and the variability and their interaction.
From page 133...
... Assimilation methods currently being used are often obsolete, and many observations are not being assimilated as part of the forecast cycle. To enable assimilation of all available observations of the coupled climate system, operational centers should implement state-of-the-art 4-D Var, Ensemble Kalman Filters, or hybrids of these in their data assimilation systems.
From page 134...
... MJO A concerted effort on improving the prediction quality associated with the MJO should be undertaken and coordinated with research activities. The path forward on this should include focused process studies, model improvement, and close collaboration between research and operational communities (e.g., Year of Tropical Convection (YOTC)
From page 135...
... High impact events affecting atmospheric composition Operational centers should be prepared to make ISI forecasts following unusual but high impact events such as volcanic eruptions, limited nuclear exchange, or space impacts that can cause a sudden, drastic change to the atmospheric burden of aerosols and trace gases. Research efforts should study the consequences of such high impact events on the climate system over ISI timescales and provide guidance for improving forecast systems.
From page 136...
... This will be facilitated by the use of more advanced methods for data assimilation in ISI forecast systems, such as Ensemble Kalman Filter techniques, that are able to adapt the forecast error covariance to the presence of new types of observations. Efforts to improve ISI prediction should work synergistically with efforts to develop and sustain the observing system.
From page 137...
... For example, research regarding expanded data assimilation methods could indicate the types and/or spatial and temporal resolution of data sets that could be the target of future measurement missions. Seamless Forecasting ISI prediction is the temporal and spatial bridge between numerical weather prediction and climate prediction and, as such, a key component of a seamless prediction system.
From page 138...
... In order to move closer to seamless prediction and leverage improvements in ISI prediction, transparency among forecast systems is paramount. Through the adoption of Best Practices, efforts to improve ISI predictions can be related back to model development and process knowledge.
From page 139...
... The earlier sections of this report support the conclusion that ISI forecast quality should continue to slowly improve on average in the future. For example, as operational centers move to more objective methods in translating prediction inputs into issued forecasts (O'Lenic et al.
From page 140...
... ISI forecasts also exhibit conditional accuracy; for example, forecast quality improves significantly during ENSO events. Forecasts may also be more valuable in certain instances.
From page 141...
... CLOSING REMARKS The committee's recommendations constitute a strategy to improve the quality of climate predictions at ISI timescales by expanding access to forecasting data and tools; broadening the suite of verification metrics that are used; enhancing collaboration among the operational, research, and user communities; upgrading the building blocks of the ISI forecast systems, which include observations, statistical and dynamical models, and data assimilation techniques; and pursuing research on incompletely understood processes that can contribute to predictability. This strategy is based largely on the lessons learned from historical improvements in the quality of weather and ISI forecasts.
From page 142...
... Finally, the committee stresses that improvements to ISI forecasting systems and improvements in the use of ISI forecasts are possible. In particular, adoption of Best Practices offers a near-term way to aid forecast users and researchers by enhancing access and transparency to forecast information.


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