Report of a Workshop on Predictability and Limits-To-Prediction in Hydrologic Systems (2002) / Chapter Skim
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3. Challenges in Predictability Science and Limits-to-Prediction for Hydrologic Systems
3. Challenges in Predictability Science and Limits-to-Prediction for Hydrologic Systems
Pages 15-29
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From page 15... ...
Finally, we limit discussion to the subfield of hydrometeorology out of a practical necessity, with an implied relevance to the broader discipline of hydrology. The coupled hydrometeorological predictability problem is chosen to show how a focused set of challenges in predictability science and "limits-to-prediction" research may be derived from current gaps in understanding and from the current operational needs.
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16 Predictability and Limits-To-Prediction
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Challenges in Predictability Science 17
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Water Cycle Plan, National Aeronautics and Space Administration's Global Water and Energy Cycle (GWEC) , and Natiohnal Oceanic and Atmospheric Administration's Office of Global Program GEWEX Americas Prediction Project (GAPP)
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Outside of these circumstances, the variations in seasonal precipitation do not appear to be related to antecedent or concurrent conditions at the land surface. Remote influences, such as seasonal to interannual ocean temperature anomalies (e.g., E]
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Are present global and regional climate models capable of simulating and hence predicting coupled features, and where are the current limitations? The talks by Adam Schiosser and Dag Lohmann at the workshop illustrated the range of activities in the hydrometeorological research community that focus on these questions with a variety of approaches and atmospheric and land surface models.
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Similar signal and noise separation techniques are required for other applications in hydrology where predictability associated with persistence, local factors, and remote influences needs to be separated. The research milestones related to separating the predictable and the unpredictable include estimation of the time scales over which hydrologic variables can be predicted in the real world
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In the workshop, Joe Tribbia introduced an example that demonstrated the effects of SGS parameterizations on error propagation in models and demonstrated how model-based estimates of limits-to-prediction are affected by the approach to representing SGS processes. The example of the sensitivity of atmospheric forecasts to the SGS representation of moist convection and the formation of precipitating clouds showed that mode]
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Research milestones related to the characterization of SGS processes include the following: · development of systematic ways of defining the relevant scales for a problem and how to pose models of the right complexity · designing experiments that explicitly help formulate models for unresolved scales · use of a hierarchy of models with differing levels of complexity, and use of ensembles of mode! simulations that include uncertainty to explore predictability · systematic investigation, quantification, and crosscomparison of mode!
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A key consideration in data assimilation is that the models that provide the so-called background predictions and the systems that provide the same measurements are both uncertain. The role of data assimilation is to merge these two estimates based on their degree of uncertainty and produce a combined estimate that has desirable statistical properties (e.g., unbiased, minimum variance, etc.~.
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Research milestones related to synergy between models and measurements include the following: · improve the initial conditions (and thus take better advantage of predictability associated with information present in the initial conditions) ; · allow efficient improvement of the models by comparison of short forecasts with the observations · provide community data sets on regional hydrologic systems · extend data assimilation systems to take advantage of emerging satellite data.
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Similarly, new measurement technology and network design are critically needed to improve the predictability and prediction of chemical fluxes, of transportation, and of impacts on terrestrial ecosystems and aquatic ecosystems in both inland and coastal waters. Finally, new measurements to characterize properties of the earth's "critical zone" are sorely needed for both hydrologic science and integrative studies linking hydrology with other earth and environmental sciences.
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Simulation and design studies with dense measurements from research basins can be used to evaluate tradeoffs and demonstrate data value. Research milestones related to making observations that accelerate modeling progress include the following: · development of benchmarks for monitoring systems, and implementation of special initiatives in algorithm development and assessment of new technologies · estimates of uncertainty associated with observations made at different scales and using different measurement technologies · improvement of access to existing data · development of a multiagency definition of hydrologic data requirements, development of strategies for coordinated observations, and development of effective mechanisms for data sharing and dissemination · estimation of evaporation and recharge on scales that allow linking the subsurface, surface, and atmospheric hydrologic systems use and promotion of paleo/proxy data for insights on .
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and/or statistical comparison of mode! forecasts with observations (e.g., threat scores and root mean squared errors)
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These measures need to be defined in the context of specific forecast quantities and spatial and temporal scales. Research milestones related to measuring predictability include the following: · definition of robust measures of limits-to-prediction that account for scale and inter-mittency issues and that are capable of distinguishing persistence effects · introduction of methods to infer predictability and limitsto-prediction from observa-tional data sets.
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