By National Research Council, Division on Earth and Life Studies, Board on Atmospheric Sciences and Climate, Water Science and Technology Board, Committee on Hydrologic Science
Document from the Committee on Hydrologic technology, Water and expertise Board, and the Board on Atmospheric Sciences and weather. Softcover.
Read or Download Report of a Workshop on Predictability & Limits-To-Prediction in Hydrologic Systems PDF
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Extra resources for Report of a Workshop on Predictability & Limits-To-Prediction in Hydrologic Systems
Gloabal Change Research Program (USGCRP) 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) programs. CONCEPTUAL MODEL FOR MAKING PREDICTIONS The conceptual framework for hydrometeorological predictability is that of a coupled land–atmosphere–ocean system. At the largest time and space scales, the cycling of water over continental regions can be viewed as net inflow of water vapor to a particular basin, net transfer of water from the atmosphere to the surface by excess of precipitation over local evapotranspiration, and net river discharge from the basin, which over the long run must balance the net inflow of water vapor.
2. What are the stability and feedback characteristics of two-way coupled subsurface, surface, and atmospheric hydrologic systems? How do they impact predictability? m. m. m. m. m. m. 39 Second panel discussion of science questions Chair: Christa Peters-Lidard, Georgia Institute of Technology 3. What are the conceptual and model frameworks required to define limits-toprediction in hydrologic systems? 4. What are the data and records requirements to estimate the inherent limits-toprediction directly from observations?
Looking at model results using two different SGS parameterizations provides a useful diagnostic tool for identifying discrepancies owing to SGS processes. Very high-quality observations are needed to choose the better of the two methods or to improve them, as Dr. Tribbia showed in the case of a forecasted Gulf Coast storm event. SGS processes remain the Achilles heel of many prediction systems. They represent a limit to predictability (as defined in this report) brought about by the influence of processes with disparate scales.