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- Author or Editor: G. T. Amanatidis x
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Abstract
Based on an extensive wind dataset over complex terrain, the commonly used small-angle approximation σ v ≈ σθ V is studied and found to overestimate over all wind speeds and σθ values observed. This should be anticipated due to the assumptions necessary to derive the approximation. Overestimation (of 10%–30%) is also observed in the small σθ range. The three parameters involved are further discussed to gain better understanding of the behavior of the approximation under different conditions. The standard deviation of wind direction σθ is shown to vary inversely with wind speed not only under stable, but also under convective conditions, reaching a site-dependent constant value at high wind speeds. The dependence of the ratio of the mean longitudinal wind component to the scalar mean wind speed on wind speed and σθ is examined, as well as that of the relevant standard deviations (σ u ,σ V a ). While the former obtains small values in the high-σθ, or low-wind range, or both, estimated values of the latter justify equivalence of σ u , σ V a under most conditions. Finally, the effects of wind speed and σθ on σ v are examined.
Abstract
Based on an extensive wind dataset over complex terrain, the commonly used small-angle approximation σ v ≈ σθ V is studied and found to overestimate over all wind speeds and σθ values observed. This should be anticipated due to the assumptions necessary to derive the approximation. Overestimation (of 10%–30%) is also observed in the small σθ range. The three parameters involved are further discussed to gain better understanding of the behavior of the approximation under different conditions. The standard deviation of wind direction σθ is shown to vary inversely with wind speed not only under stable, but also under convective conditions, reaching a site-dependent constant value at high wind speeds. The dependence of the ratio of the mean longitudinal wind component to the scalar mean wind speed on wind speed and σθ is examined, as well as that of the relevant standard deviations (σ u ,σ V a ). While the former obtains small values in the high-σθ, or low-wind range, or both, estimated values of the latter justify equivalence of σ u , σ V a under most conditions. Finally, the effects of wind speed and σθ on σ v are examined.
Accurate and reliable predictions and an understanding of future changes in the stratosphere are major aspects of the subject of climate change. Simulating the interaction between chemistry and climate is of particular importance, because continued increases in greenhouse gases and a slow decrease in halogen loading are expected. These both influence the abundance of stratospheric ozone. In recent years a number of coupled chemistry–climate models (CCMs) with different levels of complexity have been developed. They produce a wide range of results concerning the timing and extent of ozone-layer recovery. Interest in reducing this range has created a need to address how the main dynamical, chemical, and physical processes that determine the long-term behavior of ozone are represented in the models and to validate these model processes through comparisons with observations and other models. A set of core validation processes structured around four major topics (transport, dynamics, radiation, and stratospheric chemistry and microphysics) has been developed. Each process is associated with one or more model diagnostics and with relevant datasets that can be used for validation. This approach provides a coherent framework for validating CCMs and can be used as a basis for future assessments. Similar efforts may benefit other modeling communities with a focus on earth science research as their models increase in complexity.
Accurate and reliable predictions and an understanding of future changes in the stratosphere are major aspects of the subject of climate change. Simulating the interaction between chemistry and climate is of particular importance, because continued increases in greenhouse gases and a slow decrease in halogen loading are expected. These both influence the abundance of stratospheric ozone. In recent years a number of coupled chemistry–climate models (CCMs) with different levels of complexity have been developed. They produce a wide range of results concerning the timing and extent of ozone-layer recovery. Interest in reducing this range has created a need to address how the main dynamical, chemical, and physical processes that determine the long-term behavior of ozone are represented in the models and to validate these model processes through comparisons with observations and other models. A set of core validation processes structured around four major topics (transport, dynamics, radiation, and stratospheric chemistry and microphysics) has been developed. Each process is associated with one or more model diagnostics and with relevant datasets that can be used for validation. This approach provides a coherent framework for validating CCMs and can be used as a basis for future assessments. Similar efforts may benefit other modeling communities with a focus on earth science research as their models increase in complexity.
