Ensemble Sensitivity Analysis Of A Severe Downslope Windstorm In Complex Terrain: Implications For Forecast Predictability Scales And Targeted Observing Networks - Info and Reading Options
By Homan, Paul B.
"Ensemble Sensitivity Analysis Of A Severe Downslope Windstorm In Complex Terrain: Implications For Forecast Predictability Scales And Targeted Observing Networks" and the language of the book is English.
“Ensemble Sensitivity Analysis Of A Severe Downslope Windstorm In Complex Terrain: Implications For Forecast Predictability Scales And Targeted Observing Networks” Metadata:
- Title: ➤ Ensemble Sensitivity Analysis Of A Severe Downslope Windstorm In Complex Terrain: Implications For Forecast Predictability Scales And Targeted Observing Networks
- Author: Homan, Paul B.
- Language: English
“Ensemble Sensitivity Analysis Of A Severe Downslope Windstorm In Complex Terrain: Implications For Forecast Predictability Scales And Targeted Observing Networks” Subjects and Themes:
- Subjects: ➤ Ensemble Sensitivity Analysis - Downslope Windstorm - Complex Terrain - Numerical Weather Prediction - Flow Separation - Breaking Waves - Vertically Propagating Waves - Trapped Waves - Data Assimilation - Targeted Observing - Forecast Predictability - Bulk Richardson Number - Mesoscale - Mesonet - Observing System Simulation Experiment - WRF - Mountain Waves.
Edition Identifiers:
- Internet Archive ID: ensemblesensitiv1094537639
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"Ensemble Sensitivity Analysis Of A Severe Downslope Windstorm In Complex Terrain: Implications For Forecast Predictability Scales And Targeted Observing Networks" Description:
The Internet Archive:
Multiple mesoscale numerical weather simulations are conducted to evaluate whether Ensemble Sensitivity Analysis (ESA) is a useful tool determining the sensitivity of a severe downslope windstorm (DSWS) in complex terrain to initial conditions and assimilated observations. A 96-member ensemble is implemented with 1.33 km grid spacing. Sensitive regions are found both upstream and downstream, based on a new forecast metric that indicates the potential for turbulence and strong winds reaching the Earths surface. Approximating the effects of assimilating a perfect observation at these sensitivity locations, then executing non-linear ensemble forecasts, shows the linear approximations in ESA are reasonable. We analyze the roles of upstream wind and stability structures, and leeside conditions, in determining the strength and propagation of winds down the mountain slope and onto the adjacent plains. Results suggest that ESA is a viable method to identify observation locations to improve forecasts of fine-scale, non-linear, high-impact events such as DSWS. Also, 14 severe DSWS identified by the High Wind Alert System located at the USAF Academy, CO are modeled utilizing a deterministic WRF configuration. Analysis shows that non-wave breaking events account for the strongest DSWS and propagate further away from the mountains than breaking events at this location.
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