Nested simulations of a downslope windstorm over Cangshan mountain,Yunnan,China,have been used to demonstrate a method of topographic smoothing that preserves a relatively large amount of terrain detail compared to ty...Nested simulations of a downslope windstorm over Cangshan mountain,Yunnan,China,have been used to demonstrate a method of topographic smoothing that preserves a relatively large amount of terrain detail compared to typical smoothing procedures required for models with terrain-following grids to run stably.The simulations were carried out using the Met Office Unified Model(MetUM)to investigate downslope winds.The smoothing method seamlessly blends two terrain datasets to which uniform smoothing has been applied—one with a minimum of smoothing,the other smoothed more heavily to remove gradients that would cause model instabilities.The latter dataset dominates the blend where the steepest slopes exist,but this is localised and recedes outside these areas.As a result,increased detail is starkly apparent in depictions of flow simulated using the blend,compared to one using the default approach.This includes qualitative flow details that were absent in the latter,such as narrow shooting flows emerging from roughly 1-2 km wide leeside channels.Flow separation is more common due to steeper lee slopes.The use of targeted smoothing also results in increased lee side temporal variability at a given point during the windstorm,including over flat areas.Low-/high-pass filtering of the wind perturbation field reveals that relative spatial variability above 30 km in scale(reflecting the background flow)is similar whether or not targeting is used.Beneath this scale,when smoothing is targeted,relative flow variability decreases at the larger scales,and increases at lower scales.This seems linked to fast smaller scale flows disturbing more coherent flows(notably an along-valley current over Erhai Lake).Spatial variability of winds in the model is unsurprisingly weaker at key times than is observed across a local network sampling mesoscale variation,but results are compromised due to relatively few observation locations sampling the windstorm.Only when targeted smoothing is applied does the model capture the downslope windstorm's extension over the city of Dali at the mountain's foot,and the peak mean absolute wind.展开更多
基金supported by the UK–China Research&Innovation Partnership Fund through the Met Office Climate Science for Service Partnership(CSSP)China as part of the Newton Fund
文摘Nested simulations of a downslope windstorm over Cangshan mountain,Yunnan,China,have been used to demonstrate a method of topographic smoothing that preserves a relatively large amount of terrain detail compared to typical smoothing procedures required for models with terrain-following grids to run stably.The simulations were carried out using the Met Office Unified Model(MetUM)to investigate downslope winds.The smoothing method seamlessly blends two terrain datasets to which uniform smoothing has been applied—one with a minimum of smoothing,the other smoothed more heavily to remove gradients that would cause model instabilities.The latter dataset dominates the blend where the steepest slopes exist,but this is localised and recedes outside these areas.As a result,increased detail is starkly apparent in depictions of flow simulated using the blend,compared to one using the default approach.This includes qualitative flow details that were absent in the latter,such as narrow shooting flows emerging from roughly 1-2 km wide leeside channels.Flow separation is more common due to steeper lee slopes.The use of targeted smoothing also results in increased lee side temporal variability at a given point during the windstorm,including over flat areas.Low-/high-pass filtering of the wind perturbation field reveals that relative spatial variability above 30 km in scale(reflecting the background flow)is similar whether or not targeting is used.Beneath this scale,when smoothing is targeted,relative flow variability decreases at the larger scales,and increases at lower scales.This seems linked to fast smaller scale flows disturbing more coherent flows(notably an along-valley current over Erhai Lake).Spatial variability of winds in the model is unsurprisingly weaker at key times than is observed across a local network sampling mesoscale variation,but results are compromised due to relatively few observation locations sampling the windstorm.Only when targeted smoothing is applied does the model capture the downslope windstorm's extension over the city of Dali at the mountain's foot,and the peak mean absolute wind.
