Improvement of Numerical Weather Prediction for Complex Terrain

. 我们的动机是更好地预测与天气有关的灾害，并帮助提高与天气有关的清洁能源（风能和水电）的效率和安全性。 为了实现这一目标，我们的研究目标是提高复杂的山区和沿海地形，如在加拿大西部的数值天气预报的技能。 数值天气预报（NWP）是一种使用高性能计算机对大气流体动力学方程进行数值积分的计算工具。 求解这些方程的大型的、计算机上可用的数值预报计算机代码被称为“模型”。 没有一个模型是完美的，所以我们对计算机输出进行后处理，以纠正任何系统误差。 我们还利用“合奏”的NWP预测从许多模式来解释大气的混沌性质。 可以校准集合预报的传播，以估计不同预报结果的概率-这是优化能源管理的重要信息。 我们的以下活动将有助于实现我们的目标。 危险天气事件通常相对罕见，难以预测，因此我们将改进利用过去罕见的类似事件的“模拟天气集合”的方法。 水利水库和河流洪水预报依赖于不完善的水文模型，因此我们将驱动一个水文模型的集成与NWP模型的集成输出，以提高技能。 高分辨率天气预报将应用于智能电表的数据，并将通过这些数据得到改进。加拿大 我们将测试一种新型的遗传编程是否可以帮助预测风速的急剧变化，从而调节风力发电。 我们将改进数值预报模式，以便对山区地形进行更高分辨率的预报，从而更好地预测陡峭狭窄山谷中沿着运输走廊的泥石流、泥石流、铁路冲刷、局部洪水和雪崩。 这项工作将有助于拯救生命，降低能源成本，使运输更加可靠，并帮助加拿大工业蓬勃发展。

. Our motivation is to better predict weather-related disasters, and to help enhance the efficiency and safety of weather-related clean energy (wind and hydro). To achieve this, our research goal is to improve the skill of numerical weather prediction for complex mountainous and coastal terrains such as in Western Canada. Numerical weather prediction (NWP) is a computational tool that uses high-performance computers to numerically integrate the atmospheric fluid-dynamics equations. The large, publically available NWP computer codes that solve these equations are called "models". No model is perfect, so we post-process the computer output to correct any systematic errors. We also utilize an "ensemble" of NWP forecasts from many models to account for the chaotic nature of the atmosphere. The spread of the ensemble forecasts can be calibrated to estimate the probability of different forecast outcomes -- important information for the optimum management of energy resources. Our following activities will help achieve our goals. Hazardous weather events are often relatively rare and hard to forecast, so we will improve ways to utilize "analog weather ensembles" from past rare events that were similar. Hydro reservoirs and river flood forecasts rely on imperfect hydrologic models, so we will drive an ensemble of hydrologic models with output from an ensemble of NWP models to improve skill. High-resolution weather forecasts will be applied to, and will be improved by, data from smart electric meters being deployed in W. Canada. We will test whether a new type of genetic programming can help predict sharp changes in wind speed that modulate wind-power production. We will enhance an NWP model to enable finer-resolution forecasts in mountainous terrain for better prediction of rainfall-triggered landslides, debris flows, rail washouts, local flooding, and snow avalanches along transportation corridors in steep narrow mountain valleys. This work will help save lives, reduce energy costs, make transportation more reliable, and help Canadian industries to thrive.