Calibrated Probabilistic Mesoscale Weather Field Forecasting
经校准的中尺度天气场概率预测AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要研究天气场的空间概率预测，未涉及宏观经济预测，因此不因出现预测方法而归入宏观预测与即时预测。
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经校准的中尺度天气场概率预测
- 研究问题
- 研究资源有限的机构如何生成中尺度天气的联合概率预测，避免对数值模型输入反复扰动并多次运行所需的计算负担。
- 方法与思路
- 作者对确定性预测的输出进行扰动，用地统计模型描述预测误差，再模拟误差场生成集合，应用于西北太平洋沿岸温度预测。
- 材料陈述的贡献
- 摘要称应用中的单项天气预测区间经验校准良好，比近似气候基准更窄，并与观测空间相关结构的部分特征一致。
- 阅读关注点
- 应核对误差场模型和训练验证划分、边际与联合校准的区别，以及区间宽度和空间相关结构的具体评价方式。
- 阅读边界
- 仅据版本未注明的索引摘要，结果限于所述地区与时期的48小时温度预测；边际校准不能直接证明完整联合分布校准。
原文摘要
Probabilistic weather forecasting consists of finding a joint probability distribution for future weather quantities or events. It is typically done by using a numerical weather prediction model, perturbing the inputs to the model in various ways, and running the model for each perturbed set of inputs. The result is then viewed as an ensemble of forecasts, taken to be a sample from the joint probability distribution of the future weather quantities of interest. This is typically not feasible for mesoscale weather prediction carried out locally by organizations without the vast data and computing resources of national weather centers. Instead, we propose a simpler method that breaks with much previous practice by perturbing the outputs, or deterministic forecasts, from the model. Forecast errors are modeled using a geostatistical model, and ensemble members are generated by simulating realizations of the geostatistical model. The method is applied to 48-hour mesoscale forecasts of temperature in the North American Pacific Northwest between 2000 and 2002. The resulting forecast intervals turn out to be empirically well calibrated for individual meteorological quantities, to be sharper than those obtained from approximate climatology, and to be consistent with aspects of the spatial correlation structure of the observations.