On the skill of numerical weather prediction models to forecast atmospheric rivers over the central United States
On the skill of numerical weather prediction models to forecast atmospheric rivers over the central United States
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DOI：
10.1002/2014gl060299
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发表时间：
2013-12
影响因子：
5.2
通讯作者：
M. Nayak;G. Villarini;D. Lavers
中科院分区：
文献类型：
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作者：
M. Nayak;G. Villarini;D. Lavers
美国中部的洪水造成了巨大的社会经济损失。大气河流（ARs）是热带气旋温暖传送带内的密集水分输送的狭窄区域，可以产生大量降雨，导致洪水。AR活动的短期预测可以为改善对这些事件的准备提供基本信息。本研究的重点是验证五个数值天气预报模式在预测美国中部AR活动的技能。我们发现，这些模型一般预测AR的发生，以及在短的前置时间，随着前置时间增加到约1周的位置误差增加从一至三个小数点。技能（无论是在发生和位置错误）减少与增加前置时间。总的来说，这些模型在预测美国中部的AR活动方面并不熟练，提前时间超过7天左右。
Flooding over the central United States is responsible for large socioeconomic losses. Atmospheric rivers (ARs), narrow regions of intense moisture transport within the warm conveyor belt of extratropical cyclones, can give rise to high rainfall amounts leading to flooding. Short‐term forecasting of AR activity can provide basic information toward improving preparedness for these events. This study focuses on the verification of the skill of five numerical weather prediction models in forecasting AR activity over the central United States. We find that these models generally forecast AR occurrences well at short lead times, with location errors increasing from one to three decimal degrees as the lead time increases to about 1 week. The skill (both in terms of occurrence and location errors) decreases with increasing lead time. Overall, these models are not skillful in forecasting AR activity over the central United States beyond a lead time of about 7 days.