The use of vehicle-based observations in weather prediction and decision support

IF 2.3 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Amanda R. Siems-Anderson
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引用次数: 0

Abstract

Vehicle-based mobile observations are taken across the world every day by operational and research meteorological organizations, public transportation agencies, and private car manufacturers. Whether directly weather-related (e.g., air temperature) or not (e.g., wiper speed), the coverage and frequency of these observations holds the promise of filling in gaps between fixed observing stations and greatly improving situational awareness and weather forecasting, from road surface condition-specific applications and winter road maintenance to urban and street-level numerical weather prediction and beyond. However, in order to take advantage of these observations, the weather, water, and climate enterprise must work together with the transportation enterprise across academic, public, and private sectors to provide a mechanism for obtaining these data, so that the benefits of using these unconventional observations may be realized.

Abstract Image

在天气预测和决策支持中使用基于车辆的观测数据
世界各地的业务和研究气象组织、公共交通机构和私家车制造商每天都在进行基于车辆的移动观测。无论是直接与天气有关(如气温)还是无关(如雨刷速度),这些观测的覆盖范围和频率都有望填补固定观测站之间的空白,并极大地改善态势感知和天气预报,从路面状况特定应用和冬季道路维护到城市和街道级数值天气预报等。然而,为了利用这些观测数据,天气、水和气候企业必须与学术界、公共部门和私营部门的交通企业合作,提供获取这些数据的机制,从而实现利用这些非常规观测数据的益处。
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来源期刊
Meteorological Applications
Meteorological Applications 地学-气象与大气科学
CiteScore
5.70
自引率
3.70%
发文量
62
审稿时长
>12 weeks
期刊介绍: The aim of Meteorological Applications is to serve the needs of applied meteorologists, forecasters and users of meteorological services by publishing papers on all aspects of meteorological science, including: applications of meteorological, climatological, analytical and forecasting data, and their socio-economic benefits; forecasting, warning and service delivery techniques and methods; weather hazards, their analysis and prediction; performance, verification and value of numerical models and forecasting services; practical applications of ocean and climate models; education and training.
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