热气球的风观测及其在NWP模型中的应用

IF 2.3 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES Meteorological Applications Pub Date : 2023-07-03 DOI:10.1002/met.2128
Evert I. F. de Bruijn, Fred C. Bosveld, Siebren de Haan, Gert-Jan Marseille, Albert A. M. Holtslag
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引用次数: 0

摘要

本文报道了一种基于热气球运动的风观测方法。通过与荷兰Cabauw气象塔2018年5 - 9月的风观测数据进行对比,得到的纬向风分量和经向风分量的误差标准差分别为σu=0.65ms−1 $$ {\sigma}_u=0.65\;{\mathrm{ms}}^{-1} $$和σv=0.69ms−1 $$ {\sigma}_v=0.69\;{\mathrm{ms}}^{-1} $$。随后与HARMONIE - AROME模型短期预报的比较显示,风矢量差的标准偏差为2.5 ms−1。从HAB观测集中选取了一个快速变化的风场属于小型强化低气压的病例。将赤潮风观测应用于资料同化,作为单次观测实验的原理证明。结果表明,在复杂斜压情况下,模型状态略有改善。
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Wind observations from hot-air balloons and the application in an NWP model

In this paper, we report on a wind observation method based on the movement of hot-air balloons (HABs). A quality assessment was carried out by comparing against wind observations at the meteorological tower of Cabauw in the Netherlands during May–September 2018, and the obtained standard deviations in error were σ u = 0.65 ms 1 $$ {\sigma}_u=0.65\;{\mathrm{ms}}^{-1} $$ and σ v = 0.69 ms 1 $$ {\sigma}_v=0.69\;{\mathrm{ms}}^{-1} $$ for the measured zonal and meridional wind components, respectively. Subsequent comparison against short-term forecasts of the HARMONIE-AROME model showed a standard deviation of 2.5 ms−1 for the wind vector difference. From the HAB observation set, a case was selected with a rapidly changing wind field belonging to a small intensifying depression. The HAB wind observation was applied in data assimilation as a proof of principle for a single-observation experiment. It is shown that in a complex baroclinic situation, the model state is slightly improved.

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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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