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  • Author or Editor: Wei Shi x
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Francisco J. Tapiador, Wei-Kuo Tao, Jainn Jong Shi, Carlos F. Angelis, Miguel A. Martinez, Cecilia Marcos, Antonio Rodriguez, and Arthur Hou

Abstract

Ensembles of numerical model forecasts are of interest to operational early warning forecasters as the spread of the ensemble provides an indication of the uncertainty of the alerts, and the mean value is deemed to outperform the forecasts of the individual models. This paper explores two ensembles on a severe weather episode in Spain, aiming to ascertain the relative usefulness of each one. One ensemble uses sensible choices of physical parameterizations (precipitation microphysics, land surface physics, and cumulus physics) while the other follows a perturbed initial conditions approach. The results show that, depending on the parameterizations, large differences can be expected in terms of storm location, spatial structure of the precipitation field, and rain intensity. It is also found that the spread of the perturbed initial conditions ensemble is smaller than the dispersion due to physical parameterizations. This confirms that in severe weather situations operational forecasts should address moist physics deficiencies to realize the full benefits of the ensemble approach, in addition to optimizing initial conditions. The results also provide insights into differences in simulations arising from ensembles of weather models using several combinations of different physical parameterizations.

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Yang Shi, Jiahua Wei, Yan Ren, Zhen Qiao, Qiong Li, Xiaomei Zhu, Beiming Kang, Peichong Pan, Jiongwei Cao, Jun Qiu, Tiejian Li, and Guangqian Wang

Abstract

Acoustic agglomerations have increasingly attracted widespread attention as a cost-effective and environmentally friendly approach for fog removal and weather modification. In this study, research on precipitation interference and the agglomeration performance of droplet aerosols under large-scale acoustic waves was presented. In total, 49 field experiments in the source region of the Yellow River in the summer of 2019 were performed to reveal the influences of acoustic waves on precipitation, such as the radar reflectivity factor Z, rain rate R, and raindrop size distribution (DSD). A monitoring system that consisted of rain gauges and raindrop spectrometers was employed to monitor near-ground rainfall within a 5-km radius of the field site. The ground-based observations showed that acoustic waves could significantly affect the rainfall distribution and microstructure of precipitation particles. The average values of rainfall increased by 18.98%, 10.61%, and 8.74% within 2, 3, and 5 km, respectively, of the operation center with acoustic application. The changing trend of microphysical parameters of precipitation was roughly in line with variation of acoustic waves for stratiform cloud. Moreover, there was a good quadratic relationship between the spectral parameters λ and μ. Raindrop kinetic energy e K and the radar reflectivity factor Z both exhibited a power function relationship with R.

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