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  • Author or Editor: Yao Li x
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Ruibo Lei
,
Zhijun Li
,
Yanfeng Cheng
,
Xin Wang
, and
Yao Chen

Abstract

High-precision ice thickness observations are required to gain a better understanding of ocean–ice–atmosphere interactions and to validate numerical sea ice models. A new apparatus for monitoring sea ice and snow thickness has been developed, based on the magnetostrictive-delay-line (MDL) principle for positioning sensors. This system is suited for monitoring fixed measurement sites on undeformed ice. The apparatus presented herein has been tested on landfast ice near Zhongshan Station, East Antarctica, for about 6 months during the austral autumn and winter of 2006; valid data records from the deployment are available for more than 90% of the deployment’s duration. The apparatus’s precision has been estimated to be ±0.002 m for the deployment. Therefore, it is possible that this apparatus may become a standard for sea ice/snow thickness monitoring.

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Xudong Liang
,
Yanxin Xie
,
Jinfang Yin
,
Yi Luo
,
Dan Yao
, and
Feng Li

Abstract

Dealiasing is a common procedure in radar radial velocity quality control. Generally, there are two dealiasing steps: a continuity check and a reference check. In this paper, a modified version that uses azimuthal variance of radial velocity is introduced based on the integrating velocity–azimuth process (IVAP) method, referred to as the V-IVAP method. The new method can retrieve the averaged winds within a local area instead of averaged wind within a full range circle by the velocity–azimuth display (VAD) or the modified VAD method. The V-IVAP method is insensitive to the alias of the velocity, and provides a better way to produce reference velocities for a reference check. Instead of a continuity check, we use the IVAP method for a fine reference check because of its high-frequency filtering function. Then a dealiasing procedure with two steps of reference check is developed. The performance of the automatic dealiasing procedure is demonstrated by retrieving the wind field of a tornado. Using the dealiased radar velocities, the retrieved winds reveal a clear mesoscale vortex. A test based on radar network observations also has shown that the two-step dealiasing procedure based on V-IVAP and IVAP methods is reliable.

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William B. Willis
,
William E. Eichinger
,
John H. Prueger
,
Cathleen J. Hapeman
,
Hong Li
,
Michael D. Buser
,
Jerry L. Hatfield
,
John D. Wanjura
,
Gregory A. Holt
,
Alba Torrents
,
Sean J. Plenner
,
Warren Clarida
,
Stephen D. Browne
,
Peter M. Downey
, and
Qi Yao

Abstract

Pollutant emissions to the atmosphere commonly derive from nonpoint sources that are extended in space. Such sources may contain area, volume, line, or a combination of emission types. Currently, point measurements, often combined with models, are the primary means by which atmospheric emission rates are estimated from extended sources. Point measurement arrays often lack in spatial and temporal resolution and accuracy. In recent years, lidar has supplemented point measurements in agricultural research by sampling spatial ensembles nearly instantaneously. Here, a methodology using backscatter data from an elastic scanning lidar is presented to estimate emission rates from extended sources. To demonstrate the approach, a known amount of particulate matter was released upwind of a vegetative environmental buffer, a barrier designed to intercept emissions from animal production facilities. The emission rate was estimated downwind of the buffer, and the buffer capture efficiency (percentage of particles captured) was calculated. Efficiencies ranged from 21% to 74% and agree with the ranges previously published. A comprehensive uncertainty analysis of the lidar methodology was performed, revealing an uncertainty of 20% in the emission rate estimate; suggestions for significantly reducing this uncertainty in future studies are made. The methodology introduced here is demonstrated by estimating the efficiency of a vegetative buffer, but it can also be applied to any extended emission source for which point samples are inadequate, such as roads, animal feedlots, and cotton gin operations. It can also be applied to any pollutant for which a lidar system is configured, such as particulate matter, carbon dioxide, and ammonia.

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