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  • Author or Editor: Qin Zhang x
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Yuanhang Zhang and Yu Qin

Abstract

A comprehensive field measurement was set up in the springtime of 1993 to study the chemical characteristics of precipitation in the Lushan Mountains. The results showed that the concentrations of gaseous SO2, NOx, NH3, and HNO3 were low and precipitation was seriously acidified. The pH of all samples was less than 5.6 with an average of 4.6 and a minimum of 3.71. Sulfuric acid was the primary acidic substance to cause the acidification and nitric acid was the secondary. Both acidity and concentrations of compositions in cloud water were much higher than those in rainwater. Comparisons of different phases of precipitation showed that concentrations of ionic components in the solid phase were two to five times higher than those in rainwater, and its pH was 0.6 higher than that of rainwater. According to the measurement results, it was postulated that the acidification of precipitation was caused by joint effects of washout of local atmospheric pollutants and long-distance transportation of acidic substances.

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Zuohao Cao, Qin Xu, and Da-Lin Zhang

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Unlike the classical point vortex model, a new method is developed to extract flows induced not only by vorticity but also by divergence in a well-defined vortex core area of a cyclone. This new method is applied to diagnosing the interactions of three midlatitude cyclones (called A, B, and C) that account for a missed summer severe rainfall forecast, in which the daily precipitation predicted by the Canadian operational model is an order of magnitude smaller than the rain gauge and radar measurements. In this event, cyclone B, responsible for the severe rainfall occurrence, was advected largely by flows induced by two neighboring cyclones: A and C to the west and east, respectively. This work attempts to assess whether and to what degree the vertical tilt of the observed cyclone versus that of the forecast cyclone B is caused by advections of the environmental flows (including A- and C-induced flows) at 500 and 1000 hPa. Results show that the observed cyclone B was advected mainly by the cyclone A–induced flow at 500 hPa into a vertically tilted structure that was northwestward against the vertical shear of the environmental flow and thus favorable for upward motion and cyclone intensification around the time of severe rainfall. However, the forecast cyclone B was advected largely by the cyclone A–induced flow at 500 hPa and the cyclone C–induced flow at 1000 hPa into an increasingly northward-tilted structure that was along the vertical shear of the environmental flow and thus unfavorable for upward motion and cyclone intensification, leading to the missed forecast of severe rainfall. Suggestions are made for future improvements of model forecasts.

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Shu-Wen Zhang, Chong-Jian Qiu, and Qin Xu
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Shu-Wen Zhang, Chong-Jian Qiu, and Qin Xu

Abstract

A simple soil heat transfer model is used together with an adaptive Kalman filter to estimate the daily averaged soil volumetric water contents from diurnal variations of the soil temperatures measured at different depths. In this method, the soil water contents are estimated as control variables that regulate the variations of soil temperatures at different depths and make the model nonbiased, while the model system noise covariance matrix is estimated by the covariance-matching technique. The method is tested with soil temperature data collected during 1–31 July 2000 from the Soil Water and Temperature System (SWATS) within the Oklahoma Atmospheric Radiation Measurement (ARM) central facilities at Lamont. The estimated soil water contents are verified against the observed values, and the rms differences are found to be small. Sensitivity tests are performed, showing that the method is reliable and stable.

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Shun Liu, Chongjian Qiu, Qin Xu, and Pengfei Zhang

Abstract

A temporal interpolation is required for three-dimensional Doppler wind analysis when the precise measurement time is counted for each radar beam position. The time interpolation is traditionally done by a linear scheme either in the measurement space or in the analysis space. Because a volume scan often takes 5–10 min, the linear time interpolation is not accurate enough to capture the rapidly changing winds associated with a fast-moving and fast-growing storm. Performing the linear interpolation in a frame moving with the storm can reduce the error, but the analyzed wind field is traditionally assumed to be stationary in the moving frame. The stationary assumption simplifies the computation but ignores the time variation of the true wind field in the moving frame. By incorporating a linear time interpolation into the moving frame wind analysis, an improved scheme is developed. The merits of the new scheme are demonstrated by idealized examples and numerical experiments with simulated radar observations. The new scheme is also applied to real radar data for a supercell storm.

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Xu Qin, Jiang-she Zhang, and Xiao-dong Yan

Abstract

In this paper, the authors propose two improved mixture Weibull distribution models by adding one or two location parameters to the existing two-component mixture two-parameter Weibull distribution [MWbl(2, 2)] model. One improved model is the mixture two-parameter Weibull and three-parameter Weibull distribution [MWbl(2, 3)] model. The other improved model is the two-component mixture three-parameter Weibull distribution [MWbl(3, 3)] model. In contrast to existing literature, which has focused on the MWbl(2, 2) and the typical Weibull distribution models, the authors apply the MWbl(2, 3) model and MWbl(3, 3) model to fit the distribution of wind speed data with nearly zero percentages of null wind speed. The parameters of the two improved models are estimated by the maximum likelihood method in which the maximization problem is regarded as a nonlinear programming problem with only inequality constraints and is solved numerically by the interior-point method. The experimental results show that the mixture Weibull models proposed in this paper are more flexible than the existing models for the analysis of wind speed data in practice.

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Jerome D. Fast, Rob K. Newsom, K. Jerry Allwine, Qin Xu, Pengfei Zhang, Jeffrey Copeland, and Juanzhen Sun

Abstract

Two entirely different methods for retrieving 3D fields of horizontal winds from Next Generation Weather Radar (NEXRAD) radial velocities have been evaluated using radar wind profiler measurements to determine whether routine wind retrievals would be useful for atmospheric dispersion model applications. The first method uses a physical algorithm based on four-dimensional variational data assimilation, and the second simpler method uses a statistical technique based on an analytic formulation of the background error covariance. Both methods can be run in near–real time, but the simpler method was executed about 2.5 times as fast as the four-dimensional variational method. The observed multiday and diurnal variations in wind speed and direction were reproduced by both methods below ∼1.5 km above the ground in the vicinity of Oklahoma City, Oklahoma, during July 2003. However, wind retrievals overestimated the strength of the nighttime low-level jet by as much as 65%. The wind speeds and directions obtained from both methods were usually similar when compared with profiler measurements, and neither method outperformed the other statistically. Within a dispersion model framework, the 3D wind fields and transport patterns were often better represented when the wind retrievals were included along with operational data. Despite uncertainties in the wind speed and direction obtained from the wind retrievals that are higher than those from remote sensing radar wind profilers, the inclusion of the wind retrievals is likely to produce more realistic temporal variations in the winds aloft than would be obtained by interpolation using the available radiosondes, especially during rapidly changing synoptic- and mesoscale conditions.

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Rob K. Newsom, Larry K. Berg, Mikhail Pekour, Jerome Fast, Qin Xu, Pengfei Zhang, Qing Yang, William J. Shaw, and Julia Flaherty

Abstract

The accuracy of winds derived from Next Generation Weather Radar (NEXRAD) level-II data is assessed by comparison with independent observations from 915-MHz radar wind profilers. The evaluation is carried out at two locations with very different terrain characteristics. One site is located in an area of complex terrain within the State Line Wind Energy Center in northeastern Oregon. The other site is located in an area of flat terrain on the east-central Florida coast. The National Severe Storm Laboratory’s two-dimensional variational data assimilation (2DVar) algorithm is used to retrieve wind fields from the KPDT (Pendleton, Oregon) and KMLB (Melbourne, Florida) NEXRAD radars. Wind speed correlations at most observation height levels fell in the range from 0.7 to 0.8, indicating that the retrieved winds followed temporal fluctuations in the profiler-observed winds reasonably well. The retrieved winds, however, consistently exhibited slow biases in the range of 1–2 m s−1. Wind speed difference distributions were broad, with standard deviations in the range from 3 to 4 m s−1. Results from the Florida site showed little change in the wind speed correlations and difference standard deviations with altitude between about 300 and 1400 m AGL. Over this same height range, results from the Oregon site showed a monotonic increase in the wind speed correlation and a monotonic decrease in the wind speed difference standard deviation with increasing altitude. The poorest overall agreement occurred at the lowest observable level (~300 m AGL) at the Oregon site, where the effects of the complex terrain were greatest.

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