Statistical Characteristics of Long-Term High-Resolution Precipitable Water Vapor Data at Darwin

IF 0.9 Q4 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Kimberly Leung, A. Subramanian, S. Shen
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引用次数: 0

Abstract

This paper studies the statistical characteristics of a unique long-term high-resolution precipitable water vapor (PWV) data set at Darwin, Australia, from 12 March 2002 to 28 February 2011. To understand the convective precipitation processes for climate model development, the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) program made high-frequency radar observations of PWV at the Darwin ARM site and released the best estimates from the radar data retrievals for this time period. Based on the best estimates, we produced a PWV data set on a uniform 20-s time grid. The gridded data were sufficient to show the fractal behavior of precipitable water with Hausdorff dimension equal to 1.9. Fourier power spectral analysis revealed modulation instability due to two sideband frequencies near the diurnal cycle, which manifests as nonlinearity of an atmospheric system. The statistics of PWV extreme values and daily rainfall data show that Darwin’s PWV has El Nino Southern Oscillation (ENSO) signatures and has potential to be a predictor for weather forecasting. The right skewness of the PWV data was identified, which implies an important property of tropical atmosphere: ample capacity to hold water vapor. The statistical characteristics of this long-term high-resolution PWV data will facilitate the development and validation of climate models, particularly stochastic models.
达尔文长期高分辨率可降水量数据的统计特征
本文研究了2002年3月12日至2011年2月28日澳大利亚达尔文独特的长期高分辨率可降水量(PWV)数据集的统计特征。为了了解对流降水过程对气候模式发展的影响,美国能源部的大气辐射测量(ARM)项目在达尔文ARM站点对PWV进行了高频雷达观测,并发布了这一时期雷达数据检索的最佳估计。基于最佳估计,我们在统一的20秒时间网格上生成了PWV数据集。网格化数据足以显示可降水量的分形特征,其Hausdorff维数为1.9。傅里叶功率谱分析揭示了由于日周期附近的两个边带频率导致的调制不稳定性,这表现为大气系统的非线性。PWV极值和日降水数据的统计表明,达尔文PWV具有厄尔尼诺-南方涛动(ENSO)特征,具有预报天气的潜力。确定了PWV数据的正确偏度,这意味着热带大气的一个重要特性:充足的水汽容纳能力。这种长期高分辨率PWV数据的统计特征将有助于气候模型,特别是随机模型的开发和验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Advances in Data Science and Adaptive Analysis
Advances in Data Science and Adaptive Analysis MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
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