Statistical characteristics of long-term high-resolution precipitable water vapor data at Darwin

TitleStatistical characteristics of long-term high-resolution precipitable water vapor data at Darwin
Publication TypeJournal Article
Year of Publication2018
AuthorsLeung K., Subramanian AC, Shen S.SP
JournalAdvances in Data Science and Adaptive Analysis
Volume10
Date Published2018/10
Type of ArticleArticle
ISBN Number2424-922X
Accession NumberWOS:000457109600003
KeywordsDeep convection; extreme values; fractal dimensions; Mathematics; path; Precipitable water vapor; radar; rainfall; return period; statistical properties; west-african
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.

DOI10.1142/s2424922x18500109
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