Characteristics of wintertime daily precipitation over the Australian Snowy Mountains

Fahimeh Sarmadi, Yi Huang, Steven T Siems, Michael J Manton

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)

Abstract

The relationship between orographic precipitation, low-level thermodynamic stability, and the synoptic meteorology is explored for the Snowy Mountains of southeast Australia. A 21-yr dataset (May-October, 1995-2015) of upper-air soundings from an upwind site is used to define synoptic indicators and the low-level stability. A K-means clustering algorithmwas employed to classify the dailymeteorology into four synoptic classes. The initial classification, based only on six synoptic indicators, distinctly defines both the surface precipitation and the lowlevel stability by class. Consistent with theory, the wet classes are found to have weak low-level stability, and the dry classes have strong low-level stability. By including low-level stability as an additional input variable to the clustering method, statistically significant correlations were found between the precipitation and the low-level stability within each of the four classes. An examination of the joint PDF reveals a highly nonlinear relationship; heavy rain was associated with very weak low-level stability, and conversely, strong low-level stability was associated with very little precipitation. Building on these historical relationships, model output statistics (MOS) from a moderate resolution (12-km spatial resolution) operational forecast were used to develop stepwise regression models designed to improve the 24-h forecast of precipitation over the Snowy Mountains. A single regression model for all dayswas found to reduce theRMSEby 7%and the bias by 75%.Aclass-based regression model was found to reduce the overall RMSE by 30% and the bias by 85%.

Original languageEnglish
Pages (from-to)2849-2867
Number of pages19
JournalJournal of Hydrometeorology
Volume18
Issue number10
DOIs
Publication statusPublished - 1 Oct 2017

Keywords

  • Australia
  • Classification
  • Orographic effects
  • Precipitation
  • Regression analysis
  • Statistical techniques

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