Characteristics of Wintertime Daily Precipitation over the Australian Snowy Mountains

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 algorithm was employed to classify the daily meteorology into four synoptic classes. The initial classification, based only on six synoptic indicators, distinctly defines both the surface precipitation and the low-level 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 days was found to reduce the RMSE by 7% and the bias by 75%. A class-based regression model was found to reduce the overall RMSE by 30% and the bias by 85%.

Characteristics of the Marine Atmospheric Boundary Layer Over the Southern Ocean in Response to the Synoptic Forcing

“…A cluster analysis using only the MAC observations was chosen over other methods that depend on reanalysis products (e.g., self‐organizing maps or empirical orthogonal functions), as such products have relatively coarse resolution over the remote SO and may miss mesoscale circulations (e.g., Irving et al, ). The cluster analysis algorithm used is the K ‐means cluster analysis algorithm (Anderberg, ), which has been widely used in a variety of meteorological applications (e.g., Hande, Siems, & Manton, ; Jakob & Tselioudis, ; Pope et al, ; Sarmadi et al, ) including to identify cloud regimes over the SO (e.g., Haynes et al, ; Mason et al, ).…”

Validating the data fusion‐based drought index across Queensland, Australia, and investigating interdependencies with remote drivers

“…Following Sarmadi et al . (), the copula pdfs were used to investigate the inter‐relationships (corresponding thresholds determination) between DFDI and SOI. According to Table , the t ‐copula function has shown the best performance across all of Queensland.…”

“…Copula functions were initially introduced to evaluate the interdependencies between a multidimensional probability distribution and its lower dimensional margins (Sklar, ). Copula functions can be useful to apply when (a) there are limited pairwise observations, (b) observations are not distributed homogeneously over the feasible sample space of each variable, (c) there is an interest in determining conditional probabilities, and (d) defining the corresponding thresholds between a set of random variables in an n ‐dimensional space (Sklar, ; Madadgar and Moradkhani, ; Sarmadi et al, ). Moreover, there are a variety of studies (e.g., Drouet‐Mari and Kotz, ; Genest and Plante, ; Nicoloutsopoulos, ) showing that a set of variables with very low correlations (even zero) can appropriately convey interdependencies, highlighting the advantage of copulas over common correlations in revealing complicate and “tail” dependency structures (e.g., Madadgar and Moradkhani, ).…”

“…Recently, Sarmadi et al . () have shown that it is possible to apply probability density functions (pdf) of copulas to explore the interdependencies and two‐dimensional classifications between hydro‐meteorological variables (i.e., between precipitation and low‐level atmosphere indicators). Therefore, teleconnections between the most relevant global climate indices and regional drought conditions in Queensland are exemplary evaluated here by applying the abovementioned joint probability based approach.…”

Air temperature lapse rates and cloud cover in a hyper-oceanic climate

“…The equable oceanic climate and strong persistent influence of synoptic winds means that lapse rates should be driven by airflow over the relatively simple but prominent topography, which is orientated more or less perpendicular to the prevailing winds (90.2% of winds at the meteorological station are from the westerly sector 180–360°). Low-level atmospheric stability due to the effects of topography on wind speed and air temperature may be important, as it will determine the magnitude of orographic lifting (Sarmadi et al 2017).…”