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

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%.

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Thomas H. Chubb, Steven T. Siems, and Michael J. Manton

Abstract

Data from a precipitation gauge network in the Snowy Mountains of southeastern Australia have been analyzed to produce a new climatology of wintertime precipitation and airmass history for the region in the period 1990–2009. Precipitation amounts on the western slopes and in the high elevations (>1000 m) of the Snowy Mountains region have experienced a decline in precipitation in excess of the general decline in southeastern Australia. The contrast in the decline east and west of the ranges suggests that factors influencing orographic precipitation are of particular importance. A synoptic decomposition of precipitation events has been performed, which demonstrates that about 57% of the wintertime precipitation may be attributed to storms associated with “cutoff lows” (equatorward of 45°S). A further 40% was found to be due to “embedded lows,” with the remainder due to Australian east coast lows and several other sporadically occurring events. The declining trend in wintertime precipitation over the past two decades is most clearly seen in the intensity of precipitation due to cutoff lows and coincides with a decline in the number of systems associated with a cold frontal passage. Airmass history during precipitation events was represented by back trajectories calculated from ECMWF Interim Reanalysis data, and statistics of air parcel position were related to observations of precipitation intensity. This approach gives insight into sources of moisture during wintertime storms, identifying “moisture corridors,” which are typically important for transport of water vapor from remote sources to the Snowy Mountains region. The prevalence of these moisture corridors is associated with the southern annular mode, which corresponds to fluctuations in the strength of the westerly winds in southeastern Australia.

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Jingru Dai, Michael J. Manton, Steven T. Siems, and Elizabeth E. Ebert

Abstract

Wintertime precipitation in the Snowy Mountains provides water for agriculture, industry, and domestic use in inland southeastern Australia. Unlike most of Australia, much of this precipitation falls as snow, and it is recorded by a private network of heated tipping-bucket gauges. These observations are used in the present study to assess the accuracy of a poor man’s ensemble (PME) prediction of precipitation in the Snowy Mountains based on seven numerical weather prediction models. While the PME performs quite well, there is significant underestimation of precipitation intensity. It is shown that indicators of the synoptic environment can be used to improve the PME estimates of precipitation. Four synoptic regimes associated with different precipitation classes are identified from upper-air data. The reliability of the PME forecasts can be sharpened by considering the precipitation in each of the four synoptic classes. A linear regression, based on the synoptic classification and the PME estimate, is used to reduce the forecast errors. The potential to extend the method for forecasting purposes is discussed.

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Thomas Chubb, Michael J. Manton, Steven T. Siems, Andrew D. Peace, and Shane P. Bilish

Abstract

Wind-induced losses, or undercatch, can have a substantial impact on precipitation gauge observations, especially in alpine environments that receive a substantial amount of frozen precipitation and may be exposed to high winds. A network of NOAH II all-weather gauges installed in the Snowy Mountains since 2006 provides an opportunity to evaluate the magnitude of undercatch in an Australian alpine environment. Data from two intercomparison sites were used with NOAH II gauges with different configurations of wind fences installed: unfenced, WMO standard double fence intercomparison reference (full DFIR) fences, and an experimental half-sized double fence (half DFIR). It was found that average ambient temperature over 6-h periods was sufficient to classify the precipitation phase as snow, mixed precipitation, or rain in a statistically robust way. Empirical catch ratio relationships (i.e., the quotient of observations from two gauges), based on wind speed, ambient temperature, and measured precipitation amount, were established for snow and mixed precipitation. An adjustment scheme to correct the unfenced NOAH II gauge data using the catch ratio relationships was cross validated with independent data from two additional sites, as well as from the intercomparison sites themselves. The adjustment scheme was applied to the observed precipitation amounts at the other sites with unfenced NOAH II gauges. In the worst-case scenario, it was found that the observed precipitation amount would need to be increased by 52% to match what would have been recorded had adequate shielding been installed. However, gauges that were naturally well protected, and those below about 1400 m, required very little adjustment. Spatial analysis showed that the average seasonal undercatch was between 6% and 15% for gauges above 1000 m MSL.

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