Estimation of Wind-Induced Losses from a Precipitation Gauge Network in the Australian Snowy Mountains

Chubb, Thomas; Manton, M. J.; Siems, Steven T.; Peace, Andrew D.; Bilish, Shane P.

doi:10.1175/jhm-d-14-0216.1

Cited by 30 publications

(32 citation statements)

References 25 publications

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“…Interestingly, the relative bias was reduced for higher precipitation amounts. This is consistent with Chubb et al (2015), who found that undercatch was reduced for higher precipitation rates (other factors being equal). It may also reflect the tendency for very heavy precipitation to be in the form of rain rather than snow, making the precipitation measurement less affected by wind speed (Chubb et al, 2015).…”

Section: Accuracy Of the Awap Rainfall Analysis In The Snowy Mountainssupporting

confidence: 92%

“…This is consistent with Chubb et al (2015), who found that undercatch was reduced for higher precipitation rates (other factors being equal). It may also reflect the tendency for very heavy precipitation to be in the form of rain rather than snow, making the precipitation measurement less affected by wind speed (Chubb et al, 2015). Figure 7a shows the spatial distribution of the mean May-September precipitation amount derived using the Barnes analysis procedure described above and gauge data from both the BOM and SHL.…”

Section: Accuracy Of the Awap Rainfall Analysis In The Snowy Mountainssupporting

confidence: 92%

“…The highest seasonal value in the AWAP analysis was 775 mm, but many of the SHL gauges reported in excess of 1000 mm, which is more consistent with the longer-term values presented by Chubb et al (2011) for the same season and region. These discrepancies are in excess of the catch ratio corrections suggested by Chubb et al (2015), but could at least in part be attributed to poor performance of tipping bucket gauges in alpine conditions. There is a spatial pattern to the bias, with some gauges on the western slopes of the mountains recording considerably more than the analysis amount, and conversely several gauges on the eastern slopes reported less than the analysis.…”

Section: Accuracy Of the Awap Rainfall Analysis In The Snowy Mountainsmentioning

confidence: 67%

“…Some of these were at existing Noah-II sites with no wind fences, and in most cases the unfenced gauges were removed after a year or so. Chubb et al (2015) evaluated the impact of the double fences on the catch efficiency of the Noah-II gauges, and found that a fenced gauge could record as much as 50 percent more precipitation in certain conditions at the most exposed locations in the Snowy Mountains. A more realistic seasonal value for this "under-catch" is probably around 15 percent, as was recorded at a site at 1586 m elevation, where 33 percent of the total precipitation was identified as either snow or mixed-phase precipitation.…”

Section: Snowy Hydro Precipitation Gauge Datamentioning

confidence: 99%

“…At elevations above 1400 m, precipitation frequently falls as snow in the wintertime, but the tipping-bucket type gauges deployed here are not wellsuited to recording frozen precipitation, especially in windy conditions (Chubb et al, 2015), or when frozen precipitation rates greater than about 3 mm/hr overcome the gauge heating capability (Gorman, 2003). Moreover, the BOM rain gauge network is quite sparse in the mountainous areas, which is known to be problematic for the representation of the spatial variability of precipitation.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the AWAP daily precipitation spatial analysis with an independent gauge network in the Snowy Mountains

Chubb¹,

Manton²,

Siems³

et al. 2016

JSHESS

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The Bureau of Meteorology's Australian Water Availability Project (AWAP) daily precipitation analysis provides high resolution rainfall data by interpolating rainfall gauge data, but when evaluated against a spatially dense independent gauge network in the Snowy Mountains large systematic biases are identified. Direct comparisons with the gauge data in May-September between 2007 and 2014 reveal average root mean square errors of about 4.5 mm, which is slightly greater than the average daily precipitation amount, and the errors are larger for higher elevation gauges. A standard Barnes objective analysis is performed on the combined set of independent gauges and Bureau of Meteorology gauges in the region to examine the spatial characteristics of the differences. The largest differences are found on the western (windward) slopes, where the Barnes analysis is up to double the value of the AWAP analysis. These differences are attributed to a) the lack of Bureau of Meteorology gauges in the area to empirically represent the precipitation climatology, and b) the inability of the AWAP analysis to account for the steep topography exposed to the prevailing winds. At high elevation (>1400 m) the Barnes analysis suggests that the precipitation amount is about fifteen percent greater than that of the AWAP analysis, where the difficulties of measuring frozen precipitation likely have a large impact.

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Section: Accuracy Of the Awap Rainfall Analysis In The Snowy Mountainssupporting

confidence: 92%

Section: Accuracy Of the Awap Rainfall Analysis In The Snowy Mountainssupporting

confidence: 92%

Section: Accuracy Of the Awap Rainfall Analysis In The Snowy Mountainsmentioning

confidence: 67%

Section: Snowy Hydro Precipitation Gauge Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluation of the AWAP daily precipitation spatial analysis with an independent gauge network in the Snowy Mountains

Chubb¹,

Manton²,

Siems³

et al. 2016

JSHESS

Self Cite

View full text Add to dashboard Cite

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A Computational Fluid‐Dynamics Assessment of the Improved Performance of Aerodynamic Rain Gauges

Colli

Pollock

Stagnaro

et al. 2018

Water Resources Research

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The airflow surrounding any catching‐type rain gauge when impacted by wind is deformed by the presence of the gauge body, resulting in the acceleration of wind above the orifice of the gauge, which deflects raindrops and snowflakes away from the collector (the wind‐induced undercatch). The method of mounting a gauge with the collector at or below the level of the ground, or the use of windshields to mitigate this effect, is often not practicable. The physical shape of a gauge has a significant impact on its collection efficiency. In this study, we show that appropriate “aerodynamic” shapes are able to reduce the deformation of the airflow, which can reduce undercatch. We have employed computational fluid‐dynamic simulations to evaluate the time‐averaged airflow realized around “aerodynamic” rain gauge shapes when impacted by wind. Terms of comparison are provided by the results obtained for two standard “conventional” rain gauge shapes. The simulations have been run for different wind speeds and are based on a time‐averaged Reynolds‐Averaged Navier‐Stokes model. The shape of the aerodynamic gauges is shown to have a positive impact on the time‐averaged airflow patterns observed around the orifice compared to the conventional shapes. Furthermore, the turbulent air velocity fields for the aerodynamic shapes present “recirculating” structures, which may improve the particle‐catching capabilities of the gauge collector.

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Energy balance and snowmelt drivers of a marginal subalpine snowpack

Bilish

McGowan

Callow

2018

Hydrological Processes

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Snowmelt from the seasonal snowpack in the Australian Alps is a significant source of water for irrigated agriculture, electricity generation, and environmental flows in the Murray–Darling Basin. Previous studies have reported negative decadal to multidecadal trends in maximum snow depth, snow season duration, and snow‐covered area. Here, we characterise the energy balance of this marginal maritime snowpack for the first time. Turbulent fluxes measured using the eddy covariance and bulk aerodynamic methods are compared; discrepancies are attributed to the differing assumptions of the methods and characteristics of the measurement site. We examine the variability of the individual energy balance components and the drivers of snowmelt, and we find that incoming longwave radiation is the dominant control on snowmelt, providing more than 80% of the total energy to the snowpack over the season. During a midwinter rain‐on‐snow event, the advected rain heat flux provided 8% of the daily total, with the incoming longwave flux still accounting for almost 80%. The ground heat flux contributes a small proportion of the seasonal total but increases in patchy or intermittent snow cover. Comparing these results with those of studies in other maritime locations, we find that the turbulent fluxes are likely to make a proportionally higher contribution to the energy balance due to the short Australian snow season, underpinning the sensitivity of this environment to climate variability and change. These results extend the limited body of knowledge on highly marginal snowpacks and may be relevant to other regions with no direct measurements of the energy balance.

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Estimation of Wind-Induced Losses from a Precipitation Gauge Network in the Australian Snowy Mountains

Cited by 30 publications

References 25 publications

Evaluation of the AWAP daily precipitation spatial analysis with an independent gauge network in the Snowy Mountains

Evaluation of the AWAP daily precipitation spatial analysis with an independent gauge network in the Snowy Mountains

A Computational Fluid‐Dynamics Assessment of the Improved Performance of Aerodynamic Rain Gauges

Energy balance and snowmelt drivers of a marginal subalpine snowpack

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