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

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

doi:10.22499/3.6601.006

Cited by 22 publications

(20 citation statements)

References 31 publications

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“…Developing gridding precipitation indices from daily precipitation observations is not forthright since averaging daily precipitation information from numerous stations and interpolating it into gridded precipitation data dampens the extremes and misrepresents the spatial and temporal variability that exists in the original station data 5 . If the rain gauges are extremely sparse, acquiring a correct distribution of rainfall through interpolation is even more difficult and the low density of precipitation stations leads to significant uncertainties, especially in regions where the rainfall varies significantly in space and time [32][33][34] . Comparing to conventional interpolation method, the ANUSPLIN show its priority in modelling the precipitation under complex terrain and data-scarce condition.…”

Section: Discussionmentioning

confidence: 99%

Long-term spatio-temporal precipitation variations in China with precipitation surface interpolated by ANUSPLIN

Guo

Zhang

Meng

et al. 2020

Sci Rep

109

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Climate changes significantly impact environmental and hydrological processes. Precipitation is one of the most significant climatic parameters and its variability and trends have great influences on environmental and socioeconomic development. We investigate the spatio-temporal variability of precipitation occurrence frequency, mean precipitation depth, PVI and total precipitation in China based on long-term precipitation series from 1961 to 2015. As China's topography is diverse and precipitation is affected by topography strongly, ANUSPLIN can model the effect of topography on precipitation effectively is adopted to generate the precipitation interpolation surface. Mann-Kendall trend analysis and simple linear regression was adopted to examine long-term trend for these indicators. The results indicate ANUSPLIN precipitation surface is reliable and the precipitation variation show different regional and seasonal trend. For example, there is a sporadic with decreasing frequency precipitation trend in spring and a uniform with increasing frequency trend in summer in Yangtze Plain, which may affect spring ploughing and alteration of flood risk for this main rice-production areas of China. In northwestern China, there is a uniform with increasing precipitation frequency and intensity trend, which is beneficial for this arid region. Our study could be helpful for other counties with similar climate types. Climate changes, including change in regional precipitation pattern, have modified hydrological processes and will continue to reshape the hydrology. Precipitation is an important meteorological parameter, and is a key player in regional hydrological processes 1,2. It is an important natural source of water for vegetation, especially in dry regions, and its variability and trends have great influences on plant growth and crop production. Variation in total quantity and temporal variability of precipitation has significant effect on soil water supply, water stress as well as metabolic and physiological functions of vegetation, further determined the impact of precipitation variability on ecosystem structure 3. It is of practical interest to access how climate changes have altered precipitation spatially and temporally and therefore to improve our insight into precipitation variability analyses. Gauge-based precipitation is the main data source for precipitation. However, in situ precipitation data of rain gauge station are not always available. As precipitation measurements are scarce and the measuring stations are distributed unevenly in space, the rain gauge network might be inefficient in demonstrating the spatial distribution of precipitation and capturing precipitation events completely. Consequently, interpolation is introduced to examine the spatial distribution of precipitation. To assess the spatial variability and trends of precipitation indicators, the procedures of spatial interpolation broadly included two strategies, interpolating of the station indices directly and interpolating of the precipitation a...

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Section: Discussionmentioning

confidence: 99%

Long-term spatio-temporal precipitation variations in China with precipitation surface interpolated by ANUSPLIN

Guo

Zhang

Meng

et al. 2020

Sci Rep

109

View full text Add to dashboard Cite

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“…The errors are larger at high elevations (SY and TA) where gauges are fewer, and when there is frozen precipitation, and/or topography is exposed to prevailing winds (Chubb et al, 2016).…”

Section: Comparison With Daily Rainfall Analysismentioning

confidence: 99%

BARRA v1.0: Kilometre-scale downscaling of an Australian regional atmospheric reanalysis over four midlatitude domains

Su¹,

Eizenberg²,

Jakob³

et al. 2020

Preprint

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Abstract. The development of convection-permitting models (CPMs) in numerical weather prediction has facilitated the creation of km-scale (1–4 km) regional reanalysis and climate projections. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) also aims to realise the benefits of these high-resolution models over Australian sub-regions for applications such as fire danger research, by nesting them in BARRA's 12 km regional reanalysis (BARRA-R). Four mid-latitude sub-regions are centred on Perth in Western Australia, Adelaide in South Australia, Sydney in New South Wales (NSW), and Tasmania. The resulting 29-year 1.5 km downscaled reanalyses (BARRA-C) are assessed for their added skill over BARRA-R and global reanalyses for near-surface parameters (temperature, wind and precipitation) at observation locations and against independent 5 km gridded analyses. BARRA-C demonstrates better agreement with point observations for temperature and wind, particularly in topographically complex regions and coastal regions. BARRA-C also improves upon BARRA-R in terms of intensity and timing of precipitation during the thunderstorm seasons in NSW, and spatial patterns of sub-daily rain fields during storm events. However, as a hindcast-only system, BARRA-C largely inherits the domain-averaged biases and temporal variations of biases from BARRA-R. Further, BARRA-C reflects known issues of CPMs: overestimation of heavy rain rates and rain cells, and underestimation of light rain occurrence.

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“…This daily adjustment does not directly account for any orographic mechanisms that may enhance/redistribute precipitation, rather it is assumed that such processes are observed by the surface network. Chubb et al (2016) employed an independent network of surface observations over the Snowy Mountains of southeast Australia to identify biases in the AWAP precipitation product arising from the orography. Focussing on wintertime precipitation, they found that the AWAP precipitation strongly underestimated precipitation by up to 50% on the immediate upwind (westerly) slope of the Snowy Mountains, while weakly overestimated downwind precipitation by 10-20%.…”

Section: Discussionmentioning

confidence: 99%

“…The verification scores described in this section are obtained from Ebert (2008) and have commonly been used to evaluate precipitation forecasts (e.g. Johnson et al 2013;Chubb et al 2016). The AWAP daily precipitation, which is produced from surface precipitation observations, is defined as the "ground truth" to evaluate the ACCESS-VT precipitation forecasts.…”

Section: Analysis Methods -Verification Scoresmentioning

confidence: 99%

Wintertime orographic precipitation over western Tasmania

Lewis¹,

Huang²,

Siems³

et al. 2018

JSHESS

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The wintertime (April - October) precipitation across western Tasmania (west of 147°E) has been studied for two years (2014 and 2015). Using the AWAP precipitation analysis, the average daily rainfall across western Tasmania was found to be 4.49 mm day-1 for all winter days and 6.99 mm day-1 for rain days (average precipitation greater than 1 mm day-1). Rain days were observed for ~63% of all days during the winter months. Rain days were frequently recorded after the pas-sage of a cold front, when winds are typically from the west and southwest, off the open Southern Ocean. The daily precipitation was found to be highly correlated (r = 0.55) with the 12 UTC ERA-Interim 1000 m wind speed at a point upwind of Tasmania, roughly 100 km off the west coast.Given the highly variable meteorology of the Southern Ocean storm track and the complex topography, western Tasmania is a natural testbed for studying orographic precipitation. Both locally blocked and unblocked flows, caused by changes in the low-level thermodynamic stability, occur frequently over the course of a winter with a stable environment having a lower average precipitation rate (3.66 mm day-1) than an unstable environment (8.40 mm day-1), although only a weak correlation (r = -0.07) was found between precipitation and Ĥ2(the square of non-dimensional mountain height).Simulated precipitation from the Australian Bureau of Meteorology’s ACCESS-VT model was found to underestimate the AWAP precipitation by ~20%. The greatest negative relative errors between the AWAP and ACCESS-VT precipitation in unblocked flow were in the lee of the mountains, over central and south-central Tasmania. For days when the flow was blocked, this region had large positive relative errors in precipitation. Over the upwind side of western Tasmania, ACCESS-VT underestimated precipitation in comparison to AWAP in both un-blocked and blocked flows. However, the network of surface sites is quite sparse over this region, which limits our confidence in both the ACCESS-VT and the AWAP precipitation products. A more detailed investigation is necessary to better appreciate limitations in the ACCESS-VT forecasts in this region.

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

Cited by 22 publications

References 31 publications

Long-term spatio-temporal precipitation variations in China with precipitation surface interpolated by ANUSPLIN

Long-term spatio-temporal precipitation variations in China with precipitation surface interpolated by ANUSPLIN

BARRA v1.0: Kilometre-scale downscaling of an Australian regional atmospheric reanalysis over four midlatitude domains

Wintertime orographic precipitation over western Tasmania

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