SILO Patched Point data for Narrabri (54120) and Gunnedah (55023) stations in the Namoi subregion

## **Abstract** \n\nThis dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.\n\n\n\nSILO is a Queensland Government database containing continuous daily climate data for Australia from 1889 to present. Gridded datasets are constructed by spatially interpolating the observed point data. Continuous point datasets are constructed by supplementing the available point data with interpolated estimates when observed data are missing.\n\n## **Purpose** \n\nSILO provides climate datasets that are ready to use. Raw observational data typically contain missing data and are only available at the location of meteorological recording stations. SILO provides point datasets with no missing data and gridded datasets which cover mainland Australia and some islands.\n\n## **Dataset History** \n\nLineage statement:\n\n(A)\tProcessing System Version History\n\n\\*\tPrior to 2001\n\nThe interpolation system used the algorithm detailed in Jeffrey et al.1\n\n\\*\t2001-2009\n\nThe normalisation procedure was modified. Observational rainfall, when accumulated over a sufficient period and raised to an appropriate fractional power, is (to a reasonable approximation) normally distributed. In the original procedure the fractional power was fixed at 0.5 and a normal distribution was fitted to the transformed data using a maximum likelihood technique. A Kolmogorov-Smirnov test was used to test the goodness of fit, with a threshold value of 0.8. In 2001 the procedure was modified to allow the fractional power to vary between 0.4 and 0.6. The normalisation parameters (fractional power, mean and standard deviation) at each station were spatially interpolated using a thin plate smoothing spline.\n\n\\*\t2009-2011\n\nThe normalisation procedure was modified. The Kolmogorov-Smirnov test was removed, enabling normalisation parameters to be computed for all stations having sufficient data. Previously parameters were only computed for those stations having data that were adequately modelled by a normal distribution, as determined by the Kolmogorov-Smirnov test.\n\n\\*\tJanuary 2012 - November 2012\n\nThe normalisation procedure was modified:\n\no\tThe Kolmogorov-Smirnoff test was reintroduced, with a threshold value of 0.1.\n\no\tData from Bellenden Ker Top station were included in the computation of normalisation parameters. The station was previously omitted on the basis of having insufficient data. It was forcibly included to ensure the steep rainfall gradient in the region was reflected in the normalisation parameters.\n\no\tThe elevation data used when interpolating normalisation parameters were modified. Previously a mean elevation was assigned to each station, taken from the nearest grid cell in a 0.05° 0.05° digital elevation model. The procedure was modified to use the actual station elevation instead of the mean. In mountainous regions the discrepancy was substantial and cross validation tests showed a significant improvement in error statistics.\n\no\tThe station data are normalised using: (i) a power parameter extracted from the nearest pixel in the gridded power surface. The surface was obtained by interpolating the power parameters fitted at station locations using a maximum likelihood algorithm; and (ii) mean and standard deviation parameters which had been fitted at station locations using a smoothing spline. Mean and standard deviation parameters were fitted at the subset of stations having at least 40 years of data, using a maximum likelihood algorithm. The fitted data were then spatially interpolated to construct: (a) gridded mean and standard deviation surfaces (for use in a subsequent de-normalisation procedure); and (b) interpolated estimates of the parameters at all station locations (not just the subset having long data records). The parameters fitted using maximum likelihood (at the subset of stations having long data records) may differ from those fitted by the interpolation algorithm, owing to the smoothing nature of the spline algorithm which was used. Previously, station data were normalised using mean and standard deviation parameters which were taken from the nearest pixel in the respective mean and standard deviation surfaces.\n\n\\*\tNovember 2012 - May 2013\n\nThe algorithm used for selecting monthly rainfall data for interpolation was modified. Prior to November 2012, the system was as follows:\n\no\tAccumulated monthly rainfall was computed by the Bureau of Meteorology;\n\no\tRainfall accumulations spanning the end of a month were assigned to the last month included in the accumulation period;\n\no\tA monthly rainfall value was provided for all stations which submitted at least one daily report. Zero rainfall was assumed for all missing values; and\n\no\tSILO imposed a complex set of ad-hoc rules which aimed to identify stations which had ceased reporting in real time. In such cases it would not be appropriate to assume zero rainfall for days when a report was not available. The rules were only applied when processing data for January 2001 and onwards.\n\nIn November 2012 a modified algorithm was implemented:\n\no\tSILO computed the accumulated monthly rainfall by summing the daily reports;\n\no\tRainfall accumulations spanning the end of a month were discarded;\n\no\tA monthly rainfall value was not computed for a given station if any day throughout the month was not accounted for - either through a daily report or an accumulation; and\n\no\tThe SILO ad-hoc rules were not applied.\n\n\\*\tMay 2013 - current\n\nThe algorithm used for selecting monthly rainfall data for interpolation was modified. The modified algorithm is only applied to datasets for the period October 2001 - current and is as follows:\n\no\tSILO computes the accumulated monthly rainfall by summing the daily reports;\n\no\tRainfall accumulations spanning the end of a month are pro-rata distributed onto the two months included in the accumulation period;\n\no\tA monthly rainfall value is computed for all stations which have at least 21 days accounted for throughout the month. Zero rainfall is assumed for all missing values; and\n\no\tThe SILO ad-hoc rules are applied when processing data for January 2001 and onwards.\n\nDatasets for the period January 1889-September 2001 are prepared using the system that was in effect prior to November 2012.\n\n\n\nLineage statement:\n\n(A)\tProcessing System Version History\n\nNo changes have been made to the processing system since SILO's inception.\n\n(B)\tMajor Historical Data Updates\n\n\\*\tAll observational data and station coordinates were updated in 2009. \n\n\\*\tStation coordinates were updated on 26 January 2012.\n\n \n\nProcess step:\n\nThe observed data are interpolated using a tri-variate thin plate smoothing spline, with latitude, longitude and elevation as independent variables.4 A two-pass interpolation system is used. All available observational data are interpolated in the first pass and residuals computed for all data points. The residual is the difference between the observed and interpolated values. Data points with high residuals may be indicative of erroneous data and are excluded from a subsequent interpolation which generates the final gridded surface. The surface covers the region 112˚E - 154˚E, 10˚S - 44˚S on a regular 0.05˚ × 0.05˚grid and is restricted to land areas on mainland Australia and some islands.\n\n\n\n\n\nGridded datasets for the period 1957-current are obtained by interpolation of the raw data. \n\nGridded datasets for the period 1957-current are obtained by interpolation of the raw data. Gridded datasets for the period 1889-1956 were constructed using an anomaly interpolation technique. The daily departure from the long term mean is interpolated, and the gridded dataset is constructed by adding the gridded anomaly to the gridded long term mean. The long term means were constructed using data from the period 1957-2001. The anomaly interpolation technique is described in Rayner et al.6 \n\n\n\nThe observed and interpolated datasets evolve as new data becomes available and the existing data are improved through quality control procedures. Modifications gradually decrease over time, with most datasets undergoing little change 12 months after the date of observation.\n\n## **Dataset Citation** \n\n"Queensland Department of Science, Information Technology, Innovation and the Arts" (2013) SILO Patched Point data for Narrabri (54120) and Gunnedah (55023) stations in the Namoi subregion. Bioregional Assessment Source Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/0a018b43-58d3-4b9e-b339-4dae8fd54ce8.

## 摘要

本数据集及其元数据声明由第三方提供给生物区域评估项目（Bioregional Assessment Programme），并按原始提供形式呈现于此。

SILO是昆士兰州政府的数据库，包含1889年至今澳大利亚的连续日气候数据。网格数据集通过对观测点数据进行空间插值构建；当观测数据缺失时，通过插值估计补充现有点数据，从而构建连续点数据集。

## 目的

SILO提供可直接使用的气候数据集。原始观测数据通常存在缺失值，且仅在气象观测站位置可获取。SILO提供无缺失值的点数据集及覆盖澳大利亚大陆与部分岛屿的网格数据集。

## 数据集历史

谱系声明：

(A) 处理系统版本历史

* 2001年前

插值系统使用Jeffrey等人详述的算法。

* 2001-2009年

修改归一化程序：观测降雨量在累积足够时间后取适当分数幂，可近似正态分布。原程序将分数幂固定为0.5，使用最大似然法拟合转换后的数据；通过Kolmogorov-Smirnov检验（Kolmogorov-Smirnov test）验证拟合优度，阈值为0.8。2001年修改为允许分数幂在0.4-0.6范围内变化；各站点的归一化参数（分数幂、均值、标准差）通过薄板平滑样条（thin plate smoothing spline）进行空间插值。

* 2009-2011年

修改归一化程序：移除Kolmogorov-Smirnov检验，允许所有数据充足的站点计算归一化参数。此前仅对通过该检验、数据可由正态分布充分建模的站点计算参数。

* 2012年1月-11月

修改归一化程序：
- 重新引入Kolmogorov-Smirnov检验，阈值调整为0.1；
- 将Bellenden Ker Top站数据纳入归一化参数计算（此前因数据不足被排除，强制纳入以反映该区域陡峭的降雨梯度）；
- 修改插值归一化参数时使用的海拔数据：原方法采用0.05°×0.05°数字高程模型中最近网格单元的平均海拔，现改为使用实际站点海拔（山区差异显著，交叉验证显示误差统计显著改善）；
- 站点数据归一化方式调整：(i) 使用网格幂表面中最近像素提取的幂参数（该表面通过最大似然算法插值站点拟合的幂参数获得）；(ii) 使用平滑样条拟合的均值和标准差参数——在至少有40年数据的站点子集上，通过最大似然算法拟合均值和标准差，再空间插值构建：(a) 网格均值和标准差表面（用于后续反归一化）；(b) 所有站点位置的参数插值估计（不限于长数据记录子集）。由于样条算法的平滑特性，长数据记录子集通过最大似然拟合的参数可能与插值算法拟合的参数存在差异。此前站点数据归一化使用的均值和标准差参数取自对应表面的最近像素。

* 2012年11月-2013年5月

修改月度降雨量数据选择算法：2012年11月前系统流程为：
- 澳大利亚气象局（Bureau of Meteorology）计算累积月度降雨量；
- 跨月末的累积降雨量分配至累积期包含的最后一个月；
- 提交至少一份日报的站点提供月度降雨量值，缺失值假设为0；
- SILO应用一套复杂的临时规则识别停止实时报告的站点（此类情况下缺失日报不应假设为0），规则仅适用于2001年1月及以后的数据处理。

2012年11月实施修改后的算法：
- SILO通过求和日报计算累积月度降雨量；
- 跨月末的累积降雨量予以丢弃；
- 当月任何一天未通过日报或累积记录覆盖时，不计算该站点月度降雨量值；
- 不应用SILO临时规则。

* 2013年5月至今

修改月度降雨量数据选择算法，仅适用于2001年10月至今的数据集：
- SILO通过求和日报计算累积月度降雨量；
- 跨月末的累积降雨量按比例分配至包含的两个月；
- 当月至少21天有记录时，计算该站点月度降雨量值，缺失值假设为0；
- 2001年1月及以后的数据处理应用SILO临时规则。

1889年1月-2001年9月的数据集使用2012年11月前的系统制备。

谱系声明：

(A) 处理系统版本历史

自SILO成立以来，处理系统未发生变化。

(B) 主要历史数据更新

* 2009年更新所有观测数据和站点坐标。

* 2012年1月26日更新站点坐标。

处理步骤：

观测数据使用三变量薄板平滑样条（thin plate smoothing spline）插值，以纬度、经度、海拔为自变量。采用双次插值系统：第一次插值所有可用观测数据，计算所有数据点的残差（观测值与插值值之差）；高残差数据点可能指示错误数据，排除后进行第二次插值生成最终网格表面。该表面覆盖112°E-154°E、10°S-44°S区域，采用0.05°×0.05°规则网格，限于澳大利亚大陆及部分岛屿的陆地区域。

1957年至今的网格数据集通过原始数据插值获得；1889-1956年的网格数据集采用异常插值技术（anomaly interpolation technique）构建：插值日值与长期均值的偏差，将网格偏差与网格长期均值相加得到网格数据集（长期均值使用1957-2001年数据构建）。异常插值技术详见Rayner等人的研究。

观测和插值数据集随新数据可用及现有数据通过质量控制改进而演变。修改随时间逐渐减少，多数数据集在观测日期12个月后变化很小。

## 数据集引用

"Queensland Department of Science, Information Technology, Innovation and the Arts" (2013) SILO Patched Point data for Narrabri (54120) and Gunnedah (55023) stations in the Namoi subregion. Bioregional Assessment Source Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/0a018b43-58d3-4b9e-b339-4dae8fd54ce8.