Frequency and magnitude biases in the ‘Fryberger’ model, with implications for characterizing geomorphically effective winds
Frequency and magnitude biases in the ‘Fryberger’ model, with implications for characterizing geomorphically effective winds
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DOI：
10.1016/j.geomorph.2004.09.030
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发表时间：
2005-05
期刊：
影响因子：
3.9
通讯作者：
K. I. Pearce;I. Walker
中科院分区：
文献类型：
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作者：
K. I. Pearce;I. Walker
‘Fryberger模型’使用标准化的风数据来估计区域风沙沉积漂移潜力(DP)，这使得能够对风沙沙丘景观进行解释和分类。本文识别了模式中系统性的频率和震级偏差，其原因是：(I)风向扇区范围的变化，它影响用于确定输送风出现百分比的频段大小，以及(Ii)在用于DP计算的震级加权因子中使用风速等级的中点值，而不是更具统计代表性的值(例如，平均值、中值)或最小分类(完整节点)的风速。使用来自加拿大爱德华王子岛斯坦霍普的非机密数据来检验这些偏见的重要性。由于风数据聚合到不同大小的方向扇区而产生的频率偏差，在该数据集的DP和RDP估计中产生了统计上的显著差异。当36点(10 S度)的数据被缩减到16个扇区时，与原始数据的16个相等的扇区(22.5°)相比，主方向多接收一个方向类别，DP值和RDP值的幅度分别下降1.4%和2.8%。这是由于该数据集的中间方向类表示不足造成的。对RDD(小于1°)和方向变率(RDP/DP)的影响不显著。震级偏差是由于在DP计算中使用风速等级的中点值，而不是其他具有统计代表性的风速测量值造成的。由于风速分布通常是正偏态的，与使用平均值、中位数和整节(最小分类)值得出的数据集相比，中点值对总DP的高估高达34%，RDP高估高达22%。这种差异在单个风速-风向类别水平和总体(方向总和)水平上都具有统计学意义，并导致RDP向量向南移动3-5°。虽然这些频率-震级偏差的影响是特定的和温和的，但在复杂的(即，多模式)风况和/或从基本方向上有更频繁的高震级风的环境中，对RDP估计的影响可能更显著。考虑到更精确、更未分类的风数据的可用性增加，这些系统性的频率-震级偏差中的大多数都可以避免。关于减少这些偏差造成的误差的建议包括：(I)使用可用的度风数据并将其归类为16个相等的22.5°风向扇区，(Ii)在DP计算中使用风速等级统计平均值或最小分类的整节点值，以及(Iii)认识到将36点(10 S度)数据转换为16个方向等级可能会导致对主要方向的频率偏差，并将导致DP和RDP估计的不准确程度取决于风向。需要进一步研究，以评估这种偏差在不同风况下的影响，以及局部供应和运输限制因素对区域范围的沙丘地貌动力学和流动性评估的影响。这些发现也适用于使用分类风速-风向数据的其他应用，例如粉尘或污染物羽流扩散模型。
The ‘Fryberger model’ uses standardized wind data to estimate regional aeolian sediment drift potential (DP), which allows for interpretation and classification of aeolian dune landscapes. This paper identifies systematic frequency and magnitude biases in the model that result from: (i) variations in wind direction sector range, which affects frequency bin size for determining percent occurrence of transporting winds, and (ii) use of wind speed class mid-point values over more statistically representative values (e.g., mean, median) or minimally classified (whole knot) wind speeds in the magnitude weighting factor for DP calculations. The significance of these biases is tested using unclassified data from Stanhope, Prince Edward Island, Canada. Frequency bias resulting from wind data aggregation into direction sectors of varying size produces statistically significant discrepancies in DP and RDP estimates for this dataset. When 36-point (10 s of degree) data are reduced to 16 sectors, cardinal directions receive one extra direction class and the magnitude of DP and RDP values decreases by 1.4% and 2.8%, respectively, compared to 16 equal (22.5°) sectors derived from original data. This results from under-representation of intermediate direction classes for this dataset. The effects on RDD (less than 1°) and directional variability ratio (RDP/DP) are insignificant. Magnitude biases result from the use of the wind speed class mid-point values instead of other statistically representative measures of wind speed in DP calculations. In that wind speed distributions are often positively skewed, mid-point values yield an over-estimate of total DP by as much as 34% and RDP by up to 22% for this dataset over those derived using mean, median and whole knot (minimally classified) values. This difference is statistically significantly at both the individual wind speed–direction category level and at the aggregate (directionally summed) level and causes RDP vectors to shift 3–5° to the south. Though the impacts of these frequency–magnitude biases are site-specific and modest, the effects on RDP estimation may be more significant in complex (i.e., multi-modal) wind regimes and/or in environments with more frequent high-magnitude winds from the cardinal directions. Given the increased availability of more precise, unclassified wind data, most of these systematic frequency–magnitude biases can be avoided. Recommendations on reducing inaccuracies imposed by these biases include: (i) using to-the-degree wind data where available and categorizing into 16 equal 22.5° direction sectors, (ii) using either wind speed class statistical mean values or minimally classified whole knot values in DP calculations, and (iii) recognizing that converting 36-point (10 s of degrees) data to 16 direction classes may introduce a frequency bias toward the cardinal directions and will cause inaccuracies in DP and RDP estimates of an amount that depends on the wind regime. Further research is needed to assess the implications of such biases in different wind regimes and into the influence of localized supply- and transport-limiting factors on regional-scale assessments of dune morphodynamics and mobility. These findings are also relevant for other applications that use categorized wind speed–direction data such as dust or contaminant plume dispersion modelling.