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Derivation of a new continuous adjustment function for correcting wind-induced loss of solid precipitation: results of a Norwegian field study

机译:推导用于校正风引起的固体降水损失的新的连续调节函数:挪威野外研究的结果

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摘要

Precipitation measurements exhibit large coldseason biases due to under-catch in windy conditions. These uncertainties affect water balance calculations, snowpack monitoring and calibration of remote sensing algorithms and land surface models. More accurate data would improve the ability to predict future changes in water resources and mountain hazards in snow-dominated regions. In 2010, a comprehensive test site for precipitation measurements was established on a mountain plateau in southern Norway. Automatic precipitation gauge data are compared with data from a precipitation gauge in a Double Fence Intercomparison Reference (DFIR) wind shield construction which serves as the reference. A large number of other sensors are provided supporting data for relevant meteorological parameters. In this paper, data from three winters are used to study and determine the wind-induced under-catch of solid precipitation. Qualitative analyses and Bayesian statistics are used to evaluate and objectively choose the model that best describes the data. A continuous adjustment function and its uncertainty are derived for measurements of all types of winter precipitation (from rain to dry snow). A regression analysis does not reveal any significant misspecifications for the adjustment function, but shows that the chosen model does not describe the regression noise optimally. The adjustment function is operationally usable because it is based only on data available at standard automatic weather stations. The results show a non-linear relationship between under-catch and wind speed during winter precipitation events and there is a clear temperature dependency, mainly reflecting the precipitation type. The results allow, for the first time, derivation of an adjustment function based on measurements above 7 m s(-1). This extended validity of the adjustment function shows a stabilization of the wind-induced precipitation loss for higher wind speeds.
机译:由于大风条件下的雨量不足,降水量测量结果显示出较大的冷季偏差。这些不确定性会影响水平衡计算,积雪监测以及遥感算法和地表模型的校准。更准确的数据将提高预测雪域中水资源和山地灾害未来变化的能力。 2010年,在挪威南部的一个高山高原上建立了一个用于降水测量的综合测试站点。在双栅栏比较标准(DFIR)挡风玻璃结构中,将自动降水量仪数据与降水量仪的数据进行比较,该结构用作参考。提供了大量其他传感器,以支持有关气象参数的数据。在本文中,使用三个冬季的数据来研究和确定由风引起的固体降水量不足。使用定性分析和贝叶斯统计来评估和客观地选择最能描述数据的模型。连续调节函数及其不确定性可用于测量所有类型的冬季降水(从雨到干雪)。回归分析并未发现调整函数有任何重大的错误指定,但显示所选模型并未最佳地描述回归噪声。由于该调整功能仅基于标准自动气象站的可用数据,因此可在操作上使用。结果表明,冬季降水过程中,渔获量与风速之间存在非线性关系,并且存在明显的温度依赖性,主要反映了降水类型。该结果首次允许基于高于7 m s(-1)的测量值推导调整函数。调节功能的这种扩展的有效性表明,在更高的风速下,风致的降水损失会稳定下来。

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