Observations and Modeling of the Characteristics of Falling and Accumulating Snow

通过观测验证实验改进微物理参数化（IMPROVE）实地项目（2001年在太平洋西北部进行）获得的研究结果表明，雪粒子的细节和相关的微物理过程在与冬季气旋风暴和地形环境相关的降水发展中的重要性。然而，雪粒子通常是在中尺度模式用来开发云和降水水凝物和产生定量降水预报在地面的散装微物理方案的简单化的方式表示。为了解决这一缺陷，主要研究人员（PI）正在开发一个批量计划，该计划具有一套新的预测方程，预测雪的习惯组成，并使用更现实的习惯依赖性参数，强烈影响雪的生长及其与其他模拟水凝物物种的相互作用。 预计这种对雪的更现实的处理将导致更好的定量降水预报，特别是在降水量和分布对雪的行为变化高度敏感的山区。习惯预测模型的另一个好处是输出地面降雪的习惯组成。 这些信息对两个重要的预测参数有潜在的好处。 一个是“雪比”，或雪深与液体等效降水深度之比，这取决于粒子的习性，是将模型定量降水预报转化为雪深预报所必需的。 另一个是粒子在积雪层中的垂直分布，这可以为雪崩预测模型提供有用的额外信息。为了帮助指导雪习惯预测模型的开发，PI将利用几个IMPROVE案例的数据。 此外，PI将收集喀斯喀特山脉两个冬季降雪和积雪特性的长期观测数据集。 这些观测结果将通过一个雪实验室收集，该实验室将能够测量靠近地面的降雪颗粒的习惯，尺寸分布和下降速度;以及地面积雪的密度，液体等效降水率和剪切强度。 这些观测将持续足够长的时间，以收集关于各种各样的个人和混合习惯类型的降雪的有意义的统计数据。 这个独特的数据集对于雪习惯预测模型的开发和长期验证至关重要，并且还将解决有关地面积雪特性的其他一些重要问题，以及有关测量降雪速度，大小，液体等效降水率和颗粒习惯的方法和仪器。智力优势：建模和观测工作将提高对不同习惯的雪粒子如何在不同气象条件下发展的理解;习惯特征如何影响雪与其他水文气象类的相互作用;以及雪粒子的习惯最终如何影响地面降水的分布和性质。 观测数据集还将提供有关降雪和积雪特性测量技术的宝贵信息。更广泛的影响：在预报模式中加强雪的微物理过程的表现将最终导致降水预报的改进。努力更好地了解和预测降水量和分布对社会有直接的潜在利益。此外，PI将通过网页、出席适当的会议和研讨会以及与业务模型组和预报员的直接互动，向预报界和公众广泛传播研究结果。

Research results obtained from the Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE) field project (carried out in the Pacific Northwest in 2001) have demonstrated the importance of the details of snow particles and associated microphysical processes in the development of precipitation associated with winter-time cyclonic storms and orographic environments. However, snow particles are generally represented in simplistic ways in the bulk microphysical schemes that mesoscale models use to develop cloud and precipitation hydrometeors and produce quantitative precipitation forecasts at the ground. To address this deficiency, the Principal Investigators (PIs) are developing a bulk scheme that has a new set of prognostic equations that predict the habit composition of snow and use more realistic habit-dependent parameters that strongly influence the growth of snow and its interaction with other simulated hydrometeor species. It is anticipated that this more realistic treatment of snow will lead to better quantitative precipitation forecasts, particularly over mountainous regions where the amount and distribution of precipitation are highly sensitive to changes in the behavior of snow. An added benefit of the habit prediction model is the output of the habit composition of snowfall at the ground. This information is of potential benefit for two important forecasting parameters. One is the "snow ratio", or ratio of snow depth to liquid equivalent precipitation depth, which is dependent on particle habit and is necessary for translating model quantitative precipitation forecasts into a snow depth forecast. The other is the vertical profile of particle habit in accumulating snow layers, which could provide useful additional information to avalanche forecasting models. In order to help guide the development of the snow habit prediction model, the PIs will make use of data from several IMPROVE cases. In addition, the PIs will gather a longer-term observational data set of the properties of falling and accumulating snow, over the course of two winter seasons in the Cascade Mountains. These observations will be gathered with a snow lab that will be capable of measuring the habits, size distributions, and fall speeds of falling snow particles near the ground; and the density, liquid equivalent precipitation rate, and shear strength of snow accumulation on the ground. These observations will extend for a long enough period to gather meaningful statistics on a wide variety of individual and mixed habit types of snowfall. This unique data set is essential to the development and long-term verification of the snow habit prediction model, and will also address a number of other important questions about properties of snow accumulation at the ground, and about methods and instruments for measuring snow fall speeds, sizes, liquid equivalent precipitation rate, and particle habit. Intellectual Merit: Both the modeling and observational efforts will improve understanding of how snow particles of different habits develop in different meteorological conditions; how the habit characteristics influence the interaction of snow with other hydrometeor classes; and how the habits of snow particles ultimately affect the distribution and properties of precipitation at the ground. The observational data set will also provide valuable information on measurement techniques for the properties of falling and accumulating snow. Broader Impacts: The enhancement of the representation of snow microphysical processes in a forecast model will ultimately lead to improved forecasts of precipitation. Working toward better understanding and prediction of the quantity and distribution of precipitation has direct potential benefits to society. Additionally, the PIs will broadly disseminate results of the research to the forecasting community and the public via web pages, attendance at appropriate conferences and workshops, and direct interaction with operational modeling groups and forecasters.