Constraining snowfall remote sensing using novel high-resolution three-dimensional imaging and mass measurements of hydrometeors in freefall

准确的微波遥感降雪反演对于理解云-降水相互作用、天气预报和气候研究是至关重要的。然而，可靠的降雪反演算法的发展仍然受到缺乏详细的雪花测量的严重限制，这些测量表征了雪花质量和三维形状的自然变异性。该项目的目标是(I)使用新的高分辨率测量方法获得这些数据，(Ii)利用这些测量为真实雪花的散射特性提供查找表。为了实现这些目标，我们将在犹他州现有的山地实验室使用新开发的热板设备和最近推出的自动化多角度雪花相机来获取必要的数据，该相机允许同时从多个方向收集照片。预计每台仪器将在一个冬季采集约100万片雪花，以前所未有的详细程度捕捉自然雪花的变化。这些雪花测量的模型散射特性将与(1)现有的简单理想化雪花形状的微波散射计算进行比较，这些计算迄今已用于近似真实的雪花并从遥感数据中提取降雪，以及(2)配置雷达测量降雪。在计算和实验验证的基础上，我们将编制新的查找表，量化雪花质量和3D形状的自然变化与云和降水的微波遥感之间的关系。

Accurate snowfall retrieval from microwave remote sensing is essential for understanding cloud-precipitation interactions, for weather forecasting and for climate research. However, development of reliable snowfall retrieval algorithms remains severely limited by an absence of detailed snowflake measurements that characterize the natural variability of snowflake mass and three-dimensional (3D) shape. The proposed project aims to (i) obtain these data using novel high-resolution measurement methods and (ii) exploit the measurements to provide look-up tables for the scattering properties of realistic snowflakes.To accomplish these goals, we will acquire the necessary data at an existing mountain laboratory in Utah using a newly developed hotplate device and a recently available automated multi-angle snowflake camera that allows for collecting photographs from multiple directions simultaneously. It is anticipated that each instrument will sample of order one million snowflakes over a single winter season to capture the natural snowflake variability at an unprecedented degree of detail. The modeled scattering properties for these snowflake measurements will be compared to (i) existing microwave scattering calculations for simple idealized snowflake shapes that have been used thus far to approximate real snowflakes and retrieve snowfall from remote-sensing data, and (ii) collocated radar measurements of snowfall. Based on the calculations and experimental validation, we will compile new look-up tables that quantify how the natural variability of snowflake mass and 3D shape relates to microwave remote sensing of clouds and precipitation.