Use of High Resolution Field Data to Improve Model Microphysics and Investigate Orographic Precipitation Processes

在过去的几十年里，短期数值天气预报（NWP）模式的定量降水预报只是逐渐改善。 最近的模型验证使用现场数据揭示了一些潜在的大的缺陷，散装微物理参数化（BMP）。 这些问题往往导致降水精度增加不大时，模式网格间距减少到小于几公里，即使是层状地形降水。 本项目的主要目标是利用高分辨率观测资料来验证和改进中尺度模式中的微物理，并记录地形降水的三维结构和物理机制。 该项目建立在以前的研究，在第一阶段期间，模型微物理预测进行了比较遥感（雷达）观测，在现场飞机数据，和地面测量的IPEX（山间降水实验）的情况下，在东北犹他州和IMPROVE（通过观测验证实验的微物理参数化的改进）事件在俄勒冈州瀑布。 为了在新的天气和研究预报（WRF）模式中改进BMPs，需要调查更多的情况，包括2001年2月沿着加州海岸进行的PACJET（PACJET）实验中的1-2个事件。 此外，该项目还将在北方俄勒冈州小瀑布上空完成3-4个月的高分辨率模拟，以便将模拟的云结构与WSR-88 D雷达数据进行比较。 将沿着水凝物轨迹计算高空模型微物理预算，以确定导致地面降水误差的主要微物理过程。这些额外的实地研究和长期模式与WSR-88 D数据的比较也将提供一个机会，研究一些详细的三维结构和地形降水的物理机制。 这些数据集将有助于验证该项目的理想化建模结果，该结果表明，由相对较宽的障碍物（如瀑布或山脉）引起的上游倾斜重力波可以增强高空的冰生成，并有助于用冰雪播种较低层的馈线云。 雷达和现场飞机数据将有助于说明迎风地形云和微物理是如何被高空的山脉环流所改变的。 这些观测结果还将与高分辨率的案例研究模拟相结合。 总的来说，通过增加对地形降水过程的理解和改进BMPs，该项目将改进定量降水预报，这是美国天气研究计划的一项主要举措。 BMPs的缺陷也存在于全球气候模式（GCM）中，因为对雪和云水分布做出了类似的假设。 鉴于云在决定全球气候变化方面的重要性，这项研究也有利于气候界。 该项目将涉及两名研究生的教育和培训，以及对斯托尼布鲁克大学本科生实时中尺度建模研究项目的指导。 项目成果还将在斯托尼布鲁克的中尺度气象学和数值天气预报课程中分享。 将继续与国家气象局东部区域总部合作，对预报员进行中尺度模型培训。

Quantitative precipitation forecasts from short-term numerical weather prediction (NWP) models have improved only gradually during the last few decades. Recent model verification using field data has revealed some potentially large deficiencies in bulk microphysical parameterizations (BMPs). These problems often lead to little increase in precipitation accuracy when model grid spacing is decreased to less than a few kilometers, even for stratiform orographic precipitation. The primary objectives of this project are to use high-resolution observations to verify and improve the microphysics in mesoscale models, and to document the three-dimensional structures and physical mechanisms of orographic precipitation. This project builds on previous results obtained during first phase of the research, during which the model microphysical predictions were compared against remotely sensed (radar) observations, in situ aircraft data, and ground measurements for an IPEX (Intermountain Precipitation EXperiment) case over northeast Utah and an IMPROVE (Improvement of Microphysical PaRameterization through Observational Verification Experiment) event over the Oregon Cascades. In order to make improvements to the BMPs in the new Weather and Research Forecasting (WRF) model, additional cases need to be investigated, including 1-2 events from the PACific JETs (PACJET) experiment along the California coast in February 2001. In addition, this project will complete 3-4 months of high-resolution simulations over the northern Oregon Cascades in order to compare the simulated cloud structure with WSR-88D radar data. Model microphysical budgets aloft will be calculated along hydrometeor trajectories to determine the dominant microphysical processes that result in surface precipitation errors. These additional field studies and long-term model comparisons with WSR-88D data will also provide an opportunity to study some of the detailed three-dimensional structures and physical mechanisms of orographic precipitation. These datasets will help verify the project's idealized modeling results, which suggest that an upstream-tilting gravity wave induced by relatively wide barriers, such as the Cascades or Sierras, can enhance the ice generation aloft and help seed the lower-level feeder cloud with ice and snow. Radar and in situ aircraft data will help illustrate how the windward orographic cloud and microphysics are modified by the mountain circulation aloft. These observational results will also be combined with high-resolution case study simulations. Overall, by increasing the understanding of orographic precipitation processes and improving BMPs, this project will improve quantitative precipitation forecasting, which is a major initiative of the U.S. Weather Research Program. Deficiencies in BMPs also exist in global climate models (GCMs), since similar assumptions are made for snow and cloud water distributions. Given the importance of clouds in determining global climate change, this research also benefits the climate community. This project will involve the education and training of two graduate students and the mentoring of undergraduate research projects in real-time mesoscale modeling at Stony Brook. Project results will also be shared within the mesoscale meteorology and numerical weather prediction classes at Stony Brook. Collaboration with Eastern Regional Headquarters of the National Weather Service in the area of forecaster training on mesoscale models will be continued.