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

在过去的几十年里，来自短期数值天气预报(NWP)模式的定量降水预报只是逐步改进。最近使用现场数据进行的模型验证揭示了整体微物理参数化(BMP)中的一些潜在的重大缺陷。当模式网格间距减小到小于几公里时，这些问题往往导致降水精度几乎没有增加，即使对于层状地形降水也是如此。该项目的主要目标是利用高分辨率观测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。该项目建立在研究第一阶段获得的先前结果的基础上，在此期间，将模式微物理预测与遥感(雷达)观测、现场飞机数据和地面测量进行了比较，用于犹他州东北部的一个IPEX(山间降水实验)个例和俄勒冈州级联上的一个改善(通过观测验证实验改善微物理参数化)事件。为了改进新的天气和研究预报(WRF)模式中的BMPs，需要调查更多的案例，包括2001年2月在加利福尼亚州海岸进行的太平洋喷气试验(PACJET)的1-2次事件。此外，该项目将在俄勒冈州北部完成为期3-4个月的高分辨率模拟，以便将模拟的云结构与WSR-88D雷达数据进行比较。将沿着水流星轨迹计算高空模型微物理预算，以确定导致地表降水误差的主要微物理过程。这些额外的实地研究和与WSR-88D数据的长期模型比较也将提供一个机会来研究地形降水的一些详细的三维结构和物理机制。这些数据集将有助于验证该项目的理想化模拟结果，这些结果表明，由相对较宽的屏障(如瀑布或Sierras)诱导的上游倾斜重力波可以增强高空冰层的生成，并有助于在较低层的支线云中播下冰和雪。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。总体而言，通过增加对地形降水过程的了解和改进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倡议。BMP的缺陷也存在于全球气候模型(GCM)中，因为对雪和云水的分布也做出了类似的假设。考虑到云在决定全球气候变化中的重要性，这项研究也使气候界受益。该项目将包括对两名研究生的教育和培训，以及对石溪大学实时中尺度模拟的本科生研究项目的指导。项目成果还将在石溪大学的中尺度气象学和数值天气预报课程中分享。将继续在中尺度模式预报员培训领域与国家气象局东部区域总部合作。

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.