Improvement of Microphysical PaRameterization through Observational Verfication Experiment (IMPROVE): Data Analysis and Modeling

区域中尺度模式正在成为当地天气系统业务预报和定量降水预报(QPF)的中心工具。随着时间的推移，模式分辨率不断提高，物理过程的模式参数化也变得更加复杂。即便如此，QPF的改善也相对缓慢。除了分辨率和初始条件外，影响QPF的关键模式组件是云和降水过程的整体微物理参数化(BMP)。BMP所依据的许多假设都存在相当大的不确定性。要清楚地评估BMP的性能(并改进它)的唯一方法是将模型模拟中的微物理过程和预测的水流星分布与现场(机载)和遥感(例如雷达)观测进行比较。此外，至关重要的是，微物理测量必须与风、温度和湿度的观测同时进行，以便将模拟微物理中的误差与这些其他预测场中的误差隔离开来。为此，首席调查员发起了一项题为“通过观测验证实验改进微物理参数化”的研究，将当前中尺度模式中的云和降水过程的表示与详细的测量和观测进行比较。在过去的两个冬天进行了两项实地研究：一项研究了锋面系统在接近华盛顿海岸时产生的降水；另一项研究了强湿气流穿过俄勒冈瀑布屏障时降水的地形调制。在总共26个密集观测期期间收集了丰富的数据集，这些观测期涵盖各种锋面和地形降水系统。根据这项奖励，首席调查员将继续分析改进后的数据。首先，将减少和分析观测数据，以确定导致降水发展的物理过程，并产生不同水流星种类的浓度、大小分布、反射系数等的时间和空间分布。其次(与观测分析平行)，将以~1公里的分辨率对观测到的个例进行中尺度模式模拟，利用四维数据同化来产生在运动学、热力和水汽分布方面可能的最佳模拟。与观测一样，将对模型模拟进行分析，以确定水流星物种的时间和空间分布，并将进行模型敏感性测试，以确定导致这些分布的关键物理过程。第三，通过观测确定的微物理过程和水流星分布将与模型模拟中发生的进行比较。这将为修改BMP提供基础。最后，这些修改将在改进期间研究的各种风暴系统的模型模拟以及华盛顿大学实时区域预报系统的日常业务预报运行中进行评估。这里提出的研究的性质通过改进QPF对社会具有直接的潜在好处。为了进一步扩大拟议研究的益处，并提高人们对中尺度数值天气预报模式的普遍认识，将维持一个关于改善和中尺度数值天气预报模式的通俗易懂的网站。将通过与教师的互动，鼓励在当地的K-12科学教室中使用国家天气预报网站，并鼓励PI和工作人员与业务预测社区互动，以帮助将研究成果转化为应用。

Regional mesoscale models are becoming the central tools for the operational forecasting of local weather systems and quantitative precipitation forecasting (QPF) for periods of 0-48 h. Over time model resolution has continuously increased and model parameterizations of physical processes have become more sophisticated. Even so, improvements in QPF have been comparatively slow. Apart from resolution and initial conditions, the key model component that affects QPF is the bulk microphysical parameterization (BMP) of cloud and precipitation processes. There are considerable uncertainties in many of the assumptions on which BMPs are based. The only way to clearly evaluate the performance of a BMP (and to improve it) is to compare microphysical processes and predicted hydrometeor distributions in model simulations with in situ (airborne) and remotely sensed (e.g., radar) observations. In addition, it is critically important that the microphysical measurements be obtained concurrently with observations of wind, temperature and humidity, so that errors in the simulated microphysics can be isolated from errors in these other predicted fields.To this end, the Principal Investigator initiated a study entitled "Improvement of Microphysical PaRameterization Through Observational Verification Experiment "(IMPROVE) to compare representations of cloud and precipitation processes in current mesoscale models with detailed measurements and observations. Two field studies were conducted during the past two winters: one examined precipitation produced by frontal systems as they approached the coast of Washington; and the second examined the orographic modulation of precipitation in situations of strong, moist airflow across the Oregon Cascades barrier. A rich data set was gathered during a total of 26 Intensive Observing Periods (IOPs) that covered a wide variety of frontal and orographic precipitation systems. Under this award the Principal Investigator will continue with analysis of the IMPROVE data. First, observational data will be reduced and analyzed to ascertain the physical processes leading to the development of precipitation and to produce temporal and spatial distributions of concentrations, size distributions, reflectivity factor, etc., for the various hydrometeor species. Second (and parallel to the observational analysis), mesoscale model simulations of the observed cases will be performed with resolution of ~1 km, making use of four dimensional data assimilation to produce the best possible simulations in terms of kinematic, thermal, and moisture distributions. As with the observations, the model simulations will be analyzed to ascertain temporal and spatial distributions of the hydrometeor species and model sensitivity tests will be conducted to ascertain the key physical processes that led to those distributions. Third, the microphysical processes and hydrometeor distributions determined from observations will be compared to those that occurred in the model simulations. This will provide the basis for modifications to the BMP. Finally, these modifications will be evaluated in model simulations of the wide variety of storm systems studied during IMPROVE, as well as in daily operational forecast runs of the University of Washington's real-time regional forecast system. The nature of the research proposed here has direct potential benefits to society through improved QPF. To further broaden the benefits of the proposed research, and to increase general awareness of mesoscale numerical weather prediction (NWP) models, a lay-accessible web site on IMPROVE and NWP will be maintained. The use of NWP web sites in local K-12 science classrooms will be encouraged through interactions with teachers, and the PI and staff will interact with the operational forecasting community to help the transfer of research results to applications.