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0-48小时定量降水预报的主要工具。 随着时间的推移，模型分辨率不断提高，物理过程的模型参数化也变得更加复杂。 即便如此，QPF的改善相对缓慢。 除了分辨率和初始条件，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）。 在BMP所依据的许多假设中存在相当大的不确定性。 明确评估BMP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凝物分布与现场（机载）和遥感（例如，雷达观测。 此外，至关重要的是，在观测风、温度和湿度的同时获得微物理测量结果，以便模拟微物理中的误差可以与这些其他预测场中的误差隔离开来。主要研究者发起了一项名为“通过观察验证实验改进微物理参数化“的研究（IMPROVE）比较目前中尺度模式中云和降水过程的表示与详细的测量和观测。 在过去的两个冬天进行了两个实地研究：一个研究了锋面系统产生的降水，因为他们接近华盛顿海岸;第二个研究了地形的降水调制的情况下，强，潮湿的气流穿过俄勒冈州瀑布屏障。 在总共26个密集观测期（IOPs）期间收集了丰富的数据集，涵盖了各种各样的锋面和地形降水系统。根据该合同，主要研究者将继续分析IMPROVE数据。 首先，将对观测数据进行简化和分析，以确定导致降水发展的物理过程，并得出浓度、粒径分布、反射率因子等的时空分布，各种水凝物的存在 其次（与观测分析平行），将利用四维数据同化对观测情况进行中尺度模式模拟，分辨率约为1公里，以便在运动学、热力学和湿度分布方面进行尽可能最好的模拟。 与观测一样，将对模型模拟进行分析，以确定水凝物种类的时间和空间分布，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，将从观测中确定的微物理过程和水凝物分布与模型模拟中发生的微物理过程和水凝物分布进行比较。 这将为BMP的修改提供基础。 最后，将在IMPROVE期间研究的各种风暴系统的模型模拟中以及在华盛顿大学实时区域预报系统的日常业务预报运行中对这些修改进行评估。这里提出的研究性质通过改进QPF对社会有直接的潜在利益。 为了进一步扩大拟议研究的效益，并提高公众对中尺度数值天气预报模式的认识，我们将维持一个关于IMPROVE和NWP的网站。 通过与教师的互动，将鼓励在当地K-12科学教室使用NWP网站，PI和工作人员将与业务预报界互动，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.