Impact of Improved Initialization on Warm-Season Mesoscale Rainfall Prediction in a Nonhydrostatic Eta Model

这项研究是与NSF/NOAA-美国天气研究计划支持开始的工作的继续，使用NCEP Eta模式的非静力版本，水平分辨率精细(~10公里)，以研究改进的初始化对美国中部暖季降水预报的影响。将对爱荷华州和明尼苏达州中西部上段的情况进行新的模拟，那里有密集的地表观测点网络，可以为验证和核实提供广泛的数据集。主要重点将是改进与中β强迫有关的风暴的初始化和模拟，包括冷池/外流边界、低层大气湿舌头和弱锋面情况。使用来自密集地面中层网络的数据以及卫星云数据同化将改进边界层温度和湿度的初始化。模拟中将包括对陆地-大气相互作用的详细处理。将考虑在所导出的rneo-beta初始特性的不确定范围内的扰动来执行模拟集合，以评估模型预测的可信度。一些改进初始化的技术是现成的，而另一些技术将在研究的第二阶段开发或修改。这项工作的独特之处在于，它将利用非常规观测输入，探索改进的网格初始化对局地中尺度降水预报的影响。这项研究将通过建立可用的高分辨率初始数据的最有益用途，促进业务预报的未来发展。此外，这项研究还将提高我们对中西部上部强局地风暴发展的理解。

This research is a continuation of work begun with NSF/NOAA-U.S. Weather Research Program support using a nonhydrostatic version of the NCEP Eta model with fine (~ 10 km) horizontal resolution to study the effect of improved initialization on predictions of warm-season rainfall in the central United States. New simulations will be run for cases in the upper Midwest, in Iowa and Minnesota, where there is a dense mesonetwork of surface observing sites exists that can provide extensive data sets for validation and verification. The primary emphasis will be improved initialization and simulation of storms associated with meso-beta forcing, including cold pool/outflow boundaries, lower atmosphere moist tongues and weak front situations.The use of data from the dense surface mesonetwork as well as satellite cloud data assimilation will improve initialization of the temperature and moisture in the boundary layer. A detailed treatment of land-atmosphere interaction will be included in the simulations. Ensembles of simulations will be performed considering perturbations within the range of the uncertainties in the derived rneso-beta initial characteristics to assess model prediction credibility. Some of the techniques for improved initialization are readily available, whereas others will be developed or modified during this second phase of the research. The uniqueness of the effort is that it will explore refined grid initialization impacts on local mesoscale precipitation prediction, using non-routine observational input. This research will facilitate future advances in operational forecasting by establishing the most beneficial uses of available higher resolution initial data. Also, the study will improve our understanding of development of intense local storms in the upper Midwest.