Incorporation of Observed Precipitation Estimates in Four-Dimensional Data Assimilation Schemes for Mesoscale Models

在中尺度数值天气预报方面取得重大改进的潜力在于找到新数据集（来自下一代雷达或NEXRAD、卫星、自动地面观测、风廓线仪、飞机报告等）的最佳使用方法，这些数据集将在今后十年可用。这些观测结果被纳入一个四维分析过程，产生一个与预测模型相容的初始状态。该奖项下的研究专门解决了如何在分析过程中包括降水测量的问题，以使模型的降雨率在预测开始时尽可能接近大气。主要研究人员将探索利用雨量计、卫星和雷达数据来确定低层大气中加热的垂直分布的方法。这些信息随后被纳入大气分析，在预报整合过程中，质量场和风场与强加的冷凝加热达到动态平衡。这种方法将大大减少模型预测中的“自旋上升”问题。该项目是NOAA国家气象中心（NMC）和NSF联合项目的一部分。研究的一个重要方面将是测试在NMC操作模型中开发的新算法。

A potential for significant improvement in mesoscale numerical weather prediction lies in finding the optimal use for the new data sets (from next generation radar, or NEXRAD, satellites, automated surface observations, wind profilers, aircraft reports, etc.) that will become available operationally during the next decade. These observations are incorporated into a four-dimensional analysis process that produces an initial state compatible with the forecast model. The research under this award specifically addresses the issue of how to include precipitation measurements in this analysis process so that the model's rainfall rates will resemble the atmosphere's as closely as possible as the forecast begins. The principal investigators will explore methods to using raingauge, satellite and radar data to determine the vertical distribution of heating in the lower atmosphere. This information is then incorporated into the atmospheric analysis during a pre-forecast integration in which the mass and wind fields are brought into dynamical balance with the imposed condensational heating. This approach should greatly reduced the problem of "spin-up" in the model prediction. This project is part of a joint program between NOAA's National Meteorological Center (NMC) and NSF. An important aspect of the research will be to test the new algorithms that are developed in the NMC operational models.