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

中尺度有可能得到重大改进 数值天气预报的关键在于找到 对于新的数据集（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）将成为可用的 在未来十年内， 这些观察结果 纳入四维分析过程， 产生与预测模型兼容的初始状态。 该奖项下的研究专门解决了这个问题 如何将降水测量纳入分析 过程，使模型的降雨率将类似于 在天气预报开始时尽可能接近大气层。 主要研究人员将探索使用 雨量计、卫星和雷达数据来确定垂直方向 低层大气中的热量分布。 这 然后将信息纳入大气分析 在预测前的整合过程中， 场被带入动态平衡， 冷凝加热 这种做法应大大减少 模型预测中的“自旋加速”问题。 该项目是美国国家海洋和大气管理局 国家气象中心（NMC）和NSF。 一个重要 研究的一个方面将是测试新的算法， 是在NMC操作模型中开发的。

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.