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

中尺度数值天气预报的一个显著改进的潜力在于找到新数据集(来自下一代雷达、卫星、自动地面观测、风廓线仪、飞机报告等)的最佳使用。这将在下一个十年投入运营。这些观测被结合到一个四维分析过程中，该过程产生与预报模型兼容的初始状态。该奖项下的研究专门解决了如何在分析过程中包括降水量测量的问题，以便模型的降雨率将尽可能接近预报开始时的大气降雨率。首席调查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。然后，在预报前积分期间，这些信息被合并到大气分析中，在该积分中，质量场和风场随着强制凝结加热而达到动态平衡。这种方法应该会大大减少模型预测中的“自旋”问题。该项目是美国国家海洋和大气局国家气象中心(NMC)和NSF联合项目的一部分。这项研究的一个重要方面将是测试NMC作战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.