Determination of the Effects of Non-Modality on the Data Assimilation and Numerical Weather Prediction Problems

该指数将解决数值天气预报中预测误差的非模态增长问题。 第一天后的误差进入预测有很大的垂直和水平尺度，但其初期的原因已被发现是由于在更小的空间尺度的错误，并有不同的结构，如倾斜和动力不平衡，而不是正压和地转。 这种错误行为称为非模态。 根据奇异向量分析，将确定非模态预报误差增长对天气预报和观测数据分析的潜在改进的影响，并将检查观测数据误差对预报的敏感性，以便更好地了解观测系统不完善对预报和数据同化系统的影响。 这些问题将通过应用美国宇航局全球建模和同化办公室最近开发的数据同化和天气建模系统的伴随版本来解决。这项研究有可能改善对社会活动至关重要的天气预报。

The PIs will address the non-modal growth of forecast errors in numerical weather prediction. Errors after day one into the forecast have large vertical and horizontal scales, but their incipient cause has been found to be due to errors in much smaller spatial scales and to have different structures such as being tilted and dynamically unbalanced as opposed to barotropic and geostrophic. Such error behavior is called non-modal. Based on singular vector analysis, the implications of non-modal forecast error growth for potential improvement of both weather forecasting and analysis of observational data will be determined, and forecast sensitivity with respect to observational data errors will be examined in order to better understand the impacts of the observing system imperfections on forecast and data assimilation systems. These questions will be addressed by applying the adjoint version of the data assimilation and weather modeling system recently developed at NASA's Global Modeling and Assimilation Office.This research has the potential to improve weather forecasts which are important for societal activities.