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DOI	10.1016/j.atmosres.2021.105624
	Diurnally varying background error covariances estimated in RMAPS-ST and their impacts on operational implementations
	Chen Y.; Fang K.; Chen M.; Wang H.
发表日期	2021
ISSN	0169-8095
卷号	257
英文摘要	Background error covariance (BEC) plays a key role in variational data assimilation systems. The National Meteorological Center (NMC) method has been used widely to generate forecast error samples for BEC estimation. At present, most variational-based rapid update and cycling (RUC) data assimilation and forecasting systems use a fixed BEC without consideration of diurnal variation. In this study, diurnal variation BECs were estimated using three month forecast error samples (0000 UTC 01 June to 2100 UTC 31 August 2019), which came from the Rapid-refresh Multi-scale Analysis and Prediction System-Short Term (RMAPS-ST). Series of single observation tests and one-month partial cycling data assimilation and forecasting experiments with diurnal variation BECs were carried out based on the system. The results showed the following: 1) Diurnal variation is found in the standard deviation of forecast error samples, with the minimum value of standard deviation appearing at nightfall (0900 UTC, 1700 BJT), and the maximum value appearing at the early morning (2100 UTC, 0500 BJT). The eigenvalues also show similar diurnal variation features, indicating the diurnal variation characteristics of background error are consistent in the physical space and the EOF space. 2) The diurnal variation of BEC is further verified by single observation tests, and the analysis increments well response to the BEC diurnal variation. 3) The results of one-month cycling experiments show that diurnal variation BECs could improve the assimilation and forecasting performance of RMAPS-ST. © 2021 Elsevier B.V.
英文关键词	Background error covariance; Data assimilation; Diurnal variation; Rapid update and cycling
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/236746
作者单位	Key Laboratory of Meteorological Disaster of Ministry of Education (KLME) / Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, 210044, China; Institute of Urban Meteorology, China Meteorological Administration, Beijing, 100089, China; Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, United States; NOAA/OAR/Earth System Research Laboratories /Global Systems Laboratory, Boulder, CO, United States
推荐引用方式 GB/T 7714	Chen Y.,Fang K.,Chen M.,et al. Diurnally varying background error covariances estimated in RMAPS-ST and their impacts on operational implementations[J],2021,257.
APA	Chen Y.,Fang K.,Chen M.,&Wang H..(2021).Diurnally varying background error covariances estimated in RMAPS-ST and their impacts on operational implementations.Atmospheric Research,257.
MLA	Chen Y.,et al."Diurnally varying background error covariances estimated in RMAPS-ST and their impacts on operational implementations".Atmospheric Research 257(2021).