Impacts of Assimilation Frequency on Ensemble Kalman Filter Data Assimilation and Imbalances. (24th October 2020)

• Record Type: Journal Article
• Authors: He, Huan; Lei, Lili; Whitaker, Jeffrey S.; Tan, Zhe‐Min
• Is Part Of: Journal of advances in modeling earth systems. Volume 12:Number 10(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-24
• Subjects: assimilation frequency -- assimilation imbalance -- ensemble Kalman filter -- incremental analysis update; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2020MS002187
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: The ensemble Kalman filter (EnKF) has been widely used in atmosphere, ocean, and land applications. The observing network has been significantly developed, and thus, observations with highly dense temporal resolutions have become available. To better extract information from dense temporal observations, one straightforward strategy is to increase the assimilation frequency. However, more frequent assimilation may exacerbate the model imbalance and result in degraded forecasts. To combat the imbalance caused by ensemble‐based data assimilation due to sampling error and covariance localization, three‐ and four‐dimensional incremental analysis update (IAU) were proposed, which gradually introduce the analysis increments into model rather than intermittently updating the state. The trade‐off between the assimilation frequency and imbalance is systematically explored here by using an idealized two‐layer model and the NOAA GFS. Results from the idealized two‐layer model show that increasing assimilation frequency can reduce errors for state variables that are not sensitive to imbalances. For state variable that carries the signal of the external gravity mode and is sensitive to imbalances, increasing assimilation frequency without (with) IAU reduces (increases) errors. Without IAU, more frequent updates result in smaller increments and less insertion noise, while the initialization of IAU cannot effectively mitigate the imbalances with increased assimilation frequency. Results with a low‐resolution version of the NOAA GFS demonstrate that increasing assimilation frequency from 6 to 2 h improves the errors and biases of forecasts verified with conventional and radiance observations, although gravity wave noise in the forecast is increased. Plain Language Summary: Data assimilation combines observations with prior information like model forecasts to produce the best estimate of the state for a dynamical system. For global prediction, a data assimilation interval of 6‐h has typically been used; but for high‐resolution regional models with dense spatial and temporal resolution observations such as radar and satellite data, shorter data assimilation intervals are often used. Given the significantly developed observing networks, observations with highly dense temporal resolutions and inhomogeneous spatial distributions have become available. Increasing the assimilation frequency may better extract information from dense temporal observations, but does not give the model enough time to adjust the updated state variables with potentially insertion shocks. This study systematically investigates the trade‐off between the data assimilation frequency and imbalance. Results reveal that increasing assimilation frequency can reduce errors for state variables, because more frequent updates can better fit the model to the observation and result in smaller increments/less imbalances to the model. When the incremental analysis update (IAU) is applied, increasing assimilation frequency reduces errors for state variables that are not sensitive to imbalances, but the opposite for state variables sensitive to imbalances, because the IAU with more frequent assimilation cannot effectively mitigate the imbalances. Key Points: Increasing assimilation frequency can reduce errors for state variables that are not sensitive to imbalances For variables sensitive to imbalances, increasing assimilation frequency without IAU results in less insertion noises and reduced errors Increasing assimilation frequency with IAU has lessened noise filtering and increases errors for state variables sensitive to imbalances