Impacts of Assimilation Frequency on Ensemble Kalman Filter Data Assimilation and Imbalances

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 sensiti...

The Impact of Incremental Analysis Update on Regional Simulations for Typhoons

“…Experiments with IAU have similar MWS errors to CTRL at different lead times. The results are consistent with previous studies that IAU has more prominent impacts on the state variable SLP that is more sensitive to imbalances (He et al., 2020; Lei & Whitaker, 2016).…”

“…For both TC cases, 5-day forecasts are launched every 12 hr during the TC lifetimes for each assimilation experiment. (He et al, 2020;Lei & Whitaker, 2016).…”

“…The filtering properties of EnKF‐3DIAU and EnKF‐4DIAU as well as the sensitivities to the assimilation frequency are further discussed by He et al. (2020).…”

“…At National Centers for Environmental Prediction (NCEP), 4DIAU is applied for both the deterministic 4DEnVAR analysis and EnKF ensembles, which reduces imbalances generated by the 4DEnVAR and EnKF and produces more accurate forecasts than DFI and 3DIAU (Lei & Whitaker, 2016). The filtering properties of EnKF-3DIAU and EnKF-4DIAU as well as the sensitivities to the assimilation frequency are further discussed by He et al (2020).…”

Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device

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