Research brief: Inexpensive innovation improves Ensemble Kalman filter

A known weakness of the Ensemble Kalman filter approach is that its ability to provide state estimates that closely match densely distributed observations is very limited. This paper describes a computationally inexpensive innovative variation on the technique that greatly ameliorates this difficulty.

Estimating the state of the atmosphere from observations is essential for monitoring climate and also for the initialization of weather, sub-seasonal and seasonal weather forecasts. A popular way of making such estimates is using a method called the Ensemble Kalman Filter.

A known weakness of the Ensemble Kalman filter approach is that its ability to provide state estimates that closely match densely distributed observations is very limited.

This paper describes a computationally inexpensive innovative variation on the technique that greatly ameliorates this difficulty.

Huang, B., X. Wang, and C.H. Bishop, 2019: The High-Rank Ensemble Transform Kalman Filter.Mon. Wea. Rev.,147, 3025–3043, https://doi.org/10.1175/MWR-D-18-0210.1