Initial trials of convective‐scale data assimilation with a cheaply tunable ensemble filter

Abstract

Ensemble methods are an attractive option for data assimilation (DA) in convection‐permitting models, providing flow‐dependent covariances which respect the complex balances that apply at these scales. The Met Office deterministic UK forecast currently uses variational assimilation based on predominantly climatological covariances, whilst the MOGREPS‐UK ensemble interpolates its initial perturbations from the corresponding global members. An experimental convective‐scale ensemble DA system has now been built based on a serial ensemble filter, where observations are assimilated sequentially. This has been tested in a series of one month trials assimilating sonde, surface and aircraft observations, exploring issues such as cycle length, filter type, localization and inflation. The tests use 44 perturbed ensemble members, plus an unperturbed control forecast that is updated using the same Kalman gain as the ensemble mean. The best configurations provide control forecasts which are competitive with interpolating global initial states like MOGREPS‐UK did at the time, even though the local assimilation has seen fewer observation types than the global DA. An hourly cycle performs much better than a 6‐hourly one, as suggested by theoretical arguments, even though both see the same boundary conditions and essentially the same total set of observations.The serial filter calculates the difference between each observation and its model equivalent as updated by all previous observations. We call this the Innovation After assimilating Prior Observations (IAPO). The mean square IAPO measures the error of the developing analysis using observations which are statistically independent of it, without the need to involve the forecast model. This diagnostic confirms the beneficial impact of assimilation, can be used to cheaply tune parameters such as localization radii, and contributes to evaluating the correctness of the ensemble spread.