Jonathan Flowerdew, Met Office
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 conditions from the corresponding global members. An experimental convective-scale ensemble DA system has now been built based on the 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, localisation 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 MOGREPS-UK-style downscaling, even though the local assimilation has seen fewer observation types than the global DA. As anticipated, an hourly cycle performs much better than a six-hourly one, 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 the model state as updated by all previous observations. The mean square difference provides a powerful new diagnostic, since it measures the error of the developing analysis using observations which are statistically independent, without the need to involve the forecast model. The diagnostic confirms the beneficial impact of assimilation, can be used to cheaply tune parameters such as localisation radii, and contributes to evaluating the correctness of the ensemble spread.