Near-Surface Wind Observation Impact on Forecasts: Temporal Propagation of the Analysis Increment

Abstract

This study examines the assimilation of near-surface wind observations over land to improve wind nowcasting and short-term tropospheric forecasts. A new geostatistical operator based on geophysical model output statistics (GMOS) is compared with a bilinear interpolation scheme (Bilin). The multivariate impact on forecasts and the temporal evolution of the analysis increments produced are examined as well as the influence of background error covariances on different components of the prediction system. Results show that Bilin significantly degrades surface and upper-air fields when assimilating only wind data from 4942 SYNOP stations. GMOS on the other hand produces smaller increments that are in better agreement with the model state. It leads to better short-term near-surface wind forecasts and does not deteriorate the upper-air forecasts. The information persists longer in the system with GMOS, although the local improvements do not propagate beyond 6-h lead time. Initial model tendencies indicate that the mass field is not significantly altered when using static error covariances and the boundary layer parameterizations damp the poorly balanced increment locally. Conversely, most of the analysis increment is propagated when using flow-dependent error statistics. It results in better balanced wind and mass fields and provides a more persistent impact on the forecasts. Forecast accuracy results from observing system experiments (assimilating SYNOP winds with all observations used operationally) are generally neutral. Nevertheless, forecasts and analyses from GMOS are more self-consistent than those from both Bilin and a control experiment (not assimilating near-surface winds over land) and the information from the observations persists up to 12-h lead time.

The Impact of Surface Wind Data Assimilation on the Predictability of Near-Surface Plume Advection in the Case of the Fukushima Nuclear Accident

“…Therefore, the observational datasets of surface wind velocities are usually very limited or are avoided for operational weather prediction (e.g., Japan Meteorological Agency (JMA) 2015, in Japanese) to prevent the degradation of initial conditions. To improve near-surface wind prediction, recent studies tried to assimilate surface wind observations over land (e.g., Hacker and Snyder 2005;Benjamin et al 2010;Hacker and Rostkier-Edelstein 2007;Rostkier-Edelstein and Hacker 2010;Ancell et al 2011Ancell et al , 2015Ingleby 2015;Bédard et al 2015Bédard et al , 2017. In these studies, however, the surface wind observations have an influence only on extremely shortterm and local forecasts (less than 6 h) even if they have a positive impact.…”