Impact of singular‐vector‐based satellite data thinning on NWP

Singular‐vector(SV)‐based selective satellite data thinning is applied to the Southern Hemisphere (SH) extratropics to reduce analysis uncertainty and forecast error. For two seasons, the European Centre for Medium‐Range Weather Forecasts (ECMWF) four‐dimensional variational data assimilation system has been run in five different configurations with different satellite data coverage: two reference experiments used low‐density and high‐density coverage over the globe; in the SH two SV‐based selective thinning experiments used low‐density data everywhere apart from targeted regions; and one random‐based thinning experiment used low‐density data everywhere apart from randomly defined regions. The SV‐based target regions have been defined either by daily operational SVs computed for the ECMWF Ensemble Prediction System, or by the previous year's mean seasonal distribution. Results indicate that the impact of the additional data largely depends on the season. Overall, forecast errors grow faster in the SH cold season than in the warm season. In the SH cold season, the general impact of adding data is smaller and the relative difference between the impact of the individual targeting experiments is smaller as well. In the cold season, the data assimilation system failed to extract the meteorological signal carried by the extra satellite data in sensitive regions. In the SH warm season, all experiments with more data produce a statistically more significant and longer‐lasting positive impact on forecast skill. In this season, the SV‐based targeting experiment performs best and as well as the reference experiment in which the data density is increased globally.