Assimilation of European and global Mode‐S aircraft observations into the Met Office global deterministic numerical weather prediction model

Abstract Data assimilation of in‐situ aircraft measurements has greatly benefited numerical weather prediction (NWP) forecast accuracy for decades. Recently, meteorological observations of wind and air temperature have been derived from Mode‐Selective Enhanced Surveillance (Mode‐S) aircraft reports which are used to meet air traffic and navigation requirements. The typical temporal resolution can be approximately 4 s or less per aircraft, compared to approximately seven minutes for aircraft meteorological data relay (AMDAR), with similar errors. Here, three months of European Mode‐S estimates of wind and air temperature were assimilated into the Met Office global deterministic Unified Model, to determine their impact on forecast skill. Their assimilation reduced root‐mean‐squared error (RMSE) of forecasted winds, geopotential height and temperature between 850 and 250 hPa in Europe to T + 48 and in the Northern Hemisphere to T + 96. Trials showed that wind observations provided most benefit, and that results were relatively insensitive to horizontal thinning distance. However, given the high observation density, using a coarser horizontal thinning distance of 120 km allowed independent observations to retain their impact while still benefiting from Mode‐S information. The Mode‐S input error profile can be adjusted to potentially improve the average benefit of Mode‐S. However, careful choice of thinning settings should be considered, to prevent oversaturation of Mode‐S observations and inadvertently worsening forecast skill. For the first time ever, observations from a groundbreaking global Mode‐S network were also assimilated into an NWP model. This dataset includes valuable observations from traditionally under‐observed regions such as Africa, South America, and Southeast Asia. The impact on overall forecast skill was considerable, with the global Mode‐S data contributing more significantly to improvements than the European dataset, thanks to its more comprehensive global coverage. The global Mode‐S network is poised to become an indispensable source of data for operational centres worldwide, with great potential to improve both regional and global future forecasts.