Abstract. To successfully assimilate data from a new observing system, it is necessary
to develop appropriate data selection strategies, assimilating only the
generally useful data. This development work is usually done by
trial and error using observing system experiments (OSEs), which are very
time and resource consuming. This study proposes a new, efficient
methodology to accelerate the development using ensemble forecast
sensitivity to observations (EFSO). First, non-cycled assimilation of the new
observation data is conducted to compute EFSO diagnostics for each
observation within a large sample. Second, the average EFSO conditionally
sampled in terms of various factors is computed. Third, potential data
selection criteria are designed based on the non-cycled EFSO statistics, and
tested in cycled OSEs to verify the actual assimilation impact. The
usefulness of this method is demonstrated with the assimilation of satellite
precipitation data. It is shown that the EFSO-based method can efficiently
suggest data selection criteria that significantly improve the assimilation
results.
Accelerating assimilation development for new observing systems using EFSO