Efficient Estimation of the Impact of Observing Systems using Ensemble Forecast Sensitivity to Observations (EFSO)
Tse-Chun Chen
UMDCP
Noon, 18 July in 2155
Abstract:
Massive amounts of observations are being assimilated into operational
NWP. New observing systems with high temporal, spatial, and spectral
sampling rates are being developed and deployed fairly regularly. The
need to evaluate the usefulness of these observations can not be
satisfied by the prevailing OSEs, which are computationally expensive
and have limited applicability. We demonstrate that Ensemble Forecast
Sensitivity to Observations (EFSO), which quantifies the impact of each
observation on the forecasts at low cost, could be implemented as an
online monitoring tool of the impact of each observation. Thus EFSO can
efficiently identify detrimental impact episodes and the associated
observations. To avoid such detrimental episodes, Hotta et al. (2017)
have shown EFSO-based Proactive Quality Control (PQC) can reduce
forecast error in cases of "skill dropout". We further devised two
other data denial strategies: THReshold (THR), which rejects
observation if the Moist Total Energy error impact is more detrimental
than 10-5 J-kg-1, and Beneficial Growing Mode (BGM) that only keeps
observations that are beneficial in 6-hr forecasts and continue to be
beneficial after 24 hours. We show in the presentation that both THR
and BGM outperform the original PQC method, and BGM (useful for
reanalyses) performs even better than THR.