Proactive
Quality Control based on Ensemble Forecast Sensitivity to Observation
(EFSO)
Daisuke
Hotta
UMD College Park
July 16th noon in Conference Center
Abstract:
Despite recent major improvements in numerical weather prediction (NWP)
systems, operational NWP forecasts occasionally suffer from an abrupt
drop in forecast skill, a phenomenon called 'forecast skill
dropout'. Recent studies have shown that the 'dropouts' occur
not because of the model's deficiencies but by the use of flawed
observations that the operational quality control (QC) system failed to
filter out. Thus, to minimize the occurrences of forecast skill
dropouts, we need to detect and remove such flawed observations.
A diagnostic technique called Ensemble Forecast Sensitivity to
Observation (EFSO) enables us to quantify how much each observation has
improved or degraded the forecast. A recent study (Ota et
al., 2013) has shown that it is possible to detect flawed observations
that caused regional forecast skill dropouts by using EFSO
with 24-hour lead-time and that the forecast can be improved by not
assimilating the detected observations.
Inspired by their success, in the first part of this study, we propose
a new QC method, which we call Proactive QC (PQC), in which flawed
observations are detected 6 hours after the analysis by EFSO and then
the analysis and forecast are repeated without using the detected
observations. This new QC technique is implemented and tested on a
lower-resolution version of NCEP's operational global NWP system. The
results we obtained are extremely promising; we have found that we can
detect regional forecast skill dropouts and the flawed observations
after only 6 hours from the analysis and that the rejection of the
identified flawed observations indeed improves 24-hour forecasts.