The Performance and Feasibility of Ensemble Forecast Sensitivity to Observations-based Proactive Quality Control Scheme

Abstract:

Operational numerical weather prediction (NWP) systems occasionally exhibit “forecast skill dropouts” in which the forecast skill drops to an abnormally low level, due in part to the assimilation of flawed observational data. Recent studies have shown that a diagnostic technique called Ensemble Forecast Sensitivity to Observations (EFSO) can detect such observations (Kalnay et.al 2012; Ota et al. 2013, Tellus A). Based on this technique, a new Quality Control (QC) scheme called Proactive QC (PQC) has been proposed which detects “flawed” observations using EFSO after just 6 hours forecast, when the analysis at the next cycle becomes available for verification and then repeats the analysis and forecast without using the detected observations (Hotta 2014).

In Hotta (2014), it was shown using the JCSDA S4 Testbed that the 6hr PQC reduces the 24-hour forecast errors from the detected skill dropout events. With such encouraging results we are performing preliminary experiments towards operational implementation. First, we show that offline PQC correction can significantly reduce forecast errors up to 5 days, and that the reduction and improved areal coverage can grow with synoptic weather disturbances for several days. Second, with online PQC cycle experiment the reduction of forecast error is shown to be even larger than in the offline version, since the effect could accumulate over each time we perform a PQC correction. Finally, the operational center imposes very tight schedule in order to deliver the products on time, thus the computational cost has to be minimized in order for PQC to be implemented. To avoid performing the analysis twice, which is the most expensive part of PQC, we test the accuracy of constant-K approximation, which assumes the Kalman gain K doesn’t change much given the fact that only a small subset of observation is rejected. In this presentation, we will demonstrate the performance and feasibility of PQC implementation in real-time operational environment.