Progress in forecast skill at three leading global operational NWP centers during 2015-2017 as seen in Summary Assessment Metrics (SAMs)

Ross Hoffman
  7 May, Noon, in 2155

The summary assessment metric (SAM) method is applied to an array of primary assessment metrics (PAMs) for the deterministic forecasts of some leading numerical weather prediction (NWP) centers for the years 2015-2017. The PAMs include anomaly correlation, RMSE, and absolute mean error (i.e., the absolute value of bias) for different forecast times, vertical levels, geographic domains, and variables. SAMs indicate that in terms of forecast skill ECMWF is better than NCEP, which is better than but approximately the same as UKMO. The use of SAMs allows a number of interesting features of the evolution of forecast skill to be observed. All centers improve over the three year period. NCEP short-term forecast skill substantially increases during the period. Quantitatively, the effect of the 2016 May 11 NCEP upgrade to the 4D-ensemble variational (4DEnVar) system is the largest SAM impact during the study period. However, the observed impacts are within the context of slowly improving forecast skill for operation global NWP as compared to earlier years. Clearly the systems lagging ECMWF can improve, and there is evidence from SAMs in addition to the 4DEnVar example that improvements in forecast and data assimilation systems are still leading to forecast skill improvements.