Changes in the Systematic Errors of Global Reforecasts due to an Evolving Data Assimilation System

Abstract

A global reforecast dataset was recently created for the National Centers for Environmental Prediction’s Global Ensemble Forecast System (GEFS). This reforecast dataset consists of retrospective and real-time ensemble forecasts produced for the GEFS from 1985 to present day. An 11-member ensemble was produced once daily to +15-day lead time from 0000 UTC initial conditions. While the forecast model was stable during the production of this dataset, in 2011 and several times thereafter, there were significant changes to the forecast model that was used in the data assimilation system itself, as well as changes to the assimilation system and the observations that were assimilated. These changes resulted in substantial changes in the statistical characteristics of the reforecast dataset. Such changes make it challenging to uncritically use reforecasts for statistical postprocessing, which commonly assume that forecast error and bias are approximately consistent from one year to the next. Ensuring the consistency in the statistical characteristics of past and present initial conditions is desirable but can be in tension with the expectation that prediction centers upgrade their forecast systems rapidly.