Using “Pseudo” RAOB Observations to Study GFS Skill Score Dropouts

On approximately a monthly basis, poor forecasts or skill score “Dropouts” plague GFS performance and are responsible for a 10% skill loss. Other national center forecasts, for example European Centre for Medium-range Weather Forecasts (ECMWF), often do not exhibit this loss in skill. The climatology of these events indicates differences between mass and motion fields that are due to quality control (QC) and treatment of conventional and non-conventional observations. We attempt to quantify the differences between the GFS and ECMWF treatment of observations to detect issues that when addressed will improve GFS forecasts.

A GFS analysis is made using ECMWF initial conditions converted to simulated or “pseudo” RAOB observations, and inserted into the GFS Grid point Statistical Interpolation (GSI) to create a new analysis from which new forecasts are made. These are called ECM experiments and are compared with operational forecasts. This same process of assimilating only pseudo RAOB observations can be repeated for the output of GFS operations instead of ECMWF to make control runs to compare with the ECM experiments.

The ECM results show improvement in 5-day skill scores in practically all Southern and most Northern hemispheric cases. Initial condition areas that influence the sensitivity of forecast skill can be confirmed by creating a hybrid initial condition, selectively overlaying the ECMWF pseudo-analysis over the GFS initial condition. The regions used are “patches” over special areas, e.g., where there is ambiguity in observation quality, or latitude/longitude bands to isolate problems that alter downstream 5-day forecasts to determine if QC problems originate from particular observation types. In addition to Anomaly Correlation skill scores, the influence on diagnosed quantities like baroclinicity and precipitation are measured in a companion paper. We attempt to isolate the contribution of a number of observation types to the forecast skill when dropouts occur. The goal is improvement in both automated QC and the use of observations to alleviate dropouts in model forecast skill.