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Observation System Experiments with the Hourly Updating Rapid Refresh Model Using GSI Hybrid Ensemble–Variational Data Assimilation
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Observation System Experiments with the Hourly Updating Rapid Refresh Model Using GSI Hybrid Ensemble–Variational Data Assimilation
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Journal Article
Observation System Experiments with the Hourly Updating Rapid Refresh Model Using GSI Hybrid Ensemble–Variational Data Assimilation
2017
Overview
A set of observation system experiments (OSEs) over three seasons using the hourly updated Rapid Refresh (RAP) numerical weather prediction (NWP) assimilation–forecast system identifies the importance of the various components of the North American observing system for 3–12-h RAP forecasts. Aircraft observations emerge as the strongest-impact observation type for wind, relative humidity (RH), and temperature forecasts, permitting a 15%–30% reduction in 6-h forecast error in the troposphere and lower stratosphere. Major positive impacts are also seen from rawinsondes, GOES satellite cloud observations, and surface observations, with lesser but still significant impacts from GPS precipitable water (PW) observations, satellite atmospheric motion vectors (AMVs), and radar reflectivity observations. A separate experiment revealed that the aircraft-related RH forecast improvement was augmented by 50% due specifically to the addition of aircraft moisture observations. Additionally, observations from en route aircraft and those from ascending or descending aircraft contribute approximately equally to the overall forecast skill, with the strongest impacts in the respective layers of the observations. Initial results from these OSEs supported implementation of an improved assimilation configuration of boundary layer pseudoinnovations from surface observations, as well as allowing the assimilation of satellite AMVs over land. The breadth of these experiments over the three seasons suggests that observation impact results are applicable to general forecasting skill, not just classes of phenomena during limited time periods.
