Observation Impacts In Mesoscale Numerical Weather Prediction (invited Presentation)

Abstract

The 13-km Rapid Refresh (RAP) and 3-km convective-allowing High-Resolution Rapid Refresh (HRRR) are hourly updating weather forecast models that use a specially configured version of the Advanced Research WRF (ARW) model and assimilate many novel and most conventional observation types on an hourly basis using Gridpoint Statistical Interpolation (GSI). Included in this assimilation is a procedure for initializing ongoing precipitation systems from observed radar reflectivity data, a cloud analysis to initialize stable layer clouds from METAR and satellite observations, and special techniques to enhance retention of surface observation information. The RAP is cycled hourly with forecasts to twenty one hours covering much of North America and the HRRR is run hourly out to eighteen forecast hours over a domain covering the entire conterminous United States using boundary and initial conditions from the hourly-cycled RAP. A set of observation system experiments (OSEs) are conducted over three seasons using the hourly-updated Rapid Refresh (RAP) numerical weather prediction model to demonstrate the importance of various observation types for 3-hr to 12-hr RAP forecasts. These experiments selectively remove observations from assimilation to quantify the increase in forecast error across a variety of metrics. Removal of aircraft observations yield the largest increase in forecast error for wind, relative humidity, and temperature forecasts averaged over the 1000-100 hPa layer, but many other observation types also have significant positive impacts, when assimilated, including satellite, surface, GPS-precipitable water, radar, and rawinsonde observations. Seasonal differences in the error characteristics exist, with impacts on relative humidity forecasts strongest during the spring which is characterized by strong forcing and sharp moisture gradients. Convective processes limit mesoscale observation impacts in the summertime. Retention of assimilated radar data will also be highlighted where impacts tend to persist for relatively short time periods of less than six hours. Finally, we will demonstrate the impact of modifications to assimilation methodologies of surface observations including cloud information from ceilometers. In total, these results provide insight into the relative importance of different components of the observing system as we move towards a global rapid refresh capability and provide additional motivation for mesoscale ensemble data assimilation.