Assimilating Cloud Observations in the High Resolution Rapid Refresh (HRRR)

Successful data assimilation is of paramount importance for initializing numerical models, yet assimilating cloud observations can be particularly challenging. NOAA’s Earth System Research Laboratory (ESRL) Global Systems Division (GSD) developed an option within the Gridpoint Statistical Interpolation (GSI) data assimilation system for non-variational cloud and precipitating hydrometeor analysis (HM-analysis). This approach specifies and removes cloud hydrometeors based on ceilometer and satellite cloud observations and is able to improve analyses and forecasts of clouds and precipitation in the operational 3-km High-Resolution Rapid Refresh (HRRR).

Cloud observation assimilation development for the HRRR has been underway to further improve low-level cloud retention and cloud prediction overall. To facilitate these needed improvements, experiments with cloud observation assimilation in the hybrid 3D ensemble-variational framework within GSI are ongoing. Additional development has begun to include cloud assimilation in the HRRR Ensemble (HRRRE), which is an experimental convective-scale EnKF data-assimilation system that will provide ensemble background covariances for the hybrid HRRR data assimilation. Single analysis and retrospective case study experiments examining the impact of direct cloud assimilation in the HRRR and HRRRE will be presented. The sensitivity to observation errors and the balance between cloud and moisture observation types will be discussed. Qualitative and quantitative comparisons with satellite observations and near surface conditions will be assessed.