In this presentation, infrared brightness temperatures (BTs) from the GOES-16 Advanced Baseline Imager are used to examine the accuracy of cloud forecasts using two different model approaches. The first approach will be ensemble-based, comparing simulated BTs from a 5-member ensemble where a stochastic perturbed parameter methodology is applied to the widely-used Thompson-Eidhammer cloud microphysics scheme to a 5-member ensemble with white noise perturbations added to the potential temperature fields at initialization time. The second approach compares simulated BTs from several microphysics and planetary boundary layer (PBL) schemes, as well as land surface models and surface layers.
This presentation will utilize both pixel-based and object-based statistics. Some validation metrics include the mean absolute error and mean bias error, as well as the Object-Based Threat Score and Mean-Error Distance calculation. Objects are identified using the Method for Object-Based Diagnostic Evaluation.
