Evaluating the impact of planetary boundary layer, land surface model, and microphysics parameterization schemes on cold cloud objects in simulated GOES‐16 brightness temperatures

Infrared brightness temperatures (BTs) from the Geostationary Observing Environmental Satellite-16 Advanced Baseline Imager are used to examine the ability of several microphysics and planetary boundary layer (PBL) schemes, as well as land surface models (LSM) and surface layers, to simulate upper-level clouds. Six parameterization configurations were evaluated. Cloud objects are identified using the Method for Object-Based Diagnostic Evaluation (MODE) and analyzed using the object-based threat score, mean-error distance, and pixel-based metrics including the mean absolute error and mean bias error (MBE) for matched objects where the displacement between objects has been removed. Objects are identified using either a fixed BT threshold of 235 K or the 6.5th percentile of BTs for each model configuration. Analysis of the MODE-identified cloud objects shows that, compared to a configuration with the Thompson microphysics scheme, Mellor-Yamanda-Nakanishi-Niino (MYNN) PBL, Global Forecasting System (GFS) surface layer, and Noah LSM, the configuration employing the National Severe Storms Laboratory microphysics produced more cloud objects with higher BTs. Changing the PBL from MYNN to Shin-Hong or Eddy-Diffusivity Mass-Flux also resulted in a slightly lower accuracy, though these changes result in configurations which more accurately reproduced the number of observation cloud objects and slightly reduced the high MBE. Changing the LSM from Noah to RUC reduces forecast accuracy by producing too many cloud objects with too low BTs. As the forecast hour increases, this accuracy reduction increases at a greater rate than occurred when changing the microphysics or PBL scheme and is further enhanced when using the MYNN surface layer rather than the GFS.