The overarching goal of the FASTER project is to narrow uncertainty and biases in GCMs by utilizing continuous Atmospheric Radiation Measurement (ARM) data to enhance and accelerate evaluation and improvement of parameterizations of fast processes in GCMs involving clouds, precipitation, and aerosols. The project includes a broad collaboration of investigators using models with a range of scales, from cloud to regional to global. The project has six primary objectives:

1. Construction of a fast-physics testbed to rapidly evaluate fast physics in GCMs by comparing model results against continuous long-term cloud observations made by the ARM Climate Research Facility.
2. Execution of a suite of CRM simulations for selected periods/cases to augment the fast-physics testbed. Run WRFs with different parameterizations as CRMs, CRMs with bin-microphysics, and multi-scale modeling framework.
3. Continuous evaluation of model performance to identify and determine model errors by comparing the NWP and SCM results against continuous ARM observations, and to each other. The long-time data record at the ARM sites (e.g., Southern Great Plains) permits evaluation of various statistical properties (e.g., PDFs) and recurring cloud regimes.
4. Examination and improvement of parameterizations of key cloud processes/properties (e.g., convection, microphysics and aerosol-cloud interactions), thus narrowing the range of treatments of fast processes that exert strong influences on model sensitivity so as to better constrain climate sensitivity.
5. Assessment and development of metrics of model performance. Different metrics will be applied and tested in the evaluation, and new metrics will be explored. Special care will be taken to address the issue of scale-mismatch between observations and models.
6. Incorporation of newly acquired knowledge on parameterizations into the full participating GCMs to evaluate the impact of the refined parameterizations on GCM and ascertain the improvement in the representation of fast physics in the GCMs.