Tagging systematic errors arising from different components of dynamics and physics in forecast models

TL;DRAbstract

This study summarizes the results from a large number of forecast experiments with a global model with the objective of tagging errors that arise from different components of model dynamics and physics. To sort out such errors in non-linear systems is generally very difficult. In this study, we show examples of model forecasts that illustrate deficiencies (i.e., errors) that arise in the parameterization of cumulus convection and in the planetary boundary layer (PBL) physics. Our proposed Factor Separation method enables us to tag the systematic errors in our PBL formulation of moisture convergence. We note that the PBL scheme overestimates by roughly a factor of 2 the convergence near the tropical cloud base. These errors were the largest over meso-convective regions of deep convection. With such efforts at error tagging further improvements in modeling and forecasts can be achieved. We also address further work in this area of research.

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