Model bias correction is particularly challenging because it is difficult to develop valuable representations for the biases or the mechanisms that cause them. Intermittent bias correction of background estimates in a sequential estimation scheme does not prevent the generation of bias during the integration of the model. Incremental bias correction schemes, which use bias estimates to correct model tendencies, may be more effective in guiding the model to an unbiased forecast, provided the corrections are physically meaningful.