Data assimilation and diagnosis of bias for WRF over the Gulf of Mexico

The Gulf of Mexico and adjoining coastal areas are subject to numerous weather related hazards. To help improve prediction of these hazards, we examine the performance of the WRF model with cycled three-dimensional variational data assimilation in this region. Extended periods of continuous cycling (months) provide a distilled signal of the bias in the WRF model, without contamination by biases from a separate, external analysis as would occur with cold-start simulations or data assimilation with "partial" cycling. We further isolate the most important biases by computing time averages of analysis increments from the data assimilation and of tendencies from the various physical parameterizations, and by comparing the diurnal cycle in observations and the model predictions averaged over several days.