Several different spatial domains were defined over the Pacific Ocean in order to address the needs of the forecasters at the Seattle National Weather Service Forecast Office. Comparing error scores for each spatial domain, it was seen that over a 13 month average, different models had the best initialization in various levels of the atmosphere. A similar methodology was conducted for forecast times out to 48 hours over the Pacific and rest of North America and the results varied and can be found in the MS Thesis paper. Aside from a direct comparison of raw error scores, attempts were made at correlating errors in initialization over the Pacific to errors that occur downstream at subsequent forecast times. While not all results were statistically significant, relationships were shown to exist between initial condition errors over the Pacific and forecast errors going east to the Rocky Mountains.
Data density is the dominating factor in determining the quality of the end results.
In addition, it was learned that the error quantity being measured might be located in a region whose growth rate is negative, that is, the error measured is decaying with time. Beyond the error analyses, a new modeling technique was employed to better understand the exact relationship between initial condition and forecast errors. The student attended the NCAR MM5 Adjoint Modeling class held in July 1998. The adjoint model is directly related to its corresponding forward model via adjoint theory. In the MS Thesis, attempts were made to correlate actual model initial condition errors and subsequent forecast errors.