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Despite the noted disadvantages of ASOS observations for this project, one distinct advantage may be an increased pool of sites with more consistent observations. Hence, it may be an advantage to include as many sites as possible with the expectation that more and more sites with a 30-year period of record will emerge in the future. Nonetheless, short gaps of data can be expected at any operating site, and reasonable efforts will be made to fill all missing model input data (2). 2.8 Database and software A simple flat-file database was developed to serve the needs of our two-year test set. Records held approximately 130 fields that included all input parameters, output fields for all models, and numerous flag and ancillary fields. For speed and programming simplicity, file records were a binary image of the data structure developed for the C programs, allowing rapid I/O without the need for complex data formatting. The full production database will likely be similar to the concept used in the test database. A more conventional final output format will be developed for database distribution. Most software development for the test data set involved programs to read the many and varied formats of input data to transfer the values to the database. To provide easy access to the binary data, we developed a Windows-based extractor tool, allowing a user to select any of the database fields in any order and output them in a custom-configured comma- separated value ASCII file. 3. ANALYSIS OF MODELS AND METHODS The analysis of the test data sets and evaluation of the results are treated more thoroughly elsewhere (11). The analysis data set included those hours from all sites for which measured and output data for all models were concurrent. This process completely eliminated two sites from the analysis because there were no overlapping data (Barrow, Alaska and Hermiston, Oregon). The analysis compared the measured data to the output for each model using the statistical measures of RMSE, MBE, frequency distributions, probability distributions, correlation, and autocorrelation. The analysis reveals little significant differences among the three candidate models (NRCC, SUNYA, and METSTAT). The GHI monthly mean daily total (MMDT) MBE for all models ranged from -0.06 to 1.73%, and the RSME ranged from 5 to 8%. The uncertainty of the validation data is estimated to be about 5-10%, which easily encompasses these errors. The DNI MMDT RMSE was somewhat larger, but all models were very close, ranging from 13.7-15.0%. Figure 2, which shows errors for hourly global irradiance, illustrates not only the closeness of the model performance, but also the similarity of excursions for all models in site-to-site variability. These excursions may indicate a common bias in the models or input data, or an error in the ground validation data. Whatever the cause, it is important in this context to note that all models perform similarly. Figure 3 shows measured versus modeled for DNI MMDT. Fig. 2. MBE (lines, left scale) and RMSE (Bars, right scale) for modeled hourly total GHI by site and by model. From (11) Fig. 3. Modeled versus measured DNI monthly mean daily totals, regression fit, and correlation coefficients by model 4. RECOMMENDATIONS 4PDF Image | Progress on an Updated National Solar Radiation Data Base for the United States
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