Radio Science

Differential validation of the US-TEC model



[1] This paper presents a validation and accuracy assessment of the total electron content (TEC) from US-TEC, a new product presented by the Space Environment Center over the contiguous United States (CONUS). US-TEC is a real-time operational implementation of the MAGIC code and provides TEC maps every 15 min and the line-of-sight electron content between any point within the CONUS and all GPS satellites in view. Validation of TEC is difficult since there are no absolute or true values of TEC. All methods of obtaining TEC, for instance, from GPS, ocean surface monitors (TOPEX), and lightning detectors (FORTE), have challenges that limit their accuracy. GPS data have interfrequency biases; TOPEX also has biases, and data are collected only over the oceans; and FORTE can eliminate biases, but because of the lower operating frequency, the signals suffer greater bending on the rays. Because of the difficulty in obtaining an absolute unbiased TEC measurement, a “differential” accuracy estimate has been performed. The method relies on the fact that uninterrupted GPS data along a particular receiver-satellite link with no cycle slips are very precise. The phase difference (scaled to TEC units) from one epoch to the next can be determined with an accuracy of less than 0.01 TEC units. This fact can be utilized to estimate the uncertainty in the US-TEC vertical and slant path maps. By integrating through US-TEC inversion maps at two different times, the difference in the slant TEC can be compared with the direct phase difference in the original RINEX data file for nine receivers not used in the US-TEC calculations. The results of this study, for the period of April–September 2004, showed an average root mean square error of 2.4 TEC units, which is equivalent to less than 40 cm of signal delay at the GPS L1 frequency. The accuracy estimates from this “differential” method are similar to the results from a companion paper utilizing an “absolute” validation method by comparing with FORTE data.