Proceedings with Extended Abstracts (single PDF file) - Radio ...

The resulting moments are subjected to a continuity test to identify potential bad values.Confidence is estimated for each moment based on SNR, Gaussian fit of the spectral pointsused for the moments, the results of the continuity test, and proximity to contaminants.NIMA Tuning Case Study – January 7, 2003An example of tuning NIMA for a challenging site is given. The 915 MHz 3-beam profilerwith RASS is located in Dugway, Utah where the climate is very dry, resulting in windprofiler spectra that are characterized by very low SNR. RFI and occasional point targetsoften contaminate the spectra. Examples of typical spectra are shown in Figure 4.Figure 4. Examples of typical spectra from the Dugway profiler. Note the low signal to noise at mostrange gates and the RFI-like contamination symmetrically located at roughly ¾ the Nyquist velocity.The 30-minute consensus winds calculated from the POP moments using a constant windmodel are shown in Figure 5. Only at the low ranges are the resulting winds consistent andreasonable. The winds calculated from NIMA moments generated using the default NIMAconfiguration were also unsatisfactory. These included some large wind speeds that changeddirection by 180 degrees between consensus intervals indicating contamination by theapparent symmetric RFI signals at large Doppler velocities. The tuning effort thus focusedon detecting the atmospheric signal in the low SNR regions, identifying the weak RFI signals,and improving the preference for the atmospheric signal over the RFI signals.The Doppler peak detection was tuned to increase the reliance on the curvature fields as theseappeared to have higher skill than the some of the spectral intensity fields and the curvaturemembership maps were modified slightly as well. The overall threshold for Doppler peakswas lowered to allow the detection of the very weak signals.For RFI detection, the site-specific field was utilized to help detect the RFI signals thatappeared consistently in fixed locations. The RFI feature membership maps were modified toallow more tolerance in feature width, width variance, midpoint variance, and intensityvariance as the RFI signals were also contaminated with noise.The membership maps for atmospheric feature detection were also modified to account forthe contamination by noise. In particular, the feature SNR and intensity variance mapsrequired tuning. The overall threshold for categorizing a feature as atmospheric was loweredand both the Doppler peak score and feature SNR were de-emphasized while the overlap withRFI was given increased weight to help eliminate the RFI contamination.401