Influence of Mesonet Observations on the Accuracy of Surface Analyses Generated by an Ensemble Kalman Filter

The expansion of surface mesoscale networks (mesonets) across the United States provides a high-resolution observational dataset for meteorological analysis and prediction. To clarify the impact of mesonet data on the accuracy of surface analyses, 2-m temperature, 2-m dewpoint, and 10-m wind analyses for 2-week periods during the warm and cold seasons produced through an ensemble Kalman filter (EnKF) approach are compared to surface analyses created by the Real-Time Mesoscale Analysis (RTMA). Results show in general a similarity between the EnKF analyses and the RTMA, with the EnKF exhibiting a smoother appearance with less small-scale variability. Root-mean-square (RMS) innovations are generally lower for temperature and dewpoint from the RTMA, implying a closer fit to the observations. Kinetic energy spectra computed from the two analyses reveal that the EnKF analysis spectra match more closely to the spectra computed from observations and numerical models in earlier studies. Data-denial experiments using the EnKF completed for the first week of the warm and cold seasons, as well as for two periods characterized by high mesoscale variability within the experimental domain, show that mesonet data removal imparts only minimal degradation to the analyses. This is because of the localized background covariances computed for the four surface variables having spatial scales much larger than the average spacing of mesonet stations. Results show that removing 75% of the mesonet observations has only minimal influence on the analysis.