Understanding Radar Refractivity: Sources of Uncertainty

Abstract

This study presents a 2-yr-long comparison of Weather Surveillance Radar-1988 Doppler (WSR-88D) refractivity retrievals with Oklahoma Mesonetwork (''Mesonet'') and sounding measurements and discusses some challenges to implementing radar refractivity operationally. Temporal and spatial analyses of radar refractivity exhibit high correlation with Mesonet data; however, periods of large refractivity differences between the radar and Mesonet are observed. Several sources of refractivity differences are examined to determine the cause of large refractivity differences. One source for nonklystron radars includes magnetron frequency drift, which can introduce errors up to 10 N-units if the frequency drift is not corrected. Different reference maps made at different times can ''shift'' refractivity values. A semiautomated method for producing reference maps is presented, including trade-offs for making reference maps under different conditions. Refractivity from six Mesonet stations within the clutter domain of the Oklahoma City, Oklahoma, WSR-88D (KTLX) is compared with radar refractivity retrievals. The analysis revealed that the six Mesonet stations exhibited a prominent diurnal trend in differences between radar and Mesonet refractivity measurements. The diurnal range of the refractivity differences sometimes exceeded 20 or 30 N-units in the warm season, which translated to a potential dewpoint temperature difference of several degrees Celsius. A seasonal analysis revealed that large refractivity differences primarily occurred during the warm season when refractivity is most sensitive to moisture. Ultimately, the main factor in determining the magnitude of the differences between the two refractivity platforms is the vertical gradient of refractivity because of the difference in observation height between the radar and a surface station.

Two-dimensional variational analysis of near-surface moisture from simulated radar refractivity-related phase change observations

“…The flow-dependent error covariance from EnKF also helps to optimally determine the vertical spread of surface moisture observation information. We also realize that in the real world, the refractivity observation can be more complicated than considered in this study, and certain aspects of error, such as those related to representativeness, can be very difficult to quantify (Bodine et al, 2011). Further research is needed in these areas, as well as demonstration of the impact of real refractivity observations for real cases.…”

“…This is due to difficulties in estimating observation error correlation and in inverting nondiagonal R matrix. In practice, most of the correlated errors (such as those related to radar beam elevation uncertainty, Bodine et al, 2011) can be decreased or removed through data thinning and/or bias correction (e.g., Harris and Kelly, 2001). For this study, the error variances, or the diagonal elements of matrix R, whose determination was described in the last paragraph of section 2, were small relative to the background error, the final analysis was therefore closer to the observations than to the background.…”

“…The experiments presented thus far did not considered typical random observational error, which was caused by radar phase-measurement error, ground target motion, radar beam height uncertainty, and changes in the vertical gradient of refractivity (Bodine et al, 2011). Such errors were properly simulated by adding sampled errors to the raw phase measurements, from which the PCD data were derived with or without phase wrapping.…”

Application of Compressive Sensing to Refractivity Retrieval Using Networked Weather Radars

“…High correlations can be generally found between refractivity derived from radar measurements and mesonet observations [7], [8]. Note that the vertical profile of refractivity can be estimated from Global Positioning System based method [9] and is complementary to radar measurements.…”

“…However, the resolution can be degraded due to the lack of high-quality ground targets in adjacent range gates. Note that the quality of ground target signals is affected by the changes in targets' shapes, ranges from the radar, variations in the height of targets, and precipitation delays, for example [7], [8]. A network of weather radars was proposed to address this issue, where phase measurements from multiple radars are represented by a linear model with gridded refractivity field [13].…”