No abstract

Abstract-This paper tests the hypothesis that a "universal," data-driven model can be developed based on glucose data from one diabetic subject, and subsequently applied to predict subcutaneous glucose concentrations of other subjects, even of those with different types of diabetes. We employed three separate studies, each utilizing a different continuous glucose monitoring (CGM) device, to verify the model's universality. Two out of the three studies involved subjects with type 1 diabetes and the other one with type 2 diabetes. We first filtered the subcutaneous glucose concentration data by imposing constraints on their rate of change. Then, using the filtered data, we developed data-driven autoregressive models of order 30, and used them to make short-term, 30-min-ahead glucose-concentration predictions. We used same-subject model predictions as a reference for comparisons against cross-subject and cross-study model predictions, which were evaluated using the root-mean-squared error (RMSE) and Clarke error grid analysis (EGA). We found that, for each studied subject, the average crosssubject and cross-study RMSEs of the predictions were small and indistinguishable from those obtained with the same-subject models. These observations were corroborated by EGA, where better than 99.0% of the paired sensor-predicted glucose concentrations lay in the clinically acceptable zone A. In addition, the predictive capability of the models was found not to be affected by diabetes type, subject age, CGM device, and interindividual differences. We conclude that it is feasible to develop universal glucose models that allow for clinical use of predictive algorithms and CGM devices for proactive therapy of diabetic patients.

This study demonstrates that linear depolarization ratio (LDR) values obtained from zenith-pointing Ka-band radar Doppler velocity spectra are sufficient for detecting columnar ice crystals. During a deep precipitating system over the Arctic on 7 December 2013, the radar recorded LDR values up to −15 dB at temperatures corresponding to the columnar ice crystal growth regime. These LDR values were also consistent with scattering calculations for columnar ice crystals. Enhancements in LDR were suppressed within precipitation fallstreaks because the enhanced LDR values of columnar ice crystals were masked by the returns from the particles within the fallstreaks. However, Doppler velocity spectra of LDR within the fallstreak distinguished populations of slower-falling particles with high LDR (>−15 dB) and faster-falling particles with much lower LDR, suggesting that columnar ice crystals with high LDR coexisted with larger isometric particles that produced low LDR while dominating the total copolar reflectivity, thereby decreasing LDR. The measurements suggest that the columnar ice crystals originated in liquid-cloud layers through secondary ice production.

X-band polarimetric radar observations of winter storms in northeastern Colorado on 20–21 February, 9 March, and 9 April 2013 are examined. These observations were taken by the Colorado State University–University of Chicago–Illinois State Water Survey (CSU-CHILL) radar during the Front Range Orographic Storms (FROST) project. The polarimetric radar moments of reflectivity factor at horizontal polarization ZH, differential reflectivity ZDR, and specific differential phase KDP exhibited a range of signatures at different times near the −15°C temperature level favored for dendritic ice crystal growth. In general, KDP was enhanced in these regions with ZDR decreasing and ZH increasing toward the ground, suggestive of aggregation (or riming). The largest ZDR values (~3.5–5.5 dB) were observed during periods of significant low-level upslope flow. Convective features observed when the upslope flow was weaker had the highest KDP (>1.5° km−1) and ZH (>20 dBZ) values. Electromagnetic scattering calculations using the generalized multiparticle Mie method were used to determine whether these radar signatures were consistent with dendrites. Particle size distributions (PSDs) of dendrites were retrieved for a variety of cases using these scattering calculations and the radar observations. The PSDs derived using stratiform precipitation observations were found to be reasonably consistent with previous PSD observations. PSDs derived where riming may have occurred likely had errors and deviated significantly from these previous PSD observations. These results suggest that this polarimetric radar signature may therefore be useful in identifying regions of rapidly collecting dendrites, after considering the effects of riming on the radar variables.