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Understanding the boundary-layer height and its dynamics is crucial for a wide array of applications spanning various fields. Accurate identification of the boundary-layer top contributes to improved air quality predictions, pollutant transport assessments, and enhanced numerical weather prediction through parameterization and assimilation techniques. Despite its significance, defining and observing the boundary-layer top remain challenging. Existing methods of estimating the boundary-layer height encompass radiosonde-based methods, radar-based retrievals, and more. As emerging boundary-layer observation platforms emerge, it is useful to reevaluate the efficacy of existing boundary-layer-top detection methods and explore new ones. This study introduces a fuzzy logic algorithm that leverages the synergy of multiple remote sensing boundary-layer profiling instruments: a Doppler lidar, infrared spectrometer, and microwave radiometer. By harnessing the distinct advantages of each sensing platform, the proposed method enables accurate boundary-layer height estimation both during daytime and nocturnal conditions. The algorithm is benchmarked against radiosonde-derived boundary-layer-top estimates obtained from balloon launches across diverse locations in Wisconsin, Oklahoma, and Louisiana during summer and fall. The findings reveal notable similarities between the results produced by the proposed fuzzy logic algorithm and traditional radiosonde-based approaches. However, this study delves into the nuanced differences in their behavior, providing insightful analyses about the underlying causes of the observed discrepancies. While developed with the three instruments mentioned above, the fuzzy logic boundary-layer-top detection algorithm, called BLISS-FL, could be adapted for other wind and thermodynamic profilers. BLISS-FL is released publicly, fostering collaboration and advancement within the research community. [ABSTRACT FROM AUTHOR]

Understanding the boundary-layer height and its dynamics is crucial for a wide array of applications spanning various fields. Accurate identification of the boundary-layer top contributes to improved air quality predictions, pollutant transport assessments, and enhanced numerical weather prediction through parameterization and assimilation techniques. Despite its significance, defining and observing the boundary-layer top remains challenging. Existing methods of estimating the boundary-layer height encompass radiosonde-based methods, radar-based retrievals, and more. As emerging boundary-layer observation platforms emerge, it is useful to reevaluate the efficacy of existing boundary-layer top detection methods and explore new ones. This study introduces a fuzzy-logic algorithm that leverages the synergy of multiple remote-sensing boundary-layer profiling instruments. By harnessing the distinct advantages of each sensing platform, the proposed method enables accurate boundary-layer height estimation both during daytime and nocturnal conditions. The algorithm is benchmarked against radiosonde-derived boundary-layer top estimates obtained from balloon launches across diverse locations in Wisconsin, Oklahoma, and Louisiana during summer and fall. The findings reveal notable similarities between the results produced by the proposed fuzzy-logic algorithm and traditional radiosonde-based approaches. However, this study delves into the nuanced differences in their behavior, providing insightful analyses about the underlying causes of the observed discrepancies. The fuzzy-logic boundary-layer top detection algorithm, called BLISS-FL, is released publicly fostering collaboration and advancement within the research community. [ABSTRACT FROM AUTHOR]

Raindrop size distributions (DSD) and rain rate have been estimated from polarimetric radar data using different approaches with the accuracy depending on the errors both in the radar measurements and the estimation methods. Herein, a deep neural network (DNN) technique was utilized to improve the estimation of the DSD and rain rate by mitigating these errors. The performance of this approach was evaluated using measurements from a two-dimensional video disdrometer (2DVD) at the Kessler Atmospheric and Ecological Field Station in Oklahoma as ground truth with the results compared against conventional estimation methods for the period 2006-17. Physical parameters (mass-/volume-weighted diameter and liquid water content), rain rate, and polarimetric radar variables (including radar reflectivity and differential reflectivity) were obtained from the DSD data. Three methods--physics-based inversion, empirical formula, and DNN--were applied to two different temporal domains (instantaneous and rain-event average) with three diverse error assumptions (fitting, measurement, and model errors). The DSD retrievals and rain estimates from 18 cases were evaluated by calculating the bias and root-mean-squared error (RMSE). DNN produced the best performance for most cases, with up to a 5% reduction in RMSE when model errors existed. DSD and rain estimated from a nearby polarimetric radar using the empirical and DNN methods were well correlated with the disdrometer observations; the rain-rate estimate bias of the DNN was significantly reduced (3.3%in DNN vs 50.1%in empirical). These results suggest that DNN has advantages over the physics-based and empirical methods in retrieving rain microphysics from radar observations. [ABSTRACT FROM AUTHOR]

The intrinsic uncertainty of radar-based retrievals in snow originates from a large diversity of snow growth habits, densities, and particle size distributions, all of which can make interpreting radar measurements of snow very challenging. The application of polarimetric radar for snow measurements can mitigate some of these issues. In this study, a novel polarimetric method for quantification of the extinction coefficient and visibility in snow, based on the joint use of radar reflectivity at horizontal polarization Z and specific differential phase KDP, is introduced. A large 2D-video-disdrometer snow dataset from central Oklahoma is used to derive a polarimetric bivariate power-law relation for the extinction coefficient, σ e ⁡ (K DP , Z) = γ K DP α Z β . The relation is derived for particle aspect ratios ranging from 0.5 to 0.8 and the width of the canting angle distribution ranging from 0° to 40°, values typical of aggregated snow, and validated via theoretical and analytical derivations/simulations. The multiplier of the relation is sensitive to variations in particles' densities, the width of the canting angle distribution, and particles' aspect ratios, whereas the relation's exponents are practically invariant to changes in the latter two parameters. This novel approach is applied to polarimetric S-band WSR-88D data and verified against previous studies and in situ measurements of the extinction coefficient for four snow events in the eastern United States. The polarimetric radar estimates of the extinction coefficient exhibit smaller biases in comparison to previous studies concerning the ground measurements. The results indicate that there is good potential for reliable radar estimates of visibility from polarimetric weather radars, a parameter inversely proportional to the extinction coefficient. [ABSTRACT FROM AUTHOR]