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D. D. Turner, S. Kneifel, and M. P. Cadeddu

Abstract

An improved liquid water absorption model is developed for frequencies between 0.5 and 500 GHz. The empirical coefficients for this model were retrieved from a dataset that consists of both laboratory observations of the permittivity of liquid water (primarily at temperatures above 0°C) and field observations collected by microwave radiometers in three separate locations with observations at temperatures as low as −32°C. An optimal estimation framework is used to retrieve the model’s coefficients. This framework shows that there is high information content in the observations for seven of the nine model coefficients, but that the uncertainties in all of the coefficients result in less than 15% uncertainty in the liquid water absorption coefficient for all temperatures between −32° and 0°C and frequencies between 23 and 225 GHz. Furthermore, this model is more consistent with both the laboratory and field observations over all frequencies and temperatures than other popular absorption models.

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U. Löhnert, S. Kneifel, A. Battaglia, M. Hagen, L. Hirsch, and S. Crewell

The Towards an Optimal estimation based Snow Characterization Algorithm (TOSCA) project addresses possible novel measurement synergies for deriving snowfall microphysical parameters from the ground by combining the unique information obtained from a suite of ground-based sensors: microwave radiometers (22–150 GHz), 24- and 36-GHz radar, lidar, and in situ optical disdrometer methods. During the winter of 2008/09, such instruments were deployed at the Environmental Research Station Schneefernerhaus (UFS; at 2650 m MSL) at the Zugspitze Mountain in Germany for deriving microphysical properties of snowfall. This contribution gives an overview of the measurements carried out and discusses the potential for the developments of synergetic retrieval algorithms for deriving snow water content within the vertical column. The identification of potentially valuable ground-based instrument synergy for the retrieval of snowfall parameters from the surface will also be of importance for the development of new space-borne observational techniques. Microwave radiometer measurements show that brightness temperature enhancements at 90 and 150 GHz are correlated with the radar-derived snow water path, which is supported by radiative transfer simulations. The synergy of these measurements toward an improved snow mass content, however, needs to be augmented by knowledge on water vapor, supercooled liquid water, particle size distribution, and shape, thus making clear the necessity of synergetic remote sensing and in situ measurements. The radiometric measurements also reveal the very frequent presence of supercooled water within snow clouds and its importance to microphysical diffusion and aggregation growth of snow crystals. Analysis of the disdrometer measurements shows a “secondary aggregation peak” around −12° to −15°C, a temperature range where the Wegener–Bergeron–Findeisen process is most effective and typically dendrite snow crystals forms dominate.

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Mark S. Kulie, Michael J. Hiley, Ralf Bennartz, Stefan Kneifel, and Simone Tanelli

Abstract

An observation-based study is presented that utilizes aircraft data from the 2003 Wakasa Bay Advanced Microwave Scanning Radiometer Precipitation Validation Campaign to assess recent advances in the modeling of microwave scattering properties of nonspherical ice particles in the atmosphere. Previous work has suggested that a triple-frequency (Ku–Ka–W band) reflectivity framework appears capable of identifying key microphysical properties of snow, potentially providing much-needed constraints on significant sources of uncertainty in current snowfall retrieval algorithms used for microwave remote sensing instruments. However, these results were based solely on a modeling framework. In contrast, this study considers the triple-frequency approach from an observational perspective using airborne radar observations from the Wakasa Bay field campaign. After accounting for several challenges with the observational dataset, such as beam mismatching and attenuation, observed dual-wavelength ratio results are presented that confirm both the utility of a multifrequency approach to snowfall retrieval and the validity of the unique signatures predicted by complex aggregate ice particle scattering models. This analysis provides valuable insight into the microphysics of frozen precipitation that can in turn be applied to more readily available single- and dual-frequency systems, providing guidance for future precipitation retrieval algorithms.

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Stefan Kneifel, José Dias Neto, Davide Ori, Dmitri Moisseev, Jani Tyynelä, Ian S. Adams, Kwo-Sen Kuo, Ralf Bennartz, Alexis Berne, Eugene E. Clothiaux, Patrick Eriksson, Alan J. Geer, Ryan Honeyager, Jussi Leinonen, and Christopher D. Westbrook
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Mark S. Kulie, Claire Pettersen, Aronne J. Merrelli, Timothy J. Wagner, Norman B. Wood, Michael Dutter, David Beachler, Todd Kluber, Robin Turner, Marian Mateling, John Lenters, Peter Blanken, Maximilian Maahn, Christopher Spence, Stefan Kneifel, Paul A. Kucera, Ali Tokay, Larry F. Bliven, David B. Wolff, and Walter A. Petersen

Abstract

A multisensor snowfall observational suite has been deployed at the Marquette, Michigan, National Weather Service Weather Forecast Office (KMQT) since 2014. Micro Rain Radar (MRR; profiling radar), Precipitation Imaging Package (PIP; snow particle imager), and ancillary ground-based meteorological observations illustrate the unique capabilities of these combined instruments to document radar and concomitant microphysical properties associated with northern Great Lakes snowfall regimes. Lake-effect, lake-orographic, and transition event case studies are presented that illustrate the variety of snowfall events that occur at KMQT. Case studies and multiyear analyses reveal the ubiquity of snowfall produced by shallow events. These shallow snowfall features and their distinctive microphysical fingerprints are often difficult to discern with conventional remote sensing instruments, thus highlighting the scientific and potential operational value of MRR and PIP observations. The importance of near-surface lake-orographic snowfall enhancement processes in extreme snowfall events and regime-dependent snow particle microphysical variability controlled by regime and environmental factors are also highlighted.

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