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Dmitri N. Moisseev and V. Chandrasekar

Abstract

This paper presents a method to retrieve raindrop size distributions (DSD) from slant profile dual-polarization Doppler spectra observations. It is shown that using radar measurements taken at a high elevation angle raindrop size distributions can be retrieved without making an assumption on the form of a DSD. In this paper it is shown that drop size distributions can be retrieved from Doppler power spectra by compensating for the effect of spectrum broadening and mean velocity shift. To accomplish that, spectrum deconvolution is used where the spectral broadening kernel width and wind velocity are estimated from spectral differential reflectivity measurements. Since convolution kernel is estimated from dual-polarization Doppler spectra observations and does not require observation of a clear-air signal, this method can be used by most radars capable of dual-polarization spectra measurements.

To validate the technique, sensitivity of this method to the underlying assumptions and calibration errors is evaluated on realistic simulations of radar observations. Furthermore, performance of the method is illustrated on Colorado State University–University of Chicago–Illinois State Water Survey radar (CSU–CHILL) measurements of stratiform precipitation.

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Dmitri N. Moisseev and V. Chandrasekar

Abstract

Raindrop size distributions are often assumed to follow a three-parameter gamma distribution. Since rain intensity retrieval from radar observations is an underdetermined problem, there is great interest in finding physical correlations between the parameters of the gamma distribution. One of the more common approaches is to measure naturally occurring drop size distributions (DSDs) using a disdrometer and to find DSD parameters by fitting a gamma distribution to these observations. Often the method of moments is used to retrieve the parameters of a gamma distribution from disdrometer observations.

In this work the effect of the method of moments and data filtering on the relation between the parameters of the DSD is investigated, namely, the shape μ and the slope Λ parameters. For this study the disdrometer observations were simulated. In these simulations the gamma distribution parameters Nw, D 0, and μ were randomly selected from a wide range of values that are found in rainfall. Then, using simulated disdrometer measurements, DSD parameters were estimated using the method of moments. It is shown that the statistical errors associated with data filtering of disdrometer measurements might produce a spurious relation between μ and Λ parameters. It is also shown that three independent disdrometer measurements can be used to verify the existence of such a relation.

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Dmitri N. Moisseev and V. Chandrasekar

Abstract

In this paper, spectral decompositions of differential reflectivity, differential phase, and copolar correlation coefficient are used to discriminate between weather and nonweather signals in the spectral domain. This approach gives a greater flexibility for discrimination between different types of scattering sources present in a radar observation volume. A spectral filter, which removes nonweather signals, is defined based on this method. The performance of this filter is demonstrated on the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) observations. It is shown that the resulting filter parameters are adaptively defined for each range sample and do not require an assumption on spectral properties of ground clutter.

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Cuong M. Nguyen, Dmitri N. Moisseev, and V. Chandrasekar

Abstract

A parametric time domain method (PTDM) for clutter mitigation and precipitation spectral moments’ estimation for weather radars is introduced. Use of PTDM allows for the simultaneous estimation of clutter and precipitation echo spectral moments. It is shown that this approach leads to accurate estimates of precipitation spectral moments in the presence of clutter. Based on simulations, the PTDM performance is evaluated and compared against the clutter spectral filtering technique. In this study special attention is paid to the cases of strong clutter contamination. Furthermore, both methods, the PTDM and spectral clutter filter, are illustrated using the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) observations.

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Dmitri N. Moisseev, Cuong M. Nguyen, and V. Chandrasekar

Abstract

This paper presents a clutter suppression methodology for staggered pulse repetition time (PRT) observations. It is shown that spectral moments of precipitation echoes can be accurately estimated even in cases where clutter-to-signal ratios are high by using a parametric time domain method (PTDM).

Based on radar signal simulations, the accuracy of the proposed method is evaluated for various observation conditions. The performance of PTDM is demonstrated by the implementation of the staggered PRT at the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL). Based on this study, it is found that the accuracy of the retrieval is comparable to the current state of the art methods applied to the uniformly sampled observations and that the estimated velocity is unbiased for the complete Nyquist range.

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Dmitri N. Moisseev, Christine M. H. Unal, Herman W. J. Russchenberg, and Leo P. Ligthart

Abstract

Polarization properties of radar waves that are scattered from atmospheric objects are of great interest in meteorological studies. However, polarimetric radar measurements are often not sufficiently accurate for retrieving physical properties of targets. To compensate for errors, radar polarimetric calibration is applied. Typical calibrations are performed based on measurements of point targets with known scattering matrices located in the boresight of the antenna. Such calibration takes into account the polarization state of the antenna pattern only at one point. Since radar measurements of atmospheric phenomena involve distributed targets that fill the full antenna beam, point target radar calibrations are inadequate for meteorological studies.

This paper explains in detail the effects of the complete antenna patterns on weather echoes. It is shown that the conventional polarimetric calibration can be significantly improved by incorporating light-rain (<20 dBZ) zenith-pointing measurements into the calibration procedure. As a result, the sensitivity of cross-polar measurements can be improved by 7 dB on average. Also it is shown that the bias in co-cross-polar correlation coefficient can be reduced.

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Ali Tokay, Annakaisa von Lerber, Claire Pettersen, Mark S. Kulie, Dmitri N. Moisseev, and David B. Wolff

Abstract

Performance of the Precipitation Imaging Package (PIP) for estimating the snow water equivalent (SWE) is evaluated through a comparative study with the collocated National Oceanic and Atmospheric Administration National Weather Service snow stake field measurements. The PIP together with a vertically pointing radar, a weighing bucket gauge, and a laser-optical disdrometer was deployed at the NWS Marquette, Michigan office building for a long-term field study supported by the National Aeronautics and Space Administration’s Global Precipitation Measurement mission Ground Validation program. The site was also equipped with a weather station. During the 2017-18 winter, the PIP functioned nearly uninterrupted at frigid temperatures accumulating 2345.8 mm of geometric snow depth over a total of 499 hours. This long record consists of 30 events, and the PIP-retrieved and snow stake field measured SWE differed less than 15% in every event. Two of the major events with the longest duration and the highest accumulation are examined in detail. The particle mass with a given diameter was much lower during a shallow, colder, uniform lake-effect event than in the deep, less cold, and variable synoptic event. This study demonstrated that the PIP is a robust instrument for operational use, and is reliable for deriving the bulk properties of falling snow.

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Tuukka Petäjä, Ewan J. O’Connor, Dmitri Moisseev, Victoria A. Sinclair, Antti J. Manninen, Riikka Väänänen, Annakaisa von Lerber, Joel A. Thornton, Keri Nicoll, Walt Petersen, V. Chandrasekar, James N. Smith, Paul M. Winkler, Olaf Krüger, Hannele Hakola, Hilkka Timonen, David Brus, Tuomas Laurila, Eija Asmi, Marja-Liisa Riekkola, Lucia Mona, Paola Massoli, Ronny Engelmann, Mika Komppula, Jian Wang, Chongai Kuang, Jaana Bäck, Annele Virtanen, Janne Levula, Michael Ritsche, and Nicki Hickmon

Abstract

During Biogenic Aerosols—Effects on Clouds and Climate (BAECC), the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Program deployed the Second ARM Mobile Facility (AMF2) to Hyytiälä, Finland, for an 8-month intensive measurement campaign from February to September 2014. The primary research goal is to understand the role of biogenic aerosols in cloud formation. Hyytiälä is host to the Station for Measuring Ecosystem–Atmosphere Relations II (SMEAR II), one of the world’s most comprehensive surface in situ observation sites in a boreal forest environment. The station has been measuring atmospheric aerosols, biogenic emissions, and an extensive suite of parameters relevant to atmosphere–biosphere interactions continuously since 1996. Combining vertical profiles from AMF2 with surface-based in situ SMEAR II observations allows the processes at the surface to be directly related to processes occurring throughout the entire tropospheric column. Together with the inclusion of extensive surface precipitation measurements and intensive observation periods involving aircraft flights and novel radiosonde launches, the complementary observations provide a unique opportunity for investigating aerosol–cloud interactions and cloud-to-precipitation processes in a boreal environment. The BAECC dataset provides opportunities for evaluating and improving models of aerosol sources and transport, cloud microphysical processes, and boundary layer structures. In addition, numerical models are being used to bridge the gap between surface-based and tropospheric observations.

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