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Sybille Y. Schoger, Dmitri Moisseev, Annakaisa von Lerber, Susanne Crewell, and Kerstin Ebell

Abstract

Two power-law relations linking equivalent radar reflectivity factor Z e and snowfall rate S are derived for a K-band Micro Rain Radar (MRR) and for a W-band cloud radar. For the development of these Z e –S relationships, a dataset of calculated and measured variables is used. Surface-based video-disdrometer measurements were collected during snowfall events over five winters at the high-latitude site in Hyytiälä, Finland. The data from 2014 to 2018 include particle size distributions (PSD) and their fall velocities, from which snowflake masses were derived. The K- and W-band Z e values are computed using these surface-based observations and snowflake scattering properties as provided by T-matrix and single-particle scattering tables, respectively. The uncertainty analysis shows that the K-band snowfall-rate estimation is significantly improved by including the intercept parameter N 0 of the PSD calculated from concurrent disdrometer measurements. If N 0 is used to adjust the prefactor of the Z e –S relationship, the RMSE of the snowfall-rate estimate can be reduced from 0.37 to around 0.11 mm h−1. For W-band radar, a Z e –S relationship with constant parameters for all available snow events shows a similar uncertainty when compared with the method that includes the PSD intercept parameter. To demonstrate the performance of the proposed Z e –S relationships, they are applied to measurements of the MRR and the W-band microwave radar for Arctic clouds at the Arctic research base operated by the German Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI) and the French Polar Institute Paul Emile Victor (IPEV) (AWIPEV) in Ny-Ålesund, Svalbard, Norway. The resulting snowfall-rate estimates show good agreement with in situ snowfall observations while other Z e –S relationships from literature reveal larger differences.

Open access
Annakaisa von Lerber, Dmitri Moisseev, Larry F. Bliven, Walter Petersen, Ari-Matti Harri, and V. Chandrasekar

Abstract

This study uses snow events from the Biogenic Aerosols–Effects on Clouds and Climate (BAECC) 2014 campaign to investigate the connection between properties of snow and radar observations. The general hydrodynamic theory is applied to video-disdrometer measurements to retrieve masses of falling ice particles. Errors associated with the observation geometry and the measured particle size distribution (PSD) are addressed by devising a simple correction procedure. The value of the correction factor is determined by comparison of the retrieved precipitation accumulation with weighing-gauge measurements. Derived mass–dimensional relations are represented in the power-law form m = . It is shown that the retrieved prefactor a m and exponent b m react to changes in prevailing microphysical processes. From the derived microphysical properties, event-specific relations between the equivalent reflectivity factor Z e and snowfall precipitation rate S (Z e = ) are determined. For the studied events, the prefactor of the Z eS relation varied between 53 and 782 and the exponent was in the range of 1.19–1.61. The dependence of the factors a zs and b zs on the m(D) relation and PSD are investigated. The exponent of the Z eS relation mainly depends on the exponent of the m(D) relation, whereas the prefactor a zs depends on both the intercept parameter N 0 of the PSD and the prefactors of the m(D) and υ(D) relations. Changes in a zs for a given N 0 are shown to be linked to changes in liquid water path, which can be considered to be a proxy for degree of riming.

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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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Annakaisa von Lerber, Dmitri Moisseev, David A. Marks, Walter Petersen, Ari-Matti Harri, and V. Chandrasekar

Abstract

Currently, there are several spaceborne microwave instruments suitable for the detection and quantitative estimation of snowfall. To test and improve retrieval snowfall algorithms, ground validation datasets that combine detailed characterization of snowfall microphysics and spatial precipitation measurements are required. To this endpoint, measurements of snow microphysics are combined with large-scale weather radar observations to generate such a dataset. The quantitative snowfall estimates are computed by applying event-specific relations between the equivalent reflectivity factor and snowfall rate to weather radar observations. The relations are derived using retrieved ice particle microphysical properties from observations that were carried out at the University of Helsinki research station in Hyytiälä, Finland, which is about 64 km east of the radar. For each event, the uncertainties of the estimate are also determined. The feasibility of using this type of data to validate spaceborne snowfall measurements and algorithms is demonstrated with the NASA GPM Microwave Imager (GMI) snowfall product. The detection skill and retrieved surface snowfall precipitation of the GPROF detection algorithm, versions V04A and V05A, are assessed over southern Finland. On the basis of the 26 studied overpasses, probability of detection (POD) is 0.90 for version V04A and 0.84 for version V05A, and corresponding false-alarm rates are 0.09 and 0.10, respectively. A clear dependence of detection skill on cloud echo top height is shown: POD increased from 0.8 to 0.99 (V04A) and from 0.61 to 0.94 (V05A) as the cloud echo top altitude increased from 2 to 5 km. Both versions underestimate the snowfall rate by factors of 6 (V04A) and 3 (V05A).

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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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