Annakaisa von Lerber

Observed relations between snowfall microphysics and triple-frequency radar measurements

Recently published studies of triple-frequency radar observations of snowfall have demonstrated that naturally occurring snowflakes exhibit scattering signatures that are in some cases consistent with spheroidal particle models and in others can only be explained by complex aggregates. Until recently, no in situ observations have been available to investigate links between microphysical snowfall properties and their scattering properties. In this study, we investigate for the first time relations between collocated ground-based triple-frequency observations with in situ measurements of snowfall at the ground. The three analyzed snowfall cases obtained during a recent field campaign in Finland cover light to moderate snowfall rates with transitions from heavily rimed snow to open-structured, low-density snowflakes. The observed triple-frequency signatures agree well with the previously published findings from airborne radar observations. A rich spatiotemporal structure of triple-frequency observations throughout the cloud is observed during the three cases, which often seems to be related to riming and aggregation zones within the cloud. The comparison of triple-frequency signatures from the lowest altitudes with the ground-based in situ measurements reveals that in the presence of large (>5 mm) snow aggregates, a bending away in the triple-frequency space from the curve of classical spheroid scattering models is always observed. Rimed particles appear along an almost horizontal line in the triple-frequency space, which was not observed before. Overall, the three case studies indicate a close connection of triple-frequency signatures and snow particle structure, bulk snowfall density, and characteristic size of the particle size distribution.

BAECC: A Field Campaign to Elucidate the Impact of Biogenic Aerosols on Clouds and Climate

AbstractDuring 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 Hyytiala, 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. Hyytiala 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 extensi...

Microphysical Properties of Snow and Their Link to Ze–S Relations during BAECC 2014

AbstractThis 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 am and exponent bm react to changes in prevailing microphysical processes. From the derived microphysical properties, event-specific relations between the equivalent reflectivity factor Ze and snowfall precipitation rate S (Ze = ) are determined. For the studied events, the prefactor o...

Modeling Radar Attenuation by a Low Melting Layer With Optimized Model Parameters at C-Band

At northern latitudes, it is not uncommon for a melting layer of precipitation to touch or be close to the ground. For a low elevation angle, radio waves from a surveillance weather radar scan can travel a long distance through a melting layer. The resulting attenuation can be significant and must be taken into account when radar observations are interpreted. In this paper, we use a melting layer model to derive the relations between the specific attenuation caused by propagation through a melting layer and the reflectivity factor associated with this layer. The relations derived in this paper are based on modeled attenuation values for a variety of conditions and input parameters, i.e., rain rate, snow density, and rain drop size distribution parameters. The model parameters were constrained by vertically pointing Doppler C-band radar measurements of two events. The fitting procedure is presented for two different cases, of unrimed and rimed snow, and case-specific estimates of the expected attenuation of the horizontal scanning are suggested. Based on measurements of precipitation collected on December 10, 2011, by the University of Helsinki Kumpula radar, we also demonstrate that radar signal attenuation due to propagation through a low melting layer can be on the order of 7 dB or higher over a distance of 40 km.

Quantifying the effect of riming on snowfall using ground‐based observations

Ground based observations of ice particle size distribution and ensemble mean density are used to quantify the effect of riming on snowfall. The rime mass fraction is derived from these measurements by following the approach that is used in a single ice-phase category microphysical scheme proposed for the use in numerical weather prediction models. One of the characteristics of the proposed scheme is that the prefactor of a power-law relation that links mass and size of ice particles is determined by the rime mass fraction, while the exponent does not change. To derive the rime mass fraction a mass-dimensional relation representative of unrimed snow is also determined. To check the validity of the proposed retrieval method, the derived rime mass fraction is converted to the effective liquid water path that is compared to microwave radiometer observations. Since dual-polarization radar observations are often used to detect riming, the impact of riming on dual-polarization radar variables is studied for differential reflectivity measurements. It is shown that the relation between rime mass fraction and differential reflectivity is ambiguous, other factors such as change in median volume diameter need also be considered. Given the current interest on sensitivity of precipitation to aerosol pollution, which could inhibit riming, the importance of riming for surface snow accumulation is investigated. It is found that riming is responsible for 5% to 40% of snowfall mass. The study is based on data collected at the University of Helsinki field station in Hyytiala during US DOE Biogenic Aerosols Effects on Clouds and Climate (BAECC) field campaign and the winter 2014/2015. In total 22 winter storms were analyzed and detailed analysis of two events is presented to illustrate the study.

Multifrequency microwave radiometer measurements of snow on lake ice

Airborne microwave radiometer measurements of lake ice have been performed in 2004, 2007, 2011, and 2012 over two lakes in southern Finland using radiometer systems that cover frequencies from 1.4 to 36.5 GHz. Airborne and surface data have been collected under circumstances ranging from early winter dry snow to late winter dry and wet snow conditions. Water and slush on top of ice and dry snow grain size have been determined to be the two most important parameters affecting brightness temperature.

How Does Riming Affect Dual‐Polarization Radar Observations and Snowflake Shape?

As an ice particle grows by riming its shape is expected to change, resulting in a more spherical particle at the later stages of riming. This conceptual model is at the core of the current ice microphysical schemes and used for dual-polarization radar observation based classification of hydrometeors. A quantitative relation between riming and shapes of snowflake aggregates, however, has not been established yet. This study aims to derive this relation by using surface-based precipitation and coinciding dual-polarization radar observations. The observations were collected during four winter seasons, 49 snowstorms, at University of Helsinki measurement station in Hyytiälä, Finland. Results show that relation between the differential reflectivity and reflectivity-weighted rime mass fraction is not monotonic and depends on reflectivity-weighted mean diameter. This behavior can be explained by the opposing effects of riming on dual-polarization radar observations. Riming increases particle bulk density, which leads to more pronounced dual-polarization radar signatures. As riming progresses the aspect ratio of snowflake increases slowly until the rime mass fraction value reaches a certainty value after which the aspect ratio increases more rapidly. Finally, coutilization of Ze, Zdr, and Kdp for inferring riming fraction is analyzed.

Retrieval of snowflake microphysical properties from multi-frequency radar observations

We have developed an algorithm that retrieves the size, number concentration and density of falling snow from multifrequency radar observations. This work builds on previous studies that have indicated that three-frequency radars can provide information on snow density, potentially improving the accuracy of snow parameter estimates. The algorithm is based on a Bayesian framework, using lookup tables mapping the measurement space to the state space, which allows fast and robust retrieval. In the forward model, we calculate the radar reflectivities using recently published snow scattering databases. We demonstrate the algorithm using multifrequency airborne radar observations from the OLYMPEX– RADEX field campaign, comparing the retrieval results to hydrometeor identification using ground-based polarimetric radar and also to collocated in situ observations made using another aircraft. Using these data, we examine how the availability of multiple frequencies affects the retrieval accuracy, and we test the sensitivity of the algorithm to the prior assumptions. The results suggest that multifrequency radars are substantially better than single-frequency radars at retrieving snow microphysical properties. Meanwhile, triplefrequency radars can retrieve wider ranges of snow density than dual-frequency radars and better locate regions of highdensity snow such as graupel, although these benefits are relatively modest compared to the difference in retrieval performance between dualand single-frequency radars. We also examine the sensitivity of the retrieval results to the fixed a priori assumptions in the algorithm, showing that the multifrequency method can reliably retrieve snowflake size, while the retrieved number concentration and density are affected significantly by the assumptions.

Validation of GMI snowfall observations by using a combination of weather radar and surface measurements

AbstractCurrently, 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 Hyytiala, 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 snowfa...