How dual-polarization radar observations can be used to verify model representation of secondary ice

In this paper it is discussed how dual-polarization radar observations can be used to verify model representations of secondary ice production. An event where enhanced specific differential phase, K-dp, signatures in snow occur at the altitudes where temperatures lie in the range between -8 and -3 degrees C is investigated. By combining radar and surface-based precipitation observations it is shown that these dual-polarization radar signatures are most likely caused by ice with concentrations exceeding those expected from primary ice parameterizations. It is also shown that the newly formed ice particles readily aggregate, which may explain why K-dp values seem to be capped at 0.2-0.3 degrees/km for a Cband radar. For the event of interest, multiple high-resolution (1km) Weather Research and Forecasting (WRF) model simulations are conducted. When the default versions of the Morrison microphysics schemes were used, the simulated number concentration of frozen hydrometeors is much lower than observed and the simulated ice particle concentrations are comparable with values expected from primary ice parameterizations. Higher ice concentrations, which exceed values expected from primary ice parameterizations, were simulated when adhoc thresholds for rain and cloud water mixing ratio in the Hallett-Mossop part of the Morrison scheme were removed. These results suggest that the parameterization of secondary ice production in operational weather prediction models needs to be revisited and that dual-polarization radar observations, in conjunction with ancillary observations, can be used to verify them.Peer reviewe

Snowfall retrieval at X, Ka and W bands : consistency of backscattering and microphysical properties using BAECC ground-based measurements

Radar-based snowfall intensity retrieval is investigated at centimeter and millimeter wavelengths using co-located ground-based multi-frequency radar and video-disdrometer observations. Using data from four snowfall events, recorded during the Biogenic Aerosols Effects on Clouds and Climate (BAECC) campaign in Finland, measurements of liquid-water-equivalent snowfall rate S are correlated to radar equivalent reflectivity factors Z(e), measured by the Atmospheric Radiation Measurement (ARM) cloud radars operating at X, Ka and W frequency bands. From these combined observations, power-law Z(e)-S relationships are derived for all three frequencies considering the influence of riming Using microwave radiometer observations of liquid water path, the measured precipitation is divided into lightly, moderately and heavily rimed snow. Interestingly lightly rimed snow events show a spectrally distinct signature of Z(e)-S with respect to moderately or heavily rimed snow cases. In order to understand the connection between snowflake microphysical and multi-frequency backscattering properties, numerical simulations are performed by using the particle size distribution provided by the in situ video disdrometer and retrieved ice particle masses. The latter are carried out by using both the T-matrix method (TMM) applied to soft-spheroid particle models with different aspect ratios and exploiting a pre-computed discrete dipole approximation (DDA) database for rimed aggregates. Based on the presented results, it is concluded that the soft-spheroid approximation can be adopted to explain the observed multifrequency Z(e)-S relations if a proper spheroid aspect ratio is selected. The latter may depend on the degree of riming in snowfall. A further analysis of the backscattering simulations reveals that TMM cross sections are higher than the DDA ones for small ice particles, but lower for larger particles. The differences of computed cross sections for larger and smaller particles are compensating for each other. This may explain why the soft-spheroid approximation is satisfactory for radar reflectivity simulations under study.Peer reviewe

Translating the Physics of Snowfall to Radar-Based Validation of GPM

No abstract availabl

The Precipitation Imaging Package : Assessment of Microphysical and Bulk Characteristics of Snow

Remote-sensing observations are needed to estimate the regional and global impacts of snow. However, to retrieve accurate estimates of snow mass and rate, these observations require augmentation through additional information and assumptions about hydrometeor properties. The Precipitation Imaging Package (PIP) provides information about precipitation characteristics and can be utilized to improve estimates of snowfall rate and accumulation. Here, the goal is to demonstrate the quality and utility of two higher-order PIP-derived products: liquid water equivalent snow rate and an approximation of volume-weighted density called equivalent density. Accuracy of the PIP snow rate and equivalent density is obtained through intercomparison with established retrieval methods and through evaluation with colocated ground-based observations. The results confirm the ability of the PIP-derived products to quantify properties of snow rate and equivalent density, and demonstrate that the PIP produces physically realistic snow characteristics. When compared to the National Weather Service (NWS) snow field measurements of six-hourly accumulation, the PIP-derived accumulations were biased only +2.48% higher. Additionally, this work illustrates fundamentally different microphysical and bulk features of low and high snow-to-liquid ratio events, through assessment of observed particle size distributions, retrieved mass coefficients, and bulk properties. Importantly, this research establishes the role that PIP observations and higher-order products can serve for constraining microphysical assumptions in ground-based and spaceborne remotely sensed snowfall retrievals.Peer reviewe

BAECC: a field campaign to elucidate the impact of Biogenic Aerosols on Clouds and Climate

Observations obtained during an 8-month deployment of AMF2 in a boreal environment in Hyytiälä, Finland, and the 20-year comprehensive in-situ data from SMEAR-II station enable the characterization of biogenic aerosol, clouds and precipitation, and their interactions. During “Biogenic Aerosols - Effects on Clouds and Climate (BAECC)”, the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Program deployed the ARM 2nd 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 SMEAR-II (Station for Measuring Forest Ecosystem-Atmosphere Relations), 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 allow 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

Atmospheric and Surface Processes, and Feedback Mechanisms Determining Arctic Amplification: A Review of First Results and Prospects of the (AC)3 Project

Mechanisms behind the phenomenon of Arctic amplification are widely discussed. To contribute to this debate, the (AC)3 project has been established in 2016. It comprises modeling and data analysis efforts as well as observational elements. The project has assembled a wealth of ground-based, airborne, ship-borne, and satellite data of physical, chemical, and meteorological properties of the Arctic atmosphere, cryosphere, and upper ocean that are available for the Arctic climate research community. Short-term changes and indications of long-term trends in Arctic climate parameters have been detected using existing and new data