Snowfall-Rate Retrieval for K- and W-Band Radar Measurements Designed in Hyytiälä, Finland, and Tested at Ny-Ålesund, Svalbard, Norway

Abstract

Two power law relations linking equivalent radar reflectivity factor (Ze) and snowfall rate (S) are derived for a Micro Rain Radar (MRR), which operates at K-band, and a W-band cloud radar. For the development of these Ze-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 2014-2018 includes particle size distributions (PSD) and their fall velocities, from which snowflake masses were derived. K- and W-band Ze 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 N0 of the PSD calculated from concurrent disdrometer measurements. If N0 is used to adjust the prefactor of the Ze-S relationship, the RMSE of the snowfall rate estimate can be reduced from 0.37 to around 0.11 mmh−1. For W-band, a Ze-S relationship with constant parameters for all available snow events shows a similar uncertainty compared to the method that includes the PSD intercept parameter. To demonstrate the performance of the proposed Ze-S relationships, they are applied to measurements of the MRR and the W-band Microwave Radar for Arctic Clouds at the AWIPEV Arctic research base in Ny-Ålesund, Svalbard. The resulting snowfall rate estimates show a good agreement to in situ snowfall observations while other Ze-S relationships from literature reveal larger differences.

Quantitative Precipitation Estimation over Antarctica Using Different Ze-SR Relationships Based on Snowfall Classification Combining Ground Observations

“…Schoger at al. [89] faced the large spread in the QPE using different Ze-SR relationships from the literature, similarly to what shown in Table 3. Applying Ze-SR relationships suited for different sites, including Antarctica, to the MRR data from Ny-Ålesund (Svalbard, Norway), they observed significant differences in snowfall rate and snowfall accumulation with respect to the reference measurements of a weighing pluviometer, ascribed to the different microphysical properties of the snow.…”

DeepPrecip: a deep neural network for precipitation retrievals

“…size, shape, density, and fall speed) (Matrosov et al, 2008). While these techniques have been used to great success in previous studies from Schoger et al (2021) and Levizzani et al (2011), the assumptions about snowfall and rainfall particle microphysics makes the generalization of these power laws less robust, which contributes to high uncertainty when applied across large areas with unique regional climates (Jameson and Kostinski, 2002).…”

“…Remotely sensed radar observations used in empirical, power law relationships can relate radar reflectivity (RFL) estimates (Z e ) to surface snowfall (S) or rainfall (R) rates (Eq. 1) (Matrosov et al, 2008;Kulie and Bennartz, 2009;Schoger et al, 2021):…”

Evaluating seasonal and regional distribution of snowfall in regional climate model simulations in the Arctic

“…Palerme et al (2017) and Edel et al (2020) compared snowfall climatologies from CloudSat radar observations (Stephens et al, 2008) to reanalyses for Antarctica and the Arctic. In situ instruments such as gauges and disdrometers are sparse in the Arctic area, suffer from the biases introduced by blowing and drifting snow and show generally an underestimation of snowfall under windy conditions (Goodison et al, 1998;Wolff et al, 2012;Rasmussen et al, 2012). Even more sparse are sites with extensive ground-based remote sensing instrumentation such as cloud radars and radiometers, which provide anchor points for process understanding and validation (e.g., Castellani et al, 2015;Verlinde et al, 2016;Maturilli et al, 2013;Pettersen et al, 2018;Nomokonova et al, 2019;Gierens et al, 2020;Schoger et al, 2021).…”