Abstract
Snow plays a crucial role in the hydrological cycle and energy budget of the Earth, and remote sensing instruments with the necessary spatial coverage, resolution, and temporal sampling are essential for snowfall monitoring. Among such instruments, ground-radars have scanning capability and a resolution that make it possible to obtain a 3D structure of precipitating systems or vertical profiles when used in profiling mode. Radars from space have a lower spatial resolution, but they provide a global view. However, radar-based quantitative estimates of solid precipitation are still a challenge due to the variability of the microphysical, geometrical, and electrical features of snow particles. Estimations of snowfall rate are usually accomplished using empirical, long-term relationships between the equivalent radar reflectivity factor (Ze) and the liquid-equivalent snowfall rate (SR). Nevertheless, very few relationships take advantage of the direct estimation of the microphysical characteristics of snowflakes. In this work, we used a K-band vertically pointing radar collocated with a laser disdrometer to develop Ze-SR relationships as a function of snow classification. The two instruments were located at the Italian Antarctic Station Mario Zucchelli. The K-band radar probes the low-level atmospheric layers, recording power spectra at 32 vertical range gates. It was set at a high vertical resolution (35 m), with the first trusted range gate at a height of only 100 m. The disdrometer was able to provide information on the particle size distribution just below the trusted radar gate. Snow particles were classified into six categories (aggregate, dendrite aggregate, plate aggregate, pristine, dendrite pristine, plate pristine). The method was applied to the snowfall events of the Antarctic summer seasons of 2018–2019 and 2019–2020, with a total of 23,566 min of precipitation, 15.3% of which was recognized as showing aggregate features, 33.3% dendrite aggregate, 7.3% plates aggregate, 12.5% pristine, 24% dendrite pristine, and 7.6% plate pristine. Applying the appropriate Ze-SR relationship in each snow category, we calculated a total of 87 mm water equivalent, differing from the total found by applying a unique Ze-SR. Our estimates were also benchmarked against a colocated Alter-shielded weighing gauge, resulting in a difference of 3% in the analyzed periods.
Highlights
Snow plays a crucial role in climate and weather, influencing the hydrological cycle and the energy budget of the Earth system [1,2,3]
Snowfall rate measurements performed by in-situ ground-based instruments, usually taken as reference for remote sensing estimates, are prone to the wind-induced under-catch caused by the limited mass and low falling velocity of ice hydrometeors compared to the liquid ones [7], and are affected by blowing snow effect [12,13]
The x-axis reports the snowfall rates derived from wind-corrected particle size distribution (PSD), m(D), and v(D) relationships for the different categories of particles as a function of the Results show different values of a and b for the categories of aggregates and those of pristine particles (Figure 6, upper and lower row, respectively), whereas the variability is less marked within the two groups of categories
Summary
Snow plays a crucial role in climate and weather, influencing the hydrological cycle and the energy budget of the Earth system [1,2,3]. Snowfall is a reservoir of fresh water and sensibly affects human activities impacting infrastructures, commerce, energy, and the environment [4,5,6], prompting for continuous improvements of techniques for measurements and /forecasting of snowfall events, in which operational and scientific communities are deeply committed. In this framework, the use of remote sensing instruments for snowfall observations is essential since it ensures the necessary spatial coverage and temporal sampling for monitoring purposes. Snowfall rate measurements performed by in-situ ground-based instruments, usually taken as reference for remote sensing estimates, are prone to the wind-induced under-catch caused by the limited mass and low falling velocity of ice hydrometeors compared to the liquid ones [7], and are affected by blowing snow effect [12,13]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
