Characterization of snowfall estimated by in situ and ground-based remote-sensing observations at Terra Nova Bay, Victoria Land, Antarctica

Scarchilli, Claudio; Ciardini, Virginia; Grigioni, Paolo; Iaccarino, Antonio; Silvestri, Lorenzo De; Proposito, Marco; Dolci, Stefano; Camporeale, Giuseppe; Schioppo, Riccardo; Antonelli, Adriano; Baldini, Luca; Roberto, Nicoletta; Argentini, Stefania; Bracci, Alessandro; Frezzotti, Màssimo

doi:10.1017/jog.2020.70

Cited by 18 publications

(31 citation statements)

References 78 publications

(143 reference statements)

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“…Parsivel is easy to handle, robust, and reliable, and such characteristics make it suitable for operating in a harsh environment like Antarctica. Laser disdrometers, historically used to retrieve the drop size distribution of rain, are now largely employed to study the size distribution of particles in solid precipitation [18,19,[48][49][50], although some intrinsic limitations are known [51]. Moreover, a significant shortcoming is due to the influence of wind on disdrometer measurements [52][53][54], and a large part of our analysis aims to address this issue.…”

Section: Parsivel Disdrometermentioning

confidence: 99%

“…(a) minutes with Ze MRR value lower than -5dBZ were discarded from the analysis since below that threshold, MRR data could be incomplete [14,56]; (b) minutes in which Parsivel detected less than 10 particles or the SR calculated for aggregate category is less than 0.01 mm h −1 were eliminated [18,56]; (c) only minutes with simultaneous valid measurements of MRR and Parsivel satisfying criteria (a) and (b) were included.…”

Section: Datasetmentioning

confidence: 99%

“…This work proposes a new approach in estimating snowfall rates based on three pillars, namely ground-based measurements from vertically pointing radar, coincident observations from disdrometer, and backscattering simulation of hydrometeors. These components are commonly used to quantitatively estimate solid precipitation from radar observations [14][15][16][17][18][19] among others. In such cases, quantitative precipitation estimation (QPE) is commonly obtained by comparing radar and ground sensors: the equivalent radar reflectivity factor (Ze) is linked to the liquid-equivalent snowfall rate (SR) by means of a power-law relationship (Ze = a × SR b ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Parsivel Disdrometermentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Hereinafter, we use the products of the hydrometeor classification from MASC images developed by Praz et al (2017). A preliminary processing step, following Schaer et al (2020), removes blowing snow particles from the data set. A pluviometer was also continuously measuring the precipitation rate during the event (Grazioli et al, 2017a;Genthon et al, 2018).…”

Section: Step 3: Data Analysis and Visualizationmentioning

confidence: 99%

Identification of snowfall microphysical processes from Eulerian vertical gradients of polarimetric radar variables

Planat

Gehring²,

Vignon

et al. 2021

Atmos. Meas. Tech.

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Abstract. Polarimetric radar systems are commonly used to study the microphysics of precipitation. While they offer continuous measurements with a large spatial coverage, retrieving information about the microphysical processes that govern the evolution of snowfall from the polarimetric signal is challenging. The present study develops a new method, called process identification based on vertical gradient signs (PIVSs), to spatially identify the occurrence of the main microphysical processes (aggregation and riming, crystal growth by vapor deposition and sublimation) in snowfall from dual-polarization Doppler radar scans. We first derive an analytical framework to assess in which meteorological conditions the local vertical gradients of radar variables reliably inform about microphysical processes. In such conditions, we then identify regions dominated by (i) vapor deposition, (ii) aggregation and riming and (iii) snowflake sublimation and possibly snowflake breakup, based on the sign of the local vertical gradients of the reflectivity ZH and the differential reflectivity ZDR. The method is then applied to data from two frontal snowfall events, namely one in coastal Adélie Land, Antarctica, and one in the Taebaek Mountains in South Korea. The validity of the method is assessed by comparing its outcome with snowflake observations, using a multi-angle snowflake camera, and with the output of a hydrometeor classification, based on polarimetric radar signal. The application of the method further makes it possible to better characterize and understand how snowfall forms, grows and decays in two different geographical and meteorological contexts. In particular, we are able to automatically derive and discuss the altitude and thickness of the layers where each process prevails for both case studies. We infer some microphysical characteristics in terms of radar variables from statistical analysis of the method output (e.g., ZH and ZDR distribution for each process). We, finally, highlight the potential for extensive application to cold precipitation events in different meteorological contexts.

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“…Ground-based remote sensing instruments make it possible to gain insights into precipitation formation and vertical evolution in remote areas at high altitude and latitude (e.g., Shupe et al, 2011;Grazioli et al, 2015;Gorodetskaya et al, 2015;Wen et al, 2016;Grazioli et al, 2017a;Scarchilli et al, 2020;Lubin et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Identification of snowfall microphysical processes from vertical gradients of polarimetric radar variables

Planat¹,

Gehring²,

Vignon³

et al. 2020

Preprint

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Abstract. Polarimetric radar systems are commonly used to study the microphysics of precipitation. While they offer continuous measurements with a large spatial coverage, retrieving information about the microphysical processes that govern the evolution of snowfall from the polarimetric signal is challenging. The present study develops a new method, called Process Identification based on Vertical gradient Signs (PIVS), to spatially identify the occurrence of the main microphysical processes (aggregation and riming, crystal growth by vapor deposition, and sublimation) in snowfall from dual-polarization Doppler radar scans. We first derive an analytical framework to assess in which meteorological conditions the local vertical gradients of radar variables reliably inform about microphysical processes. In such conditions, we then identify regions dominated by (i) vapor deposition, (ii) aggregation and riming and (iii) snowflake sublimation and possibly snowflake breakup based on the sign of the local vertical gradients of the reflectivity ZH and the differential reflectivity ZDR. The method is then applied to data from two frontal snowfall events: one in coastal Adélie Land, Antarctica and one in the Taebaeck mountains in South Korea. The validity of the method is assessed by comparing its outcome with snowflake observations using a Multi-Angle Snowflake Camera and with the output of a hydrometeor classification based on polarimetric radar signal. The application of the method further makes it possible to better characterize and understand how snowfall forms, grows and decays in two different geographical and meteorological contexts. For the Antarctic case study, we show that crystal growth by vapor deposition dominates above 2500 m a.g.l., aggregation and riming prevail between 1500 and 2500 m a.g.l. and snowflake sublimation by low-level katabatic winds occurs below 1500 m a.g.l.. For the event in South Korea, aggregation and riming dominate between 4000 and 4800 m a.g.l., with local sublimation below and vapor deposition above. We infer some microphysical characteristics in terms of radar variables from statistical analysis of the method output (e.g. ZH and ZDR distribution for each process). We finally highlight the potential for extensive application to cold precipitation events in different meteorological contexts.

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Characterization of snowfall estimated by in situ and ground-based remote-sensing observations at Terra Nova Bay, Victoria Land, Antarctica

Cited by 18 publications

References 78 publications

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

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

Identification of snowfall microphysical processes from Eulerian vertical gradients of polarimetric radar variables

Identification of snowfall microphysical processes from vertical gradients of polarimetric radar variables

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